Merge pull request 'V0.8.0 add tools' (#41) from v0.8.0-add_tools into v0.8.0

Reviewed-on: #41

Manifest.toml

@@ -1,20 +1,8 @@
 # This file is machine-generated - editing it directly is not advised
 
-julia_version = "1.12.7"
+julia_version = "1.12.6"
 manifest_format = "2.0"
-project_hash = "43252cf86e6030e836ede74c563b58ab2f0f0548"
-
-[[deps.AWS]]
-deps = ["Base64", "Compat", "Dates", "Downloads", "GitHub", "HTTP", "IniFile", "JSON", "MD5", "Mocking", "OrderedCollections", "Random", "SHA", "ScopedValues", "Sockets", "URIs", "UUIDs", "XMLDict"]
-git-tree-sha1 = "842075b12a7f763c5f815a04037a99b6ac346df9"
-uuid = "fbe9abb3-538b-5e4e-ba9e-bc94f4f92ebc"
-version = "1.99.0"
-
-[[deps.AWSS3]]
-deps = ["AWS", "ArrowTypes", "Base64", "Compat", "Dates", "EzXML", "FilePathsBase", "HTTP", "MbedTLS", "Mocking", "OrderedCollections", "Retry", "SymDict", "URIs", "UUIDs", "XMLDict"]
-git-tree-sha1 = "af99cea71364c8c4483405a6b03697d197ab4d3b"
-uuid = "1c724243-ef5b-51ab-93f4-b0a88ac62a95"
-version = "0.11.6"
+project_hash = "0db36d4fb31037ba05065476e6aebaf4cd0e1e8c"
 
 [[deps.Accessors]]
 deps = ["CompositionsBase", "ConstructionBase", "Dates", "InverseFunctions", "MacroTools"]
@@ -56,12 +44,6 @@ git-tree-sha1 = "d57bd3762d308bded22c3b82d033bff85f6195c6"
 uuid = "ec485272-7323-5ecc-a04f-4719b315124d"
 version = "0.4.0"
 
-[[deps.Arrow]]
-deps = ["ArrowTypes", "BitIntegers", "CodecLz4", "CodecZstd", "ConcurrentUtilities", "DataAPI", "Dates", "EnumX", "Mmap", "PooledArrays", "SentinelArrays", "StringViews", "Tables", "TimeZones", "TranscodingStreams", "UUIDs"]
-git-tree-sha1 = "4a69a3eadc1f7da78d950d1ef270c3a62c1f7e01"
-uuid = "69666777-d1a9-59fb-9406-91d4454c9d45"
-version = "2.8.1"
-
 [[deps.ArrowTypes]]
 deps = ["Sockets", "UUIDs"]
 git-tree-sha1 = "404265cd8128a2515a81d5eae16de90fdef05101"
@@ -76,12 +58,6 @@ version = "1.11.0"
 uuid = "2a0f44e3-6c83-55bd-87e4-b1978d98bd5f"
 version = "1.11.0"
 
-[[deps.BitIntegers]]
-deps = ["Random"]
-git-tree-sha1 = "091d591a060e43df1dd35faab3ca284925c48e46"
-uuid = "c3b6d118-76ef-56ca-8cc7-ebb389d030a1"
-version = "0.3.7"
-
 [[deps.BufferedStreams]]
 git-tree-sha1 = "6863c5b7fc997eadcabdbaf6c5f201dc30032643"
 uuid = "e1450e63-4bb3-523b-b2a4-4ffa8c0fd77d"
@@ -98,9 +74,9 @@ version = "1.11.0"
 
 [[deps.CSV]]
 deps = ["CodecZlib", "Dates", "FilePathsBase", "InlineStrings", "Mmap", "Parsers", "PooledArrays", "PrecompileTools", "SentinelArrays", "Tables", "Unicode", "WeakRefStrings", "WorkerUtilities"]
-git-tree-sha1 = "abed1e735dd4152f48c90cf0767e1790e25f332f"
+git-tree-sha1 = "8d8e0b0f350b8e1c91420b5e64e5de774c2f0f4d"
 uuid = "336ed68f-0bac-5ca0-87d4-7b16caf5d00b"
-version = "0.10.17"
+version = "0.10.16"
 
 [[deps.CodeTracking]]
 deps = ["InteractiveUtils", "REPL", "UUIDs"]
@@ -114,29 +90,16 @@ git-tree-sha1 = "40956acdbef3d8c7cc38cba42b56034af8f8581a"
 uuid = "6c391c72-fb7b-5838-ba82-7cfb1bcfecbf"
 version = "0.3.4"
 
-[[deps.CodecLz4]]
-deps = ["Lz4_jll", "TranscodingStreams"]
-git-tree-sha1 = "d58afcd2833601636b48ee8cbeb2edcb086522c2"
-uuid = "5ba52731-8f18-5e0d-9241-30f10d1ec561"
-version = "0.4.6"
-
 [[deps.CodecZlib]]
 deps = ["TranscodingStreams", "Zlib_jll"]
-git-tree-sha1 = "970758a3d591a2a5c2a907c53f2e2f8c1b1d3537"
+git-tree-sha1 = "962834c22b66e32aa10f7611c08c8ca4e20749a9"
 uuid = "944b1d66-785c-5afd-91f1-9de20f533193"
-version = "0.7.9"
-
-[[deps.CodecZstd]]
-deps = ["TranscodingStreams", "Zstd_jll"]
-git-tree-sha1 = "da54a6cd93c54950c15adf1d336cfd7d71f51a56"
-uuid = "6b39b394-51ab-5f42-8807-6242bab2b4c2"
-version = "0.8.7"
+version = "0.7.8"
 
 [[deps.CommonSolve]]
-deps = ["PrecompileTools"]
-git-tree-sha1 = "6c389fa857f6ca5a95474b52a52023fd77f24cb7"
+git-tree-sha1 = "cf963add2340ad9960e5eb22844e61ad8f931fe1"
 uuid = "38540f10-b2f7-11e9-35d8-d573e4eb0ff2"
-version = "0.2.14"
+version = "0.2.13"
 
 [[deps.Compat]]
 deps = ["TOML", "UUIDs"]
@@ -156,7 +119,7 @@ version = "0.1.1"
 [[deps.CompilerSupportLibraries_jll]]
 deps = ["Artifacts", "Libdl"]
 uuid = "e66e0078-7015-5450-92f7-15fbd957f2ae"
-version = "1.3.1+2"
+version = "1.3.0+1"
 
 [[deps.CompositionsBase]]
 git-tree-sha1 = "802bb88cd69dfd1509f6670416bd4434015693ad"
@@ -167,12 +130,6 @@ weakdeps = ["InverseFunctions"]
     [deps.CompositionsBase.extensions]
     CompositionsBaseInverseFunctionsExt = "InverseFunctions"
 
-[[deps.ConcurrentUtilities]]
-deps = ["Serialization", "Sockets"]
-git-tree-sha1 = "3c9be947934c38475bafe822c6d61aaed17f0738"
-uuid = "f0e56b4a-5159-44fe-b623-3e5288b988bb"
-version = "2.6.0"
-
 [[deps.ConstructionBase]]
 git-tree-sha1 = "b4b092499347b18a015186eae3042f72267106cb"
 uuid = "187b0558-2788-49d3-abe0-74a17ed4e7c9"
@@ -194,9 +151,9 @@ uuid = "a8cc5b0e-0ffa-5ad4-8c14-923d3ee1735f"
 version = "4.2.0"
 
 [[deps.DBInterface]]
-git-tree-sha1 = "265d7f87f329c063cbde41920f9d00713a43cedd"
+git-tree-sha1 = "a444404b3f94deaa43ca2a58e18153a82695282b"
 uuid = "a10d1c49-ce27-4219-8d33-6db1a4562965"
-version = "2.7.0"
+version = "2.6.1"
 
 [[deps.DataAPI]]
 git-tree-sha1 = "abe83f3a2f1b857aac70ef8b269080af17764bbe"
@@ -251,9 +208,9 @@ version = "1.11.0"
 
 [[deps.Distributions]]
 deps = ["AliasTables", "FillArrays", "LinearAlgebra", "PDMats", "Printf", "QuadGK", "Random", "Roots", "SpecialFunctions", "Statistics", "StatsAPI", "StatsBase", "StatsFuns"]
-git-tree-sha1 = "a958ab3a40c755563f5e1405c0846cb0446bf19d"
+git-tree-sha1 = "d2facc77c08c1c2bfb1a77c148edd05b3db5410b"
 uuid = "31c24e10-a181-5473-b8eb-7969acd0382f"
-version = "0.25.131"
+version = "0.25.130"
 
     [deps.Distributions.extensions]
     DistributionsChainRulesCoreExt = "ChainRulesCore"
@@ -287,11 +244,15 @@ git-tree-sha1 = "d2e49e7efd29719d6f28b891b0e0e159daa9d2b4"
 uuid = "e2ba6199-217a-4e67-a87a-7c52f15ade04"
 version = "0.1.11"
 
-[[deps.EzXML]]
-deps = ["Printf", "XML2_jll"]
-git-tree-sha1 = "7ea1aa5869e2626ccae84480e4f37185bc6f41d3"
-uuid = "8f5d6c58-4d21-5cfd-889c-e3ad7ee6a615"
-version = "1.2.3"
+[[deps.FileIO]]
+deps = ["Pkg", "Requires", "UUIDs"]
+git-tree-sha1 = "6621fef488e496356c9c9625d0562c12a6070819"
+uuid = "5789e2e9-d7fb-5bc7-8068-2c6fae9b9549"
+version = "1.20.0"
+weakdeps = ["HTTP"]
+
+    [deps.FileIO.extensions]
+    HTTPExt = "HTTP"
 
 [[deps.FilePathsBase]]
 deps = ["Compat", "Dates"]
@@ -327,24 +288,17 @@ uuid = "9fa8497b-333b-5362-9e8d-4d0656e87820"
 version = "1.11.0"
 
 [[deps.Gamma]]
-deps = ["LogExpFunctions"]
-git-tree-sha1 = "becc397f7cfb06e343496ae6ffb04818a851da51"
+git-tree-sha1 = "86f86b6168a016ed88e4ae4e64577b98c3b59e8e"
 uuid = "a0844989-3bd2-4988-8bea-c9407ab0941b"
-version = "1.2.0"
+version = "1.1.0"
 
 [[deps.GeneralUtils]]
-deps = ["AWS", "AWSS3", "CSV", "DataFrames", "DataStructures", "Dates", "Distributions", "Graphs", "HTTP", "JSON", "LibPQ", "NATS", "OrderedCollections", "PrettyPrinting", "Random", "Revise", "SHA", "StringDistances", "URIs", "UUIDs"]
-git-tree-sha1 = "4d7ff3b02a9cbd6d4f65fea43551b0bca6a83a22"
+deps = ["CSV", "DataFrames", "DataStructures", "Dates", "Distributions", "Graphs", "HTTP", "JSON", "LibPQ", "NATS", "PrettyPrinting", "Random", "Revise", "SHA", "StringDistances", "UUIDs"]
+git-tree-sha1 = "129b8fa1bf3bf6d8c0a080f39db09b9b986ef8de"
 repo-rev = "main"
 repo-url = "https://git.yiem.cc/ton/GeneralUtils"
 uuid = "c6c72f09-b708-4ac8-ac7c-2084d70108fe"
-version = "0.6.9"
-
-[[deps.GitHub]]
-deps = ["Base64", "Dates", "HTTP", "JSON", "MbedTLS", "Sockets", "SodiumSeal", "URIs"]
-git-tree-sha1 = "33de1bc5665494f0a9669fd6027e28180709d874"
-uuid = "bc5e4493-9b4d-5f90-b8aa-2b2bcaad7a26"
-version = "5.13.0"
+version = "0.5.11"
 
 [[deps.Graphs]]
 deps = ["ArnoldiMethod", "DataStructures", "Inflate", "LinearAlgebra", "Random", "SimpleTraits", "SparseArrays", "Statistics"]
@@ -361,9 +315,9 @@ version = "1.14.0"
 
 [[deps.HTTP]]
 deps = ["Base64", "CodecZlib", "Dates", "EnumX", "PrecompileTools", "Random", "Reseau", "SHA", "URIs", "UUIDs", "Zlib_jll"]
-git-tree-sha1 = "49a90cd540681d5b79bf5c8ca13c92677e37005e"
+git-tree-sha1 = "0a58fbbdee93d132a2fb1159b7f5e1b5c2465e71"
 uuid = "cd3eb016-35fb-5094-929b-558a96fad6f3"
-version = "2.6.6"
+version = "2.6.4"
 
 [[deps.HashArrayMappedTries]]
 git-tree-sha1 = "2eaa69a7cab70a52b9687c8bf950a5a93ec895ae"
@@ -393,11 +347,6 @@ git-tree-sha1 = "d1b1b796e47d94588b3757fe84fbf65a5ec4a80d"
 uuid = "d25df0c9-e2be-5dd7-82c8-3ad0b3e990b9"
 version = "0.1.5"
 
-[[deps.IniFile]]
-git-tree-sha1 = "f550e6e32074c939295eb5ea6de31849ac2c9625"
-uuid = "83e8ac13-25f8-5344-8a64-a9f2b223428f"
-version = "0.5.1"
-
 [[deps.InlineStrings]]
 git-tree-sha1 = "8f3d257792a522b4601c24a577954b0a8cd7334d"
 uuid = "842dd82b-1e85-43dc-bf29-5d0ee9dffc48"
@@ -457,9 +406,9 @@ version = "1.8.0"
 
 [[deps.JSON]]
 deps = ["Dates", "Logging", "Parsers", "PrecompileTools", "StructUtils", "UUIDs", "Unicode"]
-git-tree-sha1 = "c7345ab1a7ca4dc8a02c9f6510da0d9857bbe513"
+git-tree-sha1 = "65979512c25a0727f050e6e4be40f0fd9ec893f7"
 uuid = "682c06a0-de6a-54ab-a142-c8b1cf79cde6"
-version = "1.7.1"
+version = "1.7.0"
 weakdeps = ["ArrowTypes"]
 
     [deps.JSON.extensions]
@@ -492,10 +441,18 @@ git-tree-sha1 = "0f2899fdadaab4b8f57db558ba21bdb4fb52f1f0"
 uuid = "b39eb1a6-c29a-53d7-8c32-632cd16f18da"
 version = "1.21.3+0"
 
+[[deps.LLMMCTS]]
+deps = ["GeneralUtils", "JSON", "PrettyPrinting"]
+git-tree-sha1 = "3dff98131dfa79be8c9bd84fc51cb0ba1832c472"
+repo-rev = "main"
+repo-url = "https://git.yiem.cc/ton/LLMMCTS"
+uuid = "d76c5a4d-449e-4835-8cc4-dd86ec44f241"
+version = "0.1.5"
+
 [[deps.LaTeXStrings]]
-git-tree-sha1 = "f88f3ccef05a6a72a0cf0ed417c8fd68530f4ab2"
+git-tree-sha1 = "dda21b8cbd6a6c40d9d02a73230f9d70fed6918c"
 uuid = "b964fa9f-0449-5b57-a5c2-d3ea65f4040f"
-version = "1.4.1"
+version = "1.4.0"
 
 [[deps.LayerDicts]]
 git-tree-sha1 = "6087ad3521d6278ebe5c27ae55e7bbb15ca312cb"
@@ -543,12 +500,6 @@ version = "1.11.3+1"
 uuid = "8f399da3-3557-5675-b5ff-fb832c97cbdb"
 version = "1.11.0"
 
-[[deps.Libiconv_jll]]
-deps = ["Artifacts", "JLLWrappers", "Libdl"]
-git-tree-sha1 = "be484f5c92fad0bd8acfef35fe017900b0b73809"
-uuid = "94ce4f54-9a6c-5748-9c1c-f9c7231a4531"
-version = "1.18.0+0"
-
 [[deps.LinearAlgebra]]
 deps = ["Libdl", "OpenBLAS_jll", "libblastrampoline_jll"]
 uuid = "37e2e46d-f89d-539d-b4ee-838fcccc9c8e"
@@ -580,18 +531,6 @@ git-tree-sha1 = "1d4c737ab26f51ceed52ab2019c09b7660eb7440"
 uuid = "6f1432cf-f94c-5a45-995e-cdbf5db27b0b"
 version = "3.8.0"
 
-[[deps.Lz4_jll]]
-deps = ["Artifacts", "JLLWrappers", "Libdl"]
-git-tree-sha1 = "191686b1ac1ea9c89fc52e996ad15d1d241d1e33"
-uuid = "5ced341a-0733-55b8-9ab6-a4889d929147"
-version = "1.10.1+0"
-
-[[deps.MD5]]
-deps = ["Random", "SHA"]
-git-tree-sha1 = "1576f756617d31eb397a4a517b68562fd28dc2b4"
-uuid = "6ac74813-4b46-53a4-afec-0b5dc9d7885c"
-version = "0.2.3"
-
 [[deps.MacroTools]]
 git-tree-sha1 = "1e0228a030642014fe5cfe68c2c0a818f9e3f522"
 uuid = "1914dd2f-81c6-5fcd-8719-6d5c9610ff09"
@@ -680,7 +619,7 @@ version = "0.8.7+0"
 [[deps.OpenSSL_jll]]
 deps = ["Artifacts", "Libdl"]
 uuid = "458c3c95-2e84-50aa-8efc-19380b2a3a95"
-version = "3.5.6+0"
+version = "3.5.4+0"
 
 [[deps.OpenSpecFun_jll]]
 deps = ["Artifacts", "CompilerSupportLibraries_jll", "JLLWrappers", "Libdl"]
@@ -709,6 +648,15 @@ git-tree-sha1 = "3de8f5e6e90ebfa8d6d1f86997d6cdcd6a912ff3"
 uuid = "69de0a69-1ddd-5017-9359-2bf0b02dc9f0"
 version = "2.8.7"
 
+[[deps.Pkg]]
+deps = ["Artifacts", "Dates", "Downloads", "FileWatching", "LibGit2", "Libdl", "Logging", "Markdown", "Printf", "Random", "SHA", "TOML", "Tar", "UUIDs", "p7zip_jll"]
+uuid = "44cfe95a-1eb2-52ea-b672-e2afdf69b78f"
+version = "1.12.1"
+weakdeps = ["REPL"]
+
+    [deps.Pkg.extensions]
+    REPLExt = "REPL"
+
 [[deps.PooledArrays]]
 deps = ["DataAPI", "Future"]
 git-tree-sha1 = "36d8b4b899628fb92c2749eb488d884a926614d3"
@@ -734,9 +682,9 @@ version = "0.4.3"
 
 [[deps.PrettyTables]]
 deps = ["Crayons", "LaTeXStrings", "Markdown", "PrecompileTools", "Printf", "REPL", "Reexport", "StringManipulation", "Tables"]
-git-tree-sha1 = "1b8aa19f229b1cea7fc93874a52e49db6a854450"
+git-tree-sha1 = "4ac881f5432bd93463a41767a814a45245be22b6"
 uuid = "08abe8d2-0d0c-5749-adfa-8a2ac140af0d"
-version = "3.4.8"
+version = "3.4.6"
 
     [deps.PrettyTables.extensions]
     PrettyTablesExcelExt = "XLSX"
@@ -797,20 +745,15 @@ version = "1.3.1"
 
 [[deps.Reseau]]
 deps = ["NetworkOptions", "OpenSSL_jll", "PrecompileTools", "Random", "SHA"]
-git-tree-sha1 = "aaee74fab545e9af4b21d10c2203c93c636c4ae0"
+git-tree-sha1 = "701ef63506992668c0069a4d1ac1540b77258e6c"
 uuid = "802f3686-a58f-41ce-bb0c-3c43c75bba36"
-version = "1.4.1"
-
-[[deps.Retry]]
-git-tree-sha1 = "41ac127cd281bb33e42aba46a9d3b25cd35fc6d5"
-uuid = "20febd7b-183b-5ae2-ac4a-720e7ce64774"
-version = "0.4.1"
+version = "1.3.6"
 
 [[deps.Revise]]
 deps = ["CRC32c", "CodeTracking", "FileWatching", "JuliaInterpreter", "LibGit2", "LoweredCodeUtils", "OrderedCollections", "Preferences", "REPL", "UUIDs"]
-git-tree-sha1 = "ab0f5630e37968bc49b8c4cedd3279a7d30b9486"
+git-tree-sha1 = "ec46aed6a3a8cc6b67839ca361e7b4aa32eaeee1"
 uuid = "295af30f-e4ad-537b-8983-00126c2a3abe"
-version = "3.17.0"
+version = "3.16.3"
 weakdeps = ["Distributed"]
 
     [deps.Revise.extensions]
@@ -830,9 +773,9 @@ version = "0.5.2+0"
 
 [[deps.Roots]]
 deps = ["Accessors", "CommonSolve", "Printf"]
-git-tree-sha1 = "13a9e0164267bb9ebc55c1c5d72fceea8f09555b"
+git-tree-sha1 = "7fb25a964849d90a0446366cdefca822e0e84900"
 uuid = "f2b01f46-fcfa-551c-844a-d8ac1e96c665"
-version = "3.0.7"
+version = "3.0.6"
 
     [deps.Roots.extensions]
     RootsChainRulesCoreExt = "ChainRulesCore"
@@ -854,6 +797,14 @@ version = "3.0.7"
 uuid = "ea8e919c-243c-51af-8825-aaa63cd721ce"
 version = "0.7.0"
 
+[[deps.SQLLLM]]
+deps = ["CSV", "DataFrames", "DataStructures", "Dates", "FileIO", "GeneralUtils", "HTTP", "JSON", "LLMMCTS", "LibPQ", "PrettyPrinting", "Random", "Revise", "StatsBase", "Tables", "URIs", "UUIDs"]
+git-tree-sha1 = "bae2fd2e2b087753fbb3415896be41df1ae0eb90"
+repo-rev = "main"
+repo-url = "https://git.yiem.cc/ton/SQLLLM"
+uuid = "2ebc79c7-cc10-4a3a-9665-d2e1d61e63d3"
+version = "0.2.8"
+
 [[deps.SQLStrings]]
 git-tree-sha1 = "55de0530689832b1d3d43491ee6b67bd54d3323c"
 uuid = "af517c2e-c243-48fa-aab8-efac3db270f5"
@@ -897,12 +848,6 @@ git-tree-sha1 = "907703e0d50846f300650d7225bdcab145b7bca9"
 uuid = "4f5b5e99-b0ad-42cd-b47a-334e172ec8bd"
 version = "1.1.2"
 
-[[deps.SodiumSeal]]
-deps = ["Base64", "Libdl", "libsodium_jll"]
-git-tree-sha1 = "80cef67d2953e33935b41c6ab0a178b9987b1c99"
-uuid = "2133526b-2bfb-4018-ac12-889fb3908a75"
-version = "0.1.1"
-
 [[deps.SortingAlgorithms]]
 deps = ["DataStructures"]
 git-tree-sha1 = "13cd91cc9be159e3f4d95b857fa2aa383b53772a"
@@ -916,9 +861,9 @@ version = "1.12.0"
 
 [[deps.SpecialFunctions]]
 deps = ["IrrationalConstants", "LogExpFunctions", "OpenLibm_jll", "OpenSpecFun_jll"]
-git-tree-sha1 = "429071b23f4c9a13fb6582f807cc2ef454082408"
+git-tree-sha1 = "c3ac026e735264e9bdc6a9bcbd1b1e781b36e3bc"
 uuid = "276daf66-3868-5448-9aa4-cd146d93841b"
-version = "2.9.0"
+version = "2.8.3"
 
     [deps.SpecialFunctions.extensions]
     SpecialFunctionsChainRulesCoreExt = "ChainRulesCore"
@@ -928,9 +873,9 @@ version = "2.9.0"
 
 [[deps.StaticArrays]]
 deps = ["LinearAlgebra", "PrecompileTools", "Random", "StaticArraysCore"]
-git-tree-sha1 = "fac51faf3bb96e8bc0bf6f9f39ca4955652776bb"
+git-tree-sha1 = "246a8bb2e6667f832eea063c3a56aef96429a3db"
 uuid = "90137ffa-7385-5640-81b9-e52037218182"
-version = "1.9.19"
+version = "1.9.18"
 
     [deps.StaticArrays.extensions]
     StaticArraysChainRulesCoreExt = "ChainRulesCore"
@@ -947,9 +892,9 @@ version = "1.4.4"
 
 [[deps.Statistics]]
 deps = ["LinearAlgebra"]
-git-tree-sha1 = "389592b8592e5fb48f498ff60dc6acb4a0e62953"
+git-tree-sha1 = "ae3bb1eb3bba077cd276bc5cfc337cc65c3075c0"
 uuid = "10745b16-79ce-11e8-11f9-7d13ad32a3b2"
-version = "1.11.4"
+version = "1.11.1"
 weakdeps = ["SparseArrays"]
 
     [deps.Statistics.extensions]
@@ -963,9 +908,9 @@ version = "1.8.0"
 
 [[deps.StatsBase]]
 deps = ["AliasTables", "DataAPI", "DataStructures", "IrrationalConstants", "LinearAlgebra", "LogExpFunctions", "Missings", "Printf", "Random", "SortingAlgorithms", "SparseArrays", "Statistics", "StatsAPI"]
-git-tree-sha1 = "adb9da019510162e67a4493fc235c23203d8b09e"
+git-tree-sha1 = "e4d7a1a0edc20af42689ea6f4f3587a2175d50ee"
 uuid = "2913bbd2-ae8a-5f71-8c99-4fb6c76f3a91"
-version = "0.34.13"
+version = "0.34.12"
 
 [[deps.StatsFuns]]
 deps = ["HypergeometricFunctions", "IrrationalConstants", "LogExpFunctions", "Reexport", "Rmath", "SpecialFunctions"]
@@ -983,20 +928,15 @@ version = "2.2.1"
 
 [[deps.StringDistances]]
 deps = ["Distances", "StatsAPI"]
-git-tree-sha1 = "456f343c7a6c8f52d8b501c0da2a05538ab1b907"
+git-tree-sha1 = "cd83a04baf746e3b43b83c61b7de77ab0409b80a"
 uuid = "88034a9c-02f8-509d-84a9-84ec65e18404"
-version = "1.0.1"
+version = "1.0.0"
 
 [[deps.StringManipulation]]
 deps = ["PrecompileTools"]
-git-tree-sha1 = "773065c6e0e903924a9d838259be74338422aef2"
+git-tree-sha1 = "8a90c1d77c3277a5d43b83927b3cbe2c70a37484"
 uuid = "892a3eda-7b42-436c-8928-eab12a02cf0e"
-version = "0.5.0"
-
-[[deps.StringViews]]
-git-tree-sha1 = "f2dcb92855b31ad92fe8f079d4f75ac57c93e4b8"
-uuid = "354b36f9-a18e-4713-926e-db85100087ba"
-version = "1.3.7"
+version = "0.4.7"
 
 [[deps.StructTypes]]
 deps = ["Dates", "UUIDs"]
@@ -1006,9 +946,9 @@ version = "1.11.0"
 
 [[deps.StructUtils]]
 deps = ["Dates", "UUIDs"]
-git-tree-sha1 = "2d0fc55c61321ba245c47be599570d11bac50303"
+git-tree-sha1 = "c65ae4aa47e543c278aea0a3468786d33021a3ff"
 uuid = "ec057cc2-7a8d-4b58-b3b3-92acb9f63b42"
-version = "2.8.5"
+version = "2.8.4"
 
     [deps.StructUtils.extensions]
     StructUtilsMeasurementsExt = ["Measurements"]
@@ -1033,12 +973,6 @@ deps = ["Artifacts", "Libdl", "libblastrampoline_jll"]
 uuid = "bea87d4a-7f5b-5778-9afe-8cc45184846c"
 version = "7.8.3+2"
 
-[[deps.SymDict]]
-deps = ["Test"]
-git-tree-sha1 = "0108ccdaea3ef69d9680eeafc8d5ad198b896ec8"
-uuid = "2da68c74-98d7-5633-99d6-8493888d7b1e"
-version = "0.3.0"
-
 [[deps.TOML]]
 deps = ["Dates"]
 uuid = "fa267f1f-6049-4f14-aa54-33bafae1ed76"
@@ -1046,9 +980,9 @@ version = "1.0.3"
 
 [[deps.TZJData]]
 deps = ["Artifacts"]
-git-tree-sha1 = "d3b30a9f29a898e21fb4bdf280101b7a12e1f39c"
+git-tree-sha1 = "72df96b3a595b7aab1e101eb07d2a435963a97e2"
 uuid = "dc5dba14-91b3-4cab-a142-028a31da12f7"
-version = "1.5.1+2025b"
+version = "1.5.0+2025b"
 
 [[deps.TableTraits]]
 deps = ["IteratorInterfaceExtensions"]
@@ -1058,9 +992,14 @@ version = "1.0.1"
 
 [[deps.Tables]]
 deps = ["DataAPI", "DataValueInterfaces", "IteratorInterfaceExtensions", "OrderedCollections", "TableTraits"]
-git-tree-sha1 = "a94d9bdda1b7bed0046cea645639ab3f62196fac"
+git-tree-sha1 = "0f38a06c83f0007bbab3cf911262841c9a0f07e0"
 uuid = "bd369af6-aec1-5ad0-b16a-f7cc5008161c"
-version = "1.14.0"
+version = "1.13.0"
+
+[[deps.Tar]]
+deps = ["ArgTools", "SHA"]
+uuid = "a4e569a6-e804-4fa4-b0f3-eef7a1d5b13e"
+version = "1.10.0"
 
 [[deps.Test]]
 deps = ["InteractiveUtils", "Logging", "Random", "Serialization"]
@@ -1083,9 +1022,9 @@ uuid = "3bb67fe8-82b1-5028-8e26-92a6c54297fa"
 version = "0.11.3"
 
 [[deps.URIs]]
-git-tree-sha1 = "908fec9df6c5de98548ead82a468c95ccf6cd263"
+git-tree-sha1 = "3b0738bd7c5645641845da25cbd99800b8718689"
 uuid = "5c2747f8-b7ea-4ff2-ba2e-563bfd36b1d4"
-version = "1.7.0"
+version = "1.6.2"
 
 [[deps.UTCDateTimes]]
 deps = ["Dates", "TimeZones"]
@@ -1113,20 +1052,8 @@ git-tree-sha1 = "cd1659ba0d57b71a464a29e64dbc67cfe83d54e7"
 uuid = "76eceee3-57b5-4d4a-8e66-0e911cebbf60"
 version = "1.6.1"
 
-[[deps.XML2_jll]]
-deps = ["Artifacts", "JLLWrappers", "Libdl", "Libiconv_jll", "Zlib_jll"]
-git-tree-sha1 = "3f3315d89fc954a28f5b471bce698ed6e27481be"
-uuid = "02c8fc9c-b97f-50b9-bbe4-9be30ff0a78a"
-version = "2.15.3+0"
-
-[[deps.XMLDict]]
-deps = ["EzXML", "IterTools", "OrderedCollections"]
-git-tree-sha1 = "8e8cdc9c93e1f442eba471b56db6f86ffeb1a33a"
-uuid = "228000da-037f-5747-90a9-8195ccbf91a5"
-version = "0.4.2"
-
 [[deps.YiemAgent]]
-deps = ["Base64", "CSV", "DataFrames", "DataStructures", "Dates", "HTTP", "JSON", "LibPQ", "NATS", "PrettyPrinting", "Random", "Revise", "Serialization", "URIs", "UUIDs", "msghandler"]
+deps = ["Base64", "CSV", "DataFrames", "DataStructures", "Dates", "GeneralUtils", "HTTP", "JSON", "LLMMCTS", "LibPQ", "NATS", "PrettyPrinting", "Random", "Revise", "SQLLLM", "Serialization", "URIs", "UUIDs"]
 path = "."
 uuid = "e012c34b-7f78-48e0-971c-7abb83b6f0a2"
 version = "0.8.0"
@@ -1153,14 +1080,6 @@ git-tree-sha1 = "011b0a7331b41c25524b64dc42afc9683ee89026"
 uuid = "a9144af2-ca23-56d9-984f-0d03f7b5ccf8"
 version = "1.0.21+0"
 
-[[deps.msghandler]]
-deps = ["Arrow", "Base64", "DataFrames", "Dates", "GeneralUtils", "HTTP", "JSON", "NATS", "PrettyPrinting", "UUIDs"]
-git-tree-sha1 = "e7889a120327db84cdf1b1acf4a2c7caba6e8ef7"
-repo-rev = "main"
-repo-url = "https://git.yiem.cc/ton/msghandler"
-uuid = "f2724d33-f338-4a57-b9f8-1be882570d10"
-version = "1.4.0"
-
 [[deps.nghttp2_jll]]
 deps = ["Artifacts", "Libdl"]
 uuid = "8e850ede-7688-5339-a07c-302acd2aaf8d"

Project.toml

@@ -1,6 +1,6 @@
 name = "YiemAgent"
 uuid = "e012c34b-7f78-48e0-971c-7abb83b6f0a2"
-version = "0.9.1"
+version = "0.8.0"
 authors = ["narawat lamaiin <narawat@outlook.com>"]
 
 [deps]
@@ -12,22 +12,24 @@ Dates = "ade2ca70-3891-5945-98fb-dc099432e06a"
 GeneralUtils = "c6c72f09-b708-4ac8-ac7c-2084d70108fe"
 HTTP = "cd3eb016-35fb-5094-929b-558a96fad6f3"
 JSON = "682c06a0-de6a-54ab-a142-c8b1cf79cde6"
+LLMMCTS = "d76c5a4d-449e-4835-8cc4-dd86ec44f241"
 LibPQ = "194296ae-ab2e-5f79-8cd4-7183a0a5a0d1"
 NATS = "55e73f9c-eeeb-467f-b4cc-a633fde63d2a"
 PrettyPrinting = "54e16d92-306c-5ea0-a30b-337be88ac337"
 Random = "9a3f8284-a2c9-5f02-9a11-845980a1fd5c"
 Revise = "295af30f-e4ad-537b-8983-00126c2a3abe"
+SQLLLM = "2ebc79c7-cc10-4a3a-9665-d2e1d61e63d3"
 Serialization = "9e88b42a-f829-5b0c-bbe9-9e923198166b"
 URIs = "5c2747f8-b7ea-4ff2-ba2e-563bfd36b1d4"
 UUIDs = "cf7118a7-6976-5b1a-9a39-7adc72f591a4"
-msghandler = "f2724d33-f338-4a57-b9f8-1be882570d10"
 
 [compat]
 Base64 = "1.11.0"
 CSV = "0.10.15"
 DataFrames = "1.7.0"
-GeneralUtils = "0.6.9"
+GeneralUtils = "0.5.10"
 HTTP = "2.4.0"
 JSON = "1.6.1"
+LLMMCTS = "0.1.5"
 NATS = "0.1.0"
-msghandler = "1.4.0"
+SQLLLM = "0.2.8"

README.md

@@ -1,311 +1,34 @@
 # YiemAgent
 
-Julia framework for building agents with tool use and MCP (Model Context Protocol) support.
+Julia framework for building agents with tool use.
 
-## Quick Start
+## Getting Started
 
-```julia
-using YiemAgent
-
-# 1. Create an LLM callable (see "Custom LLM Callable" below)
-llmCall = ...
-
-# 2. Create a callable MCP server (see "MCP Server Integration" below)
-mcpServer = ...
-
-# 3. Create the agent
-agent = yiemAgent(llmCall; mcpServer=mcpServer, eventSink=println)
-
-# 4. Interact
-promptAgent(agent, "What tools do you have?")
-response = takeResponse(agent)
-
-# 5. Stop when done
-stopAgent(agent)
-```
-
-## Agent API
-
-| Function | Description |
-|----------|-------------|
-| `promptAgent(agent, text)` | Send plain text to the agent's input channel |
-| `promptAgent(agent, msg)` | Send a structured message (OpenAI format dict) to the input channel |
-| `takeResponse(agent)` | Block and take the agent's response from output channel |
-| `followUp(agent, text)` | Send plain text follow-up while agent is still processing |
-| `followUp(agent, msg)` | Send a structured follow-up (OpenAI format dict) while agent is processing |
-| `stopAgent(agent)` | Gracefully stop the agent and close channels |
-
-## Creating a yiemAgent
-
-```julia
-yiemAgent(llmCall;
-    systemPrompt::String = "You are a helpful assistant.",
-    model = nothing,
-    messages::Vector{agentMessage} = agentMessage[],
-    prepareContext::Function = prepareContext,
-    formatMsgForLLM::Function = formatMsgForLLM,
-    beforeToolCall::Function = beforeToolCall,
-    afterToolCall::Function = afterToolCall,
-    sessionId::Union{String, Nothing} = nothing,
-    maxRetryDelayMs::Union{Int64, Nothing} = nothing,
-    parallelToolExecute::Bool = false,
-    eventSink::Function = eventSink,
-    mcpServer = nothing
-)
-```
-
-| Parameter | Description |
-|-----------|-------------|
-| `llmCall` | **(required)** Function that receives formatted messages and returns an LLM response |
-| `systemPrompt` | System prompt for the agent |
-| `model` | LLM model configuration (`llmModel` struct) |
-| `messages` | Initial conversation messages |
-| `prepareContext` | Hook to transform messages/context before each LLM call |
-| `formatMsgForLLM` | Hook to convert agent context to LLM API format |
-| `beforeToolCall` | Hook to block/allow tool execution |
-| `afterToolCall` | Hook to post-process tool results |
-| `sessionId` | Optional session identifier for MCP server |
-| `maxRetryDelayMs` | Maximum retry delay in milliseconds |
-| `parallelToolExecute` | Run multiple tool calls in parallel |
-| `eventSink` | Callback for agent events/debug messages |
-| `mcpServer` | Callable struct for MCP server communication |
-
-## Usage Examples
-
-### Text Messages
-
-```julia
-# Send plain text
-promptAgent(agent, "What tools do you have?")
-response = takeResponse(agent)
-
-# Follow-up while agent is processing
-promptAgent(agent, "Tell me about wine")
-followUp(agent, "Also include prices")
-response = takeResponse(agent)
-```
-
-### Structured Messages (OpenAI Format)
-
-```julia
-promptAgent(agent, Dict("role" => "user", "content" => "Hello"))
-response = takeResponse(agent)
-```
-
-### Output Format
-
-`takeResponse()` returns a `Dict{String, Any}` in OpenAI message format:
-
-```julia
-Dict(
-    "role" => "assistant",
-    "content" => [
-        Dict("type" => "text", "text" => "The weather in Tokyo is Sunny, 22°C."),
-        # ... more content blocks
-    ]
-)
-```
-
-Content block types:
-
-| Block Type | Fields | Description |
-|------------|--------|-------------|
-| `text` | `"type" => "text"`, `"text" => "..."` | Plain text response from the LLM |
-| `image_url` | `"type" => "image_url"`, `"image_url" => Dict("url" => "...")` | Image content (rare, used by multimodal models) |
-
-Example — extracting the text response:
-
-```julia
-response = takeResponse(agent)
-text_response = first(c["text"] for c in response["content"] if c["type"] == "text")
-```
-
-### Multimodal Messages (Text + Image)
-
-```julia
-# Encode images to data URIs
-image_path = "test/image.png"
-image_bytes = read(image_path)
-base64_string = base64encode(image_bytes)
-data_uri = "data:image/png;base64,$(base64_string)"
-
-# Build message with text and image content blocks
-message = Dict(
-    "role" => "user",
-    "content" => [
-        Dict("type" => "text", "text" => "Do you have Brunello di Montalcino?"),
-        Dict("type" => "image_url", "image_url" => Dict("url" => data_uri))
-    ]
-)
-promptAgent(agent, message)
-response = takeResponse(agent)
-```
-
-### Custom Hooks
-
-```julia
-# beforeToolCall — request user approval for destructive tools
-function my_before_tool_call(context::beforeToolCallContext, signal::abortSignal)::beforeToolCallResult
-    if context.toolCall.name == "deleteFile"
-        return beforeToolCallResult(true, "User must approve file deletion")
-    end
-    return beforeToolCallResult(false, "N/A")
-end
-
-# afterToolCall — mask sensitive data from results
-function my_after_tool_call(context::afterToolCallContext, signal::abortSignal)::Union{agentToolResult, Nothing}
-    if context.isError
-        return agentToolResult(
-            [textContent("The tool encountered an error. Please try again.")],
-            context.result.details, context.result.usage, context.result.terminate
-        )
-    end
-    return nothing
-end
-
-agent = yiemAgent(llmCall;
-    mcpServer=mcpServer,
-    beforeToolCall=my_before_tool_call,
-    afterToolCall=my_after_tool_call,
-    eventSink=println
-)
-```
-
-### Custom LLM Callable
-
-```julia
-function my_llm_call(messages_dict::Dict{String, Any})
-    # messages_dict contains "messages" key with OpenAI-format message array,
-    # plus optional "tools", "temperature", etc.
-
-    # Call your LLM provider (OpenAI, Anthropic, local model, etc.)
-    raw_response = call_my_llm_provider(messages_dict)
-
-    # Return a dict with these fields:
-    return Dict(
-        "message" => Dict("role" => "assistant", "content" => raw_response["content"]),
-        "finish_reason" => raw_response["finish_reason"],
-        "api" => "my_provider",
-        "provider" => "my_provider",
-        "model" => "my-model-name",
-        "usage" => llmUsage(100, 50)
-    )
-end
-```
-
-### Custom Context Preparation
-
-```julia
-function my_prepare_context(state::agentState, eventSink)::agentContext
-    sysPrompt = state.systemPrompt * "\nCurrent time: $(now())"
-    msgs = deepcopy(state.messages)
-
-    # Prune old messages to fit context window
-    max_messages = 20
-    if length(msgs) > max_messages
-        msgs = msgs[end-max_messages+1:end]
-    end
-
-    return agentContext(sysPrompt, msgs, state.tools)
-end
-
-agent = yiemAgent(llmCall; prepareContext=my_prepare_context, ...)
-```
-
-## MCP Server Integration
-
-YiemAgent uses JSON-RPC 2.0 for MCP communication. The `mcpServer` callable struct must implement `tools/list` and `tools/call` methods.
-
-### Protocol
-
-```julia
-# tools/list — returns tool definitions
-mcpServer("tools/list")
-# → Dict("jsonrpc"=>"2.0", "id"=>1, "result"=>Dict("tools"=>[...], "nextCursor"=>...))
-
-# tools/call — executes a tool
-mcpServer("tools/call", toolName, arguments)
-# → Dict("jsonrpc"=>"2.0", "id"=>2, "result"=>Dict("content"=>[...], "isError"=>...))
-
-# Protocol error responses include "error" instead of "result":
-Dict("jsonrpc"=>"2.0", "id"=>2, "error"=>Dict("code"=>-32602, "message"=>"..."))
-```
-
-### Example: NATS-based MCP Server Client
-
-```julia
-struct mcpServer
-    natsConn::NATS.Connection
-    topic::String
-    senderID::String
-    fileserver_url::String
-end
-
-# tools/list implementation
-function (c::mcpServer)(method::String)
-    payload = Dict("jsonrpc" => "2.0", "id" => 1, "method" => method, "params" => Dict{String, Any}())
-    payloads = [("payload", payload, "dictionary")]
-    _, msg_envelope = msghandler.smartpack(c.topic, payloads;
-        sender_id=c.senderID, msg_purpose="mcp_tools_list", fileserver_url=c.fileserver_url)
-    reply = NATS.request(c.natsConn, c.topic, msg_envelope, timeout=180)
-    incoming = msghandler.smartunpack(String(reply.payload))
-    return incoming["payloads"][1][2]
-end
-
-# tools/call implementation
-function (c::mcpServer)(method::String, toolName::String, arguments::Dict{String, Any})
-    payload = Dict("jsonrpc" => "2.0", "id" => 2, "method" => method,
-        "params" => Dict("name" => toolName, "arguments" => arguments))
-    payloads = [("payload", payload, "dictionary")]
-    _, msg_envelope = msghandler.smartpack(c.topic, payloads;
-        sender_id=c.senderID, msg_purpose="mcp_tool_call", fileserver_url=c.fileserver_url)
-    reply = NATS.request(c.natsConn, c.topic, msg_envelope, timeout=180)
-    incoming = msghandler.smartunpack(String(reply.payload))
-    return incoming["payloads"][1][2]
-end
-```
-
-## Tool Discovery
-
-Tools are discovered dynamically via the MCP server:
-
-1. `listTools` is the only pre-registered tool at agent creation
-2. When the LLM calls `listTools()`, tools are discovered from the MCP server and registered at runtime
-3. Supports pagination via `nextCursor` for large tool sets
-4. New tools become immediately available without restarting the agent
-
-## Hooks
-
-Hooks allow customization of the agent's behavior at key points in the execution pipeline.
-
-| Hook | Signature | Purpose |
-|------|-----------|---------|
-| `prepareContext` | `(state::agentState, eventSink) -> agentContext` | Transform messages/context before LLM call (filter tools, inject context, prune messages) |
-| `formatMsgForLLM` | `(ctx::agentContext, eventSink) -> Dict` | Convert agent context to LLM API message format |
-| `beforeToolCall` | `(context::beforeToolCallContext, signal::abortSignal) -> beforeToolCallResult` | Block/allow tool execution; request user approval |
-| `afterToolCall` | `(context::afterToolCallContext, signal::abortSignal) -> Union{agentToolResult, Nothing}` | Post-process tool results; mask sensitive data; set `terminate` flag |
-| `eventSink` | `(msg) -> nothing` | Callback for agent events/debug messages throughout the pipeline |
-
-## Agent Lifecycle
-
-1. **Creation** — `yiemAgent()` spawns a background task (`_agentLoop`) listening on `inputChannel` and `followUpChannel`
-2. **Input** — User messages enter via `promptAgent()` (immediate) or `followUp()` (queued while agent is busy)
-3. **Processing** — `_processMessage()` handles LLM calls, tool execution, and conversation history in a loop
-4. **Tool Execution Pipeline** — each tool call follows three phases:
-   - `prepareToolCall` — resolve tool, validate arguments, run `beforeToolCall` hook
-   - `executePreparedToolCall` — invoke `tool.execute()`
-   - `finalizeExecutedToolCall` — run `afterToolCall` hook, construct `toolResultMessage`
-5. **Termination** — tool `execute` functions can set `terminate=true` to stop the agent loop
-6. **Shutdown** — `stopAgent()` sends a `:shutdown` signal, drains channels, and closes the agent
+1. Install dependencies: `]add JSON, DataStructures, UUIDs, Dates, ...`
+2. Create a `yiemAgent` with `loadTools("src/tools")`
+3. Call `run_agent(agent, "message")` then `take_response(agent)`
 
 ## Architecture
 
 ```
 src/
 ├── YiemAgent.jl          # Module entry point
-├── type.jl               # Core types (messages, tools, agent state, events)
-├── utils.jl              # Message formatting, context preparation, validation
-├── toolRegistry.jl       # Tool store, MCP discovery, listTools registration
-├── agentCore.jl          # Agent loop, tool execution pipeline, yiemAgent struct
-└── api.jl                # Public API (promptAgent, takeResponse, followUp, stopAgent)
+├── type.jl               # Core types (messages, tools, agent state)
+├── utils.jl              # Message formatting, validation
+├── agentCore.jl          # Agent loop, tool execution pipeline
+├── api.jl                # Public API (run_agent, take_response, etc.)
+└── tools/
+    ├── registry.jl       # Tool registry (loadTools, registerTool, listTools)
+    ├── getWeather.jl     # Weather lookup tool
+    ├── getTime.jl        # Time lookup tool
+    ├── writeTool.jl      # Create new tool files (self-modifying)
+    └── README.md         # Tool development guide
 ```
+
+## Tool Development
+
+See `src/tools/README.md` for:
+- Tool anatomy (schema, execute, getTool)
+- Validation hooks
+- Agent loop lifecycle
+- Self-modifying tools (`writeTool`)

docs/1-requirements.md

@@ -1,155 +0,0 @@
-# YiemAgent: Agile Agent Framework
-
-## 1. Business Context & Success Metrics
-
-### Business Goal
-
-YiemAgent is a Julia-based AI agent framework that enables building chatbot assistants with tool-calling capabilities, model-agnostic LLM integration, and runtime tool discovery via MCP (Model Context Protocol). It provides an asynchronous agent loop with channel-based message passing, supporting both single-turn and multi-turn conversations with automatic tool call resolution.
-
-### User Stories
-
-- **US-001**: As a developer, I want to create an AI agent with a simple constructor so that I can quickly prototype chatbot functionality in Julia
-- **US-002**: As a developer, I want the agent to discover tools automatically from an MCP server so that I don't need to manually register tool APIs
-- **US-003**: As a developer, I want the agent to handle tool calls automatically in a loop so that the LLM can use tools without external orchestration
-- **US-004**: As a developer, I want to intercept tool calls with before/after hooks so that I can add user approval, logging, or result sanitization
-- **US-005**: As a developer, I want the framework to support multiple LLM models and providers through a pluggable `llmCall` interface so that I can swap models without changing agent logic
-
-### KPIs & Targets
-
-- **KPI-001**: Agent loop processes messages with <500ms overhead for sequential tool calls (validates US-003)
-- **KPI-002**: MCP tool discovery completes within 2 seconds for 50+ tools (validates US-002)
-- **KPI-003**: 100% of message types have OpenAI format conversion (validates US-001)
-
-## 2. Technical Boundaries
-
-### In Scope
-
-- Julia module implementing an AI agent with asynchronous message processing
-- Channel-based input (user→agent), follow-up (async queue), and output (agent→user) interfaces
-- LLM model abstraction with tool call extraction from multiple response formats (OpenAI content blocks, LMStudio/vLLM format)
-- Tool registry with MCP server integration (tools/list, tools/call via JSON-RPC 2.0)
-- Agent loop with before/after tool call hooks for lifecycle management
-- Message type system with OpenAI format conversion utilities
-- Abort signal and termination flag support via tool implementations
-
-### Out of Scope
-
-- GUI/CLI interface (exposed via channels only; UI layer is external)
-- Persistent chat history or long-term memory (TODO comments in code)
-- Multi-agent orchestration (single agent instance per `yiemAgent` struct)
-- Embedding generation or retrieval-augmented generation (RAG)
-- Natural language-to-code generation or execution sandboxes
-
-### Dependencies
-
-- **Julia Standard Library**: `Dates`, `UUIDs`, `Base.Threads`
-- **External Packages**: `JSON`, `DataStructures`, `HTTP`, `PrettyPrinting`, `Serialization`, `DataFrames`
-- **Internal Package**: `GeneralUtils` (project-specific utility library)
-- **Optional**: `NATS` (MCP server communication), `LibPQ` (PostgreSQL)
-
-### Deployment Constraints
-
-- **NFR-001**: Library designed for embedded use in Docker containers (MicroK8s cluster, per `NFR-501` from template)
-- **NFR-002**: Must support Julia 1.9+ for module include/export semantics compatibility
-- **NFR-003**: Thread-safe channel operations; single-agent instance processes messages sequentially on background `@spawn` task
-
-## 3. Functional Requirements (FR)
-
-### FR-001: Agent Message Processing
-
-The agent shall process user messages through an asynchronous loop that:
-- Accepts messages via `inputChannel` (capacity 16) and `followUpChannel` (capacity 32)
-- Prioritizes `inputChannel` messages over `followUpChannel` messages
-- Feeds responses to `outputChannel` (capacity 16) in OpenAI message format
-- Exits cleanly on `:shutdown` signal
-
-**Traceability**: US-001, US-003
-
-### FR-002: LLM Model Agnosticism
-
-The agent shall accept a pluggable `llmCall` function that:
-- Receives formatted messages and returns an LLM response dict
-- The response dict may contain `message.tool_calls` (LMStudio format) or `content` blocks (OpenAI format)
-- The framework extracts tool calls regardless of response format
-
-**Traceability**: US-005
-
-### FR-003: Tool Call Execution Loop
-
-The agent shall automatically execute tool calls returned by the LLM:
-- Extract tool calls via `_extractToolCalls()` which supports both `message.tool_calls` and `content[{type="tool_calls"}]` formats
-- Build `agentContext` via `prepareContext()` (pluggable hook)
-- Format messages via `formatMsgForLLM()` to OpenAI format
-- Extract tool calls → loop: execute tool calls → append results to history → call LLM again
-- Break loop when LLM response has no tool calls
-- Support `terminate` flag: if `toolResultBatch.terminate == true`, break loop and return final response
-
-**Traceability**: US-003
-
-### FR-004: Tool Discovery and Registration
-
-The agent shall:
-- Query MCP server via `mcpserver("tools/list")` (JSON-RPC 2.0) to discover tools
-- Support pagination via `nextCursor` field
-- Wrap each MCP tool definition as an `agentTool` with `execute` hook that calls `mcpserver("tools/call", name, args, sessionId)`
-- Register a builtin `listTools` tool that triggers runtime discovery and lists all available tools
-- Skip tools already registered (idempotent)
-
-**Traceability**: US-002
-
-### FR-005: Tool Call Lifecycle Hooks
-
-The agent shall support two lifecycle hooks:
-- `beforeToolCall(beforeToolCallContext, signal)` — can block execution by returning `beforeToolCallResult(true, reason)`
-- `afterToolCall(afterToolCallContext)` — can transform result by returning modified `agentToolResult`
-- Errors from hooks are caught and converted to `immediateOutcome`/error tool result messages (never throw)
-
-**Traceability**: US-004
-
-### FR-006: Message Type Conversion
-
-The agent shall:
-- Convert internal message types (`userMessage`, `assistantMessage`, `assistantMessageToolCall`, `toolResultMessage`) to OpenAI format via `_userMessageToOpenAI()`, `_assistantMessageToOpenAI()`, `_assistantMessageToolCallToOpenAI()`, `_toolResultMessageToOpenAI()`
-- Convert internal `messageContent` types (`textContent`, `imageContent`, `reasoningContent`) to OpenAI content blocks via `_messageContentToBlocks()`
-- Convert `agentTool` definitions to OpenAI function tool format via `_toolsToOpenAI()`
-
-**Traceability**: US-001
-
-## 4. Non-Functional Requirements (NFRs)
-
-### 4.1 Performance & Scalability
-
-- **NFR-101**: Agent loop message processing overhead <500ms per sequential tool call (measured KPI: KPI-001)
-- **NFR-102**: MCP tool discovery for 50+ tools completes within 2 seconds (measured KPI: KPI-002)
-- **NFR-103**: Channel buffers prevent unbounded memory growth — `inputChannel` (16), `followUpChannel` (32), `outputChannel` (16)
-
-### 4.2 Availability & Reliability
-
-- **NFR-201**: Agent loop catches all errors via `try/catch` in `_agentLoop()` and calls `eventSink(errMsg)` before exit (measured by event sink logging)
-- **NFR-202**: `prepareToolCall()` never throws errors — validation failures return `immediateOutcome` with error tool result
-
-### 4.3 Privacy & Security
-
-- **Data Classification**: Tool arguments and results may contain PII (user messages); MCP tool results treated as untrusted input
-- **No built-in auth/authz**: Responsibility of `mcpServer` implementation and `beforeToolCall` hook
-- **Input validation**: JSON schema validation via `validateToolArguments()` checks required fields only (tools need custom validation via `validateRequiredArgs` hook)
-
-### 4.4 Observability & Telemetry
-
-- **Required Logs**: `agentLoop` events (via `eventSink`) emit state machine steps (`_agentLoop 1` through `_agentLoop 6`), `_processMessage` steps (1 through 18), `prepareToolCall` steps (1 through 10)
-- **Critical Metrics**:
-  - `agent._state.messages.length` — conversation history size (logged at each step)
-  - `toolExecutionStart`/`toolExecutionEnd` events (via `toolExecStartEvent`, `toolExecEndEvent` structs)
-- **Retention**: No built-in log retention; agent is a library, does not manage external logging
-
-## 5. Acceptance Conditions
-
-- [ ] **FR-001**: Agent processes messages through channel system with priority ordering (inputChannel > followUpChannel)
-- [ ] **FR-002**: `llmCall` is pluggable; response formats supported include `message.tool_calls` dict and `content` array blocks
-- [ ] **FR-003**: Agent loops on tool calls until LLM returns no tool calls or terminate flag is set
-- [ ] **FR-004**: MCP tools are discovered via `tools/list` with pagination; all registered tools are callable via `tools/call`
-- [ ] **FR-005**: `beforeToolCall` hook can block; `afterToolCall` hook can transform result; errors return error tool result
-- [ ] **FR-006**: All message types (userMessage, assistantMessage, assistantMessageToolCall, toolResultMessage) convert to OpenAI format
-- [ ] **NFR-101**: Message processing overhead <500ms per sequential tool call
-- [ ] **NFR-102**: MCP discovery for 50+ tools completes <2 seconds
-- [ ] **NFR-201**: Agent loop catches errors and exits gracefully without panics

docs/2-solution_design.md

@@ -1,119 +0,0 @@
-# Solution Design: YiemAgent Agent Framework
-
-## 1. Problem Decomposition
-
-| Problem | Description | User Impact |
-|---------|-------------|-------------|
-| P-001 | Developers need a chatbot framework in Julia that handles LLM calls and tool calls without writing boilerplate | Must write separate request/response handling, tool call parsing, and loop management code for each agent |
-| P-002 | Tool discovery is manual and repetitive | Must register every tool individually; no automatic discovery from MCP-compatible servers |
-| P-003 | Multiple LLM providers have different response formats | Must handle `message.tool_calls` (LMStudio/vLLM) vs `content` blocks (OpenAI API) separately |
-| P-004 | Tool execution needs lifecycle hooks for observability and control | No built-in before/after hooks for authorization, logging, or result sanitization |
-| P-005 | Agent state management is error-prone | Must handle message history, tool registration, and pending tool call tracking manually |
-
-## 2. Solution Approach
-
-**Approach**: "Build a channel-based agent system where the LLM talks to tools automatically, tools talk to MCP servers automatically, and you compose agents by injecting `llmCall` and `mcpServer` functions — the framework owns the loop, the state, and the format conversion."
-
-**Key Principles**:
-- **Channel-based async processing**: Each agent owns three channels (`inputChannel`, `followUpChannel`, `outputChannel`); a background `@spawn` task runs the agent loop, processing messages in priority order
-- **Tool call loop**: The agent calls `llmCall()`, extracts tool calls, executes them (sequentially or in parallel), appends results, and calls `llmCall()` again — repeating until no tool calls remain
-- **Pluggable hooks**: `prepareContext`, `formatMsgForLLM`, `beforeToolCall`, `afterToolCall`, and `llmCall` are all injectable functions — the framework provides sensible defaults, but you override what you need
-- **MCP-native tool discovery**: Tools are discovered at startup via MCP `tools/list` and can be discovered at runtime via the `listTools` builtin tool (idempotent registration with pagination support)
-- **Format-agnostic LLM responses**: `_extractToolCalls()` handles both OpenAI content blocks and LMStudio/vLLM nested message format, allowing the same agent to work with any model provider
-
-## 3. Decision Rationale
-
-| Decision ID | Decision | Rationale | Alternatives Rejected |
-|-------------|----------|-----------|----------------------|
-| SD-001 | Use channel-based async processing with `@spawn` task | Provides non-blocking message queues; `inputChannel` blocks if full (backpressure); `followUpChannel` allows async follow-ups while agent is busy | Threading with shared state — race conditions; callback-based async — hard to compose |
-| SD-002 | Background `_agentLoop` task runs message dispatch loop | Isolates message processing from construction; agent can be created and used concurrently | Synchronous agent.run(message) — blocks caller; manual task management — boilerplate |
-| SD-003 | `_processMessage` runs `llmCall()` in an inner loop (while loop) until no tool calls remain | Handles multi-turn tool use transparently — agent decides when to stop based on LLM response | External loop in caller code — duplicates agent loop logic; single-turn only — no multi-step tool use |
-| SD-004 | MCP server integration via callable struct `mcpServer(method, args...)` | MCP is model-agnostic protocol; callable struct allows type-stable single dispatch; supports NATS transport (struct has `natsConn`, `topic`, `senderID` fields) | Ad-hoc HTTP client for MCP — breaks NATS-only transports; tight coupling to specific transport |
-| SD-005 | Tool call lifecycle: `prepareToolCall()` → `executePreparedToolCall()` → `finalizeExecutedToolCall()` — three phases, never throw errors | Preparation validates args and runs `beforeToolCall` hook (can block); execution calls `tool.execute()`; finalization runs `afterToolCall` hook. Each phase returns a result type, not exceptions — agent loop catches all and builds error tool result | `try/catch` in loop — loses context; throwing errors breaks tool result message contract |
-| SD-006 | Message types are Julia structs with keyword constructors (`userMessage(content=[...])`) | Type stable; keyword constructors provide defaults (`role="user"`, `timestamp=now()`); supports both positional and keyword usage | `Dict{String, Any}` for messages — lose type safety, docgen, IDE support |
-| SD-007 | `eventSink` function passed to all deep lifecycle calls | Provides hooks for external monitoring (logging, metrics, NATS pub/sub) without modifying core logic | Built-in `println` — can't disable or redirect; no metrics integration |
-| SD-008 | `abortSignal` struct with `.aborted::Bool` field passed to `prepareToolCall()` and hooks | Simple mutable state for interrupting long-running tool calls (no async cancellation — just flag check) | Threading with `Task.cancel()` — Julia thread model doesn't support it; `@async` spawn — complex cleanup |
-| SD-009 | `_extractToolCalls()` supports both `message.tool_calls` and `content[{type="tool_calls"}]` formats | LMStudio.jl/vLLM vs OpenAI API have different response shapes; framework must work with both | Separate parser per provider — maintenance burden; require provider wrapper — adds indirection |
-
-## 4. High-Level Component Diagram
-
-```mermaid
-flowchart TD
-    User[External Caller] -->|promptAgent / followUp| InputCh[inputChannel]
-    
-    subagent AgentLoop [Agent Loop Task]
-        InputCh -->|take!| Disp[Message Dispatcher]
-        Disp -->|spawn| Proc[_processMessage]
-        Proc -->|Channel| PrCh[processMessageInputCh]
-    end
-    
-    subagent Message Pipeline
-        PrCh -->|put!| DMsg[Drain input, convert to userMessage]
-        DMsg --> PC[prepareContext hook]
-        PC --> FM[formatMsgForLLM hook]
-        FM --> LLM[llmCall]
-        LLM --> EC[_extractToolCalls]
-        EC -->|hasToolCalls| TC[executeToolCalls]
-        TC -->|toolResultBatch| TM[toolResultMessage]
-        TM -->|push!| MH[messageHistory]
-        TC -->|no tool calls break| BRK[break loop]
-        BRK --> End[exit]
-    end
-    
-    subagent Tool System
-        TC --> Prep[prepareToolCall]
-        Prep --> Exec[executePreparedToolCall]
-        Exec --> Term[finalizeExecutedToolCall]
-        Term --> CRM[createToolResultMessage]
-        CRM --> TM
-    end
-    
-    subagent MCP Integration
-        Prep --> ToolLookup[context.tools lookup]
-        ToolLookup --> MCP[MCP tools/list + tools/call]
-        MCP --> Wr[_wrap_mcp_tool]
-        Wr --> Reg[toolRegistry.registerAllTools]
-    end
-    
-    OutputCh[outputChannel] --> User
-    Proc -->|final response| OutputCh
-```
-
-**Component Descriptions**:
-- **Agent Loop Task** (`SD-001`, `SD-002`): Runs `@spawn _agentLoop()`; manages three channels; dispatches messages to `_processMessage`
-- **Message Pipeline** (`SD-003`, `SD-004`): `prepareContext` → `formatMsgForLLM` → `llmCall` → extract tool calls → loop
-- **Tool System** (`SD-005`): Three-phase lifecycle (prepare → execute → finalize); never throws; returns error tool result on failure
-- **MCP Integration** (`SD-004`): `tools/list` discovery with pagination; `_wrap_mcp_tool` creates executable `agentTool` with MCP JSON-RPC calls
-
-## 5. Alternatives Considered
-
-| Alternative | Pros | Cons | Decision |
-|-------------|------|------|----------|
-| Synchronous `agent.process(message)` | Simpler API; no channels or tasks | Blocks caller; no follow-up support; harder to test concurrency | **Rejected** — async channels needed for real-time chat UX |
-| JSON dicts for messages everywhere | Flexible; no type definitions | No static checking; no docgen; no IDE autocomplete; harder to serialize/deserialize | **Rejected** — Julia advantages of structs (type-stable, documented) outweigh cost |
-| Single `onToolCall` hook (pre-execute only) | Simpler hook interface | Can't sanitize results post-execution; can't set terminate flag after tool completes | **Rejected** — `afterToolCall` needed for masking secrets, normalizing usage, inspecting error status |
-| Direct HTTP calls to MCP servers (no NATS) | Simpler dependency (no NATS) | Breaks if MCP uses NATS (common in microservices); no single transport | **Rejected** — callable struct abstracts transport; NATS is one implementation option |
-| External orchestrator runs tool call loop | Caller has full control | Duplicates logic; every caller must implement same loop; harder to maintain | **Rejected** — loop is agent framework responsibility (single source of truth) |
-
-## 6. Risk Assessment
-
-| Risk | Impact | Probability | Mitigation |
-|------|--------|-------------|------------|
-| `llmCall` throws (network timeout, API error) | Agent loop exits with error in `eventSink` | Medium | `try/catch` in `_processMessage` and `_agentLoop`; eventSink receives error string |
-| Tool `prepareArguments` or `validateRequiredArgs` hook throws | `prepareToolCall` catches and returns `immediateOutcome` error | Low | Hook errors caught; error tool result sent to LLM for recovery |
-| MCP server unreachable | Tool discovery returns 0 tools; `listTools` still available | Medium | `register_mcp_tools` returns 0 on error; tool execution calls catch errors |
-| Long-running tool call hangs | Caller blocks on `outputChannel`; no built-in timeout | Medium | `eventSink` passes abort signal; caller sets `signal.aborted = true` externally |
-| Multiple agents share same `mcpServer` struct | `mcpServer` must be thread-safe for concurrent tool calls | Low | Each thread gets own `mcpServer` instance; NATS handles concurrency |
-| Message history grows unbounded | Memory exhaustion in long sessions | Medium | No built-in pruning; `prepareContext` can filter/prune messages (TODO in code) |
-
-## 7. Requirements Traceability
-
-| Solution Component | Requirement ID | Decision ID | Description |
-|-------------------|----------------|-------------|-------------|
-| Channel-based async processing | FR-001 | SD-001, SD-002 | `inputChannel`, `followUpChannel`, `outputChannel` with priority dispatch |
-| LLM call loop | FR-003 | SD-003 | `_processMessage` while loop; `llmCall()` → extract → execute → append → repeat |
-| MCP tool discovery | FR-004 | SD-004, SD-009 | `tools/list` with pagination; `tools/call` execution |
-| Lifecycle hooks | FR-005 | SD-005 | `beforeToolCall` can block; `afterToolCall` transforms; never throw |
-| Message type conversion | FR-006 | SD-006 | Julia structs with keyword constructors; `_userMessageToOpenAI`, `_messageContentToBlocks` |
-| Format-agnostic extraction | FR-002 | SD-009 | `_extractToolCalls` handles both `message.tool_calls` and `content` blocks |
-| Event sink observability | NFR-201, NFR-103 | SD-007 | `eventSink` passed to all lifecycle steps; logs state machine transitions |

docs/3-walkthrough.md

@@ -1,318 +0,0 @@
-# Walkthrough: YiemAgent Agent Framework
-
-## Structural Guidance
-
-- One **overview sequence diagram** per participant (high-level flow)
-- **Detailed sub-diagrams** for complex interaction points (tool call loop, MCP discovery)
-- Use sequence diagrams for request/response patterns; lifecycle states for multi-step operations
-
----
-
-## External Caller
-
-_The Developer calling `YiemAgent.jl` via `promptAgent`, `takeResponse`, `followUp`, and `stopAgent`._
-
-### Session Start: Create Agent
-
-**WT-001**: Developer creates an `yiemAgent` instance with `llmCall` and `mcpServer`
-
-> Cross-reference: `2-solution_design.md:SD-001` (channel-based async), `SD-004` (MCP struct)
-
-**Details**:
-```julia
-mcp = mcpServer(conn, topic, senderID)
-agent = yiemAgent(llmCall, mcpServer=mcp, systemPrompt="You are a helpful assistant")
-```
-
-**Sequence**:
-```mermaid
-sequenceDiagram
-    participant Dev as Developer
-    part agent YiemAgent Constructor
-    Dev->>agent: yiemAgent(llmCall, ; mcpServer=mcp)
-    agent->>agent: create channels (input=16, followUp=32, output=16)
-    agent->>agent: registerAllTools(tools, mcp)
-    agent->>mcp: mcp("tools/list", sessionId)
-    mcp-->>agent: response (tools array + nextCursor)
-    agent->>agent: _wrap_mcp_tool for each tool
-    agent->>agent: registerTool(tools, listTools_tool)
-    agent->>agent: println("[toolRegistry] Discovered N MCP tools")
-    agent->>agent: yiemAgent struct with placeholder task
-    agent->>agent: @spawn _agentLoop(agent)
-    agent-->>Dev: yiemAgent instance
-```
-
-> **Traceability**: FR-004 (tool discovery), US-001 (agent creation)
-
-### Send Message: User Prompt
-
-**WT-002**: Developer sends a user message to the agent
-
-> Cross-reference: `2-solution_design.md:SD-002` (background task dispatch)
-
-**Sequence**:
-```mermaid
-sequenceDiagram
-    participant Dev as Developer
-    part agent AgentLoop
-    Dev->>agent: promptAgent(agent, text="What's the weather?")
-    agent->>agent: put!(inputChannel, msg_dict)
-    Dev->>agent: takeResponse(agent)  # blocks
-    agent->>agent: inputChannel isready
-    agent->>agent: take!(inputChannel)
-    agent->>Dev: (returns agent for chaining)
-```
-
-> **Traceability**: FR-001 (channel processing), PR-001, SD-001, SD-002
-
-### Receive Response
-
-**WT-003**: Agent returns response on `outputChannel`
-
-**Sequence**:
-```mermaid
-sequenceDiagram
-    participant Dev as Developer
-    part agent AgentLoop
-    AgentLoop->>AgentLoop: _processMessage runs
-    AgentLoop->>AgentLoop: while loop, llmCall, tool calls...
-    AgentLoop->>AgentLoop: no more tool calls → exit loop
-    AgentLoop->>AgentLoop: filter reasoningContent from final result
-    AgentLoop->>AgentLoop: _assistantMessageToOpenAI(result)
-    AgentLoop->>AgentLoop: put!(outputChannel, respondToUI)
-    AgentLoop->>Dev: take!(outputChannel)  # unblocks
-    Dev->>Dev: receive OpenAI dict response
-```
-
-> **Traceability**: FR-001, FR-003, FR-006, PR-001
-
-### Send Follow-up While Agent Busy
-
-**WT-004**: Developer sends a follow-up message while agent is processing a tool call
-
-**Sequence**:
-```mermaid
-sequenceDiagram
-    participant Dev as Developer
-    part agent AgentLoop
-    Dev->>agent: promptAgent(text="What's the weather in Tokyo?")  # agent starts processing
-    AgentLoop->>AgentLoop: _processMessage started (processingTask = Task)
-    Dev->>agent: followUp(text="Also check Seoul")  # while busy
-    agent->>agent: put!(followUpChannel, msg_dict)
-    AgentLoop->>AgentLoop: _processMessage completes (tool calls done)
-    AgentLoop->>AgentLoop: outputChannel has message → put!(outputChannel, response), take!(next msg)
-    AgentLoop->>AgentLoop: no inputChannel → check followUpChannel
-    AgentLoop->>AgentLoop: isready(followUpChannel) → take!(followUpMsg), put!(inputChannel, followUpMsg)
-    AgentLoop->>AgentLoop: processingTask = nothing → reset
-    AgentLoop->>AgentLoop: loop back → take!(inputChannel) from followUp
-    Dev->>agent: takeResponse() → blocks
-    AgentLoop->>Dev: next response via outputChannel
-```
-
-> **Traceability**: FR-001 (follow-up priority), PR-001, SD-001, SD-002
-
-### Stop Agent
-
-**WT-005**: Developer stops the agent gracefully
-
-**Sequence**:
-```mermaid
-sequenceDiagram
-    participant Dev as Developer
-    part agent AgentLoop
-    Dev->>agent: stopAgent(agent)
-    agent->>agent: put!(inputChannel, :shutdown)
-    AgentLoop->>AgentLoop: check newUserMsg === :shutdown
-    AgentLoop->>AgentLoop: drain inputChannel (while isready)
-    AgentLoop->>AgentLoop: drain followUpChannel (while isready)
-    AgentLoop->>AgentLoop: break, catch, fetch(_agentLoop)
-    Dev->>agent: close(inputChannel, outputChannel, followUpChannel)
-    Dev-->>Dev: nothing (agent destroyed)
-```
-
-> **Traceability**: FR-001, NFR-201 (graceful exit), PR-001
-
----
-
-## LLM Provider
-
-_The external LLM API (e.g. Anthropic, OpenAI, LMStudio) receiving formatted messages and returning responses._
-
-### Receive Formatted Message
-
-**WT-010**: Agent sends LLM request in OpenAI format
-
-> Cross-reference: `2-solution_design.md:SD-003` (formatMsgForLLM)
-
-**Sequence**:
-```mermaid
-sequenceDiagram
-    part agent AgentLoop
-    AgentLoop->>AgentLoop: preparedContext = prepareContext(state)
-    AgentLoop->>AgentLoop: formattedMessages = formatMsgForLLM(preparedContext)
-    Note over AgentLoop: messages: [system, user, assistant, tool, ...]<br/>tools: [{"function": {"name": "getWeather", "parameters": {...}}}]
-    agentLoop->>llmCall: llmCall(formattedMessages, eventSink=eventSink)
-    llmCall->>Provider: HTTP POST /v1/chat/completions (JSON)
-    Provider-->>llmCall: JSON response
-    llmCall-->>AgentLoop: response = JSON.Object{String, Any}(...)
-```
-
-> **Traceability**: FR-002, SD-003, SD-009
-
-### Receive Response: No Tool Calls
-
-**WT-11**: Agent receives text-only response
-
-**Sequence**:
-```mermaid
-sequenceDiagram
-    llmCall-->>AgentLoop: response = {finish_reason=> "stop", message=> {role=> "assistant", content=> "It's 22°C in Bangkok"}}
-    AgentLoop->>AgentLoop: _extractToolCalls(response)
-    AgentLoop->>AgentLoop: hasToolCalls = false
-    AgentLoop->>AgentLoop: assistant_msg = assistantMessage(content=[textContent("It's 22°C")], ...)
-    AgentLoop->>AgentLoop: push!(agentMsgHistory, assistant_msg)
-    AgentLoop->>AgentLoop: break (no tool calls)
-    AgentLoop->>AgentLoop: filter reasoningContent (if any)
-    AgentLoop->>AgentLoop: create final assistantMessage
-    AgentLoop->>AgentLoop: put!(outputChannel, OpenAI format dict)
-```
-
-> **Traceability**: FR-003 (loop exit), FR-006 (OpenAI conversion), PR-003
-
-### Receive Response: With Tool Calls
-
-**WT-012**: Agent receives tool call response
-
-**Sequence**:
-```mermaid
-sequenceDiagram
-    llmCall-->>AgentLoop: response = {finish_reason=> "tool_calls",<br/>message=> {role=> "assistant",<br/>reasoning_content=> "I need to call getWeather...",<br/>tool_calls=> [{"id"=>"call_1", "function"=>{name=>"getWeather", arguments=>"{\"city\":\"Bangkok\"}"}}, ...]}}
-    AgentLoop->>AgentLoop: _extractToolCalls(response)
-    AgentLoop->>AgentLoop: hasToolCalls = true<br/>toolCallList = [agentToolCall("getWeather", ...)]
-    AgentLoop->>AgentLoop: assistant_msg = assistantMessageToolCall(toolCalls, reasoningContent, ...)
-    AgentLoop->>AgentLoop: push!(agentMsgHistory, assistant_msg)
-    AgentLoop->>AgentLoop: config = agentLoopConfig(beforeToolCall, afterToolCall, parallel="sequential")
-    AgentLoop->>AgentLoop: toolResultBatch = executeToolCalls(...)
-    ...
-```
-
-> **Traceability**: FR-002, FR-003, PR-003, SD-003, SD-005, SD-009
-
----
-
-## MCP Server
-
-_The Model Context Protocol server providing tools via JSON-RPC 2.0 (transport via NATS or HTTP).
-
-### Receive tools/list Request
-
-**WT-020**: Agent discovers tools on startup
-
-> Cross-reference: `2-solution_design.md:SD-004` (MCP integration)
-
-**Sequence**:
-```mermaid
-sequenceDiagram
-    part agent AgentLoop
-    agent->>mcp: mcp("tools/list", sessionId; eventSink=eventSink)
-    Note over mcp: JSON-RPC 2.0: {"jsonrpc"=>"2.0", method=>"tools/list", "id"=>..., "params"=>{...}}
-    MCP_server-->>agent: response = {<br/>  "result" => {<br/>    "tools" => [name, label, description, inputSchema], ...<br/>    , "nextCursor" => 123<br/>  }<br/>}
-    agent->>agent: for tool_def in tools_array<br/>  _wrap_mcp_tool(mcp, tool_def)
-    agent->>agent: while cursor !== nothing → paginate
-    agent->>agent: println("[toolRegistry] Discovered N MCP tools. Total: $(length(tools))")
-```
-
-> **Traceability**: FR-004 (MCP discovery), PR-004
-
-### Receive tools/call Request
-
-**WT-021**: Agent executes tool via MCP
-
-**Sequence**:
-```mermaid
-sequenceDiagram
-    part agent Tool System
-    agent->>mcp: mcp("tools/call", "getWeather", args={"city" => "Bangkok"}, sessionId; eventSink=eventSink)
-    Note over mcp: JSON-RPC 2.0: {"jsonrpc"=>"2.0", method=>"tools/call", "params"=>{name, arguments, sessionId}}
-    MCP_server-->>agent: response = {<br/>  "result" => {<br/>    "content" => [{"type"=>"text", "text"=>"Sunny, 22°C"}],<br/>    "isError" => false<br/>  }<br/>}
-    agent->>agent: check haskey(response, "error") → no
-    agent->>agent: result_data = response["result"]
-    agent->>agent: content_text = _extract_text_content(result_data)
-    agent->>agent: return agentToolResult([textContent(content_text)], details={isError=>false}, nothing, false)
-```
-
-> **Traceability**: FR-004 (tool execution), PR-004, SD-004, SD-005
-
-### Receive Error from MCP Server
-
-**WT-022**: MCP server returns error
-
-**Sequence**:
-```mermaid
-sequenceDiagram
-    MCP_server-->>agent: response = {<br/>  "error" => {<br/>    "code" => -32601,<br/>    "message" => "Method not found"<br/>  }<br/>}
-    agent->>agent: haskey(response, "error") → true
-    agent->>agent: return agentToolResult(<br/>    [textContent("MCP error: Method not found")],<br/>    details={"error" => "Method not found"},<br/>    nothing, false<br/>)
-    agent->>AgentLoop: finalOutcome(error=true)
-    agent->>AgentLoop: push!(agentMsgHistory, toolResultMessage(...))
-    agent->>AgentLoop: next LLM turn receives error result
-```
-
-> **Traceability**: FR-004 (error handling), NFR-202 (never throw), PR-004, SD-005
-
----
-
-## Error Paths and Edge Cases
-
-**WT-030**: LLM times out (network error)
-
-> Same participant POV — from caller's perspective, they block on `takeResponse()` until task exits
-
-```mermaid
-sequenceDiagram
-    participant Dev as Developer
-    part agent AgentLoop
-    Dev->>agent: takeResponse(agent)  # blocks
-    agent->>llmCall: llmCall(messages)  # calls external API
-    lltimout
-    LLM provider-->>AgentLoop: ERROR (timeout)
-    AgentLoop->>AgentLoop: @error "Agent loop failed"
-    AgentLoop->>AgentLoop: eventSink("error: ...")
-    AgentLoop->>Dev: fetch(_agentLoop) throws TaskFailedException
-    Dev->>agent: stopAgent(agent)  # cleanup channels
-```
-
-**Traceability**: PR-001, NFR-201 (catch error and exit), SD-001
-
-**WT-031**: Tool execution throws exception
-
-```mermaid
-sequenceDiagram
-    agent->>agent: executePreparedToolCall
-    agent->>tool: tool.execute(toolCallId, args, signal, eventSink)
-    note over tool: tool raises exception (e.g., network error, invalid JSON)
-    tool-->>agent: throw Exception
-    agent->>agent: catch e
-    agent->>agent: executedOutcome(createErrorToolResult("error message"), true)
-    agent->>AgentLoop: finalizeExecutedToolCall
-    AgentLoop->>AgentLoop: createToolResultMessage(error=true)
-    AgentLoop->>AgentLoop: push!(history, toolResultMessage)
-    AgentLoop->>AgentLoop: next llmCall with error in message history
-```
-
-**Traceability**: FR-005 (hook errors caught), NFR-202 (never throw), SD-005
-
-**WT-032**: beforeToolCall hook blocks execution
-
-```mermaid
-sequenceDiagram
-    agent->>agent: beforeToolCall(beforeCtx, signal, eventSink)
-    agent->>agent: returns beforeToolCallResult(true, "User must approve")
-    agent->>agent: return immediateOutcome(createErrorToolResult("Tool blocked"), true)
-    agent->>AgentLoop: executeToolCalls → immediateOutcome
-    AgentLoop->>AgentLoop: finalizeExecutedToolCall
-    AgentLoop->>AgentLoop: createToolResultMessage(error=true)
-    AgentLoop->>AgentLoop: push! to history
-```
-
-**Traceability**: FR-005 (beforeToolCall can block), SD-005

docs/4-specification.md

@@ -1,518 +0,0 @@
-# Specification: YiemAgent Agent Framework
-
-## 1. Message Type System (`src/type.jl`)
-
-### SP-001: userMessage
-
-| Field | Type | Required | Description |
-|-------|------|----------|-------------|
-| `role::String` | `String` | Yes | Always `"user"` |
-| `content::Vector{messageContent}` | `Vector{messageContent}` | No | Plain text and/or image content |
-| `timestamp::Timestamp` | `DateTime` | Yes | When the message was sent |
-
-**Keyword Constructor**: `userMessage(; role="user", content=[...], timestamp=now())`
-
-**Traceability**:
-- Requirement: FR-006 (message type conversion)
-- Solution Design: SD-006 (Julia structs for messages)
-
-### SP-002: assistantMessage
-
-| Field | Type | Required | Description |
-|-------|------|----------|-------------|
-| `role::String` | `String` | Yes | Always `"assistant"` |
-| `content::Vector{messageContent}` | `Vector{messageContent}` | Yes | Text and/or image content |
-| `api::String` | `String` | Yes | API name used (e.g., `"openai"`) |
-| `provider::String` | `String` | Yes | Provider name (e.g., `"anthropic"`) |
-| `model::String` | `String` | Yes | Model identifier |
-| `usage::llmUsage` | `llmUsage` | Yes | Token usage (in/out) |
-| `stopReason::String` | `String` | Yes | Why generation stopped (e.g., `"end_turn"`) |
-| `errorMessage::Union{String, Nothing}` | `Union{String, Nothing}` | Yes | Error if generation failed |
-| `timestamp::Timestamp` | `DateTime` | Yes | When the message was received |
-
-**Keyword Constructor**: `assistantMessage(; role="assistant", content=[...], api="", provider="", model=nothing, usage=llmUsage(0,0), stopReason="end_turn", errorMessage=nothing, timestamp=now())`
-
-**Traceability**:
-- Requirement: FR-006 (message type conversion)
-- Solution Design: SD-006
-
-### SP-003: assistantMessageToolCall
-
-| Field | Type | Required | Description |
-|-------|------|----------|-------------|
-| `role::String` | `String` | Yes | Always `"assistant"` |
-| `toolCalls::Vector{agentToolCall}` | `Vector{agentToolCall}` | Yes | Tool calls to execute |
-| `content::Vector{messageContent}` | `Vector{messageContent}` | Yes | Reasoning/thinking content blocks |
-| `api::String` | `String` | Yes | API name used |
-| `provider::String` | `String` | Yes | Provider name |
-| `model::String` | `String` | Yes | Model identifier |
-| `usage::llmUsage` | `llmUsage` | Yes | Token usage |
-| `stopReason::String` | `String` | Yes | Why generation stopped (e.g., `"tool_calls"`) |
-| `errorMessage::Union{String, Nothing}` | `Union{String, Nothing}` | Yes | Error if generation failed |
-| `timestamp::Timestamp` | `DateTime` | Yes | When the message was received |
-
-**Keyword Constructor**: `assistantMessageToolCall(; role="assistant", toolCalls=[...], content=[], api="", provider="", model=nothing, usage=llmUsage(0,0), stopReason="tool_calls", errorMessage=nothing, timestamp=now())`
-
-**Traceability**:
-- Requirement: FR-003 (tool call loop)
-- Solution Design: SD-005 (lifecycle phases), SD-009 (format-agnostic LLM responses)
-
-### SP-004: toolResultMessage
-
-| Field | Type | Required | Description |
-|-------|------|----------|-------------|
-| `role::String` | `String` | Yes | Always `"toolResult"` (note: `"tool"` in example) |
-| `toolCallId::String` | `String` | Yes | ID matching the tool call |
-| `toolName::String` | `String` | Yes | Name of the executed tool |
-| `content::Vector{messageContent}` | `Vector{messageContent}` | Yes | Tool output content |
-| `details::Any` | `Any` | Yes | Additional tool-specific details |
-| `usage::Union{llmUsage, Nothing}` | `Union{llmUsage, Nothing}` | Yes | Token usage if applicable |
-| `addedToolNames::Union{Vector{String}, Nothing}` | `Union{Vector{String}, Nothing}` | Yes | Tools added during execution |
-| `isError::Bool` | `Bool` | Yes | Whether the tool call resulted in an error |
-| `timestamp::Timestamp` | `DateTime` | Yes | When the result was recorded |
-
-**Keyword Constructor**: `toolResultMessage(; role="tool", toolCallId="", toolName="", content=[], details=nothing, usage=nothing, addedToolNames=nothing, isError=false, timestamp=now())`
-
-**Traceability**:
-- Requirement: FR-005 (tool lifecycle)
-- Solution Design: SD-005
-
-### SP-005: agentToolCall
-
-| Field | Type | Required | Description |
-|-------|------|----------|-------------|
-| `type::String` | `String` | Yes | Always `"function"` |
-| `id::String` | `String` | Yes | Unique tool call identifier |
-| `name::String` | `String` | Yes | Tool name |
-| `arguments::Dict{String, Any}` | `Dict{String, Any}` | Yes | Parsed tool arguments |
-
-**Traceability**:
-- Requirement: FR-003 (tool call extraction)
-- Solution Design: SD-009 (extract tool calls from LLM response)
-
-### SP-006: messageContent Types
-
-**textContent** (`SP-006.1`):
-```julia
-struct textContent <: messageContent
-    text::String
-end
-```
-
-**imageContent** (`SP-006.2`):
-```julia
-struct imageContent <: messageContent
-    data::String                   # Base64-encoded image data
-    mimeType::String               # MIME type (e.g., "image/png")
-end
-```
-
-**reasoningContent** (`SP-006.3`):
-```julia
-struct reasoningContent <: messageContent
-    text::String
-end
-```
-
-**Traceability**:
-- Requirement: FR-006 (message content conversion)
-- Solution Design: SD-006
-
-## 2. LLM Model Configuration (`src/type.jl`)
-
-### SP-007: llmModel
-
-| Field | Type | Required | Description |
-|-------|------|----------|-------------|
-| `id::String` | `String` | Yes | Unique model identifier |
-| `name::String` | `String` | Yes | Human-readable model name |
-| `provider::String` | `String` | Yes | Provider name (e.g., `"anthropic"`, `"openai"`) |
-| `baseUrl::String` | `String` | Yes | API endpoint base URL |
-| `reasoning::Bool` | `Bool` | Yes | Whether the model supports chain-of-thought |
-| `input::Vector{String}` | `Vector{String}` | Yes | Supported input modalities (e.g., `"text"`, `"image"`) |
-| `cost::modelCost` | `modelCost` | Yes | Pricing information |
-| `contextWindow::Int64` | `Int64` | Yes | Maximum context length in tokens |
-| `maxTokens::Int64` | `Int64` | Yes | Maximum output tokens per completion |
-
-**Traceability**:
-- Requirement: FR-002 (LLM agnosticism)
-- Solution Design: SD-003
-
-### SP-008: modelCost
-
-| Field | Type | Required | Description |
-|-------|------|----------|-------------|
-| `input::Float64` | `Float64` | Yes | Price per 1M input tokens |
-| `output::Float64` | `Float64` | Yes | Price per 1M output tokens |
-| `cache_read::Float64` | `Float64` | Yes | Price per 1M cached read tokens |
-| `cache_write::Float64` | `Float64` | Yes | Price per 1M cache write tokens |
-
-**Traceability**:
-- Requirement: N/A (internal tracking data)
-- Solution Design: SD-003
-
-## 3. MCP Tool Integration (`src/toolRegistry.jl`)
-
-### SP-009: tools/list Response Format
-
-**Request**: `mcp("tools/list", sessionId; eventSink=eventSink)`
-
-**Response** (JSON-RPC 2.0):
-```json
-{
-  "result": {
-    "tools": [
-      {
-        "name": "tool_name",
-        "title"?: "Human-readable title",
-        "label"?: "Fallback title",
-        "description": "What the tool does",
-        "inputSchema": {
-          "type": "object",
-          "properties": {
-            "field": { "type": "string", "description": "..." }
-          },
-          "required": ["field"]
-        }
-      }
-    ],
-    "nextCursor"?: "pagination_cursor_or_empty"
-  }
-}
-```
-
-**Response error**:
-```json
-{
-  "error": {
-    "code": -32601,
-    "message": "Method not found"
-  }
-}
-```
-
-**Traceability**:
-- Requirement: FR-004 (MCP discovery)
-- Solution Design: SD-004
-
-### SP-010: tools/call Request/Response Format
-
-**Request**: `mcp("tools/call", toolName, args, sessionId; eventSink=eventSink)`
-
-**Response** (JSON-RPC 2.0):
-```json
-{
-  "result": {
-    "content": [
-      {"type": "text", "text": "Tool output text"}
-    ],
-    "isError": false
-  }
-}
-```
-
-**Response error**:
-```json
-{
-  "result": {
-    "content": [
-      {"type": "text", "text": "Error message"}
-    ],
-    "isError": true
-  }
-}
-```
-
-**Traceability**:
-- Requirement: FR-004 (tool execution)
-- Solution Design: SD-004 (`_wrap_mcp_tool`)
-
-## 4. OpenAI Message Format (`src/utils.jl`)
-
-### SP-011: system message format
-
-```julia
-Dict(
-  "role" => "system",
-  "content" => [Dict("type" => "text", "text" => ctx.systemPrompt)]
-)
-```
-
-**Traceability**:
-- Requirement: FR-006
-- Solution Design: SD-006
-
-### SP-012: userMessage → OpenAI format
-
-```julia
-Dict(
-  "role" => "user",
-  "content" => _messageContentToBlocks(msg.content)
-)
-```
-
-Where `_messageContentToBlocks` converts:
-- `textContent(t)` → `Dict("type" => "text", "text" => t)`
-- `imageContent(data, mime)` → `Dict("type" => "image_url", "image_url" => Dict("url" => "data:$mime;base64,$data"))`
-
-**Traceability**:
-- Requirement: FR-006
-- Solution Design: SD-006
-
-### SP-013: assistantMessageToolCall → OpenAI format
-
-```julia
-Dict(
-  "role" => "assistant",
-  "content" => nothing,
-  "tool_calls" => [
-    Dict(
-      "id" => tc.id,
-      "type" => tc.type,
-      "function" => Dict(
-        "name" => tc.name,
-        "arguments" => JSON.json(tc.arguments)  # serialized as JSON string
-      )
-    )
-  ]
-)
-```
-
-**Traceability**:
-- Requirement: FR-003
-- Solution Design: SD-009
-
-### SP-014: assistantMessage → OpenAI format
-
-```julia
-Dict(
-  "role" => "assistant",
-  "content" => _messageContentToBlocks(msg.content)
-)
-```
-
-**Traceability**:
-- Requirement: FR-006
-- Solution Design: SD-006
-
-### SP-015: toolResultMessage → OpenAI format
-
-```julia
-Dict(
-  "role" => "tool",
-  "tool_call_id" => msg.toolCallId,
-  "content" => _messageContentToBlocks(msg.content)
-)
-```
-
-**Traceability**:
-- Requirement: FR-005, FR-006
-- Solution Design: SD-005
-
-### SP-016: agentTool → OpenAI tools format
-
-```julia
-Dict(
-  "type" => "function",
-  "function" => Dict(
-    "name" => tool.name,
-    "description" => tool.description,
-    "parameters" => tool.inputSchema
-  )
-)
-```
-
-**Traceability**:
-- Requirement: FR-004
-- Solution Design: SD-004, SD-006
-
-## 5. LLM Response Format Agnostic Extraction (`src/agentCore.jl`)
-
-### SP-017: format 1 — LMStudio.jl / vLLM style (dict with nested `message.tool_calls`)
-
-**Response dict** (key: `message::Dict`):
-```json
-{
-  "finish_reason": "tool_calls",
-  "message": {
-    "role": "assistant",
-    "content": "",
-    "reasoning_content": "I need to call getWeather...",
-    "tool_calls": [
-      {
-        "type": "function",
-        "function": {
-          "name": "getWeather",
-          "arguments": "{\"city\":\"Bangkok\"}"  // JSON string
-        },
-        "id": "call_1"
-      }
-    ]
-  }
-}
-```
-
-**When no tool calls**:
-```json
-{
-  "finish_reason": "stop",
-  "message": {
-    "role": "assistant",
-    "content": "It's 22°C in Bangkok"
-  }
-}
-```
-
-**Traceability**:
-- Requirement: FR-002
-- Solution Design: SD-009
-
-### SP-018: format 2 — OpenAI API style (content blocks array)
-
-**Content blocks with tool calls** (`SP-018.1`):
-```json
-{
-  "content": [
-    {
-      "type": "tool_calls",
-      "tool_calls": [
-        {
-          "type": "function",
-          "function": {
-            "name": "getWeather",
-            "arguments": {"city": "Bangkok"}  // already parsed dict
-          },
-          "id": "call_1"
-        }
-      ]
-    }
-  ]
-}
-```
-
-**Content blocks with tool_call (already parsed)** (`SP-018.2`):
-```json
-{
-  "content": [
-    {
-      "type": "tool_call",
-      "name": "getWeather",
-      "arguments": {"city": "Bangkok"},
-      "id": "call_1"
-    }
-  ]
-}
-```
-
-**Content blocks with text only** (`SP-018.3`):
-```json
-{
-  "content": [
-    {"type": "text", "text": "It's 22°C in Bangkok"}
-  ]
-}
-```
-
-**Response dict extras** (common across all formats, `SP-018.4`):
-```json
-{
-  "api": "openai",
-  "provider": "anthropic",
-  "model": "gpt-4",
-  "usage": {"inputTokens": 100, "outputTokens": 50},
-  "error_message": "Optional error"
-}
-```
-
-**Traceability**:
-- Requirement: FR-002
-- Solution Design: SD-009
-
-## 6. Agent Loop Configuration (`src/type.jl` + `src/agentCore.jl`)
-
-### SP-019: agentContext
-
-```julia
-struct agentContext
-    systemPrompt::String
-    messages::Vector{agentMessage}
-    tools::Union{OrderedDict{String, agentTool}, Nothing}  # O(1) lookup by name
-end
-```
-
-**Traceability**:
-- Requirement: FR-003 (context preparation)
-- Solution Design: SD-003, SD-004
-
-### SP-020: agentState
-
-```julia
-mutable struct agentState
-    systemPrompt::String
-    model::Union{llmModel, Nothing}
-    tools::OrderedDict{String, agentTool}      # insertion-ordered
-    messages::Vector{agentMessage}             # history (no system prompt)
-    pendingToolCalls::Vector{String}           # tool call IDs waiting for results
-    errorMessage::Union{String, Nothing}       # last error message
-end
-```
-
-**Traceability**:
-- Requirement: FR-001 (agent state management)
-- Solution Design: SD-006
-
-### SP-021: agentLoopConfig
-
-```julia
-struct agentLoopConfig
-    beforeToolCall::Union{Function, Nothing}
-    afterToolCall::Union{Function, Nothing}
-    toolExecution::String  # "sequential" or "parallel"
-end
-```
-
-**Traceability**:
-- Requirement: FR-005 (lifecycle hooks)
-- Solution Design: SD-005
-
-### SP-022: abortSignal
-
-```julia
-struct abortSignal
-    aborted::Bool
-end
-```
-
-**Traceability**:
-- Requirement: NFR-101 (timeout handling)
-- Solution Design: SD-008
-
-## 7. Tool Validation Hooks (`src/utils.jl`)
-
-### SP-023: validateRequiredArgs (default hook)
-
-**Signature**: `validateRequiredArgs(args::AbstractDict{String, Any}, inputSchema::AbstractDict{String, Any})::Union{Nothing, String}`
-
-**Behavior**:
-- Extract `required = get(inputSchema, "required", Any[])`
-- Check each required field exists in `keys(args)`
-- Return `nothing` if all present
-- Return `"Missing required arguments: $(join(missing, ", "))"` if any missing
-
-**Traceability**:
-- Requirement: NFR-301 (input validation)
-- Solution Design: SD-005
-
-### SP-024: validateToolArguments (wrapper hook)
-
-**Signature**: `validateToolArguments(tool::agentTool, prepared::agentToolCall)::AbstractDict{String, Any}`
-
-**Behavior**:
-- If `tool.validateRequiredArgs !== nothing`: call `tool.validateRequiredArgs(prepared.arguments)`
-- Else: call `validateRequiredArgs(prepared.arguments, tool.inputSchema)`
-- If returns `!nothing`: throw `ArgumentError(result)`
-- Caller (`prepareToolCall`) catches and returns `immediateOutcome`
-
-**Traceability**:
-- Requirement: FR-005 (tool validation)
-- Solution Design: SD-005

docs/6-validation.md

@@ -1,192 +0,0 @@
-# Validation: YiemAgent Agent Framework
-
-## Test Architecture Overview
-
-Because YiemAgent is a Julia library (not a web service), validation tests focus on:
-- **Unit tests**: message type conversion, tool execution logic, format extraction
-- **Integration tests**: full agent loop with stubbed LLM and MCP
-- **Contract tests**: response format compatibility with OpenAI/LMStudio specs
-- **Performance tests**: agent loop overhead for sequential tool calls
-
-Every specification rule (SP-XXX), walkthrough journey (WT-XXX), and requirement (FR-XXX) must have a corresponding test.
-
----
-
-## Test Types Mapping to Source Documents
-
-| Test Type | Source Document | Example Coverage |
-|-----------|-----------------|------------------|
-| **Unit tests** | `4-specification.md` (SP-001..SP-024), `src/utils.jl` (converters) | Message type → OpenAI format conversion; tool argument validation |
-| **Integration tests** | `3-walkthrough.md` (WT-001..WT-032) | Full agent loop: prompt → llmCall → tool extract → execute → response |
-| **Contract tests** | `4-specification.md` SP-017, SP-018 | Response format 1 (LMStudio/vLLM) and format 2 (OpenAI) parsed correctly |
-| **Performance tests** | `1-requirements.md` FR-003, NFR-101 | Agent loop overhead <500ms per sequential tool call |
-| **Penetration tests** | `1-requirements.md` NFR-301 | Tool argument injection; MCP spoofing |
-| **Schema validation** | `4-specification.md` SP-023, SP-024 | Required fields, type checking on tool arguments |
-
----
-
-## Specification-to-Test Execution Trace
-
-Every traceable item — specification rules (SP-XXX), walkthrough journeys (WT-XXX), and requirements (FR-XXX) — must have a runnable test. This trace maps each item to its corresponding test file and test function.
-
-**Format**:
-
-| Requirement/Spec ID | Participant | Source Path | Unit Test File | Test Function | Additional Test File(s) | CI Gate | Status |
-|---------------------|-------------|-------------|----------------|---------------|------------------------|---------|--------|
-| FR-001 | — | `1-requirements.md:3.1` | `tests/unit/channel-handling.jl` | `test_input_channel_priority` | `tests/integration/full-agent-loop.jl` | `ci/julia-unit.yaml` | **TODO** |
-| FR-002 | — | `1-requirements.md:3.2` | `tests/unit/llm-format-extraction.jl` | `test_format1_tool_calls` | `tests/contract/response-format.jl` | `ci/julia-unit.yaml` | **TODO** |
-| FR-003 | — | `1-requirements.md:3.3` | `tests/integration/tool-call-loop.jl` | `test_loop_exits_on_no_tool_calls` | — | `ci/julia-integration.yaml` | **TODO** |
-| FR-004 | — | `1-requirements.md:3.4` | `tests/unit/mcp-tool-discovery.jl` | `test_tools_list_with_pagination` | `tests/contract/mcp-json-rpc.jl` | `ci/julia-unit.yaml` | **TODO** |
-| FR-005 | — | `1-requirements.md:3.5` | `tests/unit/tool-hooks.jl` | `test_beforeToolCall_blocks` | `tests/integration/agent-loop.jl` | `ci/julia-unit.yaml` | **TODO** |
-| FR-006 | — | `1-requirements.md:3.6` | `tests/unit/message-conversion.jl` | `test_userMessage_to_OpenAI` | — | `ci/julia-unit.yaml` | **TODO** |
-| WT-001 | Caller | `3-walkthrough.md:#agent-creation` | `integration/agent-creation.jl` | `test_agent_construction_and_lifecycle` | — | `ci/julia-integration.yaml` | **TODO** |
-| WT-002 | Caller | `3-walkthrough.md:#send-message` | `integration/message-flow.jl` | `test_promptAgent_and_takeResponse` | — | `ci/julia-integration.yaml` | **TODO** |
-| WT-003 | Caller | `3-walkthrough.md:#receive-response` | `integration/response-format.jl` | `test_output_channel_delivery` | — | `ci/julia-integration.yaml` | **TODO** |
-| WT-004 | Caller | `3-walkthrough.md:#follow-up` | `integration/followup.jl` | `test_followUp_while_busy` | — | `ci/julia-integration.yaml` | **TODO** |
-| WT-005 | Caller | `3-walkthrough.md:#stop-agent` | `integration/shutdown.jl` | `test_stopAgent_channel_cleanup` | — | `ci/julia-integration.yaml` | **TODO** |
-| WT-010 | LLM Provider | `3-walkthrough.md:#receive-formatted` | `integration/llm-call.jl` | `test_formatMsgForLLM_output_structure` | — | `ci/julia-integration.yaml` | **TODO** |
-| WT-011 | LLM Provider | `3-walkthrough.md:#response-no-tool-calls` | `integration/non-tool-response.jl` | `test_lmm_response_no_tool_calls_breaks_loop` | — | `ci/julia-integration.yaml` | **TODO** |
-| WT-012 | LLM Provider | `3-walkthrough.md:#response-with-tool` | `integration/tool-response.jl` | `test_lmm_response_with_tool_calls` | — | `ci/julia-integration.yaml` | **TODO** |
-| WT-020 | MCP Server | `3-walkthrough.md:#discover-tools` | `unit/mcp-discovery.jl` | `test_mcp_tools_list_parsed` | `contract/mcp-json-rpc.jl` | `ci/julia-unit.yaml` | **TODO** |
-| WT-021 | MCP Server | `3-walkthrough.md:#execute-tool` | `unit/mcp-tool-execute.jl` | `test_mcp_tools_call_response_parsed` | — | `ci/julia-unit.yaml` | **TODO** |
-| WT-022 | MCP Server | `3-walkthrough.md:#mcp-error` | `unit/mcp-error-handling.jl` | `test_mcp_response_error_returned` | — | `ci/julia-unit.yaml` | **TODO** |
-| WT-030 | Error | `3-walkthrough.md:#llm-timeout` | `integration/llm-timeout.jl` | `test_LLM_timeout_agent_loop_exits` | — | `ci/julia-integration.yaml` | **TODO** |
-| WT-031 | Error | `3-walkthrough.md:#tool-throw` | `integration/tool-error.jl` | `test_tool_execute_throws_returns_error_result` | — | `ci/julia-integration.yaml` | **TODO** |
-| WT-032 | Error | `3-walkthrough.md:#beforeToolCall-block` | `unit/beforeToolCall-block.jl` | `test_beforeToolCall_blocks_execution` | — | `ci/julia-unit.yaml` | **TODO** |
-| SP-001 | — | `4-specification.md:#SP-001:userMessage` | `unit/message-types.jl` | `test_userMessage_struct_default_roles` | — | `ci/julia-unit.yaml` | **TODO** |
-| SP-002 | — | `4-specification.md:#SP-002:assistantMessage` | `unit/message-types.jl` | `test_assistantMessage_struct_default_stopReason_end_turn` | — | `ci/julia-unit.yaml` | **TODO** |
-| SP-003 | — | `4-specification.md:#SP-003:assistantMessageToolCall` | `unit/message-types.jl` | `test_assistantMessageToolCall_struct_default_stopReason_tool_calls` | — | `ci/julia-unit.yaml` | **TODO** |
-| SP-004 | — | `4-specification.md:#SP-004:toolResultMessage` | `unit/message-types.jl` | `test_toolResultMessage_struct_defaults` | — | `ci/julia-unit.yaml` | **TODO** |
-| SP-005 | — | `4-specification.md:#SP-005:agentToolCall` | `unit/message-types.jl` | `test_agentToolCall_type_is_always_function` | — | `ci/julia-unit.yaml` | **TODO** |
-| SP-006.1,2,3 | — | `4-specification.md:#SP-006:content-types` | `unit/content-types.jl` | `test_textContent_imageContent_reasoningContent` | — | `ci/julia-unit.yaml` | **TODO** |
-| SP-017 | — | `4-specification.md:#SP-017` | `contract/response-format.jl` | `test_lmStudio_vLLM_format_parsed` | — | `ci/julia-contract.yaml` | **TODO** |
-| SP-018.1,2,3,4 | — | `4-specification.md:#SP-018` | `contract/response-format.jl` | `test_openAI_format_blocks_parsed` | — | `ci/julia-contract.yaml` | **TODO** |
-| SP-023 | — | `4-specification.md:#SP-023` | `unit/tool-validation.jl` | `test_validateRequiredArgs_missing_fields` | — | `ci/julia-unit.yaml` | **TODO** |
-| SP-024 | — | `4-specification.md:#SP-024` | `unit/tool-validation.jl` | `test_validateToolArguments_throws_on_missing` | — | `ci/julia-unit.yaml` | **TODO** |
-| NFR-101 | — | `1-requirements.md:4.1` | `perf/agent-loop.jl` | `perf_agent_loop_overhead_<500ms` | `tests/perf/concurrency.jl` | `ci/julia-perf.yaml` | **TODO** |
-| NFR-102 | — | `1-requirements.md:4.1` | `perf/mcp-discovery.jl` | `perf_mcp_discovery_50_tools_<2s` | — | `ci/julia-perf.yaml` | **TODO** |
-| NFR-201 | — | `1-requirements.md:4.2` | `unit/error-handling.jl` | `test_agent_loop_catches_and_logs_errors` | `integration/llm-timeout.jl` | `ci/julia-unit.yaml` | **TODO** |
-
----
-
-## Trace Rules
-
-### Unit Test Rules
-
-- **Every specification struct** (SP-001..SP-008, SP-019..SP-022) must have a unit test validating:
-  - Default values for keyword arguments are set correctly
-  - Type field matches spec (e.g., `agentToolCall.type` is always `"function"`)
-  - Constructor works with both positional and keyword args
-- **Every converter function** (`_userMessageToOpenAI`, `_assistantMessageToOpenAI`, etc. SP-011..SP-016) must have a unit test:
-  - Input Julia struct → output `Dict{String, Any}` matching OpenAI spec
-  - Round-trip conversion preserves data (e.g., `textContent` text is preserved)
-- **Every tool validation function** (SP-023, SP-024) must have unit tests:
-  - Missing required field → returns error string (not throws)
-  - All required present → returns `nothing`
-  - Custom `validateRequiredArgs` hook on tool takes precedence over default
-
-### Integration Test Rules
-
-- **Every walkthrough journey** (WT-XXX) must have at least one integration test:
-  - `_agentLoop` processes messages through the full pipeline with stubbed LLM/MCP
-  - `promptAgent` → `takeResponse` round-trip works
-  - `followUp` while busy queues to `followUpChannel`, processes after current work
-  - `stopAgent` sends `:shutdown`, drains channels, closes all 3 channels
-- **Tool call lifecycle** (FR-003, FR-005):
-  - `llmCall` returns tool calls → loop calls `executeToolCalls` → results appended
-  - Tool calls → no tool calls on next `llmCall` → loop exits
-  - `toolResultBatch.terminate == true` → loop exits immediately
-- **Hook tests**:
-  - `beforeToolCall` returning `beforeToolCallResult(true, reason)` blocks execution → error tool result
-  - `afterToolCall` returning modified `agentToolResult` replaces result
-
-### Contract Test Rules
-
-- **Every response format** (SP-017, SP-018) must be parsed:
-  - Format 1: `_extractToolCalls({message => {tool_calls => [...]}})` extracts `assistantMessageToolCall`
-  - Format 2: `_extractToolCalls({content => [{type=>"tool_calls", tool_calls=[...]}}])` extracts `assistantMessageToolCall`
-  - Format 2 (no tools): `_extractToolCalls({content => [{type=>"text", text=>...}]})` extracts `assistantMessage`
-  - `reasoning_content` field → added to `assistantMessageToolCall.content`
-  - `api`, `provider`, `model`, `usage` → copied to message fields
-
-### Performance Test Rules
-
-- **NFR-101**: Agent loop overhead measurement:
-  - Stub LLM returns tool call → execute tool (1ms) → stub returns no tool calls → measure elapsed
-  - Subtract stub costs (llm call 1ms + tool 1ms)
-  - Overhead = elapsed - stub costs
-  - Must be <500ms per tool call (p95)
-- **NFR-102**: MCP discovery performance:
-  - Mock MCP server returns 200 tools (simulated, no real network)
-  - Measure `_wrap_mcp_tool` + `registerTool` for all 200 tools
-  - Must complete <2 seconds
-
-### Security/Validation Rules
-
-- **NFR-301 (input validation)**:
-  - Tool arguments with injection patterns (e.g., `{"city" => "; rm -rf /"}`) → passed through to MCP
-  - `beforeToolCall` can block dangerous tools (e.g., `toolCall.name == "delete_file"`)
-- **Penetration test**: MCP server spoofing
-  - Attacker sends `{"result" => {"tools" => [{"name" => "malicious", ...}]}}` from untrusted MCP endpoint
-  - Agent executes tool via MCP `tools/call` with attacker-controlled arguments
-  - Mitigation: `beforeToolCall` hook can block unknown tools; app should validate MCP server URL
-
----
-
-## Gap Check (Validation Review)
-
-Per ASG Framework §6, verify:
-
-- ✓ Every `SP-XXX` specification item has at least one unit test (listed in trace table above)
-- ✓ Every `WT-XXX` walkthrough step has at least one integration test (listed in trace table above)
-- ✓ Every NFR threshold has at least one performance test (NFR-101, NFR-102 in trace table)
-- ✓ Security NFRs have penetration test scenarios (NFR-301 in trace rules above)
-- ✓ Every `SP-017, SP-018` response format has contract tests (listed in trace table)
-- ✓ Every `SP-011..SP-016, SP-023, SP-024` converter/validation rule has unit tests (listed in trace table)
-
-**Status**: All rows have **TODO** status. Tests must be written before integration into production CI pipeline.
-
----
-
-## CI Pipeline
-
-```yaml
-# ci/julia-pipeline.yaml (placeholder)
-name: Julia YiemAgent CI
-on: [push, pull_request]
-
-jobs:
-  unit:
-    runs-on: ubuntu-latest
-    steps:
-      - uses: actions/checkout@v4
-      - uses: julia-actions/julia-buildpkg@latest
-      - run: julia --project=. -e 'using Pkg; Pkg.instantiate()'
-      - run: julia --project=./tests -e 'using Test; Test.@testset "Unit tests" # include tests/unit/run_tests.jl"'
-  
-  integration:
-    runs-on: ubuntu-latest
-    needs: unit
-    steps:
-      - run: julia --project=./tests -e 'using Test; Test.@testset "Integration" # include tests/integration/run_tests.jl"'
-  
-  contract:
-    runs-on: ubuntu-fail
-    needs: unit
-    steps:
-      - run: julia --project=./tests -e 'using Test; Test.@testset "Contract" # include tests/contract/run_tests.jl"'
-  
-  perf:
-    runs-on: ubuntu-latest
-    needs: [unit, integration]
-    steps:
-      - run: julia --project=./tests -e 'using Test; Test.@testset "Performance" # include tests/perf/run_tests.jl"'
-    # Fail if NFR-101 or NFR-102 thresholds breached
-  
-  lint:
-    runs-on: ubuntu-latest
-    steps:
-      - run: julia --eval 'using Aqua; Aqua.test_all(YiemAgent)'
-```
-
-**Gap Check**: Per ASG §5, every spec item maps to a CI gate. If a `SP-XXX` or `WT-XXX` row has no test file/function, the trace table row must be filled before merging.

docs/7-implementation-plan.md

@@ -1,217 +0,0 @@
-# Implementation Plan: YiemAgent Agent Framework
-
-## 1. Implementation Phases
-
-### Phase 1: Type System & Core Structures (COMPLETED)
-
-**Files**: `src/type.jl`
-
-**Completed Tasks**:
-- `IMP-001`: Define Julia struct types for messages (`userMessage`, `assistantMessage`, `assistantMessageToolCall`, `toolResultMessage`) with keyword constructors — **COMPLETED** (see `src/type.jl:95-249`)
-- `IMP-002`: Define message content types (`textContent`, `imageContent`, `reasoningContent`) with abstract `messageContent` base type — **COMPLETED** (see `src/type.jl:74-87`)
-- `IMP-003`: Define tool types (`agentTool`, `agentToolCall`) with lifecycle hooks (`prepareArguments`, `validateRequiredArgs`) — **COMPLETED** (see `src/type.jl:319-328`)
-- `IMP-004`: Define agent context/state types (`agentContext`, `agentState`) — **COMPLETED** (see `src/type.jl:357-415`)
-- `IMP-005`: Define LLM model/usage/cost types (`llmModel`, `modelCost`, `llmUsage`) — **COMPLETED** (see `src/type.jl:45-67`)
-- `IMP-006`: Define tool call lifecycle result types (`preparedToolCall`, `immediateOutcome`, `executedOutcome`, `finalizedOutcome`) — **COMPLETED** (see `src/type.jl:600-710`)
-- `IMP-007`: Define agent loop config and event types (`agentLoopConfig`, `abortSignal`, `agentToolResult`, `toolExecStartEvent`, etc.) — **COMPLETED** (see `src/type.jl:450-567`)
-- `IMP-008`: Export all types from `type` module — **COMPLETED** (see `src/type.jl:2-26`)
-
-**Validation Tests**: Unit tests for struct construction with keyword args (SP-001..SP-008, SP-019..SP-022)
-
-**Traceability**: FR-001, FR-002, FR-003, FR-004, FR-005, FR-006
-
----
-
-### Phase 2: Utility & Formatting Functions (COMPLETED)
-
-**Files**: `src/utils.jl`
-
-**Completed Tasks**:
-- `IMP-010`: Implement `prepareContext(state, eventSink) -> agentContext` — **COMPLETED** (see `src/utils.jl:112-126`)
-  - TODO: Filter tools based on user intent and tool description (`src/utils.jl:114`)
-  - TODO: Inject system prompt context (e.g., time, session state) (`src/utils.jl:118`)
-  - TODO: Prune/reorder messages (`src/utils.jl:121`)
-- `IMP-011`: Implement `formatMsgForLLM(ctx, eventSink) -> Dict{String, Any}` — **COMPLETED** (see `src/utils.jl:159-244`)
-  - Converts system prompt to system message
-  - Converts conversation messages via `_userMessageToOpenAI`, `_assistantMessageToOpenAI`, `_assistantMessageToolCallToOpenAI`, `_toolResultMessageToOpenAI`
-  - Converts `ctx.tools` to OpenAI function tool format via `_toolsToOpenAI`
-- `IMP-012`: Implement message converters (`_userMessageToOpenAI`, `_assistantMessageToOpenAI`, `_assistantMessageToolCallToOpenAI`, `_toolResultMessageToOpenAI`) — **COMPLETED** (see `src/utils.jl:321-387`)
-- `IMP-013`: Implement `_messageContentToBlocks` — converts `textContent`/`imageContent` to OpenAI content blocks — **COMPLETED** (see `src/utils.jl:396-413`)
-- `IMP-014`: Implement `_toolsToOpenAI` — converts `OrderedDict{String, agentTool}` to OpenAI tools array — **COMPLETED** (see `src/utils.jl:429-447`)
-- `IMP-015`: Implement `beforeToolCall` hook default (returns `beforeToolCallResult(false, "N/A")`) — **COMPLETED** (see `src/utils.jl:278-284`)
-- `IMP-016`: Implement `afterToolCall` hook default (returns `nothing`) — **COMPLETED** (see `src/utils.jl:310-316`)
-- `IMP-017`: Implement `validateRequiredArgs(args, inputSchema)` — validates required fields in tool arguments — **COMPLETED** (see `src/utils.jl:479-497`)
-- `IMP-018`: Implement `validateToolArguments(tool, prepared)` — wrapper that calls tool-specific or default hook — **COMPLETED** (see `src/utils.jl:533-546`)
-
-**Validation Tests**: Unit tests for converters, validation hooks, and OpenAI format output structure (SP-011..SP-016, SP-023, SP-024)
-
-**Traceability**: FR-001, FR-002, FR-003, FR-004, FR-005, FR-006
-
----
-
-### Phase 3: Tool Registry & MCP Integration (COMPLETED)
-
-**Files**: `src/toolRegistry.jl`
-
-**Completed Tasks**:
-- `IMP-020`: Implement `_extract_text_content(result)` — extracts text from MCP tool result content array — **COMPLETED** (see `src/toolRegistry.jl:19-33`)
-- `IMP-021`: Implement `_wrap_mcp_tool(mcpserver, tool_def)` — wraps MCP tool as `agentTool` with `execute` calling `mcpserver("tools/call", ...)` — **COMPLETED** (see `src/toolRegistry.jl:41-106`)
-  - Handles MCP error responses (`haskey(response, "error")`)
-  - Extracts text and `isError` from result
-- `IMP-022`: Implement `register_mcp_tools(tools, mcpserver)` — queries `tools/list` with pagination (`nextCursor`) — **COMPLETED** (see `src/toolRegistry.jl:118-186`)
-  - Handles both `result.tools` and `result.error` response shapes
-  - Skips already registered tools (idempotent)
-- `IMP-023`: Implement `registerTool(tools, tool)` — adds tool to OrderedDict — **COMPLETED** (see `src/toolRegistry.jl:200-205`)
-- `IMP-024`: Implement `listTool(tools, mcpserver)` — builtin tool that triggers runtime discovery and lists all tools — **COMPLETED** (see `src/toolRegistry.jl:235-271`)
-- `IMP-025`: Implement `registerAllTools(tools, mcpserver)` — discovers existing tools + registers `listTools` — **COMPLETED** (see `src/toolRegistry.jl:286-289`)
-
-**Validation Tests**: Unit tests for MCP tools/list parsing, pagination, tool registration idempotency; contract tests for JSON-RPC 2.0 format (SP-009, SP-010)
-
-**Traceability**: FR-002, FR-004
-
----
-
-### Phase 4: Agent Core & Loop (COMPLETED)
-
-**Files**: `src/agentCore.jl`
-
-**Completed Tasks**:
-- `IMP-030`: Define `yiemAgent` struct with channels, callbacks, MCP server — **COMPLETED** (see `src/agentCore.jl:17-95`)
-- `IMP-031`: Implement `yiemAgent` constructor — creates channels, discovers MCP tools, spawns `_agentLoop` task — **COMPLETED** (see `src/agentCore.jl:126-199`)
-- `IMP-032`: Implement `_agentLoop(agent)` — background `@spawn` task managing `inputChannel`/`followUpChannel` priority dispatch — **COMPLETED** (see `src/agentCore.jl:225-364`)
-  - Handles shutdown signal: drains all channels, breaks loop
-  - On error: catches, calls `eventSink(errMsg)`, exits loop
-- `IMP-033`: Implement `_processMessage(...)` — drains input, calls `llmCall`, extracts tool calls, executes, loops until no tool calls — **COMPLETED** (see `src/agentCore.jl:394-544`)
-  - `while true` loop: `llmCall()` → `_extractToolCalls()` → if `hasToolCalls && length > 0` execute → append results → repeat
-  - On `toolResultBatch.terminate == true`: build final response and break
-  - Catches all errors and calls `eventSink(err_msg)`
-- `IMP-034`: Implement `_extractToolCalls(response)` — handles **both** formats:
-  - Format 1: `response["message"]["tool_calls"]` (LMStudio/vLLM style) — **COMPLETED** (see `src/agentCore.jl:671-683`)
-  - Format 2: `response.content` blocks with `type => "tool_calls"` or `type => "tool_call"` (OpenAI API style) — **COMPLETED** (see `src/agentCore.jl:685-719`)
-  - Extracts `reasoning_content`, `content` text blocks, `api`, `provider`, `model`, `usage`, `finish_reason` — **COMPLETED** (see `src/agentCore.jl:723-797`)
-  - Returns `(hasToolCalls, toolCallList, assistant_message)` — **COMPLETED** (see `src/agentCore.jl:799-827`)
-- `IMP-035`: Implement `prepareToolCall(context, assistantMsg, toolCall, config, signal, eventSink)` — resolves tool, validates args, runs `beforeToolCall` hook, never throws — **COMPLETED** (see `src/agentCore.jl:974-1035`)
-- `IMP-036`: Implement `executePreparedToolCall(prep, signal, eventSink)` — calls `tool.execute(toolCallId, args, ...)` — **COMPLETED** (see `src/agentCore.jl:1078-1100`)
-- `IMP-037`: Implement `finalizeExecutedToolCall(context, msg, prep, executed, config, signal, eventSink)` — runs `afterToolCall` hook, catches errors — **COMPLETED** (see `src/agentCore.jl:1159-1198`)
-- `IMP-038`: Implement `prepareToolCallArguments(tool, toolCall)` — calls `tool.prepareArguments` if present — **COMPLETED** (see `src/agentCore.jl:908-917`)
-- `IMP-039`: Implement `createErrorToolResult(msg)` — builds `agentToolResult` with error content — **COMPLETED** (see `src/agentCore.jl:572-574`)
-- `IMP-040`: Implement `createToolResultMessage(f)` — builds `toolResultMessage` from `finalizedOutcome` — **COMPLETED** (see `src/agentCore.jl:606-612`)
-
-**Note**: Sequential and parallel execution implementations continue beyond line 1207 of `src/agentCore.jl` — implementation incomplete, requires review to ensure execution methods properly call hooks and emit lifecycle events. See IMP-041.
-
-**Validation Tests**: Integration tests for full agent loop with mocked LLM and MCP; contract tests for response format extraction (WT-001..WT-032, SP-017, SP-018)
-
-**Traceability**: FR-001, FR-002, FR-003, FR-004, FR-005
-
----
-
-### Phase 5: Public API (COMPLETED)
-
-**Files**: `src/api.jl`
-
-**Completed Tasks**:
-- `IMP-050`: Implement `promptAgent(agent, msg::AbstractDict)` — put to `inputChannel`, returns agent for chaining — **COMPLETED** (see `src/api.jl:36-39`)
-- `IMP-051`: Implement `promptAgent(agent, text::String)` — wraps text as OpenAI `Dict` and put to `inputChannel` — **COMPLETED** (see `src/api.jl:41-44`)
-- `IMP-052`: Implement `takeResponse(agent)` — blocks on `outputChannel` `take!`, returns OpenAI dict response — **COMPLETED** (see `src/api.jl:66-68`)
-- `IMP-053`: Implement `followUp(agent, msg::AbstractDict)` — put to `followUpChannel`, returns agent — **COMPLETED** (see `src/api.jl:94-97`)
-- `IMP-054`: Implement `followUp(agent, text::String)` — wraps text as OpenAI `Dict` and put to `followUpChannel` — **COMPLETED** (see `src/api.jl:99-102`)
-- `IMP-055`: Implement `stopAgent(agent)` — puts `:shutdown`, `fetch(_agentLoop)`, closes all channels — **COMPLETED** (see `src/api.jl:126-139`)
-
-**Validation Tests**: Integration tests for send/receive/stop lifecycle (WT-001..WT-005)
-
-**Traceability**: FR-001, FR-006
-
----
-
-## 2. Module Structure
-
-```
-src/
-├── YiemAgent.jl     # Parent module, includes submodules in dependency order
-├── type.jl           # All Julia struct types (exports 30+ types)
-├── utils.jl          # Formatting, validation, hooks (default implementations)
-├── toolRegistry.jl   # MCP tool discovery, registration, listTools builtin
-├── agentCore.jl      # yiemAgent struct, _agentLoop, _processMessage, extractToolCalls
-└── api.jl            # Public: promptAgent, takeResponse, followUp, stopAgent
-```
-
-**Include order** (per `src/YiemAgent.jl` lines 6-19):
-1. `type.jl` (no dependencies)
-2. `utils.jl` (depends on `type`)
-3. `toolRegistry.jl` (depends on `type`)
-4. `agentCore.jl` (depends on `type`, `utils`, `toolRegistry`)
-5. `api.jl` (depends on `type`, `utils`, `agentCore`, `toolRegistry`)
-
----
-
-## 3. Task Dependencies
-
-| Task ID | Title | Depends On | Status |
-|---------|-------|------------|--------|
-| IMP-001..IMP-008 | Type system | — | **COMPLETED** |
-| IMP-010..IMP-018 | Utils (formatMsgForLLM, converters, hooks) | IMP-001..IMP-008 | **COMPLETED** |
-| IMP-020..IMP-025 | Tool registry (MCP discovery) | IMP-003, IMP-017 | **COMPLETED** |
-| IMP-030..IMP-040 | Agent core (agent loop, tool call execution) | IMP-001..IMP-008, IMP-010..IMP-018, IMP-020..IMP-025 | **INCOMPLETE*** (see note) |
-| IMP-050..IMP-055 | Public API | IMP-030..IMP-040 | **COMPLETED** |
-
-**NOTE on IMP-030..IMP-040**: The sequential and parallel execution functions (`executeToolCallsSequential`, `executeToolCallsParallel`, `executeToolCalls`) begin after line 1207 of `src/agentCore.jl` but the full implementation is truncated in file reads and requires review. The exported functions (`executePreparedToolCall`, `prepareToolCall`, `executeToolCallsSequential`, `executeToolCallsParallel`, `executeToolCalls`) are declared (`src/agentCore.jl:4-5`) but their full bodies may be incomplete. **Requires manual validation.**
-
-**Traceability**: IMP-001..IMP-008 → SP-001..SP-008, IMP-020..IMP-025 → SP-009, SP-010; IMP-010..IMP-018 → SP-011..SP-016, SP-023, SP-024; IMP-030..IMP-040 → SP-017, SP-018, SP-019..SP-022
-
----
-
-## 4. Remaining Work (TODOs)
-
-| File | Line | Description | Priority |
-|------|------|-------------|----------|
-| `src/utils.jl` | 114 | Filter tools from `state.tools` based on user intent and tool description | High |
-| `src/utils.jl` | 118 | Add system prompt context: time, session state, user preferences | Medium |
-| `src/utils.jl` | 121 | Prune/reorder/conversation messages before sending to LLM | Medium |
-| `src/agentCore.jl` | post-1207 | Complete and validate `executeToolCallsSequential`, `executeToolCallsParallel`, `executeToolCalls` implementations (currently exported but body may be incomplete) | **Critical** |
-| `tests/unit/...` | — | Write unit tests for all struct types (IMP-001..IMP-008) and converters (SP-011..SP-016) | High |
-| `tests/unit/...` | — | Write unit tests for tool validation hooks (SP-023, SP-024) | High |
-| `tests/integration/...` | — | Write integration tests for agent loop with mocked LLM/MCP (WT-001..WT-032) | High |
-| `tests/contract/...` | — | Write contract tests for response formats (SP-017, SP-018) | Medium |
-| `tests/perf/...` | — | Write performance tests (NFR-101, NFR-102) | Medium |
-
----
-
-## 5. Build & Deployment
-
-### Build
-
-```bash
-cd /home/ton/docker-apps/sommpanion/YiemAgent
-julia --project=. -e 'using Pkg; Pkg.instantiate(); using Pkg; Pkg.precompile()'
-```
-
-### Test
-
-```bash
-julia --project=./tests -e 'using Pkg; Pkg.instantiate()'
-julia --project=./tests -e 'using Test; Test.@testset "Unit" begin include("tests/unit/run_tests.jl") end'
-julia --project=./tests -e 'using Test; Test.@testset "Integration" begin include("tests/integration/run_tests.jl") end'
-```
-
-### CI/CD Preparation
-
-- Add `.github/workflows/julia.yaml` referencing `ci/julia-pipeline.yaml` steps
-- Add `Project.toml` dependencies:
-  ```toml
-  [deps]
-  JSON = "682c8566-3430-5439-8f4e-b64832e8aaba"
-  DataStructures = "892a3cad-33e6-51e5-808c-f3d90072be70"
-  HTTP = "cd6eee30-4dd5-5329-8bad-3259ec48d0f4"
-  # plus: UUIDs, Dates, PrettyPrinting, Serialization, DataFrames, NATS, LibPQ
-  ```
-
----
-
-## 6. Risk Mitigation
-
-| Risk | Mitigation |
-|------|------------|
-| Line 1207+ of `agentCore.jl` (sequential/parallel execution) is incomplete or broken | Manually review and validate `executeToolCallsSequential`, `executeToolCallsParallel`, `executeToolCalls` against spec SP-021 (agentLoopConfig) and trace to test `test_tool_call_execution_sequence` |
-| `GeneralUtils` internal package unavailable in CI | Ensure `GeneralUtils` is installed as dependency (via `Pkg.develop` or registry) |
-| NATS `mcpServer` struct is abstract — no concrete implementation | Provide at least one mock `mcpServer` for tests that returns canned JSON responses |
-| Message history grows unbounded | Implement `prepareContext` pruning (TODO in code); add NFR-104 for max history tokens |

docs/8-runbook.md

@@ -1,445 +0,0 @@
-# Runbook: YiemAgent Agent Framework
-
-**Severity**: N/A (library — not a deployed service)
-**Estimated Time**: N/A
-
-This runbook documents operational guidance for developers working with the YiemAgent agent framework. It is not an operations runbook for a production service, but a developer handbook for debugging, troubleshooting, and extending the framework.
-
----
-
-## 1. Agent Lifecycle
-
-### 1.1: Create an Agent
-
-**Steps**:
-```julia
-using YiemAgent
-
-# 1. Implement llmCall function
-function my_llmCall(messages; eventSink=nothing)
-    # Call LLM API (e.g., OpenAI, Anthropic, LMStudio)
-    # Return dict with keys: "message", "content", "finish_reason", "api", "provider", "model", "usage"
-    eventSink("my_llmCall: sending $(length(messages["messages"])) messages"; publish=true)
-    # ... API call ...
-    return response_dict
-end
-
-# 2. Implement mcpServer callable struct
-struct MyMCPClient
-    conn::NATS.Connection   # or HTTP.Client
-    topic::String
-    senderID::String
-end
-
-function (mcp::MyMCPClient)(method::String, args...; eventSink=nothing)
-    # JSON-RPC 2.0 calls:
-    #   mcp("tools/list", sessionId; eventSink=eventSink)
-    #   mcp("tools/call", toolName, args, sessionId; eventSink=eventSink)
-    # ... NATS/HTTP call ...
-    return jsonrpc_response
-end
-
-# 3. Create agent
-mcp = MyMCPClient(nats_conn, "yiemagent:tools", "agent_1")
-agent = yiemAgent(
-    my_llmCall;
-    systemPrompt="You are a wine assistant.\nYou have tools for searching wine inventory.",
-    tools=mcp_tools,              # optional: pass pre-discovered tools
-    prepareContext=my_prepareContext,   # optional: custom context preparation
-    formatMsgForLLM=my_formatMsgForLLM, # optional: custom formatting for provider
-    beforeToolCall=my_beforeToolCall,   # optional: approval gate
-    afterToolCall=my_afterToolCall,     # optional: result sanitization
-    mcpServer=mcp,
-    parallelToolExecute=false,
-    eventSink=my_eventSink,
-)
-```
-
-> **Reference**: `2-solution_design.md:SD-001` (channel-based async), `SD-002` (background task), `SD-004` (MCP struct)
-
-### 1.2: Send a Message
-
-```julia
-# Option 1: Send a string message (automatically wrapped as OpenAI dict)
-promptAgent(agent, "What wines have notes of oak and vanilla?")
-
-# Option 2: Send a full OpenAI-format dict with images
-msg = Dict(
-    "role" => "user",
-    "content" => [
-        Dict("type" => "text", "text" => "What wines match these notes?"),
-        Dict("type" => "image_url", "image_url" => Dict("url" => "data:image/png;base64,..."))
-    ]
-)
-promptAgent(agent, msg)
-```
-
-### 1.3: Receive a Response
-
-```julia
-# Blocks until agent finishes processing (no tool calls or tool calls complete)
-response = takeResponse(agent)
-# response is a Dict in OpenAI format:
-#   Dict("role" => "assistant", "content" => [Dict("type" => "text", "text" => "...")])
-
-# For text-only response:
-if response["content"][1]["type"] == "text"
-    println(response["content"][1]["text"])
-end
-```
-
-### 1.4: Send Follow-up While Agent is Busy
-
-```julia
-# While the agent is processing tool calls (between promptAgent(...) and takeResponse(...))
-followUp(agent, "Also check European wines")
-```
-
-### 1.5: Stop the Agent
-
-```julia
-stopAgent(agent)
-# Sends :shutdown, closes all channels. Agent object is destroyed.
-```
-
----
-
-## 2. Troubleshooting
-
-### 2.1: Agent Loop Exits Silently
-
-**Symptoms**:
-- `takeResponse(agent)` hangs indefinitely
-- No output in eventSink
-
-**Steps**:
-1. Check if `eventSink` is receiving error messages:
-   ```julia
-   global error_count = 0
-   my_eventSink(msg; publish=false) = begin
-       if occursin("error", lowercase(msg)) || occursin("failed", lowercase(msg))
-           println("ERROR EVENT: $msg")
-           global error_count += 1
-       end
-   end
-   ```
-2. Check network connectivity to LLM API endpoint
-3. Verify `formatMsgForLLM` produces valid OpenAI-format message structure (`4-specification.md:SP-011..SP-016`)
-
-> **Reference**: `4-specification.md` (OpenAI format specs), `3-walkthrough.md:WT-030` (LLM timeout)
-
-### 2.2: Tool Call Not Executing
-
-**Symptoms**:
-- LLM returns `tool_calls` but agent returns text response immediately
-- `beforeToolCall` hook is missing or incorrectly implemented
-
-**Steps**:
-1. Verify `prepareContext` returns `agentContext` with `tools` field (not `nothing`) (`src/utils.jl:123`)
-2. Check `beforeToolCall` hook is not blocking execution:
-   ```julia
-   function my_beforeToolCall(ctx, signal, eventSink)
-       # Returning nothing → allows execution
-       # Returning beforeToolCallResult(true, "blocked") → blocks
-       eventSink("beforeToolCall for $(ctx.toolCall.name)"; publish=true)
-       nothing  # ← must return nothing (not beforeToolCallResult(false, "N/A"))
-   end
-   ```
-3. Verify tool exists in `context.tools[toolCall.name]` (`src/agentCore.jl:985`)
-4. Check `validateToolArguments` is not throwing (catches and returns error tool result) (`src/utils.jl:533`)
-
-> **Reference**: `4-specification.md:SP-021` (agentLoopConfig with beforeToolCall hook), `3-walkthrough.md:WT-032` (beforeToolCall blocks)
-
-### 2.3: MCP Server Returns No Tools
-
-**Symptoms**:
-- `println("[toolRegistry] Discovered 0 MCP tools."` on startup
-- Agent has no tools available at all times
-
-**Steps**:
-1. Verify `mcpServer` callable struct correctly handles `mcp("tools/list", sessionId; eventSink)`
-2. Check JSON-RPC 2.0 response shape matches `4-specification.md:SP-009`:
-   ```json
-   {
-     "result": {
-       "tools": [{"name": "...", ...}],
-       "nextCursor": ""  # empty string = no more pages
-     }
-   }
-   ```
-3. Verify `tools/list` error response uses `haskey(response, "error")` check (`src/toolRegistry.jl:128`)
-
-> **Reference**: `4-specification.md:SP-009` (tools/list format), `3-walkthrough.md:WT-020` (MCP discovery)
-
-### 2.4: LLM Response Format Not Parsed
-
-**Symptoms**:
-- Agent always treats LLM response as text, never extracts tool calls
-- LLM correctly returns `tool_calls` but agent ignores them
-
-**Steps**:
-1. Verify `llmCall` returns a dict containing `message` (LMStudio/vLLM format, `4-specification.md:SP-017`) or `content` (OpenAI format, `4-specification.md:SP-018`)
-2. Check for both format variations:
-   - Format 1: `response["message"]["tool_calls"]` (LMStudio style, see `src/agentCore.jl:671-683`)
-   - Format 2: `response.content[{type => "tool_calls"}]` (OpenAI style, see `src/agentCore.jl:700-705`)
-3. Verify `finish_reason` field matches: `"tool_calls"` triggers tool execution, `"stop"`/`"end_turn"` breaks loop (`src/agentCore.jl:723-724`)
-
-> **Reference**: `4-specification.md:SP-017, SP-018`, `3-walkthrough.md:WT-011, WT-012` (LLM response handling)
-
-### 2.5: Tool Execution Hangs
-
-**Symptoms**:
-- `tool.execute(...)` starts but never returns
-- EventSink receives `toolExecutionStart` but never `toolExecutionEnd`
-
-**Steps**:
-1. Implement `abortSignal` checking in tool execute function:
-   ```julia
-   function my_tool_execute(toolCallId, args, signal, eventSink)
-       while !signal.aborted && !all_conditions_met
-           # Long-running operation with periodic abort checks
-           sleep(0.1)
-       end
-       if signal.aborted
-           return agentToolResult([textContent("Cancelled")], Dict(), nothing, false)
-       end
-       return agentToolResult([textContent("Done")], Dict(), nothing, false)
-   end
-   ```
-2. External caller sets `signal.aborted = true` (if `abortSignal` is shared via closure)
-3. Add timeout logic to tool execute function:
-   ```julia
-   start_time = time()
-   timeout_seconds = 30.0
-   while time() - start_time < timeout_seconds && !signal.aborted
-       # operation...
-       sleep(0.5)
-   end
-   ```
-
-> **Reference**: `4-specification.md:SP-022` (abortSignal), `3-walkthrough.md:WT-030` (LLM timeout)
-
-### 2.6: Message History Grows Unbounded
-
-**Symptoms**:
-- Memory usage grows linearly with conversation length
-- LLM context window exceeded for long sessions
-
-**Steps**:
-1. Implement pruning in `prepareContext` (`src/utils.jl:112-126`):
-   ```julia
-   function my_prepareContext(state::agentState, eventSink)
-       # Prune old messages, keep last N turns
-       max_turns = 20
-       recent_msgs = isempty(state.messages) ? state.messages :
-                     state.messages[max(1, end-2*max_turns):end]
-       
-       # Optionally inject system context (time, user prefs)
-       preparedMessages = recent_msgs
-       preparedSystemPrompt = state.systemPrompt * "\nCurrent time: $(now())"
-       
-       # Filter tools (optional: keep only relevant subset)
-       filteredTools = state.tools  # or filter by intent...
-       
-       return agentContext(preparedSystemPrompt, preparedMessages, filteredTools)
-   end
-   ```
-2. Add hard limit in `_agentLoop`:
-   ```julia
-   if length(agent._state.messages) > 200
-       # Truncate oldest messages
-       agent._state.messages = agent._state.messages[end-100:end]
-   end
-   ```
-
-> **Reference**: `3-walkthrough.md` (agent message flow), `1-requirements.md:NFR-103` (channel buffer limits)
-
----
-
-## 3. Extending the Framework
-
-### 3.1: Add a Custom LLM Provider
-
-**Steps**:
-1. Implement `llmCall` function that accepts `FormattedMessages::Dict{String, Any}` and returns response dict
-2. Ensure response dict contains at least one of:
-   - `message.tool_calls` format (LMStudio/vLLM) — `4-specification.md:SP-017`
-   - `content[{type=>"tool_calls"}]` format (OpenAI) — `4-specification.md:SP-018`
-3. Response dict may optionally contain: `api`, `provider`, `model`, `usage` (`llmUsage` or dict), `finish_reason`, `error_message` (`4-specification.md:SP-017`, `SP-018.4`)
-
-```julia
-function my_openai_llmCall(messages::Dict{String, Any}; eventSink=nothing)
-    # messages["messages"] contains OpenAI-format messages
-    # messages["temperature"] (default 0.7)
-    # messages["tools"] (if tools are set)
-    
-    body = Dict(
-        "model" => "gpt-4",
-        "messages" => messages["messages"],
-        "tools" => get(messages, "tools", Any[])
-    )
-    
-    resp = HTTP.post("https://api.openai.com/v1/chat/completions", 
-                     headers, JSON.json(body))
-    json_resp = JSON.parse(String(resp.body))
-    
-    # Extract first completion
-    choice = json_resp["choices"][1]["message"]
-    
-    # Build uniform response
-    return Dict(
-        "api" => "openai",
-        "provider" => "openai",
-        "model" => "gpt-4",
-        "message" => choice,
-        "usage" => json_resp["usage"],
-        "finish_reason" => json_resp["choices"][1]["finish_reason"]
-    )
-end
-```
-
-> **Reference**: `1-requirements.md:FR-002`, `4-specification.md:SP-017, SP-018`
-
-### 3.2: Add a Custom Tool
-
-**Steps**:
-1. Define tool schema in `inputSchema` (JSON Schema format):
-   ```julia
-   schema = Dict{String, Any}(
-       "type" => "object",
-       "properties" => Dict(
-           "query" => Dict("type" => "string", "description" => "Search query"),
-           "max_results" => Dict("type" => "integer", "default" => 5)
-       ),
-       "required" => ["query"]
-   )
-   ```
-2. Implement `execute` function:
-   ```julia
-   tool = agentTool(
-       name="searchWine",
-       label="Search Wine Inventory",
-       description="Search for wines matching a query",
-       inputSchema=schema,
-       execute=(toolCallId, args, signal, eventSink) -> begin
-           query = args["query"]
-           max_results = get(args, "max_results", Dict{Any,Any}()["max_results"] => 5...)  # handle default
-           
-           wines = run_search(query, max_results)
-           
-           result_text = join(["- $(w.name) (\$$(w.price))" for w in wines], "\n")
-           return agentToolResult(
-               [textContent("Found $(length(wines)) wines:\n$result_text")],
-               Dict("count" => length(wines)),
-               nothing, false  # content, details, usage, terminate
-           )
-       end,
-       prepareArguments = nothing,
-       validateRequiredArgs = nothing,  # uses default validator
-       parallelToolExecute = false
-   )
-   
-   # Register: push!(tools, tool.name => tool)
-   ```
-3. Optionally override validation:
-   ```julia
-   function my_validate(args)
-       if haskey(args, "query") && length(args["query"]) < 3
-           return "Query must be at least 3 characters"
-       end
-       return nothing
-   end
-   
-   agentTool(
-       ...,
-       validateRequiredArgs = my_validate,  # replaces default validator
-   )
-   ```
-
-> **Reference**: `1-requirements.md:FR-004`, `4-specification.md:SP-009, SP-010`
-
-### 3.3: Implement User Approval Gate (beforeToolCall Hook)
-
-**Steps**:
-1. Implement `beforeToolCall` that blocks expensive/destructive operations:
-   ```julia
-   function my_approval_toolCall(ctx::beforeToolCallContext, signal::abortSignal, eventSink)
-       if ctx.toolCall.name in ("delete_wine", "update_price_bulk")
-           eventSink("Approval required for $(ctx.toolCall.name) with args $(ctx.args)")
-           # In production, send to UI and wait for user input
-           approved = ask_user_for_approval(ctx.toolCall.name, ctx.args)  # blocking call
-           if !approved
-               return beforeToolCallResult(true, "User denied approval for $(ctx.toolCall.name)")
-           end
-       end
-       return nothing  # allow execution
-   end
-   ```
-
-> **Reference**: `2-solution_design.md:SD-005` (lifecycle hooks), `3-walkthrough.md:WT-032` (beforeToolCall blocks)
-
-### 3.4: Implement Result Sanitization (afterToolCall Hook)
-
-**Steps**:
-1. Implement `afterToolCall` to mask sensitive data:
-   ```julia
-   function my_sanitize_toolCall(ctx::afterToolCallContext, signal::abortSignal, eventSink)
-       if ctx.toolCall.name == "getCustomerInfo"
-           # Mask sensitive fields before LLM sees result
-           result = ctx.result
-           for (i, block) in enumerate(result.content)
-               if block isa textContent
-                   masked = replace(block.text, r"\d{12}" => "***-***-****")  # mask SSN
-                   result = agentToolResult(
-                       [replace(block.text, r"\d{12}" => "***-***-****") for block in result.content],
-                       result.details,
-                       result.usage,
-                       result.terminate
-                   )
-               end
-           end
-           return result
-       end
-       return nothing  # pass through for other tools
-   end
-   ```
-
-> **Reference**: `2-solution_design.md:SD-005` (lifecycle hooks)
-
----
-
-## 4. Common Error Codes
-
-| Error Condition | Response Code | Description |
-|-----------------|---------------|-------------|
-| Tool not found | `immediateOutcome("Tool <name> not found", true)` | `context.tools[toolCall.name]` returns `nothing` |
-| Missing required args | `immediateOutcome("Missing required arguments: <fields>", true)` | `validateRequiredArgs` returns error string |
-| Custom validation failed | `immediateOutcome("<tool.validateRequiredArgs result>", true)` | Tool-specific validation hook returns error |
-| beforeToolCall blocked | `immediateOutcome("Tool execution was blocked: <reason>", true)` | `beforeToolCall` returns `beforeToolCallResult(true, reason)` |
-| Operation aborted | `immediateOutcome("Operation aborted", true)` | `signal.aborted == true` after `beforeToolCall` |
-| Tool execution crashed | `immediateOutcome("<exception message>", true)` | `preptool.execute()` throws exception |
-| MCP error | `agentToolResult(["MCP error: <err_msg>"], {..., "error"=>err_msg}, ...)` | Server returns `response["error"]` JSON-RPC envelope |
-| MCP network error | `agentToolResult(["MCP call error: <err_msg>"], {..., "error"=>err_msg}, ...)` | Exception during `mcp("tools/call", ...)` |
-
-> **Reference**: `4-specification.md:SP-009, SP-010`, `src/agentCore.jl:989, 1013, 1019, 1033, 1097`
-
----
-
-## 5. Debugging Checklist
-
-Per ASG Framework §Pre-Code Debugging Habit:
-
-| Stage | Debug Question |
-|-------|----------------|
-| **Implementation** | If a tool is not found, does `prepareToolCall` return `immediateOutcome` with "Tool not found"? |
-| **Testing** | If an LLM response has `tool_calls`, does `_extractToolCalls` return `(true, toolCallList, assistantMessageToolCall)`? |
-| **Deploying** | If the agent loop exits, does `eventSink` contain the error message? |
-
----
-
-## 6. References
-
-- **API contract**: [`4-specification.md`](4-specification.md) — message types, MCP JSON-RPC format, response extraction formats
-- **Solution decisions**: [`2-solution_design.md`](2-solution_design.md) — component architecture, lifecycle hooks, format-agnostic LLM responses
-- **Walkthroughs**: [`3-walkthrough.md`](3-walkthrough.md) — end-to-end agent flow, error paths, hook sequences
-- **Validation tests**: [`6-validation.md`](6-validation.md) — test traceability matrix mapping requirements/specs to test files

etc.jl

@@ -1,10 +1,2 @@
-# check if this column has vector embedding. if there is one, seach vector version instead
-              column_name_embedding = column_name * "_embedding"
-              if occursin(column_name_embedding, tables_schema[column_name_embedding])
-                vector_column = Dict(
-                  "table_name"=> table_name,
-                  "column_name"=> column_name_embedding,
-                  "operator"=> "vector_similarity",
-                  "value"=> column_obj["value"]
-                )
-              end
+# ── executeToolCalls() Julia pseudo code ──────────────────────────
+# Full call stack from runLoop → executeToolCalls → prepare → execute → finalize → emit

example/agent_chat_virtualCustomer.jl

@@ -1,585 +0,0 @@
-using Revise
-using JSON, JSON, Dates, UUIDs, PrettyPrinting, LibPQ, Base64, DataFrames, DataStructures
-using YiemAgent, GeneralUtils
-using Base.Threads
-
-# ---------------------------------------------- 100 --------------------------------------------- #
-
-
-
-# load config
-config = JSON.parsefile("/appfolder/app/dev/YiemAgent/test/config.json")
-# config = copy(JSON.parsefile("../mountvolume/config.json"))
-
-
-function executeSQL(sql::T) where {T<:AbstractString}
-  host = config[:externalservice][:wineDB][:host]
-  port = config[:externalservice][:wineDB][:port]
-  dbname = config[:externalservice][:wineDB][:dbname]
-  user = config[:externalservice][:wineDB][:user]
-  password = config[:externalservice][:wineDB][:password]
-  DBconnection = LibPQ.Connection("host=$host port=$port dbname=$dbname user=$user password=$password")
-  result = LibPQ.execute(DBconnection, sql)
-  close(DBconnection)
-  return result
-end
-
-function executeSQLVectorDB(sql)
-  host = config[:externalservice][:SQLVectorDB][:host]
-  port = config[:externalservice][:SQLVectorDB][:port]
-  dbname = config[:externalservice][:SQLVectorDB][:dbname]
-  user = config[:externalservice][:SQLVectorDB][:user]
-  password = config[:externalservice][:SQLVectorDB][:password]
-  DBconnection = LibPQ.Connection("host=$host port=$port dbname=$dbname user=$user password=$password")
-  result = LibPQ.execute(DBconnection, sql)
-  close(DBconnection)
-  return result
-end
-
-function text2textInstructLLM(prompt::String; maxattempt::Integer=10, modelsize::String="medium",
-    senderId=GeneralUtils.uuid4snakecase(), timeout=90,
-    llmkwargs=Dict(
-      :num_ctx => 32768,
-      :temperature => 0.5,
-    )
-  )
-  msgMeta = GeneralUtils.generate_msgMeta(
-    config[:externalservice][:loadbalancer][:mqtttopic];
-    msgPurpose="inference",
-    senderName="yiemagent",
-    senderId=senderId,
-    receiverName="text2textinstruct_$modelsize",
-    mqttBrokerAddress=config[:mqttServerInfo][:broker],
-    mqttBrokerPort=config[:mqttServerInfo][:port],
-  )
-
-  outgoingMsg = Dict(
-    :msgMeta => msgMeta,
-    :payload => Dict(
-      :text => prompt,
-      :kwargs => llmkwargs
-    )
-  )
-
-  response = nothing
-  for attempts in 1:maxattempt
-    _response = GeneralUtils.sendReceiveMqttMsg(outgoingMsg; responsetimeout=timeout, responsemaxattempt=maxattempt)
-    payload = _response[:response]
-    if _response[:success] && payload[:text] !== nothing
-      response = _response[:response][:text]
-      break
-    else
-      println("\n<text2textInstructLLM()> attempt $attempts/$maxattempt failed ", @__FILE__, ":", @__LINE__, " $(Dates.now())")
-      pprintln(outgoingMsg)
-      println("</text2textInstructLLM()> attempt $attempts/$maxattempt failed ", @__FILE__, ":", @__LINE__, " $(Dates.now())\n")
-      sleep(3)
-    end
-  end
-
-  return response
-end
-
-# get text embedding from a LLM service
-function getEmbedding(text::T) where {T<:AbstractString}
-  msgMeta = GeneralUtils.generate_msgMeta(
-    config[:externalservice][:loadbalancer][:mqtttopic];
-    msgPurpose="embedding",
-    senderName="yiemagent",
-    senderId=sessionId,
-    receiverName="textembedding",
-    mqttBrokerAddress=config[:mqttServerInfo][:broker],
-    mqttBrokerPort=config[:mqttServerInfo][:port],
-  )
-
-  outgoingMsg = Dict(
-    :msgMeta => msgMeta,
-    :payload => Dict(
-      :text => [text] # must be a vector of string
-    )
-  )
-
-  response = GeneralUtils.sendReceiveMqttMsg(outgoingMsg; responsetimeout=120, responsemaxattempt=3)
-  embedding = response[:response][:embeddings]
-  return embedding
-end
-
-function findSimilarTextFromVectorDB(text::T1, tablename::T2, embeddingColumnName::T3,
-  vectorDB::Function; limit::Integer=1
-  )::DataFrame where {T1<:AbstractString, T2<:AbstractString, T3<:AbstractString}
-  # get embedding from LLM service
-  embedding = getEmbedding(text)[1]
-  # check whether there is close enough vector already store in vectorDB. if no, add, else skip 
-  sql = """
-        SELECT *, $embeddingColumnName <-> '$embedding' as distance
-        FROM $tablename
-        ORDER BY distance LIMIT $limit;
-        """
-  response = vectorDB(sql)
-  df = DataFrame(response)
-  return df
-end
-
-function similarSQLVectorDB(query; maxdistance::Integer=100)
-  tablename = "sqlllm_decision_repository"
-  # get embedding of the query
-  df = findSimilarTextFromVectorDB(query, tablename, 
-                                  "function_input_embedding", executeSQLVectorDB)
-  # println(df[1, [:id, :function_output]])
-  row, col = size(df)
-  distance = row == 0 ? Inf : df[1, :distance]  
-  # distance = 100  # CHANGE this is for testing only
-  if row != 0 && distance < maxdistance
-    # if there is usable SQL, return it.
-    output_b64 = df[1, :function_output_base64]   # pick the closest match
-    output_str = String(base64decode(output_b64))
-    rowid = df[1, :id]
-    println("\n~~~ found similar sql. row id $rowid, distance $distance ", @__FILE__, ":", @__LINE__, " $(Dates.now())")
-    return (dict=output_str, distance=distance)
-  else
-    println("\n~~~ similar sql not found, max distance $maxdistance ", @__FILE__, ":", @__LINE__, " $(Dates.now())")
-    return (dict=nothing, distance=nothing)
-  end
-end
-
-function insertSQLVectorDB(query::T1, SQL::T2; maxdistance::Integer=3) where {T1<:AbstractString, T2<:AbstractString}
-  tablename = "sqlllm_decision_repository"
-  # get embedding of the query
-  # query = state[:thoughtHistory][:question]
-  df = findSimilarTextFromVectorDB(query, tablename, 
-                                  "function_input_embedding", executeSQLVectorDB)
-  row, col = size(df)
-  distance = row == 0 ? Inf : df[1, :distance]
-  if row == 0 || distance > maxdistance # no close enough SQL stored in the database
-    query_embedding = getEmbedding(query)[1]
-    query = replace(query, "'" => "")
-    sql_base64 = base64encode(SQL)
-    sql_ = replace(SQL, "'" => "")
-
-    sql = """
-          INSERT INTO $tablename (function_input, function_output, function_output_base64, function_input_embedding) VALUES ('$query', '$sql_', '$sql_base64', '$query_embedding');
-          """
-    # println("\n~~~ added new decision to vectorDB ", @__FILE__, ":", @__LINE__, " $(Dates.now())")
-    # println(sql)
-    _ = executeSQLVectorDB(sql)
-  end
-end
-
-
-function similarSommelierDecision(recentevents::T1; maxdistance::Integer=3
-  )::Union{AbstractDict, Nothing} where {T1<:AbstractString}
-  tablename = "sommelier_decision_repository"
-  # find similar
-  println("\n~~~ search vectorDB for this: $recentevents ", @__FILE__, " ", @__LINE__)
-  df = findSimilarTextFromVectorDB(recentevents, tablename, 
-                                  "function_input_embedding", executeSQLVectorDB)
-  row, col = size(df)
-  distance = row == 0 ? Inf : df[1, :distance]
-  if row != 0 && distance < maxdistance
-    # if there is usable decision, return it.
-    rowid = df[1, :id]
-    println("\n~~~ found similar decision. row id $rowid, distance $distance ", @__FILE__, " ", @__LINE__)
-    output_b64 = df[1, :function_output_base64]   # pick the closest match
-    _output_str = String(base64decode(output_b64))
-    output = copy(JSON.parsefile(_output_str))
-    return output
-  else
-    println("\n~~~ similar decision not found, max distance $maxdistance ", @__FILE__, " ", @__LINE__)
-    return nothing
-  end
-end
-
-
-function insertSommelierDecision(recentevents::T1, decision::T2; maxdistance::Integer=5
-  ) where {T1<:AbstractString, T2<:AbstractDict}
-  tablename = "sommelier_decision_repository"
-  # find similar
-  df = findSimilarTextFromVectorDB(recentevents, tablename, 
-                                  "function_input_embedding", executeSQLVectorDB)
-  row, col = size(df)
-  distance = row == 0 ? Inf : df[1, :distance]
-  if row == 0 || distance > maxdistance # no close enough SQL stored in the database
-    recentevents_embedding = getEmbedding(recentevents)[1]
-    recentevents = replace(recentevents, "'" => "")
-    decision_json = JSON.json(decision)
-    decision_base64 = base64encode(decision_json)
-    decision = replace(decision_json, "'" => "")
-    
-    sql = """
-          INSERT INTO $tablename (function_input, function_output, function_output_base64, function_input_embedding) VALUES ('$recentevents', '$decision', '$decision_base64', '$recentevents_embedding');
-          """
-    println("\n~~~ added new decision to vectorDB ", @__FILE__, " ", @__LINE__)
-    println(sql)
-    _ = executeSQLVectorDB(sql)
-  else
-    println("~~~ similar decision previously cached, distance $distance ", @__FILE__, " ", @__LINE__)
-  end
-end
-
-
-sessionId = GeneralUtils.uuid4snakecase()
-
-externalFunction = (
-    getEmbedding=getEmbedding,
-    text2textInstructLLM=text2textInstructLLM,
-    executeSQL=executeSQL,
-    similarSQLVectorDB=similarSQLVectorDB,
-    insertSQLVectorDB=insertSQLVectorDB,
-    similarSommelierDecision=similarSommelierDecision,
-    insertSommelierDecision=insertSommelierDecision,
-  )
-
-
-# s = "full-bodied red wine, budget 1500 USD"
-# r = YiemAgent.extractWineAttributes_1(agent, s)
-# println(r)
-
-
-# --------------------------- generating scenario and customer profile --------------------------- #
-
-function rolegenerator()
-  rolegenerator_systemmsg =
-  """
-    Your role:
-      - You are a helpful assistant 
-    Your mission:
-      - Create one random role of a potential customer of an internet wine store.
-    You must follow the following guidelines:
-     - the user only need the role, do not add your own words.
-     - the role should be detailed and realistic.
-    You should then respond to the user with:
-      Name: a name of the potential customer
-      Situation: a situation that the potential customer may be facing
-      Mission: a mission of the potential customer
-      Profile: a profile of the potential customer, including their age, gender, occupation, and other relevant information
-    You should only respond in format as described below:
-      Name: ...
-      Situation: ...
-      Mission: ...
-      Profile: ...
-      Additional_information: ...
-
-    Here are some examples:
-    Name: Jimmy
-    Situation:
-      - Your relationship with your boss is not that good. You need to improve your relationship with your boss.
-      - Your boss's wedding anniversary is coming up.
-      - You are at a wine store and start talking with the store's sommelier.
-    Mission:
-      - Ask the sommelier to provide multiple wine options, and subsequently choose one option from the presented list.
-    Profile:
-      - You are a young professional in a big company.
-      - You are avid party goer
-      - You like beer.
-      - You know nothing about wine.
-      - You have a budget of 1500usd.
-    Additional_information:
-      - your boss like spicy food.
-      - your boss is a middle-aged man.
-      - your boss likes Australian wine.
-
-    Name: Kate
-    Situation:
-      - Your husband asked you to get him a bottle of wine. He will gift the wine to his business client while dining at a German restaurant.
-      - Your husband is a business client and he will gift the wine to his business
-      - You are at a wine store and start talking with the store's sommelier.
-    Mission:
-      - Ask the sommelier to provide multiple wine options, and subsequently choose one option from the presented list.
-    Profile:
-      - You are a CEO in a startup company.
-      - You are a nerd
-      - You don't like alcohol.
-      - You have a budget of 150usd.
-      - You don't care about organic, sulfite, gluten-free, or sustainability certified wines
-    Additional_information:
-      - your husband like spicy food.
-      - your husband is a middle-aged man.
-
-    Name: John
-    Situation: 
-      - A local newspaper club wants to have a scoop about wine with local food in the U.S.
-      - You are at a wine store and start talking with the store's sommelier.
-    Mission:
-      - Ask the sommelier to provide multiple wine options, and subsequently choose one option from the presented list.
-    Profile:
-      - I'm a young guy.
-      - I prefer to express my ideas in a succinct and clear manner.
-    Additional_information:
-      - N/A
-
-    Name: Jane
-    Situation:
-      - You have catering a dinner party with French cuisine.
-      - You want to serve wine with your guests.
-      - You are at a wine store and start talking with the store's sommelier.
-    Mission:
-      - Ask the sommelier to provide multiple wine options, and subsequently choose one option from the presented list.
-    Profile:
-      - You are a young French restaurant owner.
-      - You like dry, full-bodied red wine with high tannin
-      - You don't care about organic, sulfite, gluten-free, or sustainability certified wines.
-      - You have a budget of 200 usd.
-    Additional_information:
-      - N/A
-
-    Let's begin!
-  """
-
-  header = ["Name:", "Situation:", "Mission:", "Profile:", "Additional_information:"]
-  dictkey = ["name", "situation", "mission", "profile", "additional_information"]
-  errornote = "N/A"
-
-  for attempt in 1:10
-    _prompt =
-      [
-        Dict(:name => "system", :text => rolegenerator_systemmsg),
-      ]
-    prompt = GeneralUtils.formatLLMtext(_prompt, "qwen3")
-    
-    response = text2textInstructLLM(prompt) # generated role
-    response = GeneralUtils.deFormatLLMtext(response, "qwen3")
-    think, response = GeneralUtils.extractthink(response)
-
-    # check whether response has all header
-    detected_kw = GeneralUtils.detect_keyword(header, response)
-    kwvalue = [i for i in values(detected_kw)]
-    zeroind = findall(x -> x == 0, kwvalue)
-    missingkeys = [header[i] for i in zeroind]
-    if 0 ∈ values(detected_kw)
-      errornote = "$missingkeys are missing from your previous response"
-      println("\nERROR YiemAgent rolegenerator() $errornote ", @__FILE__, ":", @__LINE__, " $(Dates.now())")
-      continue
-    elseif sum(values(detected_kw)) > length(header)
-      errornote = "\nYour previous attempt has duplicated points according to the required response format"
-      println("\nERROR YiemAgent rolegenerator() $errornote ", @__FILE__, ":", @__LINE__, " $(Dates.now())")
-      continue
-    end
-
-    responsedict = GeneralUtils.textToDict(response, header; 
-                                          dictKey=dictkey, symbolkey=true)
-    responsedict[:id] = GeneralUtils.uuid4snakecase()
-
-    responsedict[:systemmsg] = 
-    """
-    You are role playing as a CUSTOMER of a wine store and you are currently talking with a sommelier of a wine store.
-    Your profile is as follows:
-      Situation: $(responsedict[:situation])
-      Mission: $(responsedict[:mission])
-      Profile: $(responsedict[:profile])
-      Additional_information: $(responsedict[:additional_information])
-
-    You should follow the following guidelines:
-    - Focus on the lastest conversation
-    - Your like to be short and concise
-    - If you don't know an answer to sommelier's question, you should say: I don't know.
-    - If you think the store can't provide what you seek, you can leave.
-
-    You should then respond to the user with:
-      Dialogue: what you want to say to the user
-      Role: Verify that the dialogue is intended for the customer of a wine store. Can be "yes" or "no"
-    You should only respond in format as described below:
-    Dialogue: ...
-    Role: ...
-
-    Let's begin!
-    """
-
-    println("\nrolegenerator() ", @__FILE__, ":", @__LINE__, " $(Dates.now())")
-    println(responsedict)
-    return responsedict
-  end
-  error("ERROR rolegenerator() failed to generate customer role: ", @__FILE__, ":", @__LINE__, " $(Dates.now())")
-end
-
-
-# Define the external functions for the customer agent in named tuple format
-customer_externalFunction = ( 
-    text2textInstructLLM=text2textInstructLLM,
-  )
-
-
-
-
-function main()
-  agent = YiemAgent.sommelier(
-    externalFunction;
-    name="Jane",
-    id=sessionId,  # agent instance id
-    retailername="Yiem",
-    llmFormatName="qwen3"
-  )
-
-  customerDict = rolegenerator()
-  customer = YiemAgent.virtualcustomer(
-    customer_externalFunction;
-    systemmsg=customerDict[:systemmsg],
-    name=customerDict[:name],
-    id=sessionId,  # agent instance id
-    llmFormatName="qwen3"
-  )
-
-  # customer_chat = "hello"
-  
-  # YiemAgent.addNewMessage(customer, "assistant", customer_chat)
-  # # add user activity to events memory
-  # push!(customer.memory[:events],
-  #   YiemAgent.eventdict(;
-  #     event_description="the assistant talks to the user.",
-  #     timestamp=Dates.now(),
-  #     subject="assistant",
-  #     action_name="CHAT_BOX",
-  #     action_input=customer_chat,
-  #   )
-  # )
-  # println("\ncustomer respond:\n $customer_chat")
-  agent_response = YiemAgent.conversation(agent; maximumMsg=50)
-  println("\nagent respond:\n $agent_response")
-  while true
-    customer_chat = nothing
-    while customer_chat === nothing
-      customer_response = YiemAgent.conversation(customer, Dict(:text=> agent_response); 
-                                    converPartnerName=agent.name,
-                                    maximumMsg=50)
-      customer_response = GeneralUtils.deFormatLLMtext(customer_response, customer.llmFormatName)
-      customer_chat = customer_response
-
-      #[WORKING] check whether customer response the same before
-    end
-    
-    println("\ncustomer respond:\n $customer_chat")
-
-    agent_response = YiemAgent.conversation(agent;
-                                            userinput=Dict(:text=> customer_chat),
-                                            maximumMsg=50)
-    println("\nagent respond:\n $agent_response")
-
-    if haskey(agent.memory[:events][end], :thought)
-      lastAssistantAction = agent.memory[:events][end][:thought][:action_name]
-      if lastAssistantAction == "END_CONVER_GUIDELINE"    # store thoughtDict
-
-        # save a.memory[:shortmem][:decisionlog] to disk using JSON
-        println("\nsaving agent.memory[:shortmem][:decisionlog] to disk")
-        date = "$(Dates.now())"
-        date = replace(date, ':'=>'.')
-        filename = "agent_decision_log_$(date)_$(agent.id).json"
-        filepath = "/appfolder/mountvolume/appdata/log/$filename"
-        open(filepath, "w") do io
-          JSON.pretty(io, agent.memory[:shortmem][:decisionlog])
-        end
-
-        # check how many file in /appfolder/mountvolume/appdata/log/ folder now
-        logfilesnumber = length(readdir("/appfolder/mountvolume/appdata/log/"))
-        println("\nCaching conversation process done. Total $logfilesnumber files in /appfolder/mountvolume/appdata/log/ folder now.\n")
-        break
-      end
-    end
-  end
-end
-
-for i in 1:100
-  main()
-  println("\n Round $i/100 done.")
-end
-
-println("done")
-
-# prompt = 
-# """
-# <|im_start|>system
-# You are a role playing agent acting as:
-#   Name: Emily
-#   Situation: - Emily is planning her upcoming birthday party and wants to make it extra special. She has invited close friends and family, and she's looking for a unique wine that will impress them.
-#   Mission: - Emily needs to find a rare and high-quality wine that matches the theme of her party, which is a mix of classic and modern flavors. She also wants to ensure that the wine is not too expensive so that it won't break her budget.
-#   Profile: - Emily is in her late 20s, works as a marketing executive for a tech company, and has a passion for trying new things. She's organized and detail-oriented but can be spontaneous when it comes to planning events.
-#   Additional_information: - Emily loves experimenting with different types of food and wine pairings.
-
-# Your are currently talking with a sommelier.
-
-# You should follow the following guidelines:
-# - Focus on the lastest conversation
-# - If you satisfy with the sommelier's recommendation for bottle of wine(s), you should say: Thanks for you help. I will buy the wine you recommended.
-# - If you don't satisfy with the sommelier's questions or can't get a good wine recommendation, you can continue the conversation.
-
-# Let's begin!
-
-# <|im_end|>
-# <|im_start|>Jane
-# Hello! Welcome to Yiem's Wine Store. I'm Jane, your friendly sommelier. How can I assist you today? What type of wine are you in the mood for, and is there a special occasion or event on your mind?
-# <|im_end|>
-# <|im_start|>Emily
-# Hi Jane! Thank you so much for welcoming me. For my birthday party, I'm looking for something that combines classic and modern flavors. It's a mix of guests who enjoy both traditional tastes and more contemporary ones. Also, I want to make sure it won't break the bank. Any suggestions?
-# <|im_end|>
-# <|im_start|>Jane
-# Thank you for sharing your preferences, Jane! To better assist you, could you please let me know if there are any specific characteristics of wine you're looking for, such as tannin, sweetness, intensity, or acidity? Additionally, do you have any food items in mind that this wine should pair well with?
-# <|im_end|>
-# <|im_start|>Emily
-# """
-
-# llmkwargs=Dict(
-#       :num_ctx => 32768,
-#       :temperature => 0.3,
-#     )
-# r = text2textInstructLLM(prompt, llmkwargs=llmkwargs)
-# println(r)
-# println(555)
-
-# response = YiemAgent.conversation(agent, Dict(:text=> "I want to get a French red wine under 100."))
-
-
-# while true
-#   println("your respond: ")
-#   user_answer = readline()
-#   response = YiemAgent.conversation(agent, Dict(:text=> user_answer))
-#   println("\n$response")
-# end
-
-
-
-
-
-
-
-
-
-
-
-
-# """
-# Hello
-
-# I would like to get a bottle of wine for my boss but I don't know much about wine. Can you help me?
-
-# well actually, my boss is going to offer the wine to his client as a gift in a business meeting. All I know is his client like spicy food and French wine. I have a budget about 1000.
-
-# """
-
-# input = "French wine, bordeaux, under USD100, pairs with spicy food"
-# r = YiemAgent.extractWineAttributes_1(a, input)
-
-# inventory_order = "French Syrah, Viognier, full bodied, under 100"
-# r = YiemAgent.extractWineAttributes_2(a, inventory_order)
-# pprintln(r)
-
-
-# cron job 
-# @reboot sleep 50 && nvidia-smi -pm 1
-# @reboot sleep 51 && nvidia-smi -i 0 -pl 150
-# @reboot sleep 52 && nvidia-smi -i 1 -pl 150
-# @reboot sleep 53 && nvidia-smi -i 2 -pl 150
-# @reboot sleep 54 && nvidia-smi -i 3 -pl 150
-
-# @reboot sleep 55 && julia -t 2 /home/ton/work/restartContainer/main.jl
-
-# using GeneralUtils
-# msgMeta = GeneralUtils.generate_msgMeta(
-#           "/tonpc_containerServices",
-#           senderName= "somename",
-#           senderId= "1230",
-#           mqttBrokerAddress= "mqtt.yiem.cc",
-#           mqttBrokerPort= 1883,
-#         )
-# outgoingMsg = Dict(
-#   :msgMeta=> msgMeta,
-#   :payload=> "docker container restart playground-app",
-# )
-# GeneralUtils.sendMqttMsg(outgoingMsg)
-

example/config.json

@@ -1,76 +0,0 @@
-{
-  "mqttServerInfo": {
-      "description": "mqtt server info",
-      "port": 1883,
-      "broker": "mqtt.yiem.cc"
-  },
-  "testingOrProduction": {
-      "value": "testing",
-      "description": "agent status, couldbe testing or production"
-  },
-  "agentid": {
-      "value": "2b74b87a-5413-4fe2-a4d3-405891051680",
-      "description": "a unique id for this agent"
-  },
-  "agentCentralConfigTopic": {
-      "mqtttopic": "/yiem_branch_1/agent/sommelier/backend/config/api/v1.1",
-      "description": "a central agent server's topic to get this agent config"
-  },
-  "servicetopic": {
-      "mqtttopic": [
-          "/yiem/hq/agent/sommelier/backend/prompt/api_v1/testing"
-      ],
-      "description": "a topic this agent are waiting for service request"
-  },
-  "role": {
-      "value": "sommelier",
-      "description": "agent role"
-  },
-  "organization": {
-      "value": "yiem_branch_1",
-      "description": "organization name"
-  },
-  "externalservice": {
-    "loadbalancer": {
-      "mqtttopic": "/loadbalancer/requestingservice",
-      "description": "text to text service with instruct LLM"
-    },
-    "text2textinstruct": {
-        "mqtttopic": "/loadbalancer/requestingservice",
-        "description": "text to text service with instruct LLM",
-        "llminfo": {
-            "name": "llama3instruct"
-        }
-    },
-    "virtualWineCustomer_1": {
-        "mqtttopic": "/virtualenvironment/winecustomer",
-        "description": "text to text service with instruct LLM that act as wine customer",
-        "llminfo": {
-            "name": "llama3instruct"
-        }
-    },
-    "text2textchat": {
-        "mqtttopic": "/loadbalancer/requestingservice",
-        "description": "text to text service with instruct LLM",
-        "llminfo": {
-            "name": "llama3instruct"
-        }
-    },
-    "wineDB" : {
-      "description": "A wine database connection info for LibPQ client",
-      "host": "192.168.88.12",
-      "port": 10201,
-      "dbname": "wineDB",
-      "user": "yiemtechnologies",
-      "password": "yiemtechnologies@Postgres_0.0"
-    },
-    "SQLVectorDB" : {
-      "description": "A wine database connection info for LibPQ client",
-      "host": "192.168.88.12",
-      "port": 10203,
-      "dbname": "SQLVectorDB",
-      "user": "yiemtechnologies",
-      "password": "yiemtechnologies@Postgres_0.0"
-    }
-  }
-}

example/main.jl

@@ -1,706 +0,0 @@
-using JSON, JSON, Dates, UUIDs, PrettyPrinting, LibPQ, Base64, DataFrames, DataStructures
-using YiemAgent, GeneralUtils
-using Base.Threads
-
-# ---------------------------------------------- 100 --------------------------------------------- #
-
-
-""" Expected incomming MQTT message format for this service:
-  {
-    "msgMeta": {
-      "msgPurpose": "updateStatus",
-      "requestresponse": "request",
-      "timestamp": "2024-03-29T05:8:48.362",
-      "replyToMsgId": null,
-      "receiverId": null,
-      "getpost": "get",
-      "msgId": "e5c09bd8-7100-4e4e-bb43-05bee589a22c",
-      "acknowledgestatus": null,
-      "sendTopic": "/agent/wine/backend/chat/api/v1/prompt",
-      "receiverName": "agent-wine-backend",
-      "replyTopic": "/agent/wine/frontend/chat/api/v1/txt/receive",
-      "senderName": "agent-wine-frontend-chat",
-      "senderId": "0938a757-e0ee-40a9-8355-5e24906a87cd"
-    },
-    "payload" : {
-      "text": "hello"
-    }
-    
-  }
-"""
-
-
-
-
-
-# load config
-config = copy(JSON.parsefile("../mountvolume/config/config.json"))
-
-""" Instantiate an agent. One need to specify startmessage and one of gpu location info,  
-  Mqtt or Rest. start message must be comply with GeneralUtils's message format
-
-  Arguments\n
-  -----
-    channel::Channel
-      communication channel
-    sessionId::String
-      sesstion ID of the agent
-    agentName::String
-      Name of the agent
-    mqttBroker::String
-      mqtt broker e.g. "tcp://127.0.0.1:1883"
-    agentConfigTopic::String
-      main communication topic for an agent to ask for config
-    timeout::Int64
-      inactivity timeout in minutes. If timeout is reached, an agent will be terminated.
-    
-  Return\n
-  -----
-    a task represent an agent
-
-  Example\n
-  -----
-  ```jldoctest
-  julia> using YiemAgent, GeneralUtils
-  julia> msg = GeneralUtils.generate_msgMeta("/agent")
-  julia> incoming_msg = msg # assuming 1st msg was sent from other app
-  julia> agentConfigTopic = "/agent/wine/backend/config"
-  julia> task = runAgentInstance(incoming_msg, mqttBroker, agentConfigTopic, 60) 
-  ```
-
-  TODO\n
-  -----
-    [] update docstringLAMA_CONTEXT_LENGTH=40960 since the default size is 2048 as you can see in your debug log:
-    [] change how to get result of YiemAgent from let YiemAgent send msg directly to frontend,
-        to    
-          response = YiemAgent.conversation()     
-        then send response to frontend
-
-  Signature\n
-  -----
-"""
-function runAgentInstance(
-    receiveUserMsgChannel::Channel,
-    outputchannel::Channel,
-    sessionId::String,
-    config::Dict,
-    timeout::Int64,
-  )
-
-  function executeSQL(sql::T) where {T<:AbstractString}
-    host = config[:externalservice][:wineDB][:host]
-    port = config[:externalservice][:wineDB][:port]
-    dbname = config[:externalservice][:wineDB][:dbname]
-    user = config[:externalservice][:wineDB][:user]
-    password = config[:externalservice][:wineDB][:password]
-    DBconnection = LibPQ.Connection("host=$host port=$port dbname=$dbname user=$user password=$password")
-    result = LibPQ.execute(DBconnection, sql)
-    close(DBconnection)
-    return result
-  end
-  
-  function executeSQLVectorDB(sql)
-    host = config[:externalservice][:SQLVectorDB][:host]
-    port = config[:externalservice][:SQLVectorDB][:port]
-    dbname = config[:externalservice][:SQLVectorDB][:dbname]
-    user = config[:externalservice][:SQLVectorDB][:user]
-    password = config[:externalservice][:SQLVectorDB][:password]
-    DBconnection = LibPQ.Connection("host=$host port=$port dbname=$dbname user=$user password=$password")
-    result = LibPQ.execute(DBconnection, sql)
-    close(DBconnection)
-    return result
-  end
-  
-  function text2textInstructLLM(prompt::String; maxattempt::Integer=3, modelsize::String="medium",
-    senderId=GeneralUtils.uuid4snakecase(), timeout=180,
-    llmkwargs=Dict(
-      :num_ctx => 32768,
-      :temperature => 0.5,
-    ))
-    msgMeta = GeneralUtils.generate_msgMeta(
-      config[:externalservice][:loadbalancer][:mqtttopic];
-      msgPurpose="inference",
-      senderName="yiemagent",
-      senderId=senderId,
-      receiverName="text2textinstruct_$modelsize",
-      mqttBrokerAddress=config[:mqttServerInfo][:broker],
-      mqttBrokerPort=config[:mqttServerInfo][:port],
-    )
-
-    outgoingMsg = Dict(
-      :msgMeta => msgMeta,
-      :payload => Dict(
-        :text => prompt,
-        :kwargs => llmkwargs
-      )
-    )
-
-    response = nothing
-    for attempts in 1:maxattempt
-      _response = GeneralUtils.sendReceiveMqttMsg(outgoingMsg; timeout=timeout, maxattempt=maxattempt)
-      payload = _response[:response]
-      if _response[:success] && payload[:text] !== nothing
-        response = _response[:response][:text]
-        break
-      else
-        println("\n<text2textInstructLLM()> attempt $attempts/$maxattempt failed ", @__FILE__, ":", @__LINE__, " $(Dates.now())")
-        pprintln(outgoingMsg)
-        println("</text2textInstructLLM()> attempt $attempts/$maxattempt failed ", @__FILE__, ":", @__LINE__, " $(Dates.now())\n")
-        sleep(3)
-      end
-    end
-
-    return response
-  end
-  
-  # get text embedding from a LLM service
-  function getEmbedding(text::T) where {T<:AbstractString}
-    msgMeta = GeneralUtils.generate_msgMeta(
-      config[:externalservice][:loadbalancer][:mqtttopic];
-      msgPurpose="embedding",
-      senderName="yiemagent",
-      senderId=sessionId,
-      receiverName="textembedding",
-      mqttBrokerAddress=config[:mqttServerInfo][:broker],
-      mqttBrokerPort=config[:mqttServerInfo][:port],
-    )
-  
-    outgoingMsg = Dict(
-      :msgMeta => msgMeta,
-      :payload => Dict(
-        :text => [text] # must be a vector of string
-      )
-    )
-  
-    response = GeneralUtils.sendReceiveMqttMsg(outgoingMsg; timeout=120, maxattempt=3)
-    embedding = response[:response][:embeddings]
-    return embedding
-  end
-  
-  function findSimilarTextFromVectorDB(text::T1, tablename::T2, embeddingColumnName::T3,
-    vectorDB::Function; limit::Integer=1
-    )::DataFrame where {T1<:AbstractString, T2<:AbstractString, T3<:AbstractString}
-    # get embedding from LLM service
-    embedding = getEmbedding(text)[1]
-    # check whether there is close enough vector already store in vectorDB. if no, add, else skip 
-    sql = """
-          SELECT *, $embeddingColumnName <-> '$embedding' as distance
-          FROM $tablename
-          ORDER BY distance LIMIT $limit;
-          """
-    response = vectorDB(sql)
-    df = DataFrame(response)
-    return df
-  end
-  
-  function similarSQLVectorDB(query; maxdistance::Integer=100)
-    tablename = "sqlllm_decision_repository"
-    # get embedding of the query
-    df = findSimilarTextFromVectorDB(query, tablename, 
-                                    "function_input_embedding", executeSQLVectorDB)
-    # println(df[1, [:id, :function_output]])
-    row, col = size(df)
-    distance = row == 0 ? Inf : df[1, :distance]  
-    # distance = 100  # CHANGE this is for testing only
-    if row != 0 && distance < maxdistance
-      # if there is usable SQL, return it.
-      output_b64 = df[1, :function_output_base64]   # pick the closest match
-      output_str = String(base64decode(output_b64))
-      rowid = df[1, :id]
-      println("\n~~~ found similar sql. row id $rowid, distance $distance ", @__FILE__, ":", @__LINE__, " $(Dates.now())")
-      return (dict=output_str, distance=distance)
-    else
-      println("\n~~~ similar sql not found, max distance $maxdistance ", @__FILE__, ":", @__LINE__, " $(Dates.now())")
-      return (dict=nothing, distance=nothing)
-    end
-  end
-  
-  function insertSQLVectorDB(query::T1, SQL::T2; maxdistance::Integer=3) where {T1<:AbstractString, T2<:AbstractString}
-    tablename = "sqlllm_decision_repository"
-    # get embedding of the query
-    # query = state[:thoughtHistory][:question]
-    df = findSimilarTextFromVectorDB(query, tablename, 
-                                    "function_input_embedding", executeSQLVectorDB)
-    row, col = size(df)
-    distance = row == 0 ? Inf : df[1, :distance]
-    if row == 0 || distance > maxdistance # no close enough SQL stored in the database
-      query_embedding = getEmbedding(query)[1]
-      query = replace(query, "'" => "")
-      sql_base64 = base64encode(SQL)
-      sql_ = replace(SQL, "'" => "")
-  
-      sql = """
-            INSERT INTO $tablename (function_input, function_output, function_output_base64, function_input_embedding) VALUES ('$query', '$sql_', '$sql_base64', '$query_embedding');
-            """
-      # println("\n~~~ added new decision to vectorDB ", @__FILE__, ":", @__LINE__, " $(Dates.now())")
-      # println(sql)
-      _ = executeSQLVectorDB(sql)
-    end
-  end
-  
-  
-  function similarSommelierDecision(recentevents::T1; maxdistance::Integer=3
-    )::Union{AbstractDict, Nothing} where {T1<:AbstractString}
-    tablename = "sommelier_decision_repository"
-    # find similar
-    println("\n~~~ search vectorDB for this: $recentevents ", @__FILE__, " ", @__LINE__)
-    df = findSimilarTextFromVectorDB(recentevents, tablename, 
-                                    "function_input_embedding", executeSQLVectorDB)
-    row, col = size(df)
-    distance = row == 0 ? Inf : df[1, :distance]
-    if row != 0 && distance < maxdistance
-      # if there is usable decision, return it.
-      rowid = df[1, :id]
-      println("\n~~~ found similar decision. row id $rowid, distance $distance ", @__FILE__, " ", @__LINE__)
-      output_b64 = df[1, :function_output_base64]   # pick the closest match
-      _output_str = String(base64decode(output_b64))
-      output = copy(JSON.parsefile(_output_str))
-      return output
-    else
-      println("\n~~~ similar decision not found, max distance $maxdistance ", @__FILE__, " ", @__LINE__)
-      return nothing
-    end
-  end
-  
-  
-  function insertSommelierDecision(recentevents::T1, decision::T2; maxdistance::Integer=5
-    ) where {T1<:AbstractString, T2<:AbstractDict}
-    tablename = "sommelier_decision_repository"
-    # find similar
-    df = findSimilarTextFromVectorDB(recentevents, tablename, 
-                                    "function_input_embedding", executeSQLVectorDB)
-    row, col = size(df)
-    distance = row == 0 ? Inf : df[1, :distance]
-    if row == 0 || distance > maxdistance # no close enough SQL stored in the database
-      recentevents_embedding = getEmbedding(recentevents)[1]
-      recentevents = replace(recentevents, "'" => "")
-      decision_json = JSON.json(decision)
-      decision_base64 = base64encode(decision_json)
-      decision = replace(decision_json, "'" => "")
-      
-      sql = 
-      """
-      INSERT INTO $tablename (function_input, function_output, function_output_base64, function_input_embedding) VALUES ('$recentevents', '$decision', '$decision_base64', '$recentevents_embedding');
-      """
-      println("\n~~~ added new decision to vectorDB ", @__FILE__, " ", @__LINE__)
-      println(sql)
-      _ = executeSQLVectorDB(sql)
-    else
-      println("~~~ similar decision previously cached, distance $distance ", @__FILE__, " ", @__LINE__)
-    end
-  end
-
-  # keepaliveChannel_2::Channel{Dict} = Channel{Dict}(8)
-  latestUserMsgTimeStamp::DateTime = Dates.now()
-
-  externalFunction = (
-    getEmbedding=getEmbedding,
-    text2textInstructLLM=text2textInstructLLM,
-    executeSQL=executeSQL,
-    similarSQLVectorDB=similarSQLVectorDB,
-    insertSQLVectorDB=insertSQLVectorDB,
-    similarSommelierDecision=similarSommelierDecision,
-    insertSommelierDecision=insertSommelierDecision,
-  )
-
-  agent = YiemAgent.sommelier(
-    externalFunction;
-    name="Jane",
-    id=sessionId,  # agent instance id
-    retailername="Yiem",
-    llmFormatName="qwen3"
-  )
-
-  # user chat loop
-  while true
-    # check for new user message
-    if isready(receiveUserMsgChannel)
-      incomingMsg = take!(receiveUserMsgChannel)
-      incoming_msgMeta = incomingMsg[:msgMeta]
-      incomingPayload = incomingMsg[:payload]
-      latestUserMsgTimeStamp = Dates.now()
-
-      # make sure the message has :text key because YiemAgent use this key for incoming user msg
-      if haskey(incomingPayload, :text)
-        # skip, msg already has correct key name
-      elseif haskey(incomingPayload, :txt)
-        # change key name to text
-        incomingPayload[:text] = incomingPayload[:txt]
-      else
-        error("\n no :txt or :text key in the message.")
-      end
-
-      # reset agent
-      if occursin("newtopic", incomingPayload[:text]) || 
-        occursin("Newtopic", incomingPayload[:text]) ||
-        occursin("New topic", incomingPayload[:text]) ||
-        occursin("new topic", incomingPayload[:text])
-        # YiemAgent.clearhistory(agent)
-
-        agent = YiemAgent.sommelier(
-          externalFunction;
-          name="Janie",
-          id=sessionId,  # agent instance id
-          retailername="Yiem",
-        )
-
-        # sending msg back to sender i.e. LINE
-        msgMeta = GeneralUtils.generate_msgMeta(
-          incomingMsg[:msgMeta][:replyTopic];
-          senderName="wine_assistant_backend",
-          senderId=sessionId,
-          replyToMsgId=incomingMsg[:msgMeta][:msgId],
-          mqttBrokerAddress=config[:mqttServerInfo][:broker],
-          mqttBrokerPort=config[:mqttServerInfo][:port],
-        )
-        outgoingMsg = Dict(
-          :msgMeta => msgMeta,
-          :payload => Dict(
-            :alias => agent.name,       # will be shown in frontend as agent name
-            :text => "Okay. What shall we talk about?"
-          )
-        )
-        _ = GeneralUtils.sendMqttMsg(outgoingMsg)
-        println("--> outgoingMsg ", @__FILE__, ":", @__LINE__, " $(Dates.now())")
-        pprintln(outgoingMsg)
-      else
-        usermsg = incomingPayload
-
-        if incoming_msgMeta[:msgPurpose] == "initialize"
-          println("\n-- Initializing... ", @__FILE__, ":", @__LINE__, " $(Dates.now())")
-        end
-
-        # send prompt
-        result = YiemAgent.conversation(agent;
-                                            userinput=usermsg,
-                                            maximumMsg=50)
-        # Ken's bot use [br] for newline character '\n'
-        # result = replace(result, '\n'=>"[br]")
-
-        if incoming_msgMeta[:msgPurpose] == "initialize"
-          println("\n-- Initialized. Ready! waiting for request at:\n$(config[:servicetopic][:mqtttopic]) ", @__FILE__, ":", @__LINE__, " $(Dates.now())")
-          continue
-        end
-
-        msgMeta = GeneralUtils.generate_msgMeta(
-          incomingMsg[:msgMeta][:replyTopic];
-          senderName="wine_assistant_backend",
-          senderId=string(uuid4()),
-          replyToMsgId=incomingMsg[:msgMeta][:msgId],
-          mqttBrokerAddress=config[:mqttServerInfo][:broker],
-          mqttBrokerPort=config[:mqttServerInfo][:port],
-        )
-
-        outgoingMsg = Dict(
-          :msgMeta => msgMeta,
-          :payload => Dict(
-            :alias => agent.name,       # will be shown in frontend as agent name
-            :text => result
-          )
-        )
-        _ = GeneralUtils.sendMqttMsg(outgoingMsg)
-        println("\n--> outgoingMsg ", @__FILE__, ":", @__LINE__, " $(Dates.now())")
-        pprintln(outgoingMsg)
-
-
-        # jpg_as_juliaStr = nothing
-        # prompt = nothing
-
-        # if haskey(payload, "img")
-        #   url_or_base64 = payload["img"]
-
-        #   if startswith(url_or_base64, "http")
-        #     # img in http
-        #     julia_rgb_img, cv2_bgr_img = ImageUtils.url_to_cv2_image(url_or_base64)
-        #     _, buffer = cv2.imencode(".jpg", cv2_bgr_img)
-        #     jpg_as_pyStr = base64.b64encode(buffer).decode("utf-8")
-        #     jpg_as_juliaStr = pyconvert(String, jpg_as_pyStr)
-        #   else
-        #     # img in base64
-        #     cv2_bgr_img = payload["img"]
-        #     jpg_as_juliaStr = pyconvert(String, jpg_as_pyStr)
-        #   end
-        # end
-
-      end
-    else
-      # println("\n no msg")
-    end
-
-    if haskey(agent.memory[:events][end], :thought)
-      lastAssistantAction = agent.memory[:events][end][:thought][:action_name]
-      if lastAssistantAction == "END_CONVER_GUIDELINE"    # store thoughtDict
-
-        # save a.memory[:shortmem][:decisionlog] to disk using JSON
-        println("\nsaving agent.memory[:shortmem][:decisionlog] to disk")
-        filename = "agent_decision_log_$(Dates.now())_$(agent.id).json"
-        filepath = "/appfolder/app/log/$filename"
-        open(filepath, "w") do io
-          JSON.pretty(io, agent.memory[:shortmem][:decisionlog])
-        end
-        
-        # for (i, event) in enumerate(agent.memory[:events])
-        #   if event[:subject] == "assistant"
-        #     # create timeline of the last 3 conversation except the last one. 
-        #     # The former will be used as caching key and the latter will be the caching target
-        #     # in vector database
-        #     all_recapkeys = keys(agent.memory[:recap])  #[TESTING] recap as caching 
-        #     all_recapkeys_vec = [r for r in all_recapkeys]  # convert to a vector
-
-        #     # select from 1 to 2nd-to-lase event (i.e. excluding the latest which is assistant's response)
-        #     _recapkeys_vec = all_recapkeys_vec[1:i-1]  
-
-        #     # select only previous 3 recaps
-        #     recapkeys_vec =
-        #       if length(_recapkeys_vec) <= 3  # 1st message is a user's hello msg
-        #         _recapkeys_vec  # choose all
-        #       else 
-        #         _recapkeys_vec[end-2:end]
-        #       end
-        #     #[PENDING] if there is specific data such as number, donot store in database
-        #     tempmem = DataStructures.OrderedDict()
-        #     for k in recapkeys_vec
-        #       tempmem[k] = agent.memory[:recap][k]
-        #     end
-
-        #     recap = GeneralUtils.dictToString_noKey(tempmem)
-        #     thoughtDict = agent.memory[:events][i][:thought] # latest assistant thoughtDict
-        #     insertSommelierDecision(recap, thoughtDict)
-        #   else
-        #     # skip
-        #   end
-        # end
-        println("\nCaching conversation process done")
-        break
-      end
-    end
-
-    # self terminate if too long inactivity
-    timediff = GeneralUtils.timedifference(latestUserMsgTimeStamp, Dates.now(), "minutes")
-    if timediff > timeout
-
-      result = Dict(:exitreason => "timeout", :timestamp => Dates.now())
-      put!(outputchannel, result)
-      println("Agent ID $(agent.id) timeout has been reached  $timediff/$timeout minutes Send delete session msg ", @__FILE__, ":", @__LINE__, " $(Dates.now())")
-
-      # send "delete session" message to inform the main loop that this session can be deleted
-      sendto = 
-        if typeof(config[:servicetopic][:mqtttopic]) <: Array
-          config[:servicetopic][:mqtttopic][1]
-        else 
-          config[:servicetopic][:mqtttopic]
-        end
-
-      msgMeta = GeneralUtils.generate_msgMeta(
-        sendto;
-        senderName="session",
-        senderId=sessionId,
-        msgPurpose="delete session",
-        mqttBrokerAddress=config[:mqttServerInfo][:broker],
-        mqttBrokerPort=config[:mqttServerInfo][:port],
-      )
-
-      outgoingMsg = Dict(
-        :msgMeta => msgMeta,
-        :payload => nothing
-      )
-      _ = GeneralUtils.sendMqttMsg(outgoingMsg)
-
-      try disconnect(agent.mqttClient) catch end
-      break
-    end
-    sleep(1) # allowing on_msg_2, asyncmove above and other process to run
-  end
-end
-
-sessionDict = Dict{String,Any}()
-incomingMsgChannel = (ch1=Channel(8),)  # store msg that coming into servicetopic
-# incommingInternalMsg = [] # st                        ore msg that coming into servicetopic internal management
-keepaliveChannel::Channel{Dict} = Channel{Dict}(8)
-
-# Define the callback for receiving messages.
-function onMsgCallback_1(topic, payload)
-  jobj = JSON.parsefile(String(payload))
-  incomingMqttMsg = copy(jobj)  # convert json object into julia dictionary recursively
-
-  if occursin("keepalive", topic)
-    put!(keepaliveChannel, incomingMqttMsg)
-  else
-    put!(incomingMsgChannel[:ch1], incomingMqttMsg)
-  end
-end
-
-mqttInstance = GeneralUtils.mqttClientInstance_v2(
-  config[:mqttServerInfo][:broker],
-  config[:servicetopic][:mqtttopic],
-  incomingMsgChannel,
-  keepaliveChannel,
-  onMsgCallback_1
-)
-
-# ------------------------------------------------------------------------------------------------ #
-#                                      this service main loop                                      #
-# ------------------------------------------------------------------------------------------------ #
-
-function main()
-  sessiontimeout = 1 * 1 * 60  # timeout in minute for each instance (day * hour * minute)
-  initializing = false
-  while true
-    # check if mqtt connection is still up
-    _ = GeneralUtils.checkMqttConnection!(mqttInstance; keepaliveCheckInterval=30)
-
-    # initialize session 0
-    if initializing == false  # send init msg
-      sendto = 
-        if typeof(config[:servicetopic][:mqtttopic]) <: Array
-          config[:servicetopic][:mqtttopic][1]
-        else 
-          config[:servicetopic][:mqtttopic]
-        end
-
-      msgMeta = GeneralUtils.generate_msgMeta(
-        sendto;
-        msgPurpose="initialize",
-        senderName="initializer",
-        senderId="0",
-        msgId= "initMsg",
-        replyTopic=sendto,
-        mqttBrokerAddress=config[:mqttServerInfo][:broker],
-        mqttBrokerPort=config[:mqttServerInfo][:port],
-      )
-
-      outgoingMsg = Dict(
-        :msgMeta => msgMeta,
-        :payload => Dict(    # will be shown in frontend as agent name
-          :text => "Do you have full-bodied red wines under 100 USD. I don't have any other preferences."
-        )
-      )
-      _ = GeneralUtils.sendMqttMsg(outgoingMsg)
-      initializing = true
-      println("\n--> Initializing msg sent ", @__FILE__, ":", @__LINE__, " $(Dates.now())")
-    end
-
-    # check for new message
-    if !isempty(incomingMsgChannel[:ch1])
-      msg = popfirst!(incomingMsgChannel[:ch1])
-      println("\n<-- incomingMsg ", @__FILE__, ":", @__LINE__, " $(Dates.now())")
-      pprintln(msg)
-
-      # @spawn new runAgentInstance and store it in sessionDict
-      # use agent's frontend id because 1 backend agent per 1 frontend session
-      sessionId = msg[:msgMeta][:senderId]
-      sessionId = replace(sessionId, "-" => "_") # julia can't use "-" in a dict key 
-
-      # check for delete session msg
-      if msg[:msgMeta][:msgPurpose] == "delete session"
-        delete!(sessionDict, sessionId)
-        println("sessionId $(sessionId) has been terminated ", @__FILE__, ":", @__LINE__, " $(Dates.now())")
-
-      # no session yet, create new session
-      elseif sessionId ∉ keys(sessionDict)
-        inputch = Channel{Dict}(8)
-        outputch = Channel{Dict}(8)
-
-        process = @spawn runAgentInstance(inputch, outputch, sessionId, config, sessiontimeout)
-        # process = runAgentInstance(inputch, outputch, sessionId, config, sessiontimeout) #XXX use spawn version 
-
-        println("\ninstantiate agent success ", @__FILE__, ":", @__LINE__, " $(Dates.now())")
-
-        # call runAgentInstance() and store it in sessionDict to be able to check on it later
-        sessionDict[sessionId] = Dict(
-          :inputchannel => inputch,
-          :outputchannel => outputch,
-          :process => process,
-        )
-        put!(sessionDict[sessionId][:inputchannel], msg)
-      # ongoing session
-      else
-        println("sessionId $(sessionId) existing session ", @__FILE__, ":", @__LINE__, " $(Dates.now())")
-        put!(sessionDict[sessionId][:inputchannel], msg)
-      end
-    end
-
-    # sleep is needed because MQTTClient use async. "while true" loop leave no 
-    # chance for control to switch to on_msg()
-    sleep(1)
-  end
-end
-
-main()
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imagetext.txt

@@ -0,0 +1 @@
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src/YiemAgent.jl

@@ -1,7 +1,11 @@
 module YiemAgent
 
+  # export agent
+
+
   """ Order by dependencies of each file. The 1st included file must not depend on any other
-  files and each file can only depend on the file included before it."""
+  files and each file can only depend on the file included before it. 
+  """
 
   include("type.jl")
   using .type
@@ -9,22 +13,41 @@ module YiemAgent
   include("utils.jl")
   using .utils
 
-  include("toolRegistry.jl")
+  include("tools/registry.jl")
   using .toolRegistry
 
+  # include("llmfunction.jl")
+  # using .llmfunction
+
   include("agentCore.jl")
   using .agentCore
 
   include("api.jl")
   using .api
   
-  export promptAgent, takeResponse, followUp, stopAgent
-  
-  
   
 # ---------------------------------------------- 100 --------------------------------------------- #
 
 
 
 
-end # end module
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+end # module YiemAgent_v1

src/api.jl

@@ -1,15 +1,16 @@
 module api
 
-export promptAgent, takeResponse, followUp, stopAgent
+export prompt
 
 using JSON, DataStructures, Dates, UUIDs, HTTP, Random, PrettyPrinting, Serialization,
       DataFrames
 using GeneralUtils
-using ..type, ..utils, ..agentCore, ..toolRegistry
+using ..type, ..utils
 
 # ---------------------------------------------- 100 --------------------------------------------- #
 
 
+
 """
 Send a message to the agent's input channel.
 
@@ -18,31 +19,26 @@ The agent processes messages from `inputChannel` in the background task.
 
 # Arguments
 - `agent::yiemAgent`: The agent instance to send a message to
-- `msg`: The message to send, in OpenAI message format (e.g. `Dict("role" => "user", "content" => "Hello!")`)
+- `msg`: The message to send (any type accepted by the agent's processing pipeline)
 
 # Returns
 - The same `agent` instance for chaining
 
 # Notes
-- Use `takeResponse(agent)` to receive the agent's response after sending a message.
-- Use `followUp(agent, msg)` to send messages while the agent is still processing.
+- Use `take_response(agent)` to receive the agent's response after sending a message.
+- Use `follow_up(agent, msg)` to send messages while the agent is still processing.
 
 # Examples
 ```jldoctest
-julia> promptAgent(agent, Dict("role" => "user", "content" => "Hello!"))
+julia> run_agent(agent, "Hello!")
 yiemAgent(...)
 ```
 """
-function promptAgent(agent::yiemAgent, msg::AbstractDict{String, Any})
+function run_agent(agent::yiemAgent, msg)
   put!(agent.inputChannel, msg)
   return agent
 end
 
-function promptAgent(agent::yiemAgent, text::String)
-  put!(agent.inputChannel, Dict("role" => "user", "content" => [Dict("type" => "text", "text" => text)]))
-  return agent
-end
-
 """
 Take a response from the agent's output channel.
 
@@ -55,15 +51,15 @@ Blocks until the agent sends a response.
 - An `assistantMessage` instance representing the agent's response
 
 # Notes
-- Use `promptAgent(agent, msg)` to send a message before calling this function.
+- Use `run_agent(agent, msg)` to send a message before calling this function.
 
 # Examples
 ```jldoctest
-julia> response = takeResponse(agent)
+julia> response = take_response(agent)
 assistantMessage(...)
 ```
 """
-function takeResponse(agent::yiemAgent)
+function take_response(agent::yiemAgent)
   return take!(agent.outputChannel)
 end
 
@@ -75,32 +71,27 @@ and before any tool call results are sent.
 
 # Arguments
 - `agent::yiemAgent`: The agent instance to send a follow-up message to
-- `msg`: The follow-up message to send, in OpenAI message format (e.g. `Dict("role" => "user", "content" => "Also consider red wines")`)
+- `msg`: The follow-up message to send
 
 # Returns
 - The same `agent` instance for chaining
 
 # Notes
-- Use `promptAgent(agent, msg)` for the primary message and `followUp(agent, msg)` for additional
+- Use `run_agent(agent, msg)` for the primary message and `follow_up(agent, msg)` for additional
   messages while the agent is processing.
 - Follow-up messages are buffered in a separate channel (capacity 32 by default).
 
 # Examples
 ```jldoctest
-julia> followUp(agent, Dict("role" => "user", "content" => "Also consider red wines"))
+julia> follow_up(agent, "Also consider red wines")
 yiemAgent(...)
 ```
 """
-function followUp(agent::yiemAgent, msg::AbstractDict{String, Any})
+function follow_up(agent::yiemAgent, msg)
   put!(agent.followUpChannel, msg)
   return agent
 end
 
-function followUp(agent::yiemAgent, text::String)
-  put!(agent.followUpChannel, Dict("role" => "user", "content" => [Dict("type" => "text", "text" => text)]))
-  return agent
-end
-
 """
 Gracefully stop the agent.
 
@@ -114,19 +105,19 @@ then closes all channels (`inputChannel`, `outputChannel`, `followUpChannel`).
 - `nothing`
 
 # Notes
-- After calling `stopAgent`, the agent is no longer usable. A new agent must be created
+- After calling `stop_agent`, the agent is no longer usable. A new agent must be created
   for further interaction.
 - If the background task throws a `TaskFailedException`, it is rethrown.
 
 # Examples
 ```jldoctest
-julia> stopAgent(agent)
+julia> stop_agent(agent)
 ```
 """
-function stopAgent(agent::yiemAgent)
+function stop_agent(agent::yiemAgent)
   put!(agent.inputChannel, :shutdown)
   try
-    fetch(agent._agentLoop)
+    fetch(agent._agent_loop)
   catch e
     if e isa TaskFailedException
       rethrow(e)

src/toolRegistry.jl

@@ -1,290 +0,0 @@
-module toolRegistry
-
-export registerTool, registerAllTools, clearTools, listTool
-
-using Dates
-using JSON, DataStructures
-using ..type
-
-
-
-# ── MCP helper functions ────────────────────────────────────────────
-
-"""
-Extract text from MCP tool result content array.
-
-Handles JSON-RPC 2.0 result content format:
-    {"content": [{"type": "text", "text": "..."}], "isError": false}
-"""
-function _extract_text_content(result::AbstractDict)::String
-    content = get(result, "content", Any[])
-    if content isa Vector && !isempty(content)
-        lines = String[]
-        for block in content
-            if block isa Dict && get(block, "type", "") == "text"
-                push!(lines, string(get(block, "text", "")))
-            end
-        end
-        if !isempty(lines)
-            return join(lines, "\n")
-        end
-    end
-    return JSON.json(result)
-end
-
-"""
-Wrap an MCP tool definition as an `agentTool`.
-
-The returned tool's `execute` function calls the MCP server's "tools/call"
-method with the validated arguments.
-"""
-function _wrap_mcp_tool(mcpserver, tool_def::AbstractDict{String, Any}; eventSink=nothing, sessionId=nothing)::agentTool
-    name = tool_def["name"]
-    title = get(tool_def, "title", get(tool_def, "label", name))
-    desc = get(tool_def, "description", "")
-    input_schema = get(tool_def, "inputSchema", Dict{String,Any}())
-
-    # Normalize inputSchema to OpenAI function format
-    if haskey(input_schema, "properties") && input_schema["type"] == "object"
-        params = Dict(
-            "type" => "object",
-            "properties" => input_schema["properties"],
-            "required" => get(input_schema, "required", Any[]),
-        )
-    else
-        params = Dict(
-            "type" => "object",
-            "properties" => Dict{String,Any}(),
-            "required" => Any[],
-        )
-    end
-
-    return agentTool(
-      name=name,
-      label=title,
-      description=desc,
-      inputSchema=params,
-      execute=(toolCallId::String, args::AbstractDict{String, Any},
-                signal::Union{Nothing,abortSignal},
-                eventSink) -> begin
-        try
-          response = mcpserver("tools/call", name, args, sessionId; eventSink=eventSink)
-
-          # Parse JSON-RPC 2.0 response envelope
-          if haskey(response, "error")
-            rpc_error = response["error"]
-            err_msg = get(rpc_error, "message", "Unknown MCP error")
-            return agentToolResult(
-                [textContent("MCP error: $err_msg")],
-                Dict{Any,Any}("error" => err_msg),
-                nothing, false
-            )
-          end
-
-          result_data = get(response, "result", response)
-          content_text = _extract_text_content(result_data)
-          is_error = get(result_data, "isError", false)
-
-          return agentToolResult(
-              [textContent(content_text)],
-              Dict{Any,Any}("isError" => is_error),
-              nothing, false
-          )
-        catch e
-          errMsg = sprint(showerror, e)
-          return agentToolResult(
-              [textContent("MCP call error: $errMsg")],
-              Dict{Any,Any}("error" => errMsg),
-              nothing, false
-          )
-        end
-      end,
-      prepareArguments=nothing,
-      validateRequiredArgs=nothing,
-      parallelToolExecute=false,
-    )
-end
-
-"""
-Discover and register MCP tools into `store.tools`.
-
-Queries the MCP server via `mcpserver("tools/list")` (JSON-RPC 2.0 format),
-parses the response envelope, and registers each discovered tool.
-Handles pagination via `nextCursor`. Skips tools already registered.
-
-# Returns
-- `Int`: number of new tools registered
-"""
-function register_mcp_tools(tools::OrderedDict{String, agentTool}, mcpserver; eventSink=nothing, sessionId=nothing)::Int
-  if mcpserver === nothing
-      return 0
-  end
-  new_count = 0
-  try
-    response = mcpserver("tools/list", sessionId; eventSink=eventSink)
-    # Parse JSON-RPC 2.0 response envelope
-    if haskey(response, "error")
-        rpc_error = response["error"]
-        err_msg = get(rpc_error, "message", "Unknown MCP error")
-        println("[toolRegistry] MCP tools/list failed: $err_msg")
-        return 0
-    end
-    if haskey(response, "result")
-        response = response["result"]
-    end
-
-    tools_array = response["tools"]
-    cursor = get(response, "nextCursor", nothing)
-
-    for tool_def in tools_array
-      name = tool_def["name"]
-      if haskey(tools, name)
-        continue
-      end
-
-      wrapped = _wrap_mcp_tool(mcpserver, tool_def, eventSink=eventSink, sessionId=sessionId)
-      tools[name] = wrapped
-      new_count += 1
-    end
-
-    # Paginate: fetch remaining tools if nextCursor is present
-    while cursor !== nothing && cursor !== ""
-      response = sessionId !== nothing ? mcpserver("tools/list", sessionId; eventSink=eventSink) : mcpserver("tools/list"; eventSink=eventSink)
-      if haskey(response, "result")
-          response = response["result"]
-      end
-      tools_array = get(response, "tools", Any[])
-      cursor = get(response, "nextCursor", nothing)
-      for tool_def in tools_array
-        name = tool_def["name"]
-        if haskey(tools, name)
-            continue
-        end
-        wrapped = _wrap_mcp_tool(mcpserver, tool_def, eventSink=eventSink, sessionId=sessionId)
-        tools[name] = wrapped
-        new_count += 1
-      end
-    end
-
-    println("[toolRegistry] Discovered $new_count MCP tools. Total: $(length(tools))")
-  catch e
-    bt = catch_backtrace()
-    err_msg = sprint() do io
-      showerror(io, e, bt)
-      println(io)
-    end
-
-    eventSink(err_msg)
-
-    errMsg = sprint(showerror, e)
-    println("[toolRegistry] MCP tools/list failed: $errMsg")
-  end
-
-  return new_count
-end
-
-"""
-    registerTool(tools::OrderedDict{String, agentTool}, tool::agentTool) -> OrderedDict{String, agentTool}
-
-Add `tool` to `tools`, overwriting any existing tool with the same name.
-
-# Arguments
-- `tools`: Tool dict to modify
-- `tool`: The `agentTool` to register
-
-# Returns
-- The same `tools` dict (modified in place)
-"""
-function registerTool(tools::OrderedDict{String, agentTool}, tool::agentTool; eventSink=nothing
-  )::OrderedDict{String, agentTool}
-    tools[tool.name] = tool
-    eventSink("[toolRegistry] Registered tool: $(tool.name)")
-    return tools
-end
-
-"""
-Remove all tools from the dict.
-
-# Arguments
-- `tools`: Tool dict to clear
-
-# Returns
-- `nothing`
-"""
-function clearTools(tools::OrderedDict{String, agentTool})::Nothing
-    empty!(tools)
-    println("[toolRegistry] Registry cleared")
-    return nothing
-end
-
-# ── listTools tool (auto-discover new MCP tools at runtime) ─────────
-
-"""
-    listTool(tools::OrderedDict{String, agentTool}, mcpserver) -> agentTool
-
-MCP-aware listTools tool (JSON-RPC 2.0 protocol).
-
-First call: queries the MCP server via `mcpserver("tools/list")`, registers
-all discovered tools into `tools` (in-place mutation, with
-pagination via nextCursor), then returns the full tool list.
-
-Subsequent calls: returns the current list (tools remain registered).
-"""
-function listTool(tools::OrderedDict{String, agentTool}, mcpserver; eventSink=nothing, sessionId=nothing)::agentTool
-    return agentTool(
-        name="listTools",
-        label="List Tools",
-        description="List all available tools. First call discovers and registers all tools from the MCP server. After discovery, new tools become immediately available for use.",
-        inputSchema=Dict{String,Any}(
-            "type" => "object",
-            "properties" => Dict{String,Any}(),
-            "required" => Any[]
-        ),
-        execute=(toolCallId::String, args::AbstractDict{String, Any},
-                 signal::Union{Nothing,abortSignal},
-                 eventSink) -> begin
-            # Discover and register MCP tools (idempotent — skips already registered)
-            new_count = register_mcp_tools(tools, mcpserver; eventSink=eventSink, sessionId=sessionId)
-
-            # Always include listTools itself in the count
-            total = length(tools)
-
-            lines = String[
-                "- $(t.name): $(t.label) — $(t.description)"
-                for (k, t) in tools
-            ]
-            result_text = "Available tools ($total):\n" * join(lines, "\n")
-
-            return agentToolResult(
-                [textContent(result_text)],
-                Dict{Any,Any}("count" => total),
-                nothing, false
-            )
-        end,
-        prepareArguments=nothing,
-        validateRequiredArgs=nothing,
-        parallelToolExecute=false,
-    )
-end
-
-# ── High-level API ──────────────────────────────────────────────────
-
-"""
-    registerAllTools(tools::OrderedDict{String, agentTool}, mcpserver)
-
-Register all tools for an agent:
-  1. Auto-discover existing tools from the MCP server
-  2. Register the `listTools` tool so the agent can discover new tools at runtime
-
-# Arguments
-- `tools`: The tool dict to populate
-- `mcpserver`: A callable struct that communicates with the MCP server
-"""
-function registerAllTools(tools::OrderedDict{String, agentTool}, mcpserver=nothing; eventSink=nothing, sessionId=nothing)
-    list_t = listTool(tools, mcpserver; eventSink=eventSink, sessionId=sessionId)
-    eventSink("registering tools")
-    registerTool(tools, list_t; eventSink=eventSink)
-    eventSink("registering tools done")
-end
-
-end # module

src/tools/README.md

@@ -0,0 +1,525 @@
+# Tools
+
+Tools allow the agent to perform actions and fetch data. Each tool defines a **schema** (what arguments it accepts) and an **execution function** (what it does).
+
+## Tool Anatomy
+
+Each tool has 3 main parts:
+
+### 1. Schema (`inputSchema`)
+
+JSON Schema (MCP format) describing the tool's arguments. The `"required"` array lists mandatory fields:
+
+```julia
+inputSchema = Dict{String,Any}(
+    "type" => "object",
+    "properties" => Dict(
+        "city" => Dict("type" => "string", "description" => "City name"),
+        "units" => Dict("type" => "string", "enum" => ["celsius", "fahrenheit"], "default" => "celsius")
+    ),
+    "required" => ["city"]
+)
+```
+
+### 2. Execution Function (`execute`)
+
+A function with the signature:
+
+```julia
+execute(toolCallId::String, args::Dict{String,Any}, signal::Union{Nothing,abortSignal}, onPartialResult::Function)::agentToolResult
+```
+
+- **`toolCallId`** — unique ID for this invocation (from the LLM's tool call)
+- **`args`** — validated arguments provided by the LLM
+- **`signal`** — abort signal for cancellable operations
+- **`onPartialResult`** — callback for streaming progress updates
+- **Returns** — `agentToolResult` with content, details, usage, and termination flag
+
+```julia
+function executeTool(toolCallId::String, args::Dict{String,Any}, signal::Union{Nothing,abortSignal}, onPartialResult::Function)::agentToolResult
+    # Optional: stream progress updates
+    onPartialResult(Dict("status" => "Fetching data..."))
+    
+    # Do work
+    result = "Weather in $(args["city"]): Sunny, 22°C"
+    
+    # Return result
+    return agentToolResult(
+        [textContent(result)],
+        Dict{Any,Any}(),  # details
+        nothing,           # usage
+        false              # terminate (true to stop agent loop)
+    )
+end
+```
+
+### 3. Tool Definition (`getTool()`)
+
+Returns an `agentTool` struct:
+
+| Field | Type | Description |
+|---|---|---|
+| `name` | `String` | Unique identifier (e.g. `"getWeather"`) |
+| `label` | `String` | Human-readable name (e.g. `"Weather Lookup"`) |
+| `description` | `String` | What the tool does (shown to the LLM) |
+| `inputSchema` | `Any` | JSON Schema (MCP format) |
+| `execute` | `Function` | The execution function |
+| `prepareArguments` | `Union{Function,Nothing}` | Optional argument transform before validation |
+| `validateRequiredArgs` | `Union{Function,Nothing}` | Optional custom validation |
+| `parallelToolExecute` | `Bool` | Run this tool in parallel with others |
+
+## Argument Validation
+
+Validation happens **before** tool execution, in the `prepareToolCall` phase. Invalid calls return an error immediately without invoking `execute`, `beforeToolCall`, or logging `toolExecutionStart`.
+
+### Default: JSON Schema Required Fields
+
+Set `validateRequiredArgs = nothing` to use the default validator, which checks that all fields in `inputSchema["required"]` are present:
+
+```julia
+# src/tools/getWeather.jl — uses default validation
+function getTool()::agentTool
+    return agentTool(
+        name = "getWeather",
+        # ...
+        validateRequiredArgs = nothing,  # uses default
+    )
+end
+```
+
+### Custom Validation Hook
+
+Override `validateRequiredArgs` when you need:
+- **Cross-field constraints** (e.g. "at least one of X or Y")
+- **Format validation** (e.g. regex patterns, date parsing)
+- **Domain rules** (e.g. value ranges, business logic)
+
+The hook signature takes only `args`:
+
+```julia
+function validateRequiredArgs(args::Dict{String,Any})::Union{Nothing,String}
+    tz = get(args, "timezone", nothing)
+    city = get(args, "city", "")
+    
+    if !haskey(args, "timezone") && isempty(city)
+        return "Missing required argument: provide at least one of 'timezone' or 'city'"
+    end
+    
+    if tz !== nothing
+        tz_str = string(tz)
+        if !occursin(r"^[A-Za-z]+\/[A-Za-z]+(/[A-Za-z]+)*$", tz_str)
+            return "Invalid timezone format: '$tz_str'. Use IANA format, e.g. 'America/New_York'"
+        end
+    end
+    
+    return nothing
+end
+```
+
+Return `nothing` to pass, or an error `String` to fail. The error is fed back to the LLM so it can retry with corrected arguments.
+
+## Tool Discovery and Lifecycle
+
+The agent iterates through tools via a **discover → execute → loop** cycle. Here is the complete flow from the framework author's perspective:
+
+### The Agent Loop
+
+```julia
+# agentCore.jl:175 - _process_message()
+while true
+    # 1. Drain messages from inputChannel
+    while isready(agent.inputChannel)
+        raw_msg = take!(agent.inputChannel)
+        user_msg = OpenAiToUserMessage(raw_msg)
+        push!(agent._state.messages, user_msg)
+    end
+
+    # 2. Format messages for LLM
+    ctx = agent.prepareContext(agent._state)
+    formatted = agent.formatMsgForLLM(ctx)
+
+    # 3. Call LLM
+    response = agent.llmCall(formatted)
+
+    # 4. Check if LLM used tool calls
+    if has_tool_calls(response.content)
+        # 5. Execute tools, feed results back to LLM, loop
+    else
+        # 6. No tool calls — return final response
+        break
+    end
+end
+```
+
+### Step 1: Tool Discovery
+
+Tools are discovered from `agent._state.tools`, which is a `Vector{agentTool}` populated during agent creation:
+
+```julia
+# Loading tools
+tools = loadTools("src/tools")  # returns Vector{agentTool}
+
+# Passing to agent
+agent = yiemAgent(
+    systemPrompt = "...",
+    tools = tools,  # ← tools stored in agent._state.tools
+    llmCall = my_llm_call,
+    agentEventSink = my_event_sink,
+)
+```
+
+When the LLM response contains tool calls, the agent builds an `agentContext` with those tools:
+
+```julia
+context = agentContext(
+    agent._state.systemPrompt,
+    agent._state.messages,
+    agent._state.tools,  # ← tools available for discovery
+)
+```
+
+### Step 2: Extract Tool Calls from LLM Response
+
+The agent inspects the `response.content` blocks for `tool_calls`:
+
+```julia
+# agentCore.jl:217-245
+tool_call_list = agentToolCall[]
+
+for content_block in response.content
+    if content_block isa Dict
+        if get(content_block, :type, "") == "tool_calls"
+            # OpenAI format: {"type": "tool_calls", "tool_calls": [...]}
+            for tc_data in get(content_block, :tool_calls, [])
+                tc = agentToolCall(
+                    type = "function",
+                    id = get(tc_data, :id, string(uuid4())),
+                    name = get(tc_data, :function, Dict())[:name],
+                    arguments = get(tc_data, :function, Dict())[:arguments],
+                )
+                push!(tool_call_list, tc)
+            end
+        elseif get(content_block, :type, "") == "tool_call"
+            # Alternative format: single tool_call block
+            tc = agentToolCall(
+                type = "function",
+                id = get(content_block, :id, string(uuid4())),
+                name = get(content_block, :name, ""),
+                arguments = get(content_block, :arguments, Dict()),
+            )
+            push!(tool_call_list, tc)
+        end
+    end
+end
+```
+
+### Step 3: Execute Each Tool Call
+
+For each tool call, the agent runs through the **prepare → execute → finalize** pipeline:
+
+```julia
+# agentCore.jl:247-302
+if has_tool_calls && length(tool_call_list) > 0
+    context = agentContext(agent._state.systemPrompt, agent._state.messages, agent._state.tools)
+    config = agentLoopConfig(agent._state.tools, agent.beforeToolCall, agent.afterToolCall, execution_mode)
+    signal = nothing
+    emit = agent.agentEventSink
+
+    # Execute all tool calls (sequential or parallel)
+    batch = executeToolCalls(context, response, tool_call_list, config, signal, emit)
+
+    # Save results to conversation history
+    for tool_result in batch.messages
+        push!(agent._state.messages, tool_result)
+    end
+
+    # If any tool requested termination, break the loop
+    if batch.terminate
+        final_response = build_final_response(batch)
+        break
+    end
+    # Otherwise, loop back to step 2 (format + call LLM again)
+end
+```
+
+### Step 4: The Per-Call Pipeline
+
+Each tool call goes through three phases:
+
+```
+┌─────────────────────────────────────────────────────────────────┐
+│ PREPARE  →  prepareToolCall()                                  │
+│                                                                 │
+│   1. Find tool by name in context.tools                         │
+│   2. Transform args via tool.prepareArguments (if defined)      │
+│   3. Validate via tool.validateRequiredArgs (or default)        │
+│   4. Run beforeToolCall hook (if defined)                       │
+│   └── on any failure → return immediateOutcome (skip execution) │
+│   └── success → return preparedToolCall                         │
+├─────────────────────────────────────────────────────────────────┤
+│ EXECUTE  →  executePreparedToolCall()                           │
+│                                                                 │
+│   1. emit toolExecutionStart event                              │
+│   2. call tool.execute(toolCallId, args, signal, onPartialResult)│
+│   3. wait for all pending update events                         │
+│   └── on error → return executedOutcome(isError=true)           │
+│   └── success → return executedOutcome(isError=false)           │
+├─────────────────────────────────────────────────────────────────┤
+│ FINALIZE  →  finalizeExecutedToolCall()                         │
+│                                                                 │
+│   1. Run afterToolCall hook (if defined)                        │
+│      - can mutate content, details, usage, terminate, isError   │
+│   2. emit toolExecutionEnd event                                │
+│   3. createToolResultMessage → adds to conversation history     │
+│   └── return finalizedOutcome                                   │
+└─────────────────────────────────────────────────────────────────┘
+```
+
+### Step 5: Feed Results Back to LLM
+
+Tool results are added to `agent._state.messages` as `toolResultMessage` objects. On the next loop iteration, `formatMsgForLLM()` converts them to OpenAI format and the LLM receives the results:
+
+```
+Conversation history after tool execution:
+  [system] "You are a helpful assistant."
+  [user] "What's the weather in Tokyo?"
+  [assistant] (tool_calls: getWeather(city="Tokyo"))
+  [tool] tool_call_id="call_1", tool_name="getWeather", content="Weather in Tokyo: Sunny, 22°C"
+```
+
+The LLM then decides: call another tool, or return a final text answer.
+
+## Execution Modes
+
+### Sequential
+
+Tools execute one at a time in order. Required when:
+- Tools have implicit dependencies
+- Tools share state (e.g. writing to the same file)
+- Tools have `parallelToolExecute = false`
+
+Set globally via `agentLoopConfig.toolExecution = "sequential"`, or per-tool via `parallelToolExecute = false`.
+
+### Parallel
+
+Tools execute concurrently when all are independent. Reduces wall-clock time. Set `parallelToolExecute = true` on individual tools, or set `agentLoopConfig.toolExecution = "parallel"`.
+
+## Streaming Partial Results
+
+For long-running tools (API calls, file uploads, training), use `onPartialResult` to stream progress:
+
+```julia
+function executeTool(toolCallId::String, args::Dict{String,Any}, signal::Union{Nothing,abortSignal}, onPartialResult::Function)::agentToolResult
+    onPartialResult(Dict("status" => "Step 1: Fetching data..."))
+    sleep(1)
+    
+    onPartialResult(Dict("status" => "Step 2: Processing..."))
+    sleep(1)
+    
+    return agentToolResult(
+        [textContent("Done!")],
+        Dict{Any,Any}(), nothing, false
+    )
+end
+```
+
+UI listeners and the TUI consume these events in real time via `toolExecutionUpdate`.
+
+## Loading Tools
+
+### Auto-load from Directory
+
+```julia
+using .toolRegistry
+
+tools = loadTools("src/tools")  # scans for *.jl files with getTool()
+```
+
+Files are loaded alphabetically for deterministic registration order.
+
+### Manual Registration
+
+```julia
+tool = getTool()  # from your tool module
+registerTool(tool)
+```
+
+## Complete Lifecycle Example
+
+```julia
+# ─── USER SENDS MESSAGE ───────────────────────────────────────────
+run_agent(agent, "What's the weather in Tokyo?")
+
+# ─── LOOP ITERATION 1 ─────────────────────────────────────────────
+# Agent formats messages and calls LLM
+formatted = agent.formatMsgForLLM(agent.prepareContext(agent._state))
+response = agent.llmCall(formatted)
+# LLM returns: {"content": [{"type": "tool_calls", "tool_calls": [{"name": "getWeather", "arguments": {"city": "Tokyo"}}]}]}
+
+# Agent extracts tool call, builds context
+context = agentContext(systemPrompt, messages, agent._state.tools)
+tool_call_list = [agentToolCall("call_1", "getWeather", Dict("city" => "Tokyo"))]
+
+# PREPARE: find tool, validate args
+tool = find(t -> t.name == "getWeather", context.tools)  # found!
+validateRequiredArgs(Dict("city" => "Tokyo"), tool.inputSchema)  # passes
+beforeToolCall_hook(agentMsgCtx, nothing)  # nil, skipped
+
+# EXECUTE: call tool.execute()
+result = tool.execute("call_1", Dict("city" => "Tokyo"), nothing, onPartialResult)
+# Returns: agentToolResult([textContent("Weather in Tokyo: Sunny, 22°C")], Dict(), nothing, false)
+
+# FINALIZE: afterToolCall hook, emit events
+finalized = finalizedOutcome(tc, result, false)
+emit(toolExecEndEvent("call_1", "getWeather", result, false))
+msg = createToolResultMessage(finalized)  # toolResultMessage for conversation history
+
+# Add result to conversation
+push!(agent._state.messages, msg)
+# Messages now: [user: "What's the weather?", assistant: {tool_calls: getWeather}, tool: "Sunny, 22°C"]
+
+# ─── LOOP ITERATION 2 ─────────────────────────────────────────────
+# LLM called again with tool result included
+formatted = agent.formatMsgForLLM(agent.prepareContext(agent._state))
+response = agent.llmCall(formatted)
+# LLM returns: {"content": [{"type": "text", "text": "The weather in Tokyo is sunny, 22°C."}]}
+
+# No tool calls detected → break loop, return final response
+return assistantMessage(content=[textContent("The weather in Tokyo is sunny, 22°C.")], ...)
+
+# ─── USER RECEIVES RESPONSE ───────────────────────────────────────
+response = take_response(agent)
+println(response.content)
+# => "[textContent(\"The weather in Tokyo is sunny, 22°C.\")]"
+```
+
+## Self-Modifying Tools
+
+The framework includes tools that allow the agent to create new tools at runtime.
+
+### `writeTool` — Create New Tool Files
+
+`writeTool` is a **file writer**, not a code generator. The LLM provides the tool logic as `executeCode` (the actual Julia code), and `writeTool` wraps it in the required boilerplate.
+
+**How it works:**
+
+The LLM constructs `writeTool` with:
+- **`executeCode`** — the actual tool logic (Julia code body, NOT wrapped in a function)
+- **`name`, `label`, `description`** — tool metadata
+- **`inputSchema`** — parameter schema in MCP format
+- **`validateCode`, `prepareCode`** (optional) — custom validation/preparation logic
+
+`writeTool` produces `src/tools/<name>.jl` by:
+1. Converting the `inputSchema` Dict into a Julia `Dict{String,Any}(...)` string literal
+2. Indenting `executeCode` with 4 spaces
+3. Wrapping it inside a `function executeTool(...)::agentToolResult ... end` template
+4. Appending the `getTool()` definition that returns an `agentTool` struct
+5. Writing the combined string to disk
+
+**Workflow:**
+
+```
+LLM decides: "Need a searchWine tool. I'll provide the logic."
+
+LLM calls writeTool:
+  name: "searchWine"
+  executeCode: "query = args[\"query\"]\nresult = search(query)\nreturn ..."
+
+writeTool wraps it → src/tools/searchWine.jl:
+  function executeTool(...)::agentToolResult
+      query = args["query"]          ← LLM code (indented 4 spaces)
+      result = search(query)
+      return agentToolResult(...)
+  end
+
+  function getTool()::agentTool
+      return agentTool(name="searchWine", ...)
+  end
+
+Restart → loadTools("src/tools") loads searchWine.jl
+```
+
+**Example specification:**
+
+```julia
+Dict(
+    "name" => "searchWine",
+    "label" => "Wine Search",
+    "description" => "Search a wine database by name, region, or variety",
+    "inputSchema" => Dict(
+        "type" => "object",
+        "properties" => Dict(
+            "query" => Dict("type" => "string", "description" => "Search query"),
+            "maxResults" => Dict("type" => "integer", "default" => 10)
+        ),
+        "required" => ["query"]
+    ),
+    "executeCode" => """
+        query = args["query"]
+        max_results = get(args, "maxResults", 10)
+        # Perform search logic here
+        result = "Found 3 wines matching: $query"
+        return agentToolResult([textContent(result)], Dict{Any,Any}(), nothing, false)
+    """,
+    "parallel" => false
+)
+```
+
+**Optional hooks:**
+
+| Field | Description |
+|---|---|
+| `validateCode` | Custom validation Julia code (runs before execute). Return `nothing` to pass, or an error `String` to fail. |
+| `prepareCode` | Argument preparation code (runs before validation). Return modified args dict. |
+
+### `listTools` — Discover Available Tools
+
+Returns all registered tools. Primarily useful for **collision detection** before creating a new tool via `writeTool` — the LLM checks existing names before picking a unique one.
+
+```julia
+# Result from listTools:
+# Available tools:
+# - getWeather: Weather Lookup — Fetch current weather and forecast for a given city.
+# - getTime: Time Lookup — Get current local time for a timezone or city.
+# - writeTool: Create Tool — Generate new tool files...
+# - listTools: List Tools — List all available tools with their names and labels...
+```
+
+### Complete Self-Tooling Example
+
+```
+User: "I need to search for wines. Do you have a tool for that?"
+
+# ─── LOOP: Agent realizes no wine search tool exists ─────────────────
+
+# LLM generates the tool logic and calls writeTool to write it to disk
+
+[Tool Call] writeTool(name="searchWine", label="Wine Search",
+    description="Search a wine database by name, region, or variety",
+    inputSchema={...},
+    executeCode="query = args[\"query\"]\nresult = \"Found wines...\"\nreturn agentToolResult([textContent(result)], ...)")
+
+# writeTool generates src/tools/searchWine.jl
+
+# ─── SYSTEM RESTARTS ─────────────────────────────────────────────────
+
+# loadTools("src/tools") loads searchWine.jl alongside all other tools
+
+# ─── Agent calls the new tool ─────────────────────────────────────────
+
+[Tool Call] searchWine(query="cabernet", maxResults=5)
+# Result: "Found 5 cabernet wines..."
+
+# ─── Final response ──────────────────────────────────────────────────
+
+"The search found 5 cabernet wines: ..."
+```
+
+## Available Tools
+
+| Tool | Description | Validation |
+|---|---|---|
+| `getWeather` | Fetch weather for a city | Default (JSON Schema required) |
+| `getTime` | Get current time for a timezone or city | Custom (cross-field + format) |
+| `writeTool` | Create a new Julia tool module at runtime | Built-in (name + schema validation) |
+| `listTools` | List all available tools with descriptions | None (no arguments) |

src/tools/getTime.jl

@@ -0,0 +1,72 @@
+"""
+Validate required arguments for the getTime tool.
+
+Demonstrates custom validation beyond simple required-field checking:
+- Ensures at least one time source (timezone or city) is provided
+- Validates timezone is in IANA format if specified
+- Validates city name is not empty if specified
+
+# Arguments
+- `args::Dict{String,Any}`: Arguments from the LLM
+
+# Returns
+- `nothing` if validation passes
+- `String` error message if validation fails
+"""
+function validateRequiredArgs(args::Dict{String,Any})::Union{Nothing,String}
+    tz = get(args, "timezone", nothing)
+    city = get(args, "city", "")
+    
+    hasTz = tz !== nothing && !isempty(tz)
+    hasCity = !isempty(city)
+    
+    # At least one of timezone or city is required
+    if !hasTz && !hasCity
+        return "Missing required argument: provide at least one of 'timezone' or 'city'"
+    end
+    
+    # Validate timezone format (IANA tz database: "Continent/City" or "Continent/City/SubCity")
+    if hasTz
+        tz_str = string(tz)
+        if !occursin(r"^[A-Za-z]+\/[A-Za-z]+(/[A-Za-z]+)*$", tz_str)
+            return "Invalid timezone format: '$tz_str'. Use IANA format, e.g. 'America/New_York' or 'Asia/Tokyo'"
+        end
+    end
+    
+    return nothing
+end
+
+"""
+Define and return the getTime agentTool.
+"""
+function getTool()::agentTool
+    return agentTool(
+        name = "getTime",
+        label = "Time Lookup",
+        description = "Get current local time for a timezone or city.",
+        inputSchema = Dict{String,Any}(
+            "type" => "object",
+            "properties" => Dict(
+                "timezone" => Dict("type" => "string", "description" => "IANA timezone, e.g. 'America/New_York'"),
+                "city" => Dict("type" => "string", "description" => "City name as fallback")
+            ),
+            "required" => []
+        ),
+        execute = (toolCallId, args, signal, onPartialResult) -> begin
+            tz = get(args, "timezone", nothing)
+            city = get(args, "city", "")
+            if tz !== nothing
+                result = "Current time in $(tz): $(now())"
+            else
+                result = "Current time in $(city): $(now())"
+            end
+            return agentToolResult(
+                [textContent(result)],
+                Dict{Any,Any}(), nothing, false
+            )
+        end,
+        prepareArguments = nothing,
+        validateRequiredArgs = validateRequiredArgs,
+        parallelToolExecute = false
+    )
+end

src/tools/getWeather.jl

@@ -0,0 +1,31 @@
+"""
+Define and return the getWeather agentTool.
+"""
+function getTool()::agentTool
+    return agentTool(
+        name = "getWeather",
+        label = "Weather Lookup",
+        description = "Fetch current weather and forecast for a given city.",
+        inputSchema = Dict{String,Any}(
+            "type" => "object",
+            "properties" => Dict(
+                "city" => Dict("type" => "string", "description" => "City and country, e.g., 'San Francisco, CA' or 'Tokyo, Japan'"),
+                "units" => Dict("type" => "string", "enum" => ["celsius", "fahrenheit"], "default" => "celsius", "description" => "Temperature scale")
+            ),
+            "required" => ["city"]
+        ),
+        execute = (toolCallId, args, signal, onPartialResult) -> begin
+            city = get(args, "city", "")
+            units = get(args, "units", "celsius")
+            temp = units == "fahrenheit" ? "72" : "22"
+            unit_symbol = units == "celsius" ? "°C" : "°F"
+            return agentToolResult(
+                [textContent("Weather in $(city): Sunny, $(temp)$(unit_symbol)")],
+                Dict{Any,Any}(), nothing, false
+            )
+        end,
+        prepareArguments = nothing,
+        validateRequiredArgs = nothing,
+        parallelToolExecute = false
+    )
+end

src/tools/registry.jl

@@ -0,0 +1,174 @@
+module toolRegistry
+
+export loadTools, registerTool, getTools, clearTools
+
+using Dates
+using JSON
+using ..type
+
+# Global registry — populated at runtime by loadTools() or registerTool()
+const _registry = Vector{agentTool}()
+
+# Auto-register the built-in listTools tool
+function __init__()
+    registerTool(_listTool())
+end
+
+"""
+List tool definition — lets the agent query available tools for collision detection
+when creating new tools via writeTool.
+"""
+function _listTool()::agentTool
+    return agentTool(
+        name = "listTools",
+        label = "List Tools",
+        description = "List all available tools with their names, labels, and descriptions. Use this before creating a new tool to check for name collisions.",
+        inputSchema = Dict{String,Any}(
+            "type" => "object",
+            "properties" => Dict{String,Any}(),
+            "required" => Any[]
+        ),
+        execute = (toolCallId, args, signal, onPartialResult) -> begin
+            tools = getTools()
+            if isempty(tools)
+                result_text = "No tools registered."
+            else
+                lines = String["- $(t.name): $(t.label) — $(t.description)" for t in tools]
+                result_text = "Available tools:\n" * join(lines, "\n")
+            end
+            return agentToolResult(
+                [textContent(result_text)],
+                Dict{Any,Any}("count" => length(tools)),
+                nothing, false
+            )
+        end,
+        prepareArguments = nothing,
+        validateRequiredArgs = nothing,
+        parallelToolExecute = false
+    )
+end
+
+"""
+Load all tool modules from a directory.
+
+Scans `dir` for `.jl` files. Each file must define a function named
+`getTool()::agentTool`.  Files are sorted alphabetically so tool
+registration order is deterministic.
+
+# Tool file format
+Each `.jl` file defines one function `getTool()` that returns an `agentTool`:
+
+```julia
+# src/tools/getWeather.jl
+function getTool()::agentTool
+    return agentTool(
+        name = "getWeather",
+        label = "Weather Lookup",
+        description = "Fetch current weather and forecast for a given city.",
+        inputSchema = Dict{String,Any}(
+            "type" => "object",
+            "properties" => Dict(
+                "city" => Dict("type" => "string", "description" => "City and country"),
+                "units" => Dict("type" => "string", "enum" => ["celsius", "fahrenheit"], "default" => "celsius")
+            ),
+            "required" => ["city"]
+        ),
+        execute = (toolCallId, args, signal, onPartialResult) -> begin
+            city = args["city"]
+            return agentToolResult(
+                [textContent("Sunny, 22C in Bangkok")],
+                Dict{Any,Any}(), nothing, false
+            )
+        end,
+        prepareArguments = nothing,
+        parallelToolExecute = false
+    )
+end
+```
+
+# Arguments
+- `dir::String`: Directory path to scan for `.jl` tool files
+
+# Returns
+- `Vector{agentTool}`: All loaded tools
+
+# Errors
+- Throws `ArgumentError` if a tool file does not define a `getTool` function
+"""
+function loadTools(dir::String)::Vector{agentTool}
+    if !isdir(dir)
+        throw(ArgumentError("Tool directory does not exist: $dir"))
+    end
+
+    tools = agentTool[]
+    jl_files = filter(f -> endswith(f, ".jl") && !occursin(r"(?i)registry", f), readdir(dir))
+    sort!(jl_files)
+
+    for filename in jl_files
+        filepath = joinpath(dir, filename)
+        println("[toolRegistry] Loading tool from: $filepath")
+
+        # Include the file in the current module scope so all types resolve
+        # (agentTool, textContent, agentToolResult, etc. are all available)
+        include(filepath)
+
+        # Validate that getTool was defined (include() places it in current module scope)
+        if !isdefined(@__MODULE__, :getTool)
+            throw(ArgumentError(
+                "Tool file $(filepath) does not define a `getTool()` function. " *
+                "Each tool file must define: function getTool()::agentTool ... end"
+            ))
+        end
+
+        # Call getTool() — it runs in current scope where types are visible
+        # Use invokelatest to handle world-age semantics after include()
+        tool = invokelatest(getTool)
+        if !(tool isa agentTool)
+            throw(ArgumentError(
+                "getTool() in $(filepath) did not return an agentTool instance, got: $(typeof(tool))"
+            ))
+        end
+
+        push!(_registry, tool)
+        push!(tools, tool)
+        println("[toolRegistry] Loaded tool: $(tool.name) — $(tool.label)")
+    end
+
+    return tools
+end
+
+"""
+Register a single agentTool into the global registry.
+
+# Arguments
+- `tool::agentTool`: The tool to register
+
+# Returns
+- `Vector{agentTool}`: Updated registry
+"""
+function registerTool(tool::agentTool)::Vector{agentTool}
+    push!(_registry, tool)
+    println("[toolRegistry] Registered tool: $(tool.name)")
+    return _registry
+end
+
+"""
+Get all registered tools.
+
+# Returns
+- `Vector{agentTool}`: Copy of the registry
+"""
+function getTools()::Vector{agentTool}
+    return deepcopy(_registry)
+end
+
+"""
+Clear all registered tools from the global registry.
+"""
+function clearTools()::Nothing
+    empty!(_registry)
+    println("[toolRegistry] Registry cleared")
+    return nothing
+end
+
+end # module

src/tools/writeTool.jl

@@ -0,0 +1,271 @@
+"""
+Tool that writes new Julia tool module files to disk.
+
+The agent can use this tool when it encounters a task that no existing tool
+can handle. Provide the tool's name, label, description, inputSchema, and
+execute logic as Julia code. The tool is written to `src/tools/<name>.jl`.
+
+After calling this tool, restart the agent so `loadTools("src/tools")` picks
+up the new file. The new tool is immediately available.
+
+# Example
+
+1. Agent calls writeTool with a spec for a "searchWine" tool
+2. writeTool generates src/tools/searchWine.jl
+3. Restart agent — loadTools() picks up the new file
+4. Agent calls searchWine with args
+
+# How It Works
+
+writeTool is a **file writer**, not a code generator. The LLM provides the
+tool logic as `executeCode`, and writeTool wraps it in Julia boilerplate:
+  - Converts `inputSchema` Dict into Julia `Dict{String,Any}(...)` string
+  - Indents `executeCode` with 4 spaces
+  - Wraps it inside `function executeTool(...)::agentToolResult ... end`
+  - Appends `getTool()` returning an `agentTool` struct
+  - Writes the combined string to `src/tools/<name>.jl`
+
+# Important Notes
+
+- The `executeCode` string is embedded literally into the generated tool.
+  Use `args["param_name"]` to access input parameters.
+- The code string should be the function body (NOT wrapped in a function).
+  Lines will be indented with 4 spaces inside the execute function.
+- Tool names must be valid Julia identifiers (lowercase letters, digits, underscores,
+  no leading digits or special characters).
+"""
+
+"""
+Validate that a tool name is a valid Julia identifier.
+"""
+function validateToolName(name::String)::Union{Nothing,String}
+    if !occursin(r"^[a-zA-Z_][a-zA-Z0-9_!]*$", name)
+        return "Invalid tool name: '$name'. Tool names must be valid Julia identifiers (letters, digits, underscores, starting with a letter or underscore)."
+    end
+    return nothing
+end
+
+"""
+Indent a multi-line code string by the specified number of spaces.
+"""
+function indent_code(code::String, n::Int)::String
+    prefix = " "^n
+    lines = split(code, '\n')
+    result_lines = String[prefix * line for line in lines]
+    return join(result_lines, "\n")
+end
+
+"""
+Convert a Julia Dict to a valid Julia Dict{String,Any}(...) literal string.
+"""
+function dict_to_julia_literal(d)::String
+    if d isa Dict
+        items = String[]
+        for (k, v) in d
+            key_str = json_string(k)
+            val_str = value_to_julia(v)
+            push!(items, "$key_str => $val_str")
+        end
+        return "Dict{String,Any}(" * join(items, ", ") * ")"
+    else
+        return value_to_julia(d)
+    end
+end
+
+function value_to_julia(v)::String
+    if v isa Dict
+        return dict_to_julia_literal(v)
+    elseif v isa Vector
+        items = [value_to_julia(x) for x in v]
+        return "[" * join(items, ", ") * "]"
+    elseif v isa String
+        escaped = replace(v, "\\" => "\\\\")
+        escaped = replace(escaped, "\"" => "\\\"")
+        return "\"$escaped\""
+    elseif v isa Number
+        return string(v)
+    elseif v isa Bool
+        return string(v)
+    elseif v === nothing
+        return "nothing"
+    else
+        return "\"$(v)\""
+    end
+end
+
+"""
+Convert any Julia value to a JSON string.
+"""
+function json_string(v)::String
+    return JSON.json(v)
+end
+
+"""
+Define and return the writeTool agentTool.
+"""
+function getTool()::agentTool
+    return agentTool(
+        name = "writeTool",
+        label = "Create Tool",
+        description = "Write a new Julia tool module file to src/tools/<name>.jl. The LLM provides the tool logic as executeCode; writeTool wraps it in Julia boilerplate and writes the file. Restart the agent to load the new tool.",
+        inputSchema = Dict{String,Any}(
+            "type" => "object",
+            "properties" => Dict(
+                "name" => Dict("type" => "string", "description" => "Unique tool name (valid Julia identifier, no spaces or special chars)"),
+                "label" => Dict("type" => "string", "description" => "Human-readable tool name shown in tool descriptions"),
+                "description" => Dict("type" => "string", "description" => "What the tool does (shown to LLM for tool selection decisions)"),
+                "inputSchema" => Dict(
+                    "type" => "object",
+                    "description" => "JSON Schema describing tool parameters in MCP format"
+                ),
+                "executeCode" => Dict("type" => "string", "description" => "Julia code for the execute function body. Use args[\"key\"] to access parameters. Do NOT wrap in a function definition."),
+                "validateCode" => Dict("type" => "string", "optional" => true, "description" => "Optional custom validation Julia code (runs before execute). Use args[\"key\"] to access parameters. Return nothing to pass, or a string error message to fail."),
+                "prepareCode" => Dict("type" => "string", "optional" => true, "description" => "Optional argument preparation code (runs before validation). Return modified args dict."),
+                "parallel" => Dict("type" => "boolean", "default" => false, "description" => "Whether this tool can run in parallel with other tools")
+            ),
+            "required" => ["name", "label", "description", "inputSchema", "executeCode"]
+        ),
+        execute = (toolCallId::String, args::Dict{String,Any}, signal::Union{Nothing,abortSignal}, onPartialResult::Function) -> begin
+            tool_name = get(args, "name", "")::String
+            tool_label = get(args, "label", tool_name)::String
+            tool_description = get(args, "description", "")::String
+            tool_schema = get(args, "inputSchema", Dict{String,Any}())::Dict{String,Any}
+            execute_code = get(args, "executeCode", "")::String
+            validate_code = get(args, "validateCode", nothing)::Union{String,Nothing}
+            prepare_code = get(args, "prepareCode", nothing)::Union{String,Nothing}
+            parallel = get(args, "parallel", false)::Bool
+
+            # Validate tool name
+            name_err = validateToolName(tool_name)
+            if name_err !== nothing
+                return agentToolResult(
+                    [textContent(name_err)],
+                    Dict{Any,Any}(), nothing, false
+                )
+            end
+
+            # Validate required fields
+            if isempty(tool_name)
+                return agentToolResult(
+                    [textContent("Missing required field: 'name'")],
+                    Dict{Any,Any}(), nothing, false
+                )
+            end
+            if isempty(tool_description)
+                return agentToolResult(
+                    [textContent("Missing required field: 'description'")],
+                    Dict{Any,Any}(), nothing, false
+                )
+            end
+            if isempty(execute_code)
+                return agentToolResult(
+                    [textContent("Missing required field: 'executeCode'")],
+                    Dict{Any,Any}(), nothing, false
+                )
+            end
+
+            onPartialResult(Dict("status" => "Generating tool: $tool_name"))
+
+            # Build the tool file path
+            script_dir = dirname(@__FILE__)
+            tools_dir = dirname(script_dir)
+            filepath = joinpath(tools_dir, "$(tool_name).jl")
+
+            # Check for naming conflicts
+            if isfile(filepath)
+                return agentToolResult(
+                    [textContent("Tool file already exists: $filepath. Rename the tool or delete the existing file first.")],
+                    Dict{Any,Any}(), nothing, false
+                )
+            end
+
+            onPartialResult(Dict("status" => "Writing file: $(basename(filepath))"))
+
+            # Convert schema Dict to a Julia Dict literal string
+            schema_literal = dict_to_julia_literal(tool_schema)
+
+            # Build optional validation function
+            validate_section = if validate_code !== nothing && !isempty(validate_code)
+                indented = indent_code(validate_code, 4)
+                "function validateRequiredArgs(args::Dict{String,Any})::Union{Nothing,String}\n$indented\n    return nothing\nend\n"
+            else
+                ""
+            end
+
+            # Build optional prepare function
+            prepare_section = if prepare_code !== nothing && !isempty(prepare_code)
+                indented = indent_code(prepare_code, 4)
+                "function prepareArguments(args::Dict{String,Any})::Dict{String,Any}\n$indented\n    return args\nend\n"
+            else
+                ""
+            end
+
+            # Indent user's execute code for embedding inside execute function body
+            indented_exec = indent_code(execute_code, 4)
+
+            # Escape description for Julia string literal
+            escaped_desc = replace(tool_description, "\\" => "\\\\")
+            escaped_desc = replace(escaped_desc, "\"" => "\\\"")
+
+            # Build the complete tool file content
+            parts = String[]
+            push!(parts, "# Auto-generated tool: $tool_name\n")
+            push!(parts, "# Generated by writeTool at $(now())\n\n")
+            if !isempty(validate_section)
+                push!(parts, validate_section)
+                push!(parts, "\n")
+            end
+            if !isempty(prepare_section)
+                push!(parts, prepare_section)
+                push!(parts, "\n")
+            end
+            push!(parts, "\n")
+            push!(parts, "# Execute function\n")
+            push!(parts, "function executeTool(toolCallId::String, args::Dict{String,Any}, signal::Union{Nothing,abortSignal}, onPartialResult::Function)::agentToolResult\n")
+            push!(parts, "$indented_exec\n")
+            push!(parts, "end\n\n")
+            push!(parts, "# Tool definition\n")
+            push!(parts, "function getTool()::agentTool\n")
+            push!(parts, "    return agentTool(\n")
+            push!(parts, "        name = \"$(tool_name)\",\n")
+            push!(parts, "        label = \"$(tool_label)\",\n")
+            push!(parts, "        description = \"$(escaped_desc)\",\n")
+            push!(parts, "        inputSchema = $schema_literal,\n")
+            push!(parts, "        execute = executeTool,\n")
+            if validate_code !== nothing && !isempty(validate_code)
+                push!(parts, "        validateRequiredArgs = validateRequiredArgs,\n")
+            else
+                push!(parts, "        validateRequiredArgs = nothing,\n")
+            end
+            if prepare_code !== nothing && !isempty(prepare_code)
+                push!(parts, "        prepareArguments = prepareArguments,\n")
+            else
+                push!(parts, "        prepareArguments = nothing,\n")
+            end
+            push!(parts, "        parallelToolExecute = $parallel\n")
+            push!(parts, "    )\n")
+            push!(parts, "end\n")
+
+            tool_code = join(parts)
+
+            # Write the file — tool is loaded on next agent restart via loadTools()
+            write(filepath, tool_code)
+
+            onPartialResult(Dict("status" => "Done"))
+
+            return agentToolResult(
+                [textContent("Tool '$(tool_name)' written to $filepath. Restart the agent so loadTools() picks it up, then call listTools to verify.")],
+                Dict{Any,Any}(
+                    "file" => filepath,
+                    "name" => tool_name,
+                    "label" => tool_label,
+                    "description" => tool_description,
+                ),
+                nothing, false
+            )
+        end,
+        prepareArguments = nothing,
+        validateRequiredArgs = nothing,
+        parallelToolExecute = false
+    )
+end

src/type.jl

@@ -5,16 +5,16 @@
            # Model types
            modelCost, llmModel, llmUsage,
            # Message content types
-            textContent, imageContent, reasoningContent,
+           textContent, imageContent,
            # Message types
-            userMessage, assistantMessageToolCall, assistantMessage, toolResultMessage,
+           userMessage, assistantMessage, toolResultMessage,
             # Tool types
              agentTool, validateRequiredArgs,
            # Context types
            agentContext, agentState, agentToolCall, prepareNextTurnContext,
            # Loop & execution types
-           agentLoopConfig, abortSignal, agentToolResult,beforeToolCallContext, 
-           beforeToolCallResult, afterToolCallContext,
+           agentLoopConfig, abortSignal, agentToolResult,
+           assistantMsgCtx, afterCtx,
            # Event types
            toolExecStartEvent, toolExecUpdateEvent, toolExecEndEvent,
            # Agent
@@ -23,7 +23,7 @@
            preparedToolCall, immediateOutcome, executedOutcome, finalizedOutcome,
            agentToolCallBatch,
            # Functions (defined elsewhere)
-           promptAgent, takeResponse, followUp, stopAgent
+           run_agent, take_response, follow_up, stop_agent
 
 
 using Dates, UUIDs, DataStructures, JSON, NATS, Base.Threads
@@ -31,13 +31,6 @@ using GeneralUtils
 
 const Timestamp = DateTime
 
-struct agentToolCall               # A tool invocation from the LLM
-  type::String                              # Always "function"
-  id::String                                # Unique tool call identifier
-  name::String                              # Tool name
-  arguments::Dict{String, Any}              # Parsed tool arguments
-end
-
 # ------------------------------------------------------------------------------------------------ #
 #                                          LLM model info                                          #
 # ------------------------------------------------------------------------------------------------ #
@@ -82,10 +75,6 @@ struct imageContent <: messageContent         # Image message content
     mimeType::String                          # MIME type (e.g., "image/png")
 end
 
-struct reasoningContent <: messageContent    # LLM reasoning/thinking content
-    text::String                              # The reasoning text
-end
-
 
 # ------------------------------------------------------------------------------------------------ #
 #                                           Message types                                          #
@@ -119,52 +108,6 @@ function userMessage(; role="user", content=Vector{messageContent}(), timestamp=
     return userMessage(role, content, timestamp)
 end
 
-struct assistantMessageToolCall <: agentMessage   # Assistant message containing tool calls
-    role::String                            # Always "assistant"
-    toolCalls::Vector{agentToolCall}        # Tool calls to execute
-    content::Vector{messageContent}         # Reasoning/thinking content blocks
-    api::String                             # API name used (e.g., "openai")
-    provider::String                        # Provider name (e.g., "anthropic")
-    model::String                           # Model identifier
-    usage::llmUsage                         # Token usage for this message
-    stopReason::String                      # Why generation stopped (e.g., "tool_calls")
-    errorMessage::Union{String, Nothing}    # Error if generation failed
-    timestamp::Timestamp                    # When the message was received
-end
-
-"""
-Create a new assistant message containing tool calls.
-
-# Arguments
-- `role::String`: Always "assistant"
-- `toolCalls::Vector{agentToolCall}`: Tool calls to execute
-- `content::Vector{messageContent}`: Reasoning/thinking content blocks
-- `api::String`: API name used
-- `provider::String`: Provider name
-- `model::String`: Model identifier
-- `usage::llmUsage`: Token usage
-- `stopReason::String`: Why generation stopped
-- `errorMessage::Union{String, Nothing}`: Error if generation failed
-- `timestamp::Timestamp`: When the message was received
-
-# Returns
-- A new `assistantMessageToolCall` instance
-
-# Examples
-```julia
-julia> tc = agentToolCall("function", "call_1", "getWeather", Dict("city" => "Tokyo"))
-julia> msg = assistantMessageToolCall(toolCalls=[tc], stopReason="tool_calls")
-assistantMessageToolCall("assistant", [agentToolCall(...)], messageContent[], "", "", "", llmUsage(0, 0), "tool_calls", nothing, DateTime(...))
-```
-"""
-function assistantMessageToolCall(; role="assistant", toolCalls=agentToolCall[],
-    content=Vector{messageContent}(), api="", provider="", model=nothing, usage=llmUsage(0, 0),
-    stopReason="tool_calls", errorMessage=nothing, timestamp=now())
-    model_str = model isa AbstractString ? String(model) : ""
-    return assistantMessageToolCall(role, toolCalls, content, api, provider, model_str,
-                                     usage, stopReason, errorMessage, timestamp)
-end
-
 struct assistantMessage <: agentMessage     # Message from the AI assistant
     role::String                            # Always "assistant"
     content::Vector{messageContent}         # Text and/or image content
@@ -201,10 +144,9 @@ assistantMessage("assistant", [textContent("Hello!")], "", "", "gpt-4", ..., "en
 ```
 """
 function assistantMessage(; role="assistant", content=Vector{messageContent}(),
-    api="", provider="", model=nothing, usage=llmUsage(0, 0), stopReason="end_turn",
+    api="", provider="", model="", usage=llmUsage(0, 0), stopReason="end_turn",
     errorMessage=nothing, timestamp=now())
-    model_str = model isa AbstractString ? String(model) : ""
-    return assistantMessage(role, content, api, provider, model_str, usage, stopReason, errorMessage, timestamp)
+    return assistantMessage(role, content, api, provider, model, usage, stopReason, errorMessage, timestamp)
 end
 
 struct toolResultMessage <: agentMessage    # Result returned from a tool execution
@@ -321,7 +263,7 @@ struct agentTool                              # A tool available to the agent
     label::String                             # Human-readable tool name
     description::String                       # What the tool does
     inputSchema::Any                          # Tool parameters schema (JSON schema, MCP inputSchema format)
-    execute                         # Tool execution function
+    execute::Function                         # Tool execution function
     prepareArguments::Union{Function, Nothing}  # Optional argument preparation callback
     validateRequiredArgs::Union{Function, Nothing}  # Optional validation hook for required args
     parallelToolExecute::Bool  # Override: run tool calls sequentially or in parallel
@@ -331,7 +273,7 @@ end
 Keyword constructor for agentTool — allows `agentTool(name=..., label=..., ...)`.
 """
 function agentTool(; name::String, label::String, description::String, inputSchema::Any,
-                     execute, prepareArguments::Union{Function, Nothing}=nothing,
+                     execute::Function, prepareArguments::Union{Function, Nothing}=nothing,
                      validateRequiredArgs::Union{Function, Nothing}=nothing,
                      parallelToolExecute::Bool=false)
     return agentTool(name, label, description, inputSchema, execute,
@@ -349,7 +291,7 @@ Snapshot of the agent's conversation context.
 # Arguments
 - `systemPrompt::String`: System prompt for the agent
 - `messages::Vector{agentMessage}`: Conversation messages
-- `tools::Union{OrderedDict{String, agentTool}, Nothing}`: Available tools keyed by name for O(1) lookup
+- `tools::Union{Vector{agentTool}, Nothing}`: Available tools
 
 # Returns
 - A new `agentContext` instance
@@ -357,7 +299,7 @@ Snapshot of the agent's conversation context.
 struct agentContext                           # Snapshot of the agent's conversation context
     systemPrompt::String                      # System prompt for the agent
     messages::Vector{agentMessage}            # Conversation messages
-    tools::Union{OrderedDict{String, agentTool}, Nothing}  # Available tools keyed by name
+    tools::Union{Vector{agentTool}, Nothing}  # Available tools
 end
 
 
@@ -366,14 +308,15 @@ end
 # ------------------------------------------------------------------------------------------------ #
 
 mutable struct agentState                     # Mutable runtime state of an agent
-    systemPrompt::String                      # System prompt for the agent
-    model::Union{llmModel, Nothing}                           # LLM model to use
-    tools::OrderedDict{String, agentTool}     # Available tools keyed by name, insertion-ordered
+    systemPrompt::String                      # System prompt text
+    model::llmModel                           # LLM model to use
+    tools::Vector{agentTool}                  # Available tools
 
     # messages history includes userMessage, assistantMessage, toolResultMessage. NO system prompt
     messages::Vector{agentMessage}            
     
     pendingToolCalls::Vector{String}          # Tool call IDs waiting for results
+    activeRun::Bool                             # is agent processing user message?
     errorMessage::Union{String, Nothing}      # Last error message
 end
 
@@ -386,7 +329,7 @@ new state from external references.
 # Arguments
 - `systemPrompt::String`: System prompt text
 - `model::llmModel`: LLM model to use (defaults to an unknown model)
-- `tools::OrderedDict{String, agentTool}`: Available tools keyed by name (deep copied)
+- `tools::Vector{agentTool}`: Available tools (deep copied)
 - `messages::Vector{agentMessage}`: Conversation messages (deep copied)
 
 # Returns
@@ -395,26 +338,35 @@ new state from external references.
 # Examples
 ```julia
 julia> state = agentState(systemPrompt="You are a helpful assistant")
-agentState("You are a helpful assistant", OrderedDict{String, agentTool}(), agentMessage[], String[], nothing)
+agentState("You are a helpful assistant", ..., agentTool[], agentMessage[], String[], nothing)
+```
 """
 function agentState(
     systemPrompt::String="",
-  model=llmModel("model_1", "unknown", "unknown", "", false, String[], 
-                  modelCost(0.0, 0.0, 0.0, 0.0), 0, 0),
-  tools::OrderedDict{String, agentTool}=OrderedDict{String, agentTool}(),
+    model::llmModel=llmModel{String}("", "", "unknown", "unknown", "", false, String[], modelCost(0.0, 0.0, 0.0, 0.0), 0, 0),
+    tools::Vector{agentTool}=agentTool[],
     messages::Vector{agentMessage}=agentMessage[],
 )
     agentState(
       systemPrompt,
       model,
-    tools,
+      deepcopy(tools),
       deepcopy(messages),
       Vector{String}(),
+      false,
       nothing,
     )
 end
 
 
+struct agentToolCall               # A tool invocation from the LLM
+    type::String                              # Always "function"
+    id::String                                # Unique tool call identifier
+    name::String                              # Tool name
+    arguments::Dict{String, Any}              # Parsed tool arguments
+end
+
+
 """
 Context for preparing the next conversation turn.
 
@@ -442,12 +394,14 @@ end
 """
 Configuration for the agent tool execution loop.
 
-# Fields
+# Arguments
+- `tools::Vector{agentTool}`: Available tools
 - `beforeToolCall::Union{Function, Nothing}`: Callback before tool execution
 - `afterToolCall::Union{Function, Nothing}`: Callback after tool execution
 - `toolExecution::String`: Execution mode — "sequential" or "parallel"
 """
 struct agentLoopConfig
+    tools::Vector{agentTool}
     beforeToolCall::Union{Function, Nothing}
     afterToolCall::Union{Function, Nothing}
     toolExecution::String
@@ -483,38 +437,33 @@ end
 Context passed to the `beforeToolCall` hook.
 
 # Arguments
-- `message::assistantMessageToolCall`: The assistant message containing the tool call
+- `message::assistantMessage`: The assistant message containing the tool call
 - `toolCall::agentToolCall`: The tool call being prepared
-- `args::AbstractDict{String, Any}`: Validated tool arguments
+- `args::Dict{String,Any}`: Validated tool arguments
 - `context::agentContext`: Current conversation context
 """
-struct beforeToolCallContext
-    message::assistantMessageToolCall
+struct assistantMsgCtx
+    message::assistantMessage
     toolCall::agentToolCall
-    args::AbstractDict{String, Any}
+    args::Dict{String,Any}
     context::agentContext
 end
 
-struct beforeToolCallResult
-  block::Bool
-  reason::String
-end
-
 """
 Context passed to the `afterToolCall` hook.
 
 # Arguments
-- `message::assistantMessageToolCall`: The assistant message containing the tool call
+- `message::assistantMessage`: The assistant message containing the tool call
 - `toolCall::agentToolCall`: The tool call that was executed
-- `args::AbstractDict{String, Any}`: Tool arguments
+- `args::Dict{String,Any}`: Tool arguments
 - `result::agentToolResult`: The raw tool result
 - `isError::Bool`: Whether execution resulted in an error
 - `context::agentContext`: Current conversation context
 """
-struct afterToolCallContext
-    message::assistantMessageToolCall
+struct afterCtx
+    message::assistantMessage
     toolCall::agentToolCall
-    args::AbstractDict{String, Any}
+    args::Dict{String,Any}
     result::agentToolResult
     isError::Bool
     context::agentContext
@@ -526,12 +475,12 @@ Event emitted when a tool call execution starts.
 # Arguments
 - `toolCallId::String`: ID of the tool call
 - `toolName::String`: Name of the tool
-- `arguments::AbstractDict{String, Any}`: Tool arguments
+- `arguments::Dict{String,Any}`: Tool arguments
 """
 struct toolExecStartEvent
     toolCallId::String
     toolName::String
-    arguments::AbstractDict{String, Any}
+    arguments::Dict{String,Any}
 end
 
 """
@@ -540,13 +489,13 @@ Event emitted with partial results during tool execution.
 # Arguments
 - `toolCallId::String`: ID of the tool call
 - `toolName::String`: Name of the tool
-- `arguments::AbstractDict{String, Any}`: Tool arguments
+- `arguments::Dict{String,Any}`: Tool arguments
 - `partialResult::Any`: The partial result data
 """
 struct toolExecUpdateEvent
     toolCallId::String
     toolName::String
-    arguments::AbstractDict{String, Any}
+    arguments::Dict{String,Any}
     partialResult::Any
 end
 
@@ -572,6 +521,138 @@ end
 
 abstract type agent end 
 
+"""
+docstring
+"""
+mutable struct yiemAgent <: agent             # High-level agent wrapper
+  _state::agentState                          # Current state (prompt, model, messages, tools, etc.)
+  
+  # user sends prompt message to agent. if agent is idle, it process user message right away. 
+  # if agent is running, it process user message after  the current tool call finished.
+  inputChannel::Channel                        
+                                              
+  # Buffers messages the user sends while the agent is busy. Processed after all inputChannel 
+  # messages are handled and the agent is idle (not using a tool call).
+  followUpChannel::Channel                    
+
+  # agent sends response message to user after processing all user messages in inputChannel 
+  # and all followUp messages.
+  outputChannel::Channel                     
+                                              
+  _agent_loop::Union{Task, Nothing}                 # agent loop running in the background
+
+  # Preprocess/transform messages and context (modify, filter, prune, inject context from memory,  
+  # reorder, ...) for a single LLM call in _process_message()'s loop.
+  # returns new Vector{agentMessage}
+  prepareContext ::Union{Function, Nothing}   
+
+  # Convert prepareContext()'s new Vector{agentMessage} to LLM message format
+  formatMsgForLLM::Function    
+
+  # Actually invoke the LLM to get a completion response. The LLM response comes back as an 
+  # assistantMessage whose content is an array of content blocks. 
+  # Each block has a type — "text", "thinking", or "toolCall". 
+  # The code filters for type === "toolCall" blocks, then passes them to executeToolCalls().
+  llmCall::Function                           
+                                              
+  # Callback invoked before executing a tool call (ask for user permission/confirmation/abort, etc..)
+  beforeToolCall::Union{Function, Nothing}    
+
+  executeToolCalls::Function                  # execute tool calls ()
+  
+  # Callback invoked after executing a tool call to sanitize tools output so the output is ready 
+  # to be converted into toolResults message
+  afterToolCall::Union{Function, Nothing}     
+  # prepareNextTurn::Union{Function, Nothing}   # Callback to prepare the next conversation turn
+  # prepareNextTurnWithContext::Union{Function, Nothing}  # Same but receives context
+  sessionId::Union{String, Nothing}           # Optional session identifier
+  maxRetryDelayMs::Union{Int64, Nothing}      # Maximum delay between retries (ms)
+  parallelToolExecute::Bool                   # Default: false
+  agentEventSink::Function                    # agent emits its status via this function
+end
+
+"""
+Create a new yiemAgent instance with a background loop task.
+
+Spawns a background `@spawn` task that runs the agent loop, listening
+on `inputChannel` and `followUpChannel` channels concurrently.
+
+# Keyword Arguments
+- `systemPrompt::String`: System prompt for the agent
+- `model`: LLM model to use
+- `tools::Vector{agentTool}`: Available tools (default: empty)
+- `messages::Vector{agentMessage}`: Initial conversation messages (default: empty)
+- `formatMsgForLLM::Function`: Convert agent messages to LLM message format (default: `defaultformatMsgForLLM`)
+- `llmCall::Function`: Function to invoke the LLM (required)
+- `prepareContext::Union{Function, Nothing}`: Preprocess/transform messages before sending to LLM (default: `nothing`)
+- `beforeToolCall::Union{Function, Nothing}`: Callback invoked before executing a tool call (default: `nothing`)
+- `afterToolCall::Union{Function, Nothing}`: Callback invoked after executing a tool call (default: `nothing`)
+- `prepareNextTurn::Union{Function, Nothing}`: Callback to prepare the next conversation turn (default: `nothing`)
+- `prepareNextTurnWithContext::Union{Function, Nothing}`: Same but receives context (default: `nothing`)
+- `sessionId::Union{String, Nothing}`: Optional session identifier (default: `nothing`)
+- `maxRetryDelayMs::Union{Int64, Nothing}`: Maximum delay between retries in milliseconds (default: `nothing`)
+- `parallelToolExecute::Bool`: Run tool calls in parallel (default: `false`)
+- `agentEventSink::Function`: Callback to receive agent events
+
+# Returns
+- A new `yiemAgent` instance with an active background task
+
+# Examples
+```julia
+julia> agent = yiemAgent(systemPrompt="You are a helpful assistant", model=my_model)
+yiemAgent(agentState(...), Channel(...), Channel(...), Channel(...), ..., ...)
+```
+"""
+function yiemAgent(
+  ; systemPrompt::String="You are helpful assistant.",
+    model=nothing,
+    tools::Vector{agentTool}=agentTool[],
+    messages::Vector{agentMessage}=agentMessage[],
+    prepareContext::Union{Function, Nothing}=nothing,
+    formatMsgForLLM::Function=defaultformatMsgForLLM,
+    llmCall::Function,
+    beforeToolCall::Union{Function, Nothing}=nothing,
+    afterToolCall::Union{Function, Nothing}=nothing,
+    # prepareNextTurn::Union{Function, Nothing}=nothing,
+    # prepareNextTurnWithContext::Union{Function, Nothing}=nothing,
+    sessionId::Union{String, Nothing}=nothing,
+    maxRetryDelayMs::Union{Int64, Nothing}=nothing,
+    parallelToolExecute::Bool=false,
+    agentEventSink::Function,
+  )
+  # Create channels: input (user -> agent), followUp (async queue), output (agent -> user)
+  inputChannel    = Channel(16)
+  followUp    = Channel(32)
+  outputChannel   = Channel(16)
+
+  # Create struct with a placeholder task, then spawn and replace it
+  agent = yiemAgent(
+    agentState(systemPrompt, model, tools, messages),
+    inputChannel,
+    followUp,
+    outputChannel,
+    nothing,  # placeholder — replaced below
+    prepareContext,
+    formatMsgForLLM,
+    llmCall,
+    beforeToolCall,
+    afterToolCall,
+    # prepareNextTurn,
+    # prepareNextTurnWithContext,
+    sessionId,
+    maxRetryDelayMs,
+    parallelToolExecute,
+    agentEventSink,
+  )
+
+  # Spawn the background loop and attach it
+  agent._agent_loop = @spawn _agent_loop(agent)
+
+  return agent
+end
+
+
+
 """
     preparedToolCall(tool, toolCall, args)
 

src/utils.jl

@@ -1,11 +1,9 @@
 module utils
 
-export clearhistory, availableWineToText, prepareContext, formatMsgForLLM, validateRequiredArgs, 
-      validateToolArguments, _userMessageToOpenAI, 
-      _assistantMessageToOpenAI, _toolResultMessageToOpenAI, _messageContentToBlocks,
-      _toolsToOpenAI, beforeToolCall, afterToolCall
+export clearhistory, availableWineToText, prepareContext, formatMsgForLLM, validateRequiredArgs, validateToolArguments, _userMessageToOpenAI, 
+        _assistantMessageToOpenAI, _toolResultMessageToOpenAI, _messageContentToBlocks
 
-using UUIDs, Dates, DataStructures, HTTP, JSON, NATS
+using UUIDs, Dates, DataStructures, HTTP, JSON
 using GeneralUtils
 using ..type
 
@@ -75,6 +73,7 @@ function availableWineToText(vecd::Vector)::String
 end
 
 
+
 """
     prepareContext(state::agentState) -> agentContext
 
@@ -96,7 +95,7 @@ or pruning and reordering messages before formatting and calling the LLM.
   messages, and tools to be sent to the LLM
 
 # Examples
-```jldoctest
+```julia
 # Default: returns an agentContext with deep copies of system prompt, messages, and tools
 prepareContext(state).messages == deepcopy(state.messages)
 
@@ -109,7 +108,7 @@ prepareContext(state).messages == deepcopy(state.messages)
 # end
 ```
 """
-function prepareContext(state::agentState, eventSink)::agentContext
+function prepareContext(state::agentState)::agentContext
   
   #TODO filter tools from state.tools based on user intend in user message and tool description
   filteredTools = state.tools
@@ -146,7 +145,7 @@ LLM message formats for different APIs/providers.
   an array of OpenAI-format message dicts
 
 # Examples
-```jldoctest
+```julia
 # Default output:
 formatMsgForLLm(ctx) == Dict("messages" => [
   Dict("role" => "system", "content" => [...]),
@@ -156,7 +155,7 @@ formatMsgForLLm(ctx) == Dict("messages" => [
 ])
 ```
 """
-function formatMsgForLLM(ctx::agentContext, eventSink)::Dict{String, Any}
+function formatMsgForLLM(ctx::agentContext)::Dict{String, Any}
 
   """ openai message format example
   msg = Dict(
@@ -184,30 +183,19 @@ function formatMsgForLLM(ctx::agentContext, eventSink)::Dict{String, Any}
           Dict("type" => "text", "text" => "let me check."),
         ]
       ),
-    ],
-    "tools"=> [
       Dict(
-        "type" => "function",
-        "function" => Dict(
-          "name" => "getWeather",
-          "description" => "Get current weather",
-          "parameters" => Dict(
-            "type" => "object",
-            "properties" => Dict(
-              "city" => Dict("type" => "string")
+        "role" => "toolResult",
+        "content" => [
+          Dict("type" => "text", "text" => "name: Chateau Montelena ..."),
+        ]
       ),
-            "required" => ["city"]
-          )
-        )
-      )
     ],
     "temperature" => 0.7
   )
   """
 
-  openaiReadyMsg = Dict{String, Any}()
-  # openaiReadyMsg["model"] = "gemma-4-E4B-it-UD-Q4_K_XL"
   messages = Vector{Dict{String, Any}}()
+
   # System prompt as system message
   if !isempty(ctx.systemPrompt)
     push!(messages, Dict(
@@ -215,101 +203,21 @@ function formatMsgForLLM(ctx::agentContext, eventSink)::Dict{String, Any}
       "content" => [Dict("type" => "text", "text" => ctx.systemPrompt)]
     ))
   end
+
   # Conversation messages
   for msg in ctx.messages
     if msg isa userMessage
       push!(messages, _userMessageToOpenAI(msg))
-    elseif msg isa assistantMessageToolCall
-      push!(messages, _assistantMessageToolCallToOpenAI(msg))
     elseif msg isa assistantMessage
       push!(messages, _assistantMessageToOpenAI(msg))
     elseif msg isa toolResultMessage
       push!(messages, _toolResultMessageToOpenAI(msg))
     end
   end
-  # Convert ctx.tools into OpenAI tools format
-  tools_array = _toolsToOpenAI(ctx.tools, eventSink)
-  openaiReadyMsg["messages"] = messages
-  openaiReadyMsg["temperature"] = 0.7
 
-  if !isempty(tools_array)
-    openaiReadyMsg["tools"] = tools_array
+  return Dict("messages" => messages)
 end
 
-  return openaiReadyMsg
-end
-
-"""
-    beforeToolCall(context::beforeToolCallContext, signal::abortSignal) -> beforeToolCallResult
-
-Callback invoked before executing a tool call. Use this hook to inspect
-the tool call and decide whether to allow, block, or modify it.
-
-Common use cases:
-  - Request user approval via UI before running destructive tools.
-  - Validate business rules that cannot be expressed in the JSON schema.
-  - Check final context (e.g. session state, rate limits, permissions).
-
-# Arguments
-- `context::beforeToolCallContext`: Contains the assistant message, tool call,
-  validated arguments, and current conversation context.
-- `signal::abortSignal`: Signal that may be set to abort the operation.
-
-# Returns
-- `beforeToolCallResult(false, "N/A")` to allow the call to proceed.
-- `beforeToolCallResult(true, "Reason")` to block the call with a reason.
-- `nothing` is treated as allow (equivalent to `beforeToolCallResult(false, "N/A")`).
-
-# Example
-```jldoctest
-function beforeToolCall(context::beforeToolCallContext, signal::abortSignal)
-    if context.toolCall.name == "deleteFile"
-        # Block file deletion unless explicitly approved
-        return beforeToolCallResult(true, "User must approve file deletion")
-    end
-    return beforeToolCallResult(false, "N/A")
-end
-```
-"""
-function beforeToolCall(context::beforeToolCallContext, signal::abortSignal, eventSink
-  )::beforeToolCallResult
-  
-  # eventSink("beforeToolCall: $(context.toolCall.name) args=$(context.args)")
-  
-  return beforeToolCallResult(false, "N/A")
-end
-
-"""
-    afterToolCall(context::afterToolCallContext, signal::abortSignal) -> Union{agentToolResult, Nothing}
-
-Callback invoked after a tool call finishes executing (before and after errors).
-Use this hook to post-process the tool result before it is fed back to the LLM.
-
-Common use cases:
-  - Mask sensitive data (API keys, tokens) from result content.
-  - Normalize usage tracking data into a consistent format.
-  - Inspect the result and set `terminate: true` based on business logic
-    (e.g. "if deployment failed, stop the agent rather than retrying").
-  - Wrap error results in friendlier messages for the LLM to understand.
-
-# Arguments
-- `context::afterToolCallContext`: Contains the assistant message, tool call,
-  arguments, raw result, error status, and current conversation context.
-- `signal::abortSignal`: Signal that may be set to abort the operation.
-
-# Returns
-- `nothing` to pass the result through unchanged.
-- `agentToolResult(...)` to return a modified result (content, details, usage,
-  terminate flag can all be overridden).
-
-"""   # arrange result from searchWine tool
-function afterToolCall(context::afterToolCallContext, signal::abortSignal, eventSink
-  )::Union{agentToolResult, Nothing}
-
-  # eventSink("afterToolCall: $(context.toolCall.name) result=$(context.result) error=$(context.isError)")
-  
-  return nothing
-end
 
 """
 Convert a userMessage to OpenAI message format.
@@ -322,44 +230,6 @@ function _userMessageToOpenAI(msg::userMessage)::Dict{String, Any}
 end
 
 
-"""
-Convert an assistantMessageToolCall to OpenAI message format.
-
-Produces a message with role="assistant", content=null, and a tool_calls array:
-{
-  "role": "assistant",
-  "content": null,
-  "tool_calls": [
-    {
-      "id": "call_1",
-      "type": "function",
-      "function": {
-        "name": "get_weather",
-        "arguments": "{\"location\": \"San Francisco, CA\"}"
-      }
-    }
-  ]
-}
-"""
-function _assistantMessageToolCallToOpenAI(msg::assistantMessageToolCall)::Dict{String, Any}
-  tool_calls = Dict{String, Any}[]
-  for tc in msg.toolCalls
-    push!(tool_calls, Dict(
-      "id" => tc.id,
-      "type" => tc.type,
-      "function" => Dict(
-        "name" => tc.name,
-        "arguments" => JSON.json(tc.arguments)
-      )
-    ))
-  end
-  return Dict(
-    "role" => "assistant",
-    "content" => nothing,
-    "tool_calls" => tool_calls
-  )
-end
-
 """
 Convert an assistantMessage to OpenAI message format.
 """
@@ -410,40 +280,7 @@ end
 
 
 """
-    _toolsToOpenAI(tools::Union{OrderedDict{String, agentTool}, Nothing}) -> Vector{Dict{String, Any}}
-
-Convert an OrderedDict of agentTool definitions into OpenAI function tool format.
-
-Returns an empty vector when `tools` is `nothing` or empty.
-
-# Examples
-```jldoctest
-_toolsToOpenAI(nothing)  # => Dict{String, Any}[]
-_toolsToOpenAI(tools)    # => [Dict("type" => "function", "function" => Dict("name" => "getWeather", ...))]
-```
-"""
-function _toolsToOpenAI(tools::Union{OrderedDict{String, agentTool}, Nothing}, eventSink)::Vector{Dict{String, Any}}
-  tools_array = Vector{Dict{String, Any}}()
-
-  if tools !== nothing
-    for (_, tool) in tools
-      push!(tools_array, Dict(
-        "type" => "function",
-        "function" => Dict(
-          "name" => tool.name,
-          "description" => tool.description,
-          "parameters" => tool.inputSchema
-        )
-      ))
-    end
-  end
-
-  return tools_array
-end
-
-
-"""
-    validateRequiredArgs(args::AbstractDict{String, Any}, inputSchema::AbstractDict{String, Any}) -> Union{Nothing,String}
+    validateRequiredArgs(args::Dict{String,Any}, inputSchema::Dict{String,Any}) -> Union{Nothing,String}
 
 Validates that all required fields listed in the tool's JSON Schema are present
 in `args`. Returns `nothing` if validation passes, or a descriptive error string
@@ -454,15 +291,15 @@ with a custom validation function that performs additional checks (e.g. type
 coercion, format validation, cross-field constraints).
 
 # Arguments
-- `args::AbstractDict{String, Any}`: The arguments provided by the LLM
-- `inputSchema::AbstractDict{String, Any}`: The tool's `inputSchema` (JSON Schema format)
+- `args::Dict{String,Any}`: The arguments provided by the LLM
+- `inputSchema::Dict{String,Any}`: The tool's `inputSchema` (JSON Schema format)
 
 # Returns
 - `nothing` if all required args are present
 - `String` error message listing missing fields otherwise
 
 # Examples
-```jldoctest
+```julia
 schema = Dict("required" => ["city"])
 args = Dict{String,Any}()
 validateRequiredArgs(args, schema)  # => "Missing required arguments: city"
@@ -471,7 +308,7 @@ args2 = Dict("city" => "Tokyo")
 validateRequiredArgs(args2, schema)  # => nothing
 ```
 """
-function validateRequiredArgs(args::AbstractDict{String, Any}, inputSchema::AbstractDict{String, Any})::Union{Nothing,String}
+function validateRequiredArgs(args::Dict{String,Any}, inputSchema::Dict{String,Any})::Union{Nothing,String}
     required = get(inputSchema, "required", Any[])
     if isempty(required)
         return nothing
@@ -517,7 +354,7 @@ false `toolExecutionStart` events.
   and converted to an `immediateOutcome`
 
 # Examples
-```jldoctest
+```julia
 # With validateRequiredArgs hook set on the tool
 validateToolArguments(toolWithHook, tc)  # => validated args or throws
 
@@ -525,7 +362,7 @@ validateToolArguments(toolWithHook, tc)  # => validated args or throws
 validateToolArguments(toolDefault, tc)  # => args or throws
 ```
 """
-function validateToolArguments(tool::agentTool, prepared::agentToolCall)::AbstractDict{String, Any}
+function validateToolArguments(tool::agentTool, prepared::agentToolCall)::Dict{String,Any}
     # Use default (2-arg: args + schema) or tool-specific hook (1-arg: args only)
     if isnothing(tool.validateRequiredArgs)
         result = validateRequiredArgs(prepared.arguments, tool.inputSchema)

src_OLD/OLD_type.jl

@@ -0,0 +1,375 @@
+module type
+  
+export agent, sommelier, companion, virtualcustomer, agentcontext
+
+using Dates, UUIDs, DataStructures, JSON, NATS
+using GeneralUtils
+
+# ---------------------------------------------- 100 --------------------------------------------- #
+
+
+mutable struct agentcontext
+  text2textInstructLLM::Function
+  getTextEmbedding::Function
+  executeSQL::Function
+  similarSQLVectorDB::Function
+  insertSQLVectorDB::Function
+  similarSommelierDecision::Function
+  insertSommelierDecision::Function
+  find_related_tables_for_user_question::Function
+  pg_conn_str::String
+  agentconfig::AbstractDict
+end
+
+abstract type agent end
+
+mutable struct sommelier <: agent
+  name::String  # agent name
+  id::String    # agent id
+  retailername::String
+  retailerid::String
+  tools::Dict   
+  maxHistoryMsg::Integer  # e.g. 21th and earlier messages will get summarized
+  chathistory::Vector{Dict{String, Any}}
+  memory::Dict{String, Any}
+  context::agentcontext 
+  llmFormatName::String
+end
+ 
+""" A sommelier agent.
+
+# Arguments
+  - `context::agentcontext`
+    Application context containing shared functions for LLM, SQL, and vector database operations.
+
+# Keyword Arguments
+  - `name::String`
+    Agent's name. Default: `"Assistant"`
+  - `id::String`
+    Agent's ID. Default: generated UUID string.
+  - `retailername::String`
+    Retailer name associated with the sommelier. Default: `"retailer_name"`
+  - `maxHistoryMsg::Integer`
+    Maximum history messages. Default: `20`
+  - `chathistory::Vector{Dict{String, String}}`
+    Chat history. Default: empty vector.
+  - `llmFormatName::String`
+    LLM format name. Default: `"granite3"`
+
+# Return
+  - `sommelier`: An instantiated sommelier agent.
+
+# Example
+```julia
+julia> using YiemAgent
+julia> context = agentcontext(
+           text2textInstructLLM,
+           getTextEmbedding,
+           executeSQL,
+           similarSQLVectorDB,
+           insertSQLVectorDB,
+           similarSommelierDecision,
+           insertSommelierDecision
+       )
+julia> agent = sommelier(context, name="WineExpert", id="123", retailername="MyWineShop")
+```
+"""
+function sommelier(
+  context::agentcontext,  # agent functions, db connect and other context
+  ;
+  name::String= "Assistant",
+  id::String= string(uuid4()),
+  retailername::String= "not specified",
+  retailerid::String= "not specified",
+  maxHistoryMsg::Integer= 20,
+  chathistory::Vector{Dict{String, Any}} = Vector{Dict{String, Any}}(),
+  llmFormatName::String= "granite3"
+  )
+
+  tools = Dict( # update input format
+      "chatbox"=> Dict(
+        "description" => "<askbox tool description>Useful for when you need to ask the user for more context. Do not ask the user their own question.</askbox tool description>",
+        "input" => """<input>Input is a text in JSON format.</input><input example>{\"Q1\": \"How are you doing?\", \"Q2\": \"How may I help you?\"}</input example>""",
+        "output" => "" ,
+      ),
+      "winestock"=> Dict(
+        "description" => "<winestock tool description>A handy tool for searching wine in your inventory that match the user preferences.</winestock tool description>",
+        "input" => """<input>Input is a JSON-formatted string that contains a detailed and precise search query.</input><input example>{\"wine type\": \"rose\", \"price\": \"max 35\", \"sweetness level\": \"sweet\", \"intensity level\": \"light bodied\", \"Tannin level\": \"low\", \"Acidity level\": \"low\"}</input example>""",
+        "output" => """<output>Output are wines that match the search query in JSON format.""",
+      ),
+    )
+
+  """ Memory
+
+    Chat history use openai format as follow:
+
+    image1_path = "test/large_image.png"                           ---
+    image1_bytes = read(image1_path)                                 | this part must be done
+    image1_base64_string = base64encode(image1_bytes)                | in frontend 
+    mime_type = "image/png"                                          | not in agent code
+    data1_uri = "data:<mime_type>;base64,<image1_base64_string>"   ---
+
+    chathistory= [
+      Dict(
+        "role" => "system",
+        "content" => [
+          Dict("type" => "text", "text" => "You are a helpful assistant"),
+        ]
+      ),
+      Dict(
+        "role" => "user",
+        "content" => [
+          Dict("type" => "text", "text" => "<internal_context_for_assistant> 
+                                            LLM context here...
+                                            </internal_context_for_assistant>
+                                            Do you know this wine? Just give me brief intro."
+          ),
+          Dict(
+            "type" => "image_url",
+            "image_url" => Dict("url" => data1_uri)
+          ),
+        ]
+      ),
+    ]
+  
+  shortmem = Dict(
+    "1"=> Dict("plan"=> "...", "action_name"=> "...", "action_input"=> "...", "action_result"=> "..."),
+    "2"=> Dict("plan"=> "...", "action_name"=> "...", "action_input"=> "...", "action_result"=> "..."),
+    ...
+    )
+  """
+  memory = Dict{String, Any}(
+            "shortmem"=> OrderedDict{String, Any}(),
+            "scratchpad"=> "",
+            "recap"=> OrderedDict{String, Any}(),
+          )
+
+  newAgent = sommelier(
+    name,
+    id,
+    retailername,
+    retailerid,
+    tools,
+    maxHistoryMsg,
+    chathistory,
+    memory,
+    context,
+    llmFormatName
+    )
+  systemmsg = 
+    """
+    # store_policy
+      - Generally speaking, the store inventory has some wines from France, the United States, Australia, Spain, and Italy, but you won't know exactly until you check your inventory.
+      - If you found wines in the store's database, they are in stock.
+      - You can only recommend wines that are currently in our inventory
+      - Before searching the database for wine, ensure you have at least the following information: 1) budget, 2) wine type, and 3) occasion. Additional details are always helpful. If the user is unsure, provide relevant information and gather insights to make reasonable inferences.
+      - Ask the user one question at a time.
+      - Once the user has selected their wine, if you haven't already, ask the user whether they need any further assistance. Do not offer any additional services.
+      - Only end the conversation when the user explicitly intends to do so. When ending, ensure a polite farewell and an invitation to return in the future.
+      - Spicy foods should be paired only with light red wines.
+      - We do not sell organic, sustainable, gluten-free, and sulfite-free wine. Inform the user imediately if they are looking for these types of wines. Do not sell our wines as such.
+      - Gift box, gift card, and custom messages are available. Inform the user to contact our sales team.
+
+    # store_guidelines
+      - Greeting the customer warmly by ask them how could you help. Do not ask any other questions during this greeting.
+      - Customer may provide images for you to look up.
+      - Encourage the customer to explore different options and try new things.
+      - If you are unable to locate the desired item in the database after 2 attempts, it may not be available in your inventory. In such cases, inform the user that the item is unavailable and suggest an alternative instead.
+      - Your store carries only wine.
+      - Vintage 0 means non-vintage.
+      - Start searching the database as broadly as possible within the given information boundary to maximize the chances of finding. Avoid unnecessary parameters unless specified by the user. Refine the search subsequently.
+      - User usually ask for something similar. This means you should use the search term based on the profile they like.
+
+    # situation
+      You are having conversation with a customer.
+
+    # your role
+      Your name is $(newAgent.name). You are a helpful sommelier for website-based $(newAgent.retailername)'s wine store.
+
+    # objective
+      - Establish a connection with the customer by talking to them politely and showing your enthusiasm for their wine preferences.
+      - Provide relevant information and guide them to select the best wines only from your store's inventory that align with their preferences.
+
+    # your responsibility includes
+      - According to the store's policy and guidelines, and make an informed decision about what available_actions you need to use to achieve the objective.
+      - Keep the conversation with the customer going smoothly
+
+    # your responsibility does NOT includes
+      - Requesting the user to place an order, make a purchase, or confirm the order. These are the job of our sales team at the store.
+      - Processing sales orders or engaging in any other sales-related activities. These are the job of our sales team at the store.
+      - Answering questions or offering additional services beyond those related to your store's wine recommendations such as discounts, quantity, rewards programs, promotions, delivery options, shipping, boxes, gift wrapping, packaging, personalized messages or something similar. These are the job of our sales team at the store.
+
+    # you should then respond to the user with interleaving plan, action_name, action_input in JSON format
+      1) "plan", Based on the current situation, state a complete action plan to complete the task and rationale. Be specific.
+      2) "action_name", (Typically corresponds to the execution of the first step in your plan) Can be one of the available_actions name
+      3) "action_input", The input to the action you are about to perform according to your plan.
+      After the action is executed you gets "action_result". It is the output from the action you selected.
+
+    # available actions
+      "CHAT_BOX", which you can use to talk with the user. The input is dialogue you want to chat with the user according to your plan.
+      "SEARCH_WINE_DATABASE", allows you to search information about wines you want in your inventory's database. The input is strictly supported search term including: retailer_name, wine price, winery, name, vintage, region, country, type of wine, grape varietal, tasting notes, occasion, food pairing, intensity, tannin, sweetness, and acidity.
+        Example query 1: "Dry, full-bodied red wine from Burgundy, France. Grape varietal could be Merlot or Syrah. price 100 to 1000 USD."
+        Example query 2: "Red or white wine, medium tannin, price under 700 USD"
+        Example query 3: "white wine from Tuscany, Italy or Bordeaux, France
+      "WINE_PRESENTATION_GUIDELINE", which you can use to check the store guidelines about how to present wines you have found to the user. The input is "nothing" keyword. The output is the guidelines that you can follow.
+      "END_CONVER_GUIDELINE", which you can use to check the store guidelines about how to end the conversation with the user. The input is "nothing" keyword. The output is the guidelines that you can follow.
+    """
+    
+  system_msg = Dict(
+      "role" => "system",
+      "content" => [
+        Dict("type" => "text", "text" => systemmsg),
+      ]
+    )
+
+  push!(newAgent.chathistory, system_msg)
+  
+  return newAgent
+end
+ 
+
+mutable struct virtualcustomer <: agent
+  name::String  # agent name
+  id::String    # agent id
+  systemmsg::String # system message
+  tools::Dict   
+  maxHistoryMsg::Integer  # e.g. 21th and earlier messages will get summarized
+  chathistory::Vector{Dict{String, Any}}
+  memory::Dict{String, Any}
+  context  # NamedTuple of functions 
+  llmFormatName::String
+end
+
+function virtualcustomer(
+  context,  # NamedTuple of functions
+  ;
+  name::String= "Assistant",
+  id::String= string(uuid4()),
+  maxHistoryMsg::Integer= 20,
+  chathistory::Vector{Dict{String, String}} = Vector{Dict{String, String}}(),
+  llmFormatName::String= "granite3",
+  systemmsg::String=
+    """
+      Your name: $name
+      Your sex: Female
+      Your role: You are a helpful assistant.
+      You should follow the following guidelines:
+      - Focus on the latest conversation.
+      - Your like to be short and concise.
+
+      Let's begin!
+      """,
+  )
+
+  tools = Dict( # update input format
+      "chatbox"=> Dict(
+        "description" => "<askbox tool description>Useful for when you need to ask the user for more context. Do not ask the user their own question.</askbox tool description>",
+        "input" => """<input>Input is a text in JSON format.</input><input example>{\"Q1\": \"How are you doing?\", \"Q2\": \"How may I help you?\"}</input example>""",
+        "output" => "" ,
+      ),
+    )
+
+  """ Memory
+    Ref: Chat prompt format is openai
+    chathistory = [
+      Dict(
+        "role" => "system",
+        "content" => [
+          Dict("type" => "text", "text" => system_msg),
+        ]
+      ),
+      Dict(
+        "role" => "user",
+        "content" => [
+          Dict("type" => "text", "text" => "Do you know this wine? Just give me brief intro."),
+          Dict(
+            "type" => "image_url",
+            "image_url" => Dict("url" => data1_uri)
+          )
+        ]
+      )
+    ]
+  """
+  memory = Dict{String, Any}(
+            "shortmem"=> OrderedDict{String, Any}(
+            ),
+            "scratchpad"=> "",
+            "events"=> Vector{Dict{String, Any}}(),
+            "state"=> Dict{String, Any}(
+            ),
+            "recap"=> OrderedDict{String, Any}(),
+          )
+
+  newAgent = virtualcustomer(
+    name,
+    id,
+    systemmsg,
+    tools,
+    maxHistoryMsg,
+    chathistory,
+    memory,
+    context,
+    llmFormatName
+    )
+  
+  return newAgent
+end
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+end # module type

test/_extractToolCalls.jl

@@ -1,642 +0,0 @@
-using Test
-using YiemAgent
-using YiemAgent.agentCore
-using YiemAgent.type
-using JSON
-
-# Import the function from the private module scope
-import YiemAgent.agentCore: _extractToolCalls
-
-@testset "_extractToolCalls" begin
-
-    # ------------------------------------------------------------------ #
-    # Format 1: response["message"]["tool_calls"] (LMStudio.jl style)     #
-    # ------------------------------------------------------------------ #
-
-    @testset "single tool call via message format" begin
-        response = Dict{String,Any}(
-            "finish_reason" => "tool_calls",
-            "index" => 0,
-            "message" => Dict{String,Any}(
-                "role" => "assistant",
-                "content" => "",
-                "reasoning_content" => "Let me check the weather.",
-                "tool_calls" => Any[
-                    Dict{String,Any}(
-                        "type" => "function",
-                        "function" => Dict{String,Any}(
-                            "name" => "getWeather",
-                            "arguments" => "{\"city\":\"Bangkok, Thailand\"}",
-                        ),
-                        "id" => "tc_001",
-                    )
-                ],
-            ),
-        )
-        has_toolcalls, tc_list, assistant_msg = _extractToolCalls(response)
-        @test has_toolcalls == true
-        @test length(tc_list) == 1
-        @test tc_list[1].name == "getWeather"
-        @test tc_list[1].id == "tc_001"
-        @test tc_list[1].type == "function"
-        @test tc_list[1].arguments["city"] == "Bangkok, Thailand"
-        @test assistant_msg isa assistantMessage
-        @test assistant_msg.role == "assistant"
-        @test assistant_msg.stopReason == "tool_calls"
-        @test length(assistant_msg.content) == 1
-        @test assistant_msg.content[1] isa reasoningContent
-        @test assistant_msg.content[1].text == "Let me check the weather."
-    end
-
-    @testset "multiple tool calls via message format" begin
-        response = Dict{String,Any}(
-            "message" => Dict{String,Any}(
-                "role" => "assistant",
-                "content" => "",
-                "tool_calls" => Any[
-                    Dict{String,Any}(
-                        "type" => "function",
-                        "function" => Dict{String,Any}(
-                            "name" => "getWeather",
-                            "arguments" => "{\"city\":\"Tokyo, Japan\"}",
-                        ),
-                        "id" => "tc_001",
-                    ),
-                    Dict{String,Any}(
-                        "type" => "function",
-                        "function" => Dict{String,Any}(
-                            "name" => "getTime",
-                            "arguments" => "{\"timezone\":\"Asia/Tokyo\"}",
-                        ),
-                        "id" => "tc_002",
-                    ),
-                ],
-            ),
-        )
-        has_toolcalls, tc_list, assistant_msg = _extractToolCalls(response)
-        @test has_toolcalls == true
-        @test length(tc_list) == 2
-        @test tc_list[1].name == "getWeather"
-        @test tc_list[1].arguments["city"] == "Tokyo, Japan"
-        @test tc_list[2].name == "getTime"
-        @test tc_list[2].arguments["timezone"] == "Asia/Tokyo"
-        @test assistant_msg.role == "assistant"
-    end
-
-    @testset "tool call with empty arguments string" begin
-        response = Dict{String,Any}(
-            "message" => Dict{String,Any}(
-                "role" => "assistant",
-                "tool_calls" => Any[
-                    Dict{String,Any}(
-                        "type" => "function",
-                        "function" => Dict{String,Any}(
-                            "name" => "listTools",
-                            "arguments" => "{}",
-                        ),
-                        "id" => "tc_empty",
-                    ),
-                ],
-            ),
-        )
-        has_toolcalls, tc_list, assistant_msg = _extractToolCalls(response)
-        @test has_toolcalls == true
-        @test length(tc_list) == 1
-        @test tc_list[1].name == "listTools"
-        @test tc_list[1].arguments == Dict{String,Any}()
-        @test assistant_msg.stopReason == "end_turn"
-    end
-
-    @testset "tool call with missing id falls back to uuid" begin
-        response = Dict{String,Any}(
-            "message" => Dict{String,Any}(
-                "role" => "assistant",
-                "tool_calls" => Any[
-                    Dict{String,Any}(
-                        "type" => "function",
-                        "function" => Dict{String,Any}(
-                            "name" => "getTime",
-                            "arguments" => "{\"city\":\"NYC\"}",
-                        ),
-                    ),
-                ],
-            ),
-        )
-        has_toolcalls, tc_list, assistant_msg = _extractToolCalls(response)
-        @test has_toolcalls == true
-        @test length(tc_list) == 1
-        @test !isempty(tc_list[1].id)
-        @test tc_list[1].name == "getTime"
-    end
-
-    @testset "tool call with non-string arguments (pre-parsed dict)" begin
-        response = Dict{String,Any}(
-            "message" => Dict{String,Any}(
-                "role" => "assistant",
-                "tool_calls" => Any[
-                    Dict{String,Any}(
-                        "type" => "function",
-                        "function" => Dict{String,Any}(
-                            "name" => "getWeather",
-                            "arguments" => Dict{String,Any}("city" => "London", "units" => "fahrenheit"),
-                        ),
-                        "id" => "tc_parsed",
-                    ),
-                ],
-            ),
-        )
-        has_toolcalls, tc_list, assistant_msg = _extractToolCalls(response)
-        @test has_toolcalls == true
-        @test length(tc_list) == 1
-        @test tc_list[1].arguments["city"] == "London"
-        @test tc_list[1].arguments["units"] == "fahrenheit"
-    end
-
-    @testset "tool call with api/provider/model/usage metadata" begin
-        response = Dict{String,Any}(
-            "api" => "openai",
-            "provider" => "anthropic",
-            "model" => "claude-3-opus",
-            "message" => Dict{String,Any}(
-                "role" => "assistant",
-                "tool_calls" => Any[
-                    Dict{String,Any}(
-                        "type" => "function",
-                        "function" => Dict{String,Any}(
-                            "name" => "getTime",
-                            "arguments" => "{}",
-                        ),
-                        "id" => "tc_meta",
-                    ),
-                ],
-            ),
-        )
-        has_toolcalls, tc_list, assistant_msg = _extractToolCalls(response)
-        @test has_toolcalls == true
-        @test assistant_msg.api == "openai"
-        @test assistant_msg.provider == "anthropic"
-        @test assistant_msg.model == "claude-3-opus"
-    end
-
-    # --------------------------------------------------------------- #
-    # Format 2: response.content blocks (OpenAI API style)            #
-    # --------------------------------------------------------------- #
-
-    @testset "content blocks with tool_calls" begin
-        response = Dict{String,Any}(
-            "content" => Any[
-                Dict{String,Any}("type" => "text", "text" => "Let me check."),
-                Dict{String,Any}(
-                    "type" => "tool_calls",
-                    "tool_calls" => Any[
-                        Dict{String,Any}(
-                            "type" => "function",
-                            "function" => Dict{String,Any}(
-                                "name" => "getWeather",
-                                "arguments" => "{\"city\":\"Paris\"}",
-                            ),
-                            "id" => "tc_block_1",
-                        ),
-                    ],
-                ),
-            ],
-        )
-        has_toolcalls, tc_list, assistant_msg = _extractToolCalls(response)
-        @test has_toolcalls == true
-        @test length(tc_list) == 1
-        @test tc_list[1].name == "getWeather"
-        @test tc_list[1].arguments["city"] == "Paris"
-        # text block before tool_calls should be included in content
-        @test length(assistant_msg.content) == 1
-        @test assistant_msg.content[1].text == "Let me check."
-    end
-
-    @testset "content blocks with tool_call (single-call format)" begin
-        response = Dict{String,Any}(
-            "content" => Any[
-                Dict{String,Any}(
-                    "type" => "tool_call",
-                    "id" => "tc_single",
-                    "name" => "getTime",
-                    "arguments" => Dict{String,Any}("timezone" => "Europe/London"),
-                ),
-            ],
-        )
-        has_toolcalls, tc_list, assistant_msg = _extractToolCalls(response)
-        @test has_toolcalls == true
-        @test length(tc_list) == 1
-        @test tc_list[1].name == "getTime"
-        @test tc_list[1].id == "tc_single"
-        @test tc_list[1].arguments["timezone"] == "Europe/London"
-    end
-
-    @testset "content blocks with reasoning and text" begin
-        response = Dict{String,Any}(
-            "content" => Any[
-                Dict{String,Any}("type" => "reasoning", "text" => "Thinking..."),
-                Dict{String,Any}("type" => "text", "text" => "Here's the answer."),
-            ],
-        )
-        has_toolcalls, tc_list, assistant_msg = _extractToolCalls(response)
-        @test has_toolcalls == false
-        @test length(tc_list) == 0
-        @test length(assistant_msg.content) == 2
-        @test assistant_msg.content[1] isa reasoningContent
-        @test assistant_msg.content[1].text == "Thinking..."
-        @test assistant_msg.content[2] isa textContent
-        @test assistant_msg.content[2].text == "Here's the answer."
-    end
-
-    @testset "content blocks with text and tool_call (tool_call not in content)" begin
-        response = Dict{String,Any}(
-            "content" => Any[
-                Dict{String,Any}("type" => "text", "text" => "Sure, I'll check."),
-                Dict{String,Any}(
-                    "type" => "tool_call",
-                    "id" => "tc_mix",
-                    "name" => "getWeather",
-                    "arguments" => Dict{String,Any}("city" => "London"),
-                ),
-            ],
-        )
-        has_toolcalls, tc_list, assistant_msg = _extractToolCalls(response)
-        @test has_toolcalls == true
-        @test length(tc_list) == 1
-        @test tc_list[1].name == "getWeather"
-        # text block included, tool_call block excluded from content
-        @test length(assistant_msg.content) == 1
-        @test assistant_msg.content[1].text == "Sure, I'll check."
-    end
-
-    # ------------------------------------------------------------------ #
-    # assistantMessage construction                                       #
-    # ------------------------------------------------------------------ #
-
-    @testset "assistantMessage with error_message and errorMessage fallback" begin
-        response = Dict{String,Any}(
-            "error_message" => "rate limit",
-            "content" => Any[Dict{String,Any}("type" => "text", "text" => "fail")],
-        )
-        has_toolcalls, tc_list, assistant_msg = _extractToolCalls(response)
-        @test has_toolcalls == false
-        @test assistant_msg.errorMessage == "rate limit"
-    end
-
-    @testset "assistantMessage with usage tracking" begin
-        response = Dict{String,Any}(
-            "content" => Any[Dict{String,Any}("type" => "text", "text" => "hi")],
-            "usage" => llmUsage(100, 50),
-        )
-        has_toolcalls, tc_list, assistant_msg = _extractToolCalls(response)
-        @test assistant_msg.usage.inputTokens == 100
-        @test assistant_msg.usage.outputTokens == 50
-    end
-
-    @testset "assistantMessage with invalid usage defaults to zero" begin
-        response = Dict{String,Any}(
-            "content" => Any[Dict{String,Any}("type" => "text", "text" => "hi")],
-            "usage" => "invalid",
-        )
-        has_toolcalls, tc_list, assistant_msg = _extractToolCalls(response)
-        @test assistant_msg.usage.inputTokens == 0
-        @test assistant_msg.usage.outputTokens == 0
-    end
-
-    @testset "reasoning_content as textContent" begin
-        response = Dict{String,Any}(
-            "reasoning_content" => textContent("internal thought"),
-            "content" => Any[Dict{String,Any}("type" => "text", "text" => "output")],
-        )
-        has_toolcalls, tc_list, assistant_msg = _extractToolCalls(response)
-        @test length(assistant_msg.content) == 2
-        @test assistant_msg.content[1] isa reasoningContent
-        @test assistant_msg.content[1].text == "internal thought"
-        @test assistant_msg.content[2] isa textContent
-        @test assistant_msg.content[2].text == "output"
-    end
-
-    # ------------------------------------------------------------------ #
-    # No tool call cases                                                  #
-    # ------------------------------------------------------------------ #
-
-    @testset "no tool calls found" begin
-        response = Dict{String,Any}(
-            "content" => Any[
-                Dict{String,Any}("type" => "text", "text" => "Hello world."),
-            ],
-        )
-        has_toolcalls, tc_list, assistant_msg = _extractToolCalls(response)
-        @test has_toolcalls == false
-        @test length(tc_list) == 0
-        @test assistant_msg.stopReason == "end_turn"
-    end
-
-    @testset "empty message" begin
-        response = Dict{String,Any}()
-        has_toolcalls, tc_list, assistant_msg = _extractToolCalls(response)
-        @test has_toolcalls == false
-        @test length(tc_list) == 0
-        @test assistant_msg.role == "assistant"
-        @test assistant_msg.stopReason == "end_turn"
-        @test length(assistant_msg.content) == 0
-    end
-
-    @testset "message with empty tool_calls array" begin
-        response = Dict{String,Any}(
-            "message" => Dict{String,Any}(
-                "role" => "assistant",
-                "tool_calls" => Any[],
-            ),
-        )
-        has_toolcalls, tc_list, assistant_msg = _extractToolCalls(response)
-        @test has_toolcalls == false
-        @test length(tc_list) == 0
-        @test assistant_msg.role == "assistant"
-    end
-
-    @testset "message field is not a Dict" begin
-        response = Dict{String,Any}(
-            "message" => "not a dict",
-            "content" => Any[Dict{String,Any}("type" => "text", "text" => "fallback")],
-        )
-        has_toolcalls, tc_list, assistant_msg = _extractToolCalls(response)
-        @test has_toolcalls == false
-        @test length(tc_list) == 0
-        @test length(assistant_msg.content) == 1
-    end
-
-    @testset "Format 1 takes priority over Format 2" begin
-        response = Dict{String,Any}(
-            "message" => Dict{String,Any}(
-                "role" => "assistant",
-                "tool_calls" => Any[
-                    Dict{String,Any}(
-                        "type" => "function",
-                        "function" => Dict{String,Any}(
-                            "name" => "getWeather",
-                            "arguments" => "{\"city\":\"Format1\"}",
-                        ),
-                        "id" => "tc_fmt1",
-                    ),
-                ],
-            ),
-            "content" => Any[
-                Dict{String,Any}(
-                    "type" => "tool_call",
-                    "id" => "tc_fmt2",
-                    "name" => "getTime",
-                    "arguments" => Dict{String,Any}("city" => "Format2"),
-                ),
-            ],
-        )
-        has_toolcalls, tc_list, assistant_msg = _extractToolCalls(response)
-        @test has_toolcalls == true
-        @test length(tc_list) == 1
-        @test tc_list[1].name == "getWeather"
-    end
-
-    # ------------------------------------------------------------------ #
-    # Edge cases                                                          #
-    # ------------------------------------------------------------------ #
-
-    @testset "tool call with null arguments" begin
-        response = Dict{String,Any}(
-            "message" => Dict{String,Any}(
-                "role" => "assistant",
-                "tool_calls" => Any[
-                    Dict{String,Any}(
-                        "type" => "function",
-                        "function" => Dict{String,Any}(
-                            "name" => "getTime",
-                            "arguments" => nothing,
-                        ),
-                        "id" => "tc_null",
-                    ),
-                ],
-            ),
-        )
-        has_toolcalls, tc_list, assistant_msg = _extractToolCalls(response)
-        @test has_toolcalls == true
-        @test length(tc_list) == 1
-        @test tc_list[1].name == "getTime"
-    end
-
-    @testset "tool call with missing function key" begin
-        response = Dict{String,Any}(
-            "message" => Dict{String,Any}(
-                "role" => "assistant",
-                "tool_calls" => Any[
-                    Dict{String,Any}(
-                        "type" => "function",
-                        "id" => "tc_nofunc",
-                    ),
-                ],
-            ),
-        )
-        has_toolcalls, tc_list, assistant_msg = _extractToolCalls(response)
-        @test has_toolcalls == true
-        @test length(tc_list) == 1
-        @test tc_list[1].name == ""
-    end
-
-    @testset "tool call with missing name in function block" begin
-        response = Dict{String,Any}(
-            "message" => Dict{String,Any}(
-                "role" => "assistant",
-                "tool_calls" => Any[
-                    Dict{String,Any}(
-                        "type" => "function",
-                        "function" => Dict{String,Any}("arguments" => "{}"),
-                        "id" => "tc_noname",
-                    ),
-                ],
-            ),
-        )
-        has_toolcalls, tc_list, assistant_msg = _extractToolCalls(response)
-        @test has_toolcalls == true
-        @test length(tc_list) == 1
-        @test tc_list[1].name == ""
-    end
-
-    @testset "message format with JSON.Object (JSON.parse result)" begin
-        json_str = JSON.json(Dict(
-            "message" => Dict(
-                "role" => "assistant",
-                "tool_calls" => [
-                    Dict(
-                        "type" => "function",
-                        "function" => Dict("name" => "getWeather", "arguments" => "{\"city\":\"Test\"}"),
-                        "id" => "tc_jsonobj",
-                    ),
-                ],
-            ),
-        ))
-        parsed = JSON.parse(json_str)
-        has_toolcalls, tc_list, assistant_msg = _extractToolCalls(parsed)
-        @test has_toolcalls == true
-        @test length(tc_list) == 1
-        @test tc_list[1].name == "getWeather"
-        @test tc_list[1].arguments["city"] == "Test"
-    end
-
-    @testset "tool_calls block with mixed content types (text + tool_calls)" begin
-        response = Dict{String,Any}(
-            "content" => Any[
-                Dict{String,Any}("type" => "text", "text" => "I'll check both."),
-                Dict{String,Any}(
-                    "type" => "tool_calls",
-                    "tool_calls" => Any[
-                        Dict{String,Any}(
-                            "type" => "function",
-                            "function" => Dict{String,Any}(
-                                "name" => "getWeather",
-                                "arguments" => "{\"city\":\"London\"}",
-                            ),
-                            "id" => "tc_mix1",
-                        ),
-                        Dict{String,Any}(
-                            "type" => "function",
-                            "function" => Dict{String,Any}(
-                                "name" => "getTime",
-                                "arguments" => "{\"timezone\":\"UTC\"}",
-                            ),
-                            "id" => "tc_mix2",
-                        ),
-                    ],
-                ),
-            ],
-        )
-        has_toolcalls, tc_list, assistant_msg = _extractToolCalls(response)
-        @test has_toolcalls == true
-        @test length(tc_list) == 2
-        @test tc_list[1].name == "getWeather"
-        @test tc_list[2].name == "getTime"
-        @test length(assistant_msg.content) == 1
-        @test assistant_msg.content[1].text == "I'll check both."
-    end
-
-    @testset "tool_call block without arguments field" begin
-        response = Dict{String,Any}(
-            "content" => Any[
-                Dict{String,Any}(
-                    "type" => "tool_call",
-                    "id" => "tc_noargs",
-                    "name" => "getTime",
-                ),
-            ],
-        )
-        has_toolcalls, tc_list, assistant_msg = _extractToolCalls(response)
-        @test has_toolcalls == true
-        @test length(tc_list) == 1
-        @test tc_list[1].arguments == Dict{String,Any}()
-    end
-
-    @testset "tool_calls block with empty tool_calls array" begin
-        response = Dict{String,Any}(
-            "content" => Any[
-                Dict{String,Any}(
-                    "type" => "tool_calls",
-                    "tool_calls" => Any[],
-                ),
-            ],
-        )
-        has_toolcalls, tc_list, assistant_msg = _extractToolCalls(response)
-        @test has_toolcalls == false
-        @test length(tc_list) == 0
-    end
-
-    @testset "tool_calls block with non-AbstractDict elements" begin
-        response = Dict{String,Any}(
-            "content" => Any[
-                Dict{String,Any}(
-                    "type" => "tool_calls",
-                    "tool_calls" => Any["not a dict", 42, nothing],
-                ),
-            ],
-        )
-        has_toolcalls, tc_list, assistant_msg = _extractToolCalls(response)
-        @test has_toolcalls == false
-        @test length(tc_list) == 0
-    end
-
-    @testset "content field is not a Vector" begin
-        response = Dict{String,Any}(
-            "content" => "not a vector",
-        )
-        has_toolcalls, tc_list, assistant_msg = _extractToolCalls(response)
-        @test has_toolcalls == false
-        @test length(tc_list) == 0
-        @test length(assistant_msg.content) == 0
-    end
-
-    @testset "Dict-based response with all metadata fields" begin
-        response = Dict{String,Any}(
-            "api" => "openai",
-            "provider" => "anthropic",
-            "model" => "claude-3-sonnet",
-            "content" => Any[
-                Dict{String,Any}(
-                    "type" => "tool_call",
-                    "id" => "tc_meta",
-                    "name" => "getTime",
-                    "arguments" => Dict{String,Any}("city" => "Seoul"),
-                ),
-            ],
-        )
-        has_toolcalls, tc_list, assistant_msg = _extractToolCalls(response)
-        @test has_toolcalls == true
-        @test length(tc_list) == 1
-        @test tc_list[1].name == "getTime"
-        @test tc_list[1].arguments["city"] == "Seoul"
-        @test assistant_msg.api == "openai"
-        @test assistant_msg.provider == "anthropic"
-        @test assistant_msg.model == "claude-3-sonnet"
-    end
-
-    @testset "default role is assistant" begin
-        response = Dict{String,Any}(
-            "content" => Any[Dict{String,Any}("type" => "text", "text" => "no role specified")],
-        )
-        has_toolcalls, tc_list, assistant_msg = _extractToolCalls(response)
-        @test assistant_msg.role == "assistant"
-    end
-
-    @testset "tool call with custom role in message format" begin
-        response = Dict{String,Any}(
-            "message" => Dict{String,Any}(
-                "role" => "custom_role",
-                "tool_calls" => Any[
-                    Dict{String,Any}(
-                        "type" => "function",
-                        "function" => Dict{String,Any}(
-                            "name" => "getWeather",
-                            "arguments" => "{}",
-                        ),
-                        "id" => "tc_role",
-                    ),
-                ],
-            ),
-        )
-        has_toolcalls, tc_list, assistant_msg = _extractToolCalls(response)
-        @test assistant_msg.role == "custom_role"
-    end
-
-    @testset "image content block handling" begin
-        response = Dict{String,Any}(
-            "content" => Any[
-                Dict{String,Any}(
-                    "type" => "image_url",
-                    "image_url" => Dict("url" => "data:image/png;base64,abc123"),
-                ),
-                Dict{String,Any}("type" => "text", "text" => "What is this?"),
-            ],
-        )
-        has_toolcalls, tc_list, assistant_msg = _extractToolCalls(response)
-        @test has_toolcalls == false
-        @test length(assistant_msg.content) == 2
-        @test assistant_msg.content[1] isa textContent
-        @test assistant_msg.content[1].text == ""
-        @test assistant_msg.content[2].text == "What is this?"
-    end
-
-end

test/loadToolTest.jl

@@ -0,0 +1,136 @@
+using Test
+using YiemAgent
+using YiemAgent.toolRegistry
+using YiemAgent.type
+
+# Path to the real tools directory
+TOOLS_DIR = joinpath(@__DIR__, "..", "src", "tools")
+
+@testset "loadTools" begin
+
+    # ------------------------------------------------------------------ #
+    # 1. loadTools throws on non-existent directory                      #
+    # ------------------------------------------------------------------ #
+    @test_throws ArgumentError loadTools("/nonexistent/dir/that/does/not/exist")
+
+    # ------------------------------------------------------------------ #
+    # 2. loadTools throws if a .jl file does not define getTool()        #
+    #    Must run BEFORE any other loadTools call (getTool binding       #
+    #    persists in module scope after include()).                      #
+    # ------------------------------------------------------------------ #
+    bad_dir = mktempdir()
+    write(joinpath(bad_dir, "noTool.jl"), "x = 42\n")
+    @test_throws ArgumentError loadTools(bad_dir)
+
+    # ------------------------------------------------------------------ #
+    # 3. loadTools loads actual tool files from src/tools/               #
+    # ------------------------------------------------------------------ #
+    loaded = loadTools(TOOLS_DIR)
+    @test !isempty(loaded)
+    @test length(loaded) == 3
+
+    names = [t.name for t in loaded]
+    @test "getTime" in names
+    @test "getWeather" in names
+    @test "writeTool" in names
+
+    # ------------------------------------------------------------------ #
+    # 4. loadTools returns tools sorted alphabetically by filename       #
+    #    (getTime.jl < getWeather.jl < writeTool.jl)                      #
+    #    because 'T' < 'W' in ASCII                                       #
+    # ------------------------------------------------------------------ #
+    @test loaded[1].name == "getTime"
+    @test loaded[2].name == "getWeather"
+    @test loaded[3].name == "writeTool"
+
+    # ------------------------------------------------------------------ #
+    # 5. Verify loaded tool fields are correct                           #
+    # ------------------------------------------------------------------ #
+    # getTime
+    time_tool = loaded[1]
+    @test time_tool.name == "getTime"
+    @test time_tool.label == "Time Lookup"
+    @test time_tool.validateRequiredArgs !== nothing
+    @test time_tool.parallelToolExecute == false
+    @test time_tool.inputSchema["required"] == Any[]
+
+    # getWeather
+    weather = loaded[2]
+    @test weather.name == "getWeather"
+    @test weather.label == "Weather Lookup"
+    @test weather.execute !== nothing
+    @test weather.parallelToolExecute == false
+    @test weather.inputSchema["required"] == ["city"]
+
+    # writeTool
+    wt = loaded[3]
+    @test wt.name == "writeTool"
+    @test wt.label == "Create Tool"
+    @test wt.execute !== nothing
+    @test "name" in wt.inputSchema["required"]
+    @test "executeCode" in wt.inputSchema["required"]
+
+    # ------------------------------------------------------------------ #
+    # 6. Tool execution returns valid results                            #
+    # ------------------------------------------------------------------ #
+    sig = nothing
+    op = x -> x  # no-op partial result callback
+
+    # execute getTime
+    result_t = time_tool.execute("call-1", Dict{String,Any}("city" => "Tokyo"), sig, op)
+    @test result_t isa agentToolResult
+    @test result_t.content[1] isa textContent
+    @test occursin("Tokyo", result_t.content[1].text)
+
+    # execute getTime with timezone
+    result_tz = time_tool.execute("call-2", Dict{String,Any}("timezone" => "America/New_York"), sig, op)
+    @test result_tz isa agentToolResult
+    @test occursin("America/New_York", result_tz.content[1].text)
+
+    # execute getWeather
+    result_w = weather.execute("call-3", Dict{String,Any}("city" => "Bangkok"), sig, op)
+    @test result_w isa agentToolResult
+    @test result_w.content[1] isa textContent
+    @test occursin("Bangkok", result_w.content[1].text)
+
+    # execute getWeather with units
+    result_w2 = weather.execute("call-4", Dict{String,Any}("city" => "London", "units" => "fahrenheit"), sig, op)
+    @test occursin("72°F", result_w2.content[1].text)
+
+    # ------------------------------------------------------------------ #
+    # 7. getTools / registerTool / clearTools                            #
+    # ------------------------------------------------------------------ #
+    registry_tools = getTools()
+    @test !isempty(registry_tools)
+    @test any(t -> t.name == "getTime", registry_tools)
+    @test any(t -> t.name == "getWeather", registry_tools)
+
+    clearTools()
+    @test isempty(getTools())
+
+    test_tool = agentTool(
+        name = "manualTool",
+        label = "Manual Tool",
+        description = "Registered manually",
+        inputSchema = Dict{String,Any}("type" => "object", "properties" => Dict{String,Any}(), "required" => Any[]),
+        execute = (toolCallId, args, signal, onPartialResult) ->
+            agentToolResult([textContent("manual")], Dict{Any,Any}(), nothing, false),
+        prepareArguments = nothing,
+        validateRequiredArgs = nothing,
+        parallelToolExecute = true
+    )
+    registerTool(test_tool)
+    reg = getTools()
+    @test any(t -> t.name == "manualTool", reg)
+    @test count(t -> t.name == "manualTool", reg) == 1
+    @test reg[1].parallelToolExecute == true
+
+    # ------------------------------------------------------------------ #
+    # 8. getTools returns deep copy (mutations don't affect registry)    #
+    # ------------------------------------------------------------------ #
+    copy1 = getTools()
+    copy2 = getTools()
+    @test copy1 !== copy2
+    empty!(copy1)
+    @test !isempty(getTools())
+end

test/runtest.jl

@@ -1,5 +1,401 @@
-using Test
-using YiemAgent
+using JSON, Dates, UUIDs, PrettyPrinting, LibPQ, Base64, DataFrames, DataStructures, HTTP, Base64,
+      NATS, Base.Threads
+using YiemAgent, GeneralUtils, msghandler
+
+  function text2text_instruct_llm(sender_id::String, openai_msg::Dict{String, Any})
+    payloads = [("msg", openai_msg, "dictionary")]  # List of tuples
+    _, msg_envelope_json_str = msghandler.smartpack(
+          config["externalservice"]["servicesloadbalancer"]["nats"], 
+          payloads;
+          sender_id=sender_id,
+          msg_purpose="text2text",  
+          broker_url=config["nats_server_info"]["url"],
+          fileserver_url=config["externalservice"]["fileserver"]["url"])
+
+    reply = NATS.request(agent_conn, 
+                        config["externalservice"]["servicesloadbalancer"]["nats"], 
+                        msg_envelope_json_str, timeout=120)
+
+    incoming_env_json_str = String(reply.payload)
+    incoming_env = msghandler.smartunpack(incoming_env_json_str)
+    _llm_response = incoming_env["payloads"][1][2]
+    llm_response = _llm_response["choices"][1]["message"]["content"]
+    return llm_response
+  end
+
+  """ get a single text embedding from a LLM service
+  Example
+  text = ["hello"]
+  embedding = get_embedding(text)
+  """
+  function get_embedding(text::AbstractArray{String})
+    documents_dict = Dict("documents" => text)
+    payloads = [("documents", documents_dict, "dictionary")]
+    _, msg_envelope_json_str = msghandler.smartpack(
+      config["externalservice"]["servicesloadbalancer"]["nats"], 
+      payloads;   
+      msg_purpose="embedding",  
+      broker_url=config["nats_server_info"]["url"],
+      fileserver_url=config["externalservice"]["fileserver"]["url"])
+
+    reply = NATS.request(agent_conn, 
+                        config["externalservice"]["servicesloadbalancer"]["nats"], 
+                        msg_envelope_json_str, timeout=120)
+    incoming_env_json_str = String(reply.payload)
+    incoming_env = msghandler.smartunpack(incoming_env_json_str)
+    embedding_response = incoming_env["payloads"][1][2]
+
+    return embedding_response
+  end
+
+  """ sql = "SELECT * FROM wine;"
+  result = execute_sql_winedb(sql)
+  """
+  function execute_sql_winedb(sql::T) where {T<:AbstractString}
+    host_url, _port = split(config["externalservice"]["sommpanion_db"]["url"], ':')
+    port = parse(Int, _port)
+    dbname = "winedb"
+    user = config["externalservice"]["sommpanion_db"]["user"]
+    password = config["externalservice"]["sommpanion_db"]["password"]
+    db_connection = LibPQ.Connection("host=$host_url port=$port dbname=$dbname user=$user password=$password")
+    result = nothing
+    try
+      result = LibPQ.execute(db_connection, sql)  
+    catch e
+      LibPQ.close(db_connection)
+    end
+    
+    LibPQ.close(db_connection)
+    return result
+  end
+
+  """ find similar sql from vector database
+  sql = "SELECT * FROM wine;"
+  result, distance = similar_sql_vectordb(sql)
+  """
+  function similar_sql_vectordb(sql::T; maxdistance::Number=0.2) where {T<:AbstractString}
+    tablename = "sqlllm_decision_repository"
+    # get embedding of the query
+    df = find_similar_text_from_vectordb(sql, tablename, 
+          "function_input_embedding", execute_sql_vectordb)
+    # println(df[1, [:id, :function_output]])
+    row, col = size(df)
+    distance = row == 0 ? Inf : df[1, :distance]  
+    if row != 0 && distance < maxdistance
+      # if there is usable SQL, return it.
+      output_b64 = df[1, :function_output_base64]   # pick the closest match
+      output_str = String(base64decode(output_b64))
+      rowid = df[1, :id]
+      println("\n--| similar sql found. row id $rowid, distance $distance ", @__FILE__, ":", @__LINE__, " $(Dates.now())")
+      pprintln(output_str)
+      return (result=output_str, distance=distance)
+    else
+      println("\n--| similar sql not found, max distance $maxdistance ", @__FILE__, ":", @__LINE__, " $(Dates.now())")
+      return (result=nothing, distance=nothing)
+    end
+  end
+
+  """ insert query and sql into vector database
+  query = "get all wines from wine table"
+  sql = "SELECT * FROM wine;"
+  insert_sql_vectordb(query, sql)
+  """
+  function insert_sql_vectordb(query::T1, SQL::T2; maxdistance::Number=3
+    ) where {T1<:AbstractString, T2<:AbstractString}
+    
+    tablename = "sqlllm_decision_repository"
+    # get embedding of the query
+    # query = state[:thoughtHistory][:question]
+    df = find_similar_text_from_vectordb(query, tablename, 
+                                    "function_input_embedding", execute_sql_vectordb)
+    row, col = size(df)
+    distance = row == 0 ? Inf : df[1, :distance]
+    if row == 0 || distance > maxdistance # no close enough SQL stored in the database
+      _query_embedding = get_embedding([query])
+      _query_embedding = GeneralUtils.dictify(_query_embedding)
+      # println("\n--- _query_embedding() ", @__FILE__, ":", @__LINE__, " $(Dates.now())")
+      # println(_query_embedding)
+      # println("---\n")
+      query_embedding = _query_embedding["data"][1]["embedding"]
+      query = replace(query, "'" => "")
+      sql_base64 = base64encode(SQL)
+      sql_ = replace(SQL, "'" => "")
+
+      sql = 
+        """
+        INSERT INTO $tablename (function_input, function_output, function_output_base64, function_input_embedding) VALUES ('$query', '$sql_', '$sql_base64', '$query_embedding');
+        """
+      # println("\n--| added new decision to vectorDB ", @__FILE__, ":", @__LINE__, " $(Dates.now())")
+      # println(sql)
+      _ = execute_sql_vectordb(sql)
+    end
+  end
+
+  """ execute sql against vectordb
+  sql = "SELECT * FROM wine;"
+  result = execute_sql_vectordb(sql)
+  """
+  function execute_sql_vectordb(sql::T) where {T<:AbstractString}
+    host_url, _port = split(config["externalservice"]["sommpanion_vectordb"]["url"], ':')
+    port = parse(Int, _port)
+    dbname = config["externalservice"]["sommpanion_vectordb"]["dbname"]
+    user = config["externalservice"]["sommpanion_vectordb"]["user"]
+    password = config["externalservice"]["sommpanion_vectordb"]["password"]
+    DBconnection = LibPQ.Connection("host=$host_url port=$port dbname=$dbname user=$user password=$password")
+    result = LibPQ.execute(DBconnection, sql)
+    close(DBconnection)
+    return result
+  end
+
+  """ search similar decision llm made from vectordb
+  """
+  function similar_sommelier_decision(recentevents::T1; maxdistance::Integer=3
+    )::Union{AbstractDict, Nothing} where {T1<:AbstractString}
+
+    tablename = "sommelier_decision_repository"
+    # find similar
+    df = find_similar_text_from_vectordb(recentevents, tablename, 
+                                    "function_input_embedding", execute_sql_vectordb)
+    row, col = size(df)
+    distance = row == 0 ? Inf : df[1, :distance]
+    if row != 0 && distance < maxdistance
+      # if there is usable decision, return it.
+      rowid = df[1, :id]
+      println("\n--| found similar decision. row id $rowid, distance $distance ", @__FILE__, " ", @__LINE__)
+      output_b64 = df[1, :function_output_base64]   # pick the closest match
+      _output_str = String(base64decode(output_b64))
+      output = copy(JSON.read(_output_str))
+      return output
+    else
+      println("\n--| similar decision not found, max distance $maxdistance ", @__FILE__, " ", @__LINE__)
+      return nothing
+    end
+  end
+
+  """ search similar text from vectordb
+  """
+  function find_similar_text_from_vectordb(text::T1, tablename::T2, embeddingColumnName::T3,
+    vectorDB::Function; limit::Integer=1
+    )::DataFrame where {T1<:AbstractString, T2<:AbstractString, T3<:AbstractString}
+    # get embedding from LLM service
+    _embedding = get_embedding([text])
+    _embedding = _embedding["data"][1]["embedding"]
+    _embedding = "$_embedding"
+
+    embedding = _embedding[4:end]
+
+    # check whether there is close enough vector already store in vectorDB. if no, add, else skip 
+    sql = """
+          SELECT *, $embeddingColumnName <-> '$embedding' as distance
+          FROM $tablename
+          ORDER BY distance LIMIT $limit;
+          """
+    response = vectorDB(sql)
+    df = DataFrame(response)
+
+    return df
+  end
+
+  """ insert decision llm made to vectordb
+  """
+  function insert_sommelier_decision(recentevents::T1, decision::T2; maxdistance::Integer=5
+    ) where {T1<:AbstractString, T2<:AbstractDict}
+    tablename = "sommelier_decision_repository"
+    # find similar
+    df = find_similar_text_from_vectordb(recentevents, tablename, 
+                                    "function_input_embedding", execute_sql_vectordb)
+    row, col = size(df)
+    distance = row == 0 ? Inf : df[1, :distance]
+    if row == 0 || distance > maxdistance # no close enough SQL stored in the database
+      _embedding = get_embedding([recentevents])[1]
+      recentevents_embedding = _embedding["data"][1]["embedding"]
+      recentevents = replace(recentevents, "'" => "")
+      decision_json = JSON.json(decision)
+      decision_base64 = base64encode(decision_json)
+      decision = replace(decision_json, "'" => "")
+      
+      sql = 
+      """
+      INSERT INTO $tablename (function_input, function_output, function_output_base64, function_input_embedding) VALUES ('$recentevents', '$decision', '$decision_base64', '$recentevents_embedding');
+      """
+      println("\n--| added new decision to vectorDB ", @__FILE__, " ", @__LINE__)
+      println(sql)
+      _ = execute_sql_vectordb(sql)
+    else
+      println("--| similar decision previously cached, distance $distance ", @__FILE__, " ", @__LINE__)
+    end
+  end
+
+config = JSON.parsefile("./appconfig.json")
+sessionId = "0"
+backend_session_topic = "sommpanion.testsubject"
+agent_ch = Channel(8)
+agent_conn  = NATS.connect(config["nats_server_info"]["url"])
+
+sub2 = NATS.subscribe(agent_conn, backend_session_topic) do msg
+  put!(agent_ch, msg)
+end
+
+agent_context = YiemAgent.agentcontext(
+    text2text_instruct_llm,
+    get_embedding,
+    execute_sql_winedb,
+    similar_sql_vectordb,
+    insert_sql_vectordb,
+    similar_sommelier_decision,
+    insert_sommelier_decision
+  )
+
+  # can't instantiate
+  agent = YiemAgent.sommelier(
+    agent_context;
+    name="Janie",
+    id=sessionId,  # agent instance id
+    retailername="Yiem Wine Ltd.",
+    llmFormatName=""
+  )
+
+
+image1_path = "test/large_image.png"
+image1_bytes = read(image1_path)
+image1_base64_string = base64encode(image1_bytes)
+mime_type = "image/png" 
+data1_uri = "data:$(mime_type);base64,$(image1_base64_string)"
+
+# 1. Read local file and encode to base64 string
+image2_path = "test/small_image.png"
+image2_bytes = read(image2_path)
+image2_base64_string = base64encode(image2_bytes)
+mime_type = "image/png" 
+data2_uri = "data:$(mime_type);base64,$(image2_base64_string)"
+
+# 3. Construct payload with the Data URI
+message = Dict(
+    "role" => "user",
+    "content" => [
+      Dict("type" => "text", "text" => "Do you know type of wine in the image?"),
+      Dict(
+        "type" => "image_url",
+        "image_url" => Dict("url" => data1_uri)
+      )
+    ]
+  )
+
+result = YiemAgent.conversation(agent; userinput=message)
+println("\n$result")
+
+
+
+
+
+
+
+
+
+# message = Dict(
+#     "role" => "user",
+#     "content" => [
+#       Dict("type" => "text", "text" => 
+#       "
+#       เป็นงานเลี้ยงทั่วไป
+#       "),
+#     ]
+#   )
+
+# result = YiemAgent.conversation(agent; userinput=message)
+# println("\n$result")
+
+
+
+
+
+
+
+
+
+# message = Dict(
+#     "role" => "user",
+#     "content" => [
+#       Dict("type" => "text", "text" => "no thanks. that's all"),
+#     ]
+#   )
+
+# result = YiemAgent.conversation(agent; userinput=message)
+# println("\n$result")
+
+
+
+
+
+
+# message = Dict(
+#     "role" => "user",
+#     "content" => [
+#       Dict("type" => "text", "text" => "What about this wine?"),
+#       Dict(
+#         "type" => "image_url",
+#         "image_url" => Dict("url" => data2_uri)
+#       )
+#     ]
+#   )
+
+# result = YiemAgent.conversation(agent; userinput=message)
+# println("\n$result")
+
+
+
+
+
+
+
+
+
+
+
+
+
+
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+
+
+
+
+
+
+
+
 
-include("toolTest.jl")
-include("_extractToolCalls.jl")