update

2026-08-24 15:16:28 +07:00 · 2026-08-24 13:33:20 +07:00 · 2026-08-21 22:39:51 +07:00 · 2026-08-21 22:34:27 +07:00 · 2026-08-21 13:13:38 +07:00 · 2026-08-21 07:13:52 +07:00
23 changed files with 2251 additions and 4062 deletions
@@ -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"
+project_hash = "3ff1783eadf40ccb51801954aa0a8df935689752"
 [[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"
 [[deps.Accessors]]
 deps = ["CompositionsBase", "ConstructionBase", "Dates", "InverseFunctions", "MacroTools"]
@@ -98,9 +86,9 @@ 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"]
@@ -122,9 +110,9 @@ 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"
+version = "0.7.8"
 [[deps.CodecZstd]]
 deps = ["TranscodingStreams", "Zstd_jll"]
@@ -133,10 +121,9 @@ uuid = "6b39b394-51ab-5f42-8807-6242bab2b4c2"
 version = "0.8.7"
 [[deps.CommonSolve]]
-deps = ["PrecompileTools"]
+git-tree-sha1 = "cf963add2340ad9960e5eb22844e61ad8f931fe1"
 git-tree-sha1 = "6c389fa857f6ca5a95474b52a52023fd77f24cb7"
 uuid = "38540f10-b2f7-11e9-35d8-d573e4eb0ff2"
-version = "0.2.14"
+version = "0.2.13"
 [[deps.Compat]]
 deps = ["TOML", "UUIDs"]
@@ -156,7 +143,7 @@ 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"
@@ -194,9 +181,9 @@ 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 +238,9 @@ 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 +274,15 @@ git-tree-sha1 = "d2e49e7efd29719d6f28b891b0e0e159daa9d2b4"
 uuid = "e2ba6199-217a-4e67-a87a-7c52f15ade04"
 version = "0.1.11"
-[[deps.EzXML]]
+[[deps.FileIO]]
-deps = ["Printf", "XML2_jll"]
+deps = ["Pkg", "Requires", "UUIDs"]
-git-tree-sha1 = "7ea1aa5869e2626ccae84480e4f37185bc6f41d3"
+git-tree-sha1 = "6621fef488e496356c9c9625d0562c12a6070819"
-uuid = "8f5d6c58-4d21-5cfd-889c-e3ad7ee6a615"
+uuid = "5789e2e9-d7fb-5bc7-8068-2c6fae9b9549"
-version = "1.2.3"
+version = "1.20.0"
 weakdeps = ["HTTP"]
    [deps.FileIO.extensions]
    HTTPExt = "HTTP"
 [[deps.FilePathsBase]]
 deps = ["Compat", "Dates"]
@@ -327,24 +318,17 @@ uuid = "9fa8497b-333b-5362-9e8d-4d0656e87820"
 version = "1.11.0"
 [[deps.Gamma]]
-deps = ["LogExpFunctions"]
+git-tree-sha1 = "86f86b6168a016ed88e4ae4e64577b98c3b59e8e"
 git-tree-sha1 = "becc397f7cfb06e343496ae6ffb04818a851da51"
 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"]
+deps = ["CSV", "DataFrames", "DataStructures", "Dates", "Distributions", "Graphs", "HTTP", "JSON", "LibPQ", "NATS", "PrettyPrinting", "Random", "Revise", "SHA", "StringDistances", "UUIDs"]
-git-tree-sha1 = "4d7ff3b02a9cbd6d4f65fea43551b0bca6a83a22"
+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"
+version = "0.5.11"
 [[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"
 [[deps.Graphs]]
 deps = ["ArnoldiMethod", "DataStructures", "Inflate", "LinearAlgebra", "Random", "SimpleTraits", "SparseArrays", "Statistics"]
@@ -361,9 +345,9 @@ 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 +377,6 @@ 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 +436,9 @@ 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 +471,18 @@ 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 +530,6 @@ 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"
@@ -586,12 +567,6 @@ 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 +655,7 @@ 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 +684,15 @@ 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 +718,9 @@ 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 +781,15 @@ 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"
+version = "1.3.6"
 [[deps.Retry]]
 git-tree-sha1 = "41ac127cd281bb33e42aba46a9d3b25cd35fc6d5"
 uuid = "20febd7b-183b-5ae2-ac4a-720e7ce64774"
 version = "0.4.1"
 [[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 +809,9 @@ 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 +833,14 @@ 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 +884,6 @@ 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 +897,9 @@ 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 +909,9 @@ 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 +928,9 @@ 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 +944,9 @@ 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"]
@@ -983,15 +964,15 @@ 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"
+version = "0.4.7"
 [[deps.StringViews]]
 git-tree-sha1 = "f2dcb92855b31ad92fe8f079d4f75ac57c93e4b8"
@@ -1006,9 +987,9 @@ 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 +1014,6 @@ 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 +1021,9 @@ 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 +1033,14 @@ 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 +1063,9 @@ 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 +1093,8 @@ 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"
@@ -1154,12 +1122,12 @@ uuid = "a9144af2-ca23-56d9-984f-0d03f7b5ccf8"
 version = "1.0.21+0"
 [[deps.msghandler]]
-deps = ["Arrow", "Base64", "DataFrames", "Dates", "GeneralUtils", "HTTP", "JSON", "NATS", "PrettyPrinting", "UUIDs"]
+deps = ["Arrow", "Base64", "DataFrames", "Dates", "GeneralUtils", "HTTP", "JSON", "NATS", "PrettyPrinting", "Revise", "UUIDs"]
-git-tree-sha1 = "e7889a120327db84cdf1b1acf4a2c7caba6e8ef7"
+git-tree-sha1 = "e82a79cf6602541ea25409aded57b2ace4a7c29f"
 repo-rev = "main"
 repo-url = "https://git.yiem.cc/ton/msghandler"
 uuid = "f2724d33-f338-4a57-b9f8-1be882570d10"
-version = "1.4.0"
+version = "1.2.1"
 [[deps.nghttp2_jll]]
 deps = ["Artifacts", "Libdl"]
@@ -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]
@@ -26,8 +26,8 @@ msghandler = "f2724d33-f338-4a57-b9f8-1be882570d10"
 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"
 NATS = "0.1.0"
-msghandler = "1.4.0"
+msghandler = "1.2.1"
@@ -1,311 +1,139 @@
-# YiemAgent
+# YiemAgent
-
+
-Julia framework for building agents with tool use and MCP (Model Context Protocol) support.
+Julia framework for building agents with tool use and MCP (Model Context Protocol) support.
-
+
-## Quick Start
+## Getting Started
-
+
-```julia
+1. Install dependencies: `]add JSON, DataStructures, UUIDs, Dates, NATS, DataFrames`
-using YiemAgent
+2. Create a callable `mcpServer` struct that communicates with an MCP server via NATS
-
+3. Create a `yiemAgent` with an LLM callable and MCP server:
-# 1. Create an LLM callable (see "Custom LLM Callable" below)
+   ```julia
-llmCall = ...
+   agent = YiemAgent.yiemAgent(llmCall; mcpServer=mcpServer, eventSink=yourSink)
-
+   ```
-# 2. Create a callable MCP server (see "MCP Server Integration" below)
+4. Call `promptAgent(agent, message)` then `takeResponse(agent)`
-mcpServer = ...
+
-
+## Architecture
-# 3. Create the agent
+
-agent = yiemAgent(llmCall; mcpServer=mcpServer, eventSink=println)
+```
-
+src/
-# 4. Interact
+├── YiemAgent.jl          # Module entry point
-promptAgent(agent, "What tools do you have?")
+├── type.jl               # Core types (messages, tools, agent state)
-response = takeResponse(agent)
+├── utils.jl              # Message formatting, context preparation, validation
-
+├── agentCore.jl          # Agent loop, tool execution pipeline
-# 5. Stop when done
+├── api.jl                # Public API (promptAgent, takeResponse, followUp, stopAgent)
-stopAgent(agent)
+└── toolRegistry.jl       # Tool store, MCP discovery, listTools registration
-```
+```
-
+
-## Agent API
+## MCP Protocol
-
+
-| Function | Description |
+YiemAgent uses JSON-RPC 2.0 for MCP communication. The `mcpServer` callable struct must implement:
-|----------|-------------|
+
-| `promptAgent(agent, text)` | Send plain text to the agent's input channel |
+```julia
-| `promptAgent(agent, msg)` | Send a structured message (OpenAI format dict) to the input channel |
+# tools/list — returns tool definitions
-| `takeResponse(agent)` | Block and take the agent's response from output channel |
+mcpServer("tools/list")  # → Dict("jsonrpc"=>"2.0", "id"=>1, "result"=>Dict("tools"=>[...], "nextCursor"=>...))
-| `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 |
+# tools/call — executes a tool
-| `stopAgent(agent)` | Gracefully stop the agent and close channels |
+mcpServer("tools/call", toolName, arguments)  # → Dict("jsonrpc"=>"2.0", "id"=>2, "result"=>Dict("content"=>[...], "isError"=>...))
-
+```
-## Creating a yiemAgent
+
-
+Protocol error responses include `"error"` instead of `"result"`:
-```julia
+```julia
-yiemAgent(llmCall;
+Dict("jsonrpc"=>"2.0", "id"=>2, "error"=>Dict("code"=>-32602, "message"=>"..."))
-    systemPrompt::String = "You are a helpful assistant.",
+```
-    model = nothing,
+
-    messages::Vector{agentMessage} = agentMessage[],
+### Implementing the MCP Server Client
-    prepareContext::Function = prepareContext,
+
-    formatMsgForLLM::Function = formatMsgForLLM,
+You must provide a callable struct that communicates with your MCP server. Example using NATS:
-    beforeToolCall::Function = beforeToolCall,
+
-    afterToolCall::Function = afterToolCall,
+```julia
-    sessionId::Union{String, Nothing} = nothing,
+struct mcpServer
-    maxRetryDelayMs::Union{Int64, Nothing} = nothing,
+  natsConn::NATS.Connection
-    parallelToolExecute::Bool = false,
+  topic::String
-    eventSink::Function = eventSink,
+  senderID::String
-    mcpServer = nothing
+  fileserver_url::String
-)
+end
-```
+
-
+# tools/list implementation
-| Parameter | Description |
+function (c::mcpServer)(method::String)
-|-----------|-------------|
+  if method != "tools/list"
-| `llmCall` | **(required)** Function that receives formatted messages and returns an LLM response |
+    error("mcpServer: unexpected method '$method' (expected 'tools/list')")
-| `systemPrompt` | System prompt for the agent |
+  end
-| `model` | LLM model configuration (`llmModel` struct) |
+
-| `messages` | Initial conversation messages |
+  payload = Dict("jsonrpc" => "2.0", "id" => 1, "method" => method, "params" => Dict{String, Any}())
-| `prepareContext` | Hook to transform messages/context before each LLM call |
+  payloads = [("payload", payload, "dictionary")]
-| `formatMsgForLLM` | Hook to convert agent context to LLM API format |
+  _, msg_envelope_json_str = msghandler.smartpack(
-| `beforeToolCall` | Hook to block/allow tool execution |
+        c.topic, payloads;
-| `afterToolCall` | Hook to post-process tool results |
+        sender_id=c.senderID,
-| `sessionId` | Optional session identifier for MCP server |
+        msg_purpose="mcp_tools_list",
-| `maxRetryDelayMs` | Maximum retry delay in milliseconds |
+        fileserver_url=c.fileserver_url)
-| `parallelToolExecute` | Run multiple tool calls in parallel |
+
-| `eventSink` | Callback for agent events/debug messages |
+  reply = NATS.request(c.natsConn, c.topic, msg_envelope_json_str, timeout=180)
-| `mcpServer` | Callable struct for MCP server communication |
+  incoming_env = msghandler.smartunpack(String(reply.payload))
-
+  return incoming_env["payloads"][1][2]
-## Usage Examples
+end
-
+
-### Text Messages
+# tools/call implementation
-
+function (c::mcpServer)(method::String, toolName::String, arguments::Dict{String, Any})
-```julia
+  if method != "tools/call"
-# Send plain text
+    error("mcpServer: unexpected method '$method' (expected 'tools/call')")
-promptAgent(agent, "What tools do you have?")
+  end
-response = takeResponse(agent)
+
-
+  payload = Dict(
-# Follow-up while agent is processing
+    "jsonrpc" => "2.0",
-promptAgent(agent, "Tell me about wine")
+    "id" => 2,
-followUp(agent, "Also include prices")
+    "method" => method,
-response = takeResponse(agent)
+    "params" => Dict(
-```
+      "name" => toolName,
-
+      "arguments" => arguments
-### Structured Messages (OpenAI Format)
+    )
-
+  )
-```julia
+  payloads = [("payload", payload, "dictionary")]
-promptAgent(agent, Dict("role" => "user", "content" => "Hello"))
+  _, msg_envelope_json_str = msghandler.smartpack(
-response = takeResponse(agent)
+        c.topic, payloads;
-```
+        sender_id=c.senderID,
-
+        msg_purpose="mcp_tool_call",
-### Output Format
+        fileserver_url=c.fileserver_url)
-
+
-`takeResponse()` returns a `Dict{String, Any}` in OpenAI message format:
+  reply = NATS.request(c.natsConn, c.topic, msg_envelope_json_str, timeout=180)
-
+  incoming_env = msghandler.smartunpack(String(reply.payload))
-```julia
+  return incoming_env["payloads"][1][2]
-Dict(
+end
-    "role" => "assistant",
+```
-    "content" => [
+
-        Dict("type" => "text", "text" => "The weather in Tokyo is Sunny, 22°C."),
+See `test/runtest.jl` for the complete working example.
-        # ... more content blocks
+
-    ]
+## Tool Discovery
-)
+
-```
+Tools are discovered dynamically via MCP server:
-
+
-Content block types:
+1. `listTool` is the only pre-registered tool
-
+2. When the LLM calls `listTools()`, tools are discovered from the MCP server and registered at runtime
-| Block Type | Fields | Description |
+3. Supports pagination via `nextCursor` for large tool sets
-|------------|--------|-------------|
+
-| `text` | `"type" => "text"`, `"text" => "..."` | Plain text response from the LLM |
+## Agent API
-| `image_url` | `"type" => "image_url"`, `"image_url" => Dict("url" => "...")` | Image content (rare, used by multimodal models) |
+
-
+| Function | Description |
-Example — extracting the text response:
+|----------|-------------|
-
+| `promptAgent(agent, msg)` | Send a message to the agent's input channel |
-```julia
+| `takeResponse(agent)` | Block and take the agent's response from output channel |
-response = takeResponse(agent)
+| `followUp(agent, msg)` | Send a follow-up message while agent is still processing |
-text_response = first(c["text"] for c in response["content"] if c["type"] == "text")
+| `stopAgent(agent)` | Gracefully stop the agent and close channels |
-```
+
-
+## Agent Lifecycle
-### Multimodal Messages (Text + Image)
+
-
+1. `yiemAgent()` spawns a background task (`_agentLoop`) listening on `inputChannel` and `followUpChannel`
-```julia
+2. User messages enter via `promptAgent()` or `followUp()`
-# Encode images to data URIs
+3. `_processMessage()` handles LLM calls, tool execution, and conversation history
-image_path = "test/image.png"
+4. Tool execution follows three phases: `prepareToolCall` → `executePreparedToolCall` → `finalizeExecutedToolCall`
-image_bytes = read(image_path)
+5. `beforeToolCall`/`afterToolCall` hooks allow pre/post-processing of tool calls
-base64_string = base64encode(image_bytes)
+6. Tool `execute` functions can set `terminate=true` to stop the agent loop
-data_uri = "data:image/png;base64,$(base64_string)"
+
-
+## Hooks
-# Build message with text and image content blocks
+
-message = Dict(
+| Hook | Signature | Purpose |
-    "role" => "user",
+|------|-----------|---------|
-    "content" => [
+| `prepareContext` | `(state, sink, llmCall) -> ctx` | Transform messages/context before LLM call |
-        Dict("type" => "text", "text" => "Do you have Brunello di Montalcino?"),
+| `formatMsgForLLM` | `(ctx, sink) -> dict` | Convert agent context to LLM API format |
-        Dict("type" => "image_url", "image_url" => Dict("url" => data_uri))
+| `beforeToolCall` | `(context, signal) -> result` | Block/allow tool execution |
-    ]
+| `afterToolCall` | `(context, signal) -> result` | Post-process tool results |
-)
+| `eventSink` | `(msg) -> nothing` | Callback for agent events/debug messages |
 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
 ## 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)
 ```
@@ -1,54 +0,0 @@
 {
  "nats_server_info": {
    "description": "nats server",
    "url": "nats.yiem.cc"
  },
  "testingOrProduction": "testing",
  "agentId": "2b74b87a-5413-4fe2-a4d3-405891051680",
  "agentCentralConfigSubject": "/yiem/hq/agent/sommelier/backend/config/api/v1.1",
  "this_service_name": "agent_backend",
  "this_service_input_channel": {
    "mqtt": [
        "/yiem/hq/agent/sommpanion/backend/db/api_v1"
    ],
    "nats": [
      "sommpanion.backend.agentbackend.v1.inbox"
    ]
  },
  "agentRole": "sommelier",
  "organization": "yiem_hq",
  "externalservice": {
    "servicesloadbalancer": {
      "nats": "sommpanion.backend.servicesloadbalancer.v1.inbox" 
    },
    "textembedding": {
        "url": "textembedding.api.v1"
    },
    "textimage_to_text_llm": {
        "url": "https://llmcoder.yiem.cc/v1/chat/completions",
        "modelname": "Qwen3.6-35B-A3B-UD-Q4_K_M"
    },
    "virtualWineCustomer_1": {
        "serviceSubject": "",
        "modelName": "qwen3:8b"
    },
    "sommpanion_db" : {
      "description": "A database connection info for LibPQ client",
      "url": "192.168.88.106:5432",
      "dbname": "winedb",
      "user": "admin",
      "password": "admin@Sommpanion_0.0"
    },
    "sommpanion_vectordb" : {
      "description": "A wine database connection info for LibPQ client",
      "url": "192.168.88.106:5433",
      "dbname": "vectordb",
      "user": "admin",
      "password": "admin@Sommpanion_0.0"
    },
    "fileserver": {
    "description": "temporary file server",
    "url": "https://fileserver.yiem.cc"
    }
  }
 }
@@ -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
@@ -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 |
@@ -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
@@ -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
@@ -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.
@@ -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 |
@@ -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
@@ -1,10 +0,0 @@
 # 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
@@ -0,0 +1,11 @@
 check my understanding
 1) if LLM didn't use tool calls, assistantMessage get pushed into agent._state.messages and it will be the latest message in agent._state.messages. then _agentLoop() can pick it as the output to outputChannel
 2) if LLM use tool calls, assistantMessageToolCall get pushed into agent._state.messages. then toolResult get pushed into agent._state.messages. if toolResultBatch.terminate is false then _processMessage() loop continue
 3) if LLM use tool calls, assistantMessageToolCall get pushed into agent._state.messages. then toolResult get pushed into agent._state.messages. if toolResultBatch.terminate is true then final_response message get pushed into agent._state.messages. _processMessage() loop exit. then _agentLoop() can pick it as the output to outputChannel
 Is my understanding correct?
 the user can provide NATS connection to MCP server by adding agent.mcpserver (a callable struct) for communication with MCP server just like agent.llmCall (also a callable struct). I think communicating with MCP server is just send/receive JSON text right?
 Moreover, for simplicity I want to all tools into an MCP server so an agent can be instantiated with only listTools() in tool store then populate tools from MCP server later.
 what do you think?
@@ -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)
@@ -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"
    }
  }
 }
@@ -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__, " $(Dates.now())")
        put!(sessionDict[sessionId][:inputchannel], msg)
      end
    end
    # sleep is needed because MQTTClient use async. "while true" loop leave no 
    # chance for control to switch to on_msg()
    sleep(1)
  end
 end
 main()
@@ -0,0 +1 @@
 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
@@ -19,46 +19,14 @@ mutable struct yiemAgent <: agent             # High-level agent wrapper
  # user sends prompt message to agent. if agent is idle, it process user message right away. 
  # if agent is running, it process user message after  the current tool call finished.
  # Expect each message in OpenAI chat format:
  # image1_path = "test/large_image.png"
  # image1_bytes = read(image1_path)
  # image1_base64_string = base64encode(image1_bytes)
  # mime_type = "image/png" 
  # data1_uri = "data:$(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 have Brunello di Montalcino from Tenuta CastelGiocon?
  #       "),
  #       Dict(
  #         "type" => "image_url",
  #         "image_url" => Dict("url" => data1_uri)
  #       )
  #     ]
  #   )
  inputChannel::Channel                        
-  # Buffers follow-up messages the user sends while the agent is busy. Uses the same
+  # Buffers messages the user sends while the agent is busy. Processed after all inputChannel 
-  # OpenAI chat format as inputChannel. Processed after all inputChannel messages are
+  # messages are handled and the agent is idle (not using a tool call).
  # handled and the agent is idle (not in the middle of a tool call).
  followUpChannel::Channel                    
-  # agent sends response message to user after processing all user messages in inputChannel
+  # agent sends response message to user after processing all user messages in inputChannel 
-  # and all followUp messages. Response is in OpenAI message format:
+  # and all followUp messages.
  #   Dict("role" => "assistant", "content" => [
  #     Dict("type" => "text", "text" => "Response text here")
  #   ])
  outputChannel::Channel                     
  _agentLoop::Union{Task, Nothing}                 # agent loop running in the background
@@ -76,8 +44,81 @@ mutable struct yiemAgent <: agent             # High-level agent wrapper
  # Each block has a type — "text", "thinking", or "toolCall". 
  # The code filters for type === "toolCall" blocks, then passes them to executeToolCalls().
  llmCall                         
-                 
+                                               
-  # check input and output format at MCP server doc
+  # Callable struct for MCP server communication.
  # Called as: mcpServer("tools/list") → returns JSON-RPC 2.0 parsed response
  #            mcpServer("tools/call", toolName, arguments) → returns JSON-RPC 2.0 parsed response
  #
  # # Example (weather tool)
  #   # User provides a callable struct
  #   struct MyMCPClient
  #     natsConn::NATS.Connection
  #     topic::String
  #     senderID::String
  #     fileserver_url::String
  #   end
  #
  #   function (c::MyMCPClient)(method::String)
  #       payload = Dict("jsonrpc" => "2.0", "id" => 1, "method" => method, "params" => Dict{String, Any}())
  #       payloads = [("payload", payload, "dictionary")]
  #       _, msg_envelope_json_str = 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_json_str, timeout=180)
  #       incoming_env = msghandler.smartunpack(String(reply.payload))
  #       return incoming_env["payloads"][1][2]
  #   end
  #
  #   function (c::MyMCPClient)(method::String, toolName::String, arguments::AbstractDict{String, Any})
  #       payload = Dict("jsonrpc" => "2.0", "id" => 2, "method" => method,
  #                      "params" => Dict("name" => toolName, "arguments" => arguments))
  #       payloads = [("payload", payload, "dictionary"),]
  #       _, msg_envelope_json_str = 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_json_str, timeout=180)
  #       incoming_env = msghandler.smartunpack(String(reply.payload))
  #       return incoming_env["payloads"][1][2]
  #   end
  #
  #   # "tools/list" input:
  #     mcpServer("tools/list")
  #     # sending out payload before smart packed by msghandler:
  #     Dict("jsonrpc" => "2.0", "id" => 1, "method" => "tools/list", "params" => Dict{String, Any}())
  #     # expected return after smart unpacked by msghandler:
  #     Dict(
  #       "jsonrpc" => "2.0", "id" => 1,
  #       "result" => Dict(
  #         "tools" => [
  #           Dict(
  #             "name" => "getWeather",
  #             "description" => "Fetch current weather for a city.",
  #             "inputSchema" => Dict("type"=>"object",
  #                                   "properties" => Dict("city"=>Dict("type"=>"string", "description"=>"City name"),
  #                                                       "units"=>Dict("type"=>"string", "enum"=>["celsius","fahrenheit"], "default"=>"celsius")),
  #                                   "required" => ["city"])
  #           )
  #         ],
  #         "nextCursor" => nothing
  #       )
  #     )
  #
  #   # "tools/call" input:
  #     mcpServer("tools/call", "getWeather", Dict("city"=>"Tokyo", "units"=>"celsius"))
  #     # sending out payload before smart packed by msghandler:
  #     Dict("jsonrpc" => "2.0", "id" => 2, "method" => "tools/call",
  #          "params" => Dict("name" => "getWeather", "arguments" => Dict("city"=>"Tokyo", "units"=>"celsius")))
  #     # expected return after smart unpacked by msghandler (success):
  #     Dict("jsonrpc" => "2.0", "id" => 2,
  #          "result" => Dict("content" => [{"type" => "text", "text" => "Weather in Tokyo: Sunny, 22°C"}],
  #                           "isError" => false))
  #     # expected return after smart unpacked by msghandler (tool error):
  #     Dict("jsonrpc" => "2.0", "id" => 2,
  #          "result" => Dict("content" => [{"type" => "text", "text" => "Error: City 'Atlantis' not found."}],
  #                           "isError" => true))
  #     # expected return after smart unpacked by msghandler (protocol error):
  #     Dict("jsonrpc" => "2.0", "id" => 2,
  #          "error" => Dict("code" => -32602, "message" => "Missing required argument 'city'"))
  mcpServer                        
  # Callback invoked before executing a tool call (ask for user permission/confirmation/abort, etc..)
@@ -146,31 +187,10 @@ function yiemAgent(
  followUp    = Channel(32)
  outputChannel   = Channel(16)
  if mcpServer === nothing
    error(""" please implement the following MCP server interface
    struct mcpServer
      natsConn::NATS.Connection
      topic::String
      senderID::String
    end
    # MCP protocol: tools/list — returns tool definitions (JSON-RPC 2.0)
    # check input and output format from MCP server doc
    function (mcp::mcpServer)(method::String, sessionId::String; eventSink=nothing)::AbstractDict{String, <:Any}
      error("please implement MCP server tools/list interface")
    end
    # MCP protocol: tools/call — executes a tool (JSON-RPC 2.0)
    function (mcp::mcpServer)(method::String, toolName::String, arguments::Dict{String, Any}, 
      sessionId::String; eventSink=nothing)::AbstractDict{String, <:Any}
      error("please implement MCP server interface")
    end
    """)
  end
  tools = OrderedDict{String, agentTool}()
  registerAllTools(tools, mcpServer; eventSink=eventSink, sessionId=sessionId)
-  eventSink("instantiate an agent")
+
  # Create struct with a placeholder task, then spawn and replace it
  agent = yiemAgent(
    agentState(systemPrompt, model, tools, messages),
@@ -194,7 +214,7 @@ function yiemAgent(
  # Spawn the background loop and attach it
  agent._agentLoop = @spawn _agentLoop(agent)
-  eventSink("agent instantiated")
+
  return agent
 end
@@ -260,36 +280,42 @@ function _agentLoop(agent::yiemAgent)
      agent.inputChannel -> nothing
      agent.followUpChannel -> nothing
    """
-    agent.eventSink("_agentLoop starting")
+
    while true
      while newUserMsg === nothing 
        if isready(agent.inputChannel)
          agent.eventSink("_agentLoop 1 agent._state.messages length $(length(agent._state.messages))")
          # agent process new user msg immediately after the current tool call finished.
          newUserMsg = take!(agent.inputChannel)   
-          agent.eventSink("take new user message")
+          agent.eventSink("new user msg")
        else
          # check followUp message after _processMessage() is done
          if typeof(processingTask) == Task && istaskdone(processingTask) == true
-            agent.eventSink("_processMessage() done")
+            agent.eventSink("_agentLoop 2 agent._state.messages length $(length(agent._state.messages))")
            # if agent runs is done but followUpChannel has messages,
            # put new message in inputChannel instead
            if isready(agent.followUpChannel)
              agent.eventSink("_agentLoop 3 agent._state.messages length $(length(agent._state.messages))")
              while isready(agent.followUpChannel)
                agent.eventSink("put followUp message to input channel")
                followUpMsg = take!(agent.followUpChannel)
                put!(agent.inputChannel, followUpMsg)
              end
              processingTask = nothing        # reset
              result = nothing                # reset
            else  # _processMessage() done and no followUp message.
-              agent.eventSink("no followUp message")
+              agent.eventSink("_agentLoop 4 agent._state.messages length $(length(agent._state.messages))")
              result = deepcopy(agent._state.messages[end])
              agent.eventSink("_agentLoop 4-1 ")
              # filter out reasoningContent in-place
              filter!(c -> !(c isa reasoningContent), result.content)
              agent.eventSink("_agentLoop 5 ")
              # format output
              respondToUI = _assistantMessageToOpenAI(result)
              agent.eventSink("_agentLoop 6 ")
              put!(agent.outputChannel, respondToUI)
-              agent.eventSink("put result to output channel")
+              if !isempty(result.content) && result.content[1] isa textContent
                agent.eventSink(result.content[1].text)
              end
              processingTask = nothing        # reset
              result = nothing                # reset
            end
@@ -319,7 +345,7 @@ function _agentLoop(agent::yiemAgent)
      else
        # spawn new _processMessage() if it is not already running.
        if processingTask === nothing
-          agent.eventSink("spawn new _processMessage()")
+          agent.eventSink("_agentLoop 5 agent._state.messages length $(length(agent._state.messages))")
          # discard all messages in followUpChannel
          while isready(agent.followUpChannel)
            _ = take!(agent.followUpChannel)
@@ -341,10 +367,8 @@ function _agentLoop(agent::yiemAgent)
          ) 
        end
        put!(processMessageInputCh, newUserMsg)
        agent.eventSink("put new user msg to processMessageInputCh")
        newUserMsg = nothing # reset
      end
    end
  catch e
    bt = catch_backtrace()
@@ -352,7 +376,7 @@ function _agentLoop(agent::yiemAgent)
        showerror(io, e, bt)
        println(io)
    end
-    agent.eventSink(errMsg; publish=true)
+    eventSink(errMsg)
    # On any error, send error response and exit the loop
    @error "Agent loop failed" error=e
@@ -400,11 +424,10 @@ function _processMessage(
  afterToolCall::Union{Function, Nothing},
  parallelToolExecute::Bool,
 )::Nothing
  eventSink("_processMessage 1 _state.messages length $(length(agentMsgHistory))")
  # loop until llmCall() response didn't use tool calls
  final_response = nothing
  eventSink("▶ _processMessage: entered")
  """ example message in inputChannel
  Dict(
      "role" => "user",
@@ -422,39 +445,48 @@ function _processMessage(
    try
      # Drain inputChannel and convert OpenAI-format messages to userMessage type
      while isready(inputChannel)
        eventSink("_processMessage 2 _state.messages length $(length(agentMsgHistory))")
        newUserMsg_openai = take!(inputChannel)
        eventSink("_processMessage 3 _state.messages length $(length(agentMsgHistory))")
        if newUserMsg_openai === :shutdown
          eventSink("_processMessage 4 _state.messages length $(length(agentMsgHistory))")
          # Re-emit shutdown signal for the loop to handle
          put!(inputChannel, :shutdown)
          break
        end
        eventSink("_processMessage 5 _state.messages length $(length(agentMsgHistory))")
        newUserMsg = OpenAiToUserMessage(newUserMsg_openai)
        push!(agentMsgHistory, newUserMsg)
-        eventSink("▶ _processMessage: user msg pushed")
+        eventSink("_processMessage 6 _state.messages length $(length(agentMsgHistory))")
      end
      eventSink("_processMessage 7 _state.messages length $(length(agentMsgHistory))")
      # call prepareContext()
      state = agentState(systemPrompt, nothing, tools, agentMsgHistory)
      eventSink("_processMessage 8 _state.messages length $(length(agentMsgHistory))")
      preparedContext = prepareContext(state, eventSink)
      eventSink("_processMessage 9 _state.messages length $(length(agentMsgHistory))")
      # Call formatMessagesForLLM() to format for LLM
      formattedMessages = formatMessagesForLLM(preparedContext, eventSink)
      eventSink("_processMessage 10 formattedMessages $formattedMessages")    
      eventSink("▶ _processMessage: context")
      eventSink("Thinking...")
      """ response example
      response = JSON.Object{String, Any}("finish_reason" => "tool_calls", "index" => 0, "message" => JSON.Object{String, Any}("role" => "assistant", "content" => "", "reasoning_content" => "Here's a thinking process:\n\n1.  **Identify User Request**: The user is asking for the weather in Bangkok.\n2.  **Locate Relevant Tool**: I have a `getWeather` function available.\n3.  **Check Function Parameters**:\n   - `city` (required): City and country, e.g., 'San Francisco, CA' or 'Tokyo, Japan'\n   - `units` (optional, default \"celsius\"): Temperature scale (\"celsius\" or \"fahrenheit\")\n4.  **Prepare Parameters**:\n   - `city`: \"Bangkok, Thailand\" (adding country for clarity, though just \"Bangkok\" might work, following the example format is safer)\n   - `units`: Not specified, so I'll use the default (\"celsius\")\n5.  **Execute Tool Call**: Call `getWeather` with `city: \"Bangkok, Thailand\"`\n6.  **Anticipate Response**: The function will return current weather and forecast data for Bangkok. I'll then format it nicely for the user.\n   - *Self-Correction/Verification during thought*: The prompt says \"city: City and country, e.g., 'San Francisco, CA' or 'Tokyo, Japan'\". I'll use \"Bangkok, Thailand\". The `units` parameter is optional, so I'll omit it to use the default.\n   - Proceed with tool call.✅\n", "tool_calls" => Any[JSON.Object{String, Any}("type" => "function", "function" => JSON.Object{String, Any}("name" => "getWeather", "arguments" => "{\"city\":\"Bangkok, Thailand\"}"), "id" => "6fOilR5QPcdppbAAHluhkRyUDu3oWMAL")]))
      """
-      response = llmCall(formattedMessages, eventSink=eventSink)
+      response = llmCall(formattedMessages)
      eventSink("LLM response " * string(response))
      eventSink("_processMessage 11 _state.messages length $(length(agentMsgHistory))")
      # Extract tool calls from LLM response content blocks
-      hasToolCalls, toolCallList, assistant_msg = _extractToolCalls(response, eventSink)
+      hasToolCalls, toolCallList, assistant_msg = _extractToolCalls(response)
      eventSink("hasToolCalls: $hasToolCalls\ntoolCallList: $toolCallList _state.messages length $(length(agentMsgHistory))")
      eventSink("assistant_msg " * string(assistant_msg))
      eventSink("_processMessage 11-1")
      # Add assistant message (tool calls or text) to history for next LLM turn
      push!(agentMsgHistory, assistant_msg)
      eventSink("▶ _processMessage: LLM response")
      if hasToolCalls && length(toolCallList) > 0
        eventSink("▶ _processMessage: tool calls")
        # Build context and config for executeToolCalls
        config = agentLoopConfig(
@@ -464,16 +496,21 @@ function _processMessage(
        )
        signal = abortSignal(false)
        eventSink("_processMessage 12 _state.messages length $(length(agentMsgHistory))")
        # call executeToolCalls()
        toolResultBatch = executeToolCalls(preparedContext, assistant_msg, toolCallList, config, 
                                          signal, eventSink)      
-        eventSink("▶ _processMessage: tool batch")
+        eventSink("_processMessage 13 _state.messages length $(length(agentMsgHistory))")
        # save toolResults to messages
        for toolResult in toolResultBatch.messages
          eventSink("toolResult " * string(toolResult))
          push!(agentMsgHistory, toolResult)
        end
        eventSink("_processMessage 14 _state.messages length $(length(agentMsgHistory))")
        if toolResultBatch.terminate
-          eventSink("▶ _processMessage: TERMINATE branch")
+          eventSink("_processMessage 15 _state.messages length $(length(agentMsgHistory))")
          # If toolResultBatch requested termination, build a final response
          final_content = [textContent("Tool execution completed.")]
          for toolResult in toolResultBatch.messages
@@ -487,6 +524,7 @@ function _processMessage(
              end
            end
          end
          eventSink("_processMessage 16 _state.messages length $(length(agentMsgHistory))")
          final_response = assistantMessage(
            role="assistant",
            content=final_content,
@@ -505,7 +543,7 @@ function _processMessage(
          break
        end
      else
-        eventSink("▶ _processMessage: FINAL response")
+        eventSink("_processMessage 17 _state.messages length $(length(agentMsgHistory))")
        # LLM did not use tool calls — 
        break
      end
@@ -516,12 +554,12 @@ function _processMessage(
            println(io)
        end
-        eventSink(err_msg; publish=true)
+        eventSink(err_msg)
        eventSink("▶ _processMessage: CAUGHT ERROR")
    end
  end
-  eventSink("▶ _processMessage: exited")
+
  eventSink("_processMessage 18 _state.messages length $(length(agentMsgHistory))")
  return nothing
 end
@@ -552,7 +590,7 @@ agentToolResult([textContent("text", "Tool not found")], Dict{Any,Any}())
 ```
 """
 function createErrorToolResult(msg::String)::agentToolResult
-  return agentToolResult([textContent("text", msg)], Dict{Any,Any}())
+  return agentToolResult([textContent("text", msg)], dict{any,any}())
 end
 """
@@ -631,12 +669,9 @@ The message is constructed from:
  1)  llm_useTool = JSON.Object{String, Any}("finish_reason" => "tool_calls", "index" => 0, "message" => JSON.Object{String, Any}("role" => "assistant", "content" => "", "reasoning_content" => "Here's a thinking process:\n\n1.  **Identify User Request**: The user is asking for the weather in Bangkok.\n2.  **Locate Relevant Tool**: I have a `getWeather` function available.\n3.  **Check Function Parameters**:\n   - `city` (required): City and country, e.g., 'San Francisco, CA' or 'Tokyo, Japan'\n   - `units` (optional, default \"celsius\"): Temperature scale (\"celsius\" or \"fahrenheit\")\n4.  **Prepare Parameters**:\n   - `city`: \"Bangkok, Thailand\" (adding country for clarity, though just \"Bangkok\" might work, following the example format is safer)\n   - `units`: Not specified, so I'll use the default (\"celsius\")\n5.  **Execute Tool Call**: Call `getWeather` with `city: \"Bangkok, Thailand\"`\n6.  **Anticipate Response**: The function will return current weather and forecast data for Bangkok. I'll then format it nicely for the user.\n   - *Self-Correction/Verification during thought*: The prompt says \"city: City and country, e.g., 'San Francisco, CA' or 'Tokyo, Japan'\". I'll use \"Bangkok, Thailand\". The `units` parameter is optional, so I'll omit it to use the default.\n   - Proceed with tool call.✅\n", "tool_calls" => Any[JSON.Object{String, Any}("type" => "function", "function" => JSON.Object{String, Any}("name" => "getWeather", "arguments" => "{\"city\":\"Bangkok, Thailand\"}"), "id" => "6fOilR5QPcdppbAAHluhkRyUDu3oWMAL")]))
  2)  llm_notUseTool = JSON.Object{String, Any}("finish_reason" => "stop", "index" => 0, "message" => JSON.Object{String, Any}("role" => "assistant", "content" => "The weather in London, UK is currently Sunny with a temperature of 22°C.", "reasoning_content" => "The user asked for the weather in London.\nI called the `getWeather` tool for London, UK.\nThe response indicates it's Sunny and 22°C.\nI will convey this information to the user.\n"))
 """
-function _extractToolCalls(response, eventSink=nothing)
+function _extractToolCalls(response)
    hasToolCalls = false
    toolCallList = agentToolCall[]
    if eventSink !== nothing
        eventSink("▶ _extractToolCalls: parsing response")
    end
    # Helper: parse args (JSON string -> Dict, or pass through)
    parse_args(raw) = raw isa AbstractDict && !(raw isa Dict{String,Any}) ? 
@@ -847,10 +882,10 @@ stopping the agent when other tools in the batch did not intend to terminate.
 julia> shouldTerminate(finalizedOutcome[])
 false
-julia> shouldTerminate([finalizedOutcome(tc, agentToolResult([], Dict{Any,Any}(), Dict{Any,Any}(), false), false) for _ in 1:2])
+julia> shouldTerminate([finalizedOutcome(tc, agentToolResult([], dict{any,any}(), dict{any,any}(), false), false) for _ in 1:2])
 false
-julia> shouldTerminate([finalizedOutcome(tc, agentToolResult([], Dict{Any,Any}(), Dict{Any,Any}(), true), false) for _ in 1:2])
+julia> shouldTerminate([finalizedOutcome(tc, agentToolResult([], dict{any,any}(), dict{any,any}(), true), false) for _ in 1:2])
 true
 ```
 """
@@ -964,40 +999,46 @@ function prepareToolCall(
  signal::abortSignal,
  eventSink
 )::Union{preparedToolCall,immediateOutcome}
  eventSink("prepareToolCall 1")
  # pick a called tool from tool store
  eventSink("▶ prepareToolCall: resolving")
  tool = get(context.tools, toolCall.name, nothing) 
  if tool === nothing
-    eventSink("▶ prepareToolCall: NOT FOUND")
+    eventSink("prepareToolCall 2")
    return immediateOutcome(createErrorToolResult("Tool $toolCall.name not found"), true)
  end
  try
    eventSink("prepareToolCall 3")
    # 1. prepare arguments (tool-specific transform)
    prepared = prepareToolCallArguments(tool, toolCall)
    eventSink("prepared " * string(prepared.arguments))
    eventSink("prepareToolCall 4")
    validatedArgs = validateToolArguments(tool, prepared)
-    eventSink("▶ prepareToolCall: validated")
+    eventSink("validatedArgs " * string(validatedArgs))
    eventSink("prepareToolCall 5")
    # 2. beforeToolCall hook — can block
    if config.beforeToolCall !== nothing
      eventSink("prepareToolCall 6")
      before = config.beforeToolCall(
        beforeToolCallContext(assistantMsg, toolCall, validatedArgs, context), 
-        signal,
+        signal
        eventSink
        )
-
+      eventSink("prepareToolCall 7")
      if signal.aborted
        eventSink("prepareToolCall 8")
        return immediateOutcome(createErrorToolResult("Operation aborted"), true)
      end
      if before !== nothing && before.block
        eventSink("prepareToolCall 9")
        return immediateOutcome(
          createErrorToolResult(get(before, :reason, "Tool execution was blocked")), true)
      end
    end
-
+    eventSink("prepareToolCall 10")
    return preparedToolCall(tool, toolCall, validatedArgs)
  catch e
    bt = catch_backtrace()
@@ -1006,7 +1047,8 @@ function prepareToolCall(
        println(io)
    end
-    eventSink(errMsg; publish=true)
+    eventSink(errMsg)
    return immediateOutcome(createErrorToolResult(sprint(showerror, e)), true)
  end
 end
@@ -1044,7 +1086,7 @@ emitted after the result has already been captured.
 ```julia
 # Successful execution
 executePreparedToolCall(prep, nothing, emit)
-# => executedOutcome(agentToolResult([textContent("text", "Found 3 wines")], Dict{Any,Any}(), Dict{Any,Any}()), false)
+# => executedOutcome(agentToolResult([textContent("text", "Found 3 wines")], dict{any,any}(), dict{any,any}()), false)
 # Execution error
 executePreparedToolCall(prep, nothing, emit)
@@ -1057,11 +1099,13 @@ function executePreparedToolCall(
    signal::Union{Nothing,abortSignal},
    eventSink,
  )::executedOutcome
  eventSink("executePreparedToolCall 1")
-  eventSink("▶ execute: START")
+  try #WORKING
  try
    result = prep.tool.execute(prep.toolCall.id, prep.args, signal, eventSink)
-    eventSink("▶ execute: END")
+    eventSink("executePreparedToolCall 2")
    eventSink("tool execute raw result: " * sprint(show, result))
    eventSink("executePreparedToolCall 3")
    return executedOutcome(result, false)
  catch e
    bt = catch_backtrace()
@@ -1069,12 +1113,10 @@ function executePreparedToolCall(
        showerror(io, e, bt)
        println(io)
    end
-    eventSink(errMsg; publish=true)
+    eventSink(errMsg)
    eventSink("▶ execute: ERROR")
    return executedOutcome(createErrorToolResult(sprint(showerror, e)), true)
  end
 end
@@ -1143,20 +1185,23 @@ function finalizeExecutedToolCall(
    signal::Union{Nothing,abortSignal},
    eventSink
 )::finalizedOutcome
-
+  eventSink("finalizeExecutedToolCall 1")
  eventSink("▶ finalize: START")
  result = executed.result
  isError = executed.isError
  eventSink("finalizeExecutedToolCall 2")
  if config.afterToolCall !== nothing
    try
      after = config.afterToolCall(
        afterToolCallContext(assistantMsg, prep.toolCall, prep.args, result, isError, context), 
-        signal,
+        signal
        eventSink
      )
      eventSink("finalizeExecutedToolCall 3")
      if after !== nothing
-        result = after
+        result = merge(result, dict(:content=>get(after,:content,result.content),
-        isError = false
+                                    :details=>get(after,:details,result.details),
                                    :usage=>get(after,:usage,result.usage),
                                    :terminate=>get(after,:terminate,result.terminate)))
        isError = get(after, :isError, isError)
      end
    catch e
      bt = catch_backtrace()
@@ -1164,13 +1209,13 @@ function finalizeExecutedToolCall(
          showerror(io, e, bt)
          println(io)
      end
-      eventSink(errMsg; publish=true)
+      eventSink(errMsg)
      result = createErrorToolResult(sprint(showerror, e))
      isError = true
    end
  end
-  eventSink("▶ finalize: END")
+  eventSink("finalizeExecutedToolCall 4")
  return finalizedOutcome(prep.toolCall, result, isError)
 end
@@ -1236,27 +1281,39 @@ function executeToolCallsSequential(
    signal::abortSignal,
    eventSink,
 )::agentToolCallBatch
  eventSink("executeToolCallsSequential 1")
  finalizedCalls = finalizedOutcome[]
  messages = toolResultMessage[]
  eventSink("▶ executeToolCallsSequential: batch start")
  for tc in toolCalls
    eventSink("start tool execute $(tc.id), $(tc.name), $(tc.arguments)")
    prep = prepareToolCall(context, assistantMsg, tc, config, signal, eventSink)
    eventSink("executeToolCallsSequential " * string(prep.args))
    if prep isa immediateOutcome
      eventSink("executeToolCallsSequential 2-1")
      finalized = finalizedOutcome(tc, prep.result, prep.isError)
      eventSink("executeToolCallsSequential 2-2")
    else
      eventSink("executeToolCallsSequential 3")
      executed = executePreparedToolCall(prep, signal, eventSink)
      eventSink("executeToolCallsSequential 3-1")
      finalized = finalizeExecutedToolCall(context, assistantMsg, prep, executed, config,
                                          signal, eventSink)
      eventSink("executeToolCallsSequential 3-2")
    end
    eventSink("executeToolCallsSequential 4")
    eventSink("finalized $(finalized.toolCall.id), $(finalized.toolCall.name),
                          $(finalized.result), $(finalized.isError)")
    push!(messages, createToolResultMessage(finalized))
    push!(finalizedCalls, finalized)
    eventSink("executeToolCallsSequential 5")
    if signal !== nothing && signal.aborted
        break
    end
  end
-  eventSink("▶ executeToolCallsSequential: batch end")
+  eventSink("executeToolCallsSequential 6")
  return agentToolCallBatch(messages, shouldTerminate(finalizedCalls))
 end
@@ -1327,24 +1384,23 @@ function executeToolCallsParallel(
 )::agentToolCallBatch
  entries = Union{finalizedOutcome,Task}[]
  eventSink("▶ executeToolCallsParallel: batch start")
  for tc in toolCalls
-    toolExecStartEvent(tc.id, tc.name, tc.arguments)
+    eventSink(toolExecStartEvent(tc.id, tc.name, tc.arguments))
    prep = prepareToolCall(context, assistantMsg, tc, config, signal, eventSink)
    if prep isa immediateOutcome
      finalized = finalizedOutcome(tc, prep.result, prep.isError)
-      toolExecEndEvent(finalized.toolCall.id, finalized.toolCall.name,
+      eventSink(toolExecEndEvent(finalized.toolCall.id, finalized.toolCall.name,
-                            finalized.result, finalized.isError)
+                            finalized.result, finalized.isError))
      push!(entries, finalized)
    else
      t = Task() do
        executed = executePreparedToolCall(prep, signal, eventSink)
        finalized = finalizeExecutedToolCall(context, assistantMsg, prep, executed, config, signal)
-        toolExecEndEvent(finalized.toolCall.id, finalized.toolCall.name,
+        eventSink(toolExecEndEvent(finalized.toolCall.id, finalized.toolCall.name,
-                              finalized.result, finalized.isError)
+                              finalized.result, finalized.isError))
        return finalized
      end
      schedule(t)
@@ -1367,7 +1423,6 @@ function executeToolCallsParallel(
      push!(messages, createToolResultMessage(f))
  end
  eventSink("▶ executeToolCallsParallel: batch end")
  return agentToolCallBatch(messages, shouldTerminate(finalizedCalls))
 end
@@ -1430,6 +1485,7 @@ function executeToolCalls(
    eventSink,
  )::agentToolCallBatch
  eventSink("_executeToolCalls 1")
  hasSequential = false
  for tc in toolCalls
      t = get(context.tools, tc.name, nothing)
@@ -1438,11 +1494,13 @@ function executeToolCalls(
          break
      end
  end
-  eventSink("▶ executeToolCalls: dispatch")
+  eventSink("_executeToolCalls 2")
  if config.toolExecution == "sequential" || hasSequential
    eventSink("_executeToolCalls 3")
    return executeToolCallsSequential(context, assistantMsg, toolCalls, config, signal,
                                      eventSink)
  else
    eventSink("_executeToolCalls 4")
    return executeToolCallsParallel(context, assistantMsg, toolCalls, config, signal,
                                    eventSink)
  end
@@ -1483,16 +1541,13 @@ OpenAiToUserMessage(msg)
 # => userMessage("user", [textContent("Hello")], DateTime(...))
 ```
 """
-function OpenAiToUserMessage(msg::AbstractDict, eventSink=nothing)::userMessage
+function OpenAiToUserMessage(msg::Dict)::userMessage
    content_blocks = Vector{messageContent}()
    if eventSink !== nothing
        eventSink("▶ OpenAiToUserMessage: parsing")
    end
    raw_content = get(msg, "content", Any[])
    if raw_content isa Vector
        for block in raw_content
-            if typeof(block) <: AbstractDict
+            if block isa Dict
                block_type = get(block, "type", "")
                if block_type == "text"
                    text = get(block, "text", "")
@@ -1511,9 +1566,6 @@ function OpenAiToUserMessage(msg::AbstractDict, eventSink=nothing)::userMessage
        end
    end
    if eventSink !== nothing
        eventSink("▶ OpenAiToUserMessage: done")
    end
    return userMessage(content=content_blocks)
 end
@@ -1584,6 +1636,11 @@ end
@@ -38,11 +38,6 @@ function promptAgent(agent::yiemAgent, msg::AbstractDict{String, Any})
  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.
@@ -96,11 +91,6 @@ function followUp(agent::yiemAgent, msg::AbstractDict{String, Any})
  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.
@@ -60,48 +60,48 @@ function _wrap_mcp_tool(mcpserver, tool_def::AbstractDict{String, Any}; eventSin
    end
    return agentTool(
-      name=name,
+        name=name,
-      label=title,
+        label=title,
-      description=desc,
+        description=desc,
-      inputSchema=params,
+        inputSchema=params,
-      execute=(toolCallId::String, args::AbstractDict{String, Any},
+        execute=(toolCallId::String, args::AbstractDict{String, Any},
-                signal::Union{Nothing,abortSignal},
+                 signal::Union{Nothing,abortSignal},
-                eventSink) -> begin
+                 eventSink) -> begin
-        try
+            try
-          response = mcpserver("tools/call", name, args, sessionId; eventSink=eventSink)
+                response = mcpserver("tools/call", name, args, sessionId)
-          # Parse JSON-RPC 2.0 response envelope
+                # Parse JSON-RPC 2.0 response envelope
-          if haskey(response, "error")
+                if haskey(response, "error")
-            rpc_error = response["error"]
+                    rpc_error = response["error"]
-            err_msg = get(rpc_error, "message", "Unknown MCP error")
+                    err_msg = get(rpc_error, "message", "Unknown MCP error")
-            return agentToolResult(
+                    return agentToolResult(
-                [textContent("MCP error: $err_msg")],
+                        [textContent("MCP error: $err_msg")],
-                Dict{Any,Any}("error" => err_msg),
+                        Dict{Any,Any}("error" => err_msg),
-                nothing, false
+                        nothing, false
-            )
+                    )
-          end
+                end
-          result_data = get(response, "result", response)
+                result_data = get(response, "result", response)
-          content_text = _extract_text_content(result_data)
+                content_text = _extract_text_content(result_data)
-          is_error = get(result_data, "isError", false)
+                is_error = get(result_data, "isError", false)
-          return agentToolResult(
+                return agentToolResult(
-              [textContent(content_text)],
+                    [textContent(content_text)],
-              Dict{Any,Any}("isError" => is_error),
+                    Dict{Any,Any}("isError" => is_error),
-              nothing, false
+                    nothing, false
-          )
+                )
-        catch e
+            catch e
-          errMsg = sprint(showerror, e)
+                errMsg = sprint(showerror, e)
-          return agentToolResult(
+                return agentToolResult(
-              [textContent("MCP call error: $errMsg")],
+                    [textContent("MCP call error: $errMsg")],
-              Dict{Any,Any}("error" => errMsg),
+                    Dict{Any,Any}("error" => errMsg),
-              nothing, false
+                    nothing, false
-          )
+                )
-        end
+            end
-      end,
+        end,
-      prepareArguments=nothing,
+        prepareArguments=nothing,
-      validateRequiredArgs=nothing,
+        validateRequiredArgs=nothing,
-      parallelToolExecute=false,
+        parallelToolExecute=false,
    )
 end
@@ -121,7 +121,9 @@ function register_mcp_tools(tools::OrderedDict{String, agentTool}, mcpserver; ev
  end
  new_count = 0
  try
-    response = mcpserver("tools/list", sessionId; eventSink=eventSink)
+    eventSink("register_mcp_tools 1")
    response = mcpserver("tools/list", sessionId)
    eventSink("register_mcp_tools 2")
    # Parse JSON-RPC 2.0 response envelope
    if haskey(response, "error")
        rpc_error = response["error"]
@@ -137,41 +139,41 @@ function register_mcp_tools(tools::OrderedDict{String, agentTool}, mcpserver; ev
    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
    # Paginate: fetch remaining tools if nextCursor is present
    while cursor !== nothing && cursor !== ""
        response = sessionId !== nothing ? mcpserver("tools/list", sessionId) : mcpserver("tools/list")
        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)
+        showerror(io, e, bt)
-      println(io)
+        println(io)
    end
    eventSink(err_msg)
@@ -198,7 +200,7 @@ Add `tool` to `tools`, overwriting any existing tool with the same name.
 function registerTool(tools::OrderedDict{String, agentTool}, tool::agentTool; eventSink=nothing
  )::OrderedDict{String, agentTool}
    tools[tool.name] = tool
-    eventSink("[toolRegistry] Registered tool: $(tool.name)")
+    println("[toolRegistry] Registered tool: $(tool.name)")
    return tools
 end
@@ -245,6 +247,7 @@ function listTool(tools::OrderedDict{String, agentTool}, mcpserver; eventSink=no
                 eventSink) -> begin
            # Discover and register MCP tools (idempotent — skips already registered)
            new_count = register_mcp_tools(tools, mcpserver; eventSink=eventSink, sessionId=sessionId)
            eventSink("tools dump: " * sprint(show, tools))
            # Always include listTools itself in the count
            total = length(tools)
@@ -282,9 +285,7 @@ Register all tools for an agent:
 """
 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
@@ -2,8 +2,8 @@ module utils
 export clearhistory, availableWineToText, prepareContext, formatMsgForLLM, validateRequiredArgs, 
      validateToolArguments, _userMessageToOpenAI, 
-      _assistantMessageToOpenAI, _toolResultMessageToOpenAI, _messageContentToBlocks,
+      _assistantMessageToOpenAI, _toolResultMessageToOpenAI, _messageContentToBlocks, _toolsToOpenAI,
-      _toolsToOpenAI, beforeToolCall, afterToolCall
+      beforeToolCall, afterToolCall, eventSink
 using UUIDs, Dates, DataStructures, HTTP, JSON, NATS
 using GeneralUtils
@@ -96,7 +96,7 @@ 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)
@@ -146,7 +146,7 @@ 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" => [...]),
@@ -208,6 +208,7 @@ function formatMsgForLLM(ctx::agentContext, eventSink)::Dict{String, Any}
  openaiReadyMsg = Dict{String, Any}()
  # openaiReadyMsg["model"] = "gemma-4-E4B-it-UD-Q4_K_XL"
  messages = Vector{Dict{String, Any}}()
  eventSink("formatMsgForLLM 1")
  # System prompt as system message
  if !isempty(ctx.systemPrompt)
    push!(messages, Dict(
@@ -215,6 +216,7 @@ function formatMsgForLLM(ctx::agentContext, eventSink)::Dict{String, Any}
      "content" => [Dict("type" => "text", "text" => ctx.systemPrompt)]
    ))
  end
  eventSink("formatMsgForLLM 2")
  # Conversation messages
  for msg in ctx.messages
    if msg isa userMessage
@@ -227,8 +229,10 @@ function formatMsgForLLM(ctx::agentContext, eventSink)::Dict{String, Any}
      push!(messages, _toolResultMessageToOpenAI(msg))
    end
  end
  eventSink("formatMsgForLLM 3")
  # Convert ctx.tools into OpenAI tools format
  tools_array = _toolsToOpenAI(ctx.tools, eventSink)
  eventSink("formatMsgForLLM 4")
  openaiReadyMsg["messages"] = messages
  openaiReadyMsg["temperature"] = 0.7
@@ -261,7 +265,7 @@ Common use cases:
 - `nothing` is treated as allow (equivalent to `beforeToolCallResult(false, "N/A")`).
 # Example
-```jldoctest
+```julia
 function beforeToolCall(context::beforeToolCallContext, signal::abortSignal)
    if context.toolCall.name == "deleteFile"
        # Block file deletion unless explicitly approved
@@ -271,10 +275,14 @@ function beforeToolCall(context::beforeToolCallContext, signal::abortSignal)
 end
 ```
 """
-function beforeToolCall(context::beforeToolCallContext, signal::abortSignal, eventSink
+function beforeToolCall(context::beforeToolCallContext, signal::abortSignal
  )::beforeToolCallResult
-  # eventSink("beforeToolCall: $(context.toolCall.name) args=$(context.args)")
+  # final context check
  # seek user approval via UI
  # other check
  return beforeToolCallResult(false, "N/A")
 end
@@ -302,15 +310,22 @@ Common use cases:
 - `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
+function afterToolCall(context::afterToolCallContext, signal::abortSignal
  )::Union{agentToolResult, Nothing}
-  # eventSink("afterToolCall: $(context.toolCall.name) result=$(context.result) error=$(context.isError)")
+  # modify context.result if needed and return agentToolResult
  return nothing
 end
 #TODO
 function eventSink(x)
 end
 """
 Convert a userMessage to OpenAI message format.
 """
@@ -417,14 +432,15 @@ Convert an OrderedDict of agentTool definitions into OpenAI function tool format
 Returns an empty vector when `tools` is `nothing` or empty.
 # Examples
-```jldoctest
+```julia
 _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}}()
-
+  eventSink("_toolsToOpenAI 1")
  eventSink(string(typeof(tools)))
  if tools !== nothing
    for (_, tool) in tools
      push!(tools_array, Dict(
@@ -437,7 +453,7 @@ function _toolsToOpenAI(tools::Union{OrderedDict{String, agentTool}, Nothing}, e
      ))
    end
  end
-
+  eventSink("_toolsToOpenAI 2")
  return tools_array
 end
@@ -462,7 +478,7 @@ coercion, format validation, cross-field constraints).
 - `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"
@@ -517,7 +533,7 @@ 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
@@ -0,0 +1,113 @@
 using Revise, JSON, Dates, UUIDs, PrettyPrinting, LibPQ, Base64, DataFrames, DataStructures, HTTP, Base64,
      NATS, Base.Threads
 using YiemAgent, GeneralUtils, msghandler
 """ Debug 
 using JSON, NATS, msghandler
 using NATS
 conn = NATS.connect("nats.yiem.cc")
 sub = NATS.subscribe(conn, "sommanion.debug") do msg
  payload = NATS.payload(msg)
  @info "debug" payload
  open("./log/error.log", "a") do io
    println(io, payload)
  end
 end
 NATS.publish(conn, "sommanion.debug", "order-123")
 # ---------------------------- inject this code into codebase to debug --------------------------- #
 try
  batch = someFunction(x, y, z)
 catch e
  bt = catch_backtrace()
  err_msg = sprint() do io
      showerror(io, e, bt)
      println(io)
  end
  eventSink(err_msg)
 end
 """
 struct text2textInstructLLM
  natsConn::NATS.Connection
  topic::String
  senderID::String
  fileserver_url::String
 end
 function (t::text2textInstructLLM)(openai_msg::Dict{String, Any})
  payloads = [("msg", openai_msg, "dictionary")]  # List of tuples
  _, msg_envelope_json_str = msghandler.smartpack(
        t.topic, 
        payloads;
        sender_id=t.senderID,
        msg_purpose="text2text",  
        fileserver_url=t.fileserver_url)
  reply = NATS.request(t.natsConn, t.topic, msg_envelope_json_str, timeout=180)
  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]
  return llm_response
 end
 struct eventSink
  natsConn::NATS.Connection
  topic::String
  senderID::String
 end
 function (aes::eventSink)(msg::String)
  NATS.publish(aes.natsConn, aes.topic, msg)
 end
 config = JSON.parsefile("./appconfig.json")
 agent_conn  = NATS.connect(config["nats_server_info"]["url"])
 #WORKING load tools
 text2text_llm = text2textInstructLLM(agent_conn, 
                                    config["externalservice"]["servicesloadbalancer"]["nats"], 
                                    "sender", 
                                    config["externalservice"]["fileserver"]["url"])
 debugNats = eventSink(agent_conn, "sommanion.debug", "sender")
 agent = YiemAgent.yiemAgent(
  text2text_llm;
  eventSink=debugNats
 )
 msg = Dict(
        "role" => "user",
        "content" => [
          Dict("type" => "text", "text" => "What's the weather in Bangkok?"),
          # Dict(
          #   "type" => "image_url",
          #   "image_url" => Dict("url" => "data:mime_type;base64,image2_base64_string")
          # ),
        ]
      )
 push!(agent.inputChannel, msg)
Author	SHA1	Message	Date
ton	7789748f25	update	2026-08-24 15:16:28 +07:00
ton	9193260539	update	2026-08-24 13:33:20 +07:00
ton	5f284883b3	update	2026-08-21 22:39:51 +07:00
ton	d2592ab6d6	mcp works	2026-08-21 22:34:27 +07:00
ton	da21790263	update	2026-08-21 13:13:38 +07:00
ton	c59f6bfa61	update	2026-08-21 07:13:52 +07:00
ton	3c91222462	update	2026-08-21 07:10:49 +07:00
ton	f62b8f14e7	update	2026-08-20 18:45:39 +07:00
ton	7ffb720f86	update mcp definition example	2026-08-20 09:58:55 +07:00
ton	5829c82d05	update	2026-08-17 03:04:10 +07:00
ton	c7a98f1710	Merge pull request 'V0.8.0 process message debug' (#44 ) from v0.8.0-process_message_debug into v0.8.0 Reviewed-on: #44	2026-08-16 13:22:51 +00:00
ton	c7abf844ea	update	2026-08-16 20:14:54 +07:00
ton	00447e4dde	update	2026-08-16 18:25:26 +07:00
ton	25f8468696	text message process works	2026-08-16 17:24:29 +07:00
ton	a29a82b74c	update	2026-08-16 13:36:01 +07:00
ton	fd616409dd	update	2026-08-15 20:03:01 +07:00
ton	2543e6cbf1	static tool loading	2026-08-15 16:50:28 +07:00
ton	b8067c2d33	update	2026-08-13 18:14:46 +07:00
ton	510cf6126c	update	2026-08-13 05:56:08 +07:00
ton	90fb97a4e7	update	2026-08-12 23:47:10 +07:00
ton	77adeb3a6b	update	2026-08-12 20:10:51 +07:00
ton	4e592173a6	update	2026-08-12 14:37:11 +07:00
ton	0cacb5c94a	update	2026-08-12 04:35:34 +07:00
ton	6c96409969	update	2026-08-12 04:33:19 +07:00
ton	06d51c1ee9	update	2026-08-12 04:00:09 +07:00
ton	2ad3d1df38	update	2026-08-11 19:10:37 +07:00
ton	83c7770877	update	2026-08-11 18:57:53 +07:00
ton	bad14fbe7f	update	2026-08-11 18:42:34 +07:00
ton	578e8f55bd	update	2026-08-11 18:28:03 +07:00
ton	ae3e432b02	update	2026-08-11 17:35:56 +07:00
ton	7c14390400	update	2026-08-11 17:28:25 +07:00
ton	89885c1583	update	2026-08-11 16:43:48 +07:00
ton	5a27630ccf	update	2026-08-11 12:15:05 +07:00
ton	ed91260468	update	2026-08-10 20:37:28 +07:00
ton	c13aeb3a74	Merge pull request 'V0.8.0 verify tool use' (#43 ) from v0.8.0-verify_tool_use into v0.8.0 Reviewed-on: #43	2026-08-10 13:10:57 +00:00
ton	287704778f	update	2026-08-10 20:07:15 +07:00
ton	a9fa23f01b	update	2026-08-10 19:19:58 +07:00
ton	c5cb18f0f1	update	2026-08-10 16:10:04 +07:00
ton	c78f4b023d	update	2026-08-10 14:55:09 +07:00
ton	1b69f69c7d	update	2026-08-10 13:33:45 +07:00
ton	3891099eaa	update readme	2026-08-10 10:39:30 +07:00
ton	268d340e2f	Merge pull request 'V0.8.0 use tool module' (#42 ) from v0.8.0-use_tool_module into v0.8.0 Reviewed-on: #42	2026-08-10 02:48:25 +00:00
ton	189bc2efcf	update	2026-08-10 09:43:35 +07:00
ton	92b3e4081f	update	2026-08-09 22:50:02 +07:00
ton	750eff483b	update	2026-08-09 22:08:04 +07:00
ton	5cc35c78f4	Merge pull request 'V0.8.0 add tools' (#41 ) from v0.8.0-add_tools into v0.8.0 Reviewed-on: #41	2026-08-09 11:47:02 +00:00
ton	ed5415d92a	update	2026-08-09 08:45:35 +07:00
ton	6b3d575ea0	update	2026-08-09 06:50:37 +07:00
ton	da16c80a0a	update	2026-08-09 06:27:48 +07:00
ton	069240912b	update	2026-08-09 04:45:31 +07:00
ton	2aa0d1e9a4	update	2026-08-08 19:44:06 +07:00
ton	03e1dd7628	update	2026-08-08 18:49:51 +07:00
ton	70296a3bf2	update	2026-08-08 12:21:52 +07:00
ton	75b2ce5978	update	2026-08-08 12:11:53 +07:00
ton	d8172a7fbe	Merge pull request 'V0.8.0 async think loop' (#40 ) from v0.8.0-async_think_loop into v0.8.0 Reviewed-on: #40	2026-08-08 04:13:05 +00:00
ton	d29a413159	update	2026-08-08 11:09:00 +07:00
ton	0ddbcf9ca1	update	2026-08-08 09:38:05 +07:00
ton	04e75e61b8	update	2026-08-08 08:51:07 +07:00
ton	c1ef544734	update	2026-08-08 08:04:08 +07:00
ton	e60cbc67c4	update	2026-08-07 23:13:13 +07:00
ton	8e356f06bc	update	2026-08-07 17:48:50 +07:00
ton	131973edd4	update	2026-08-07 15:55:37 +07:00
ton	0f16e045b9	update	2026-08-07 15:54:36 +07:00
ton	02e93cae57	update	2026-08-07 15:47:27 +07:00
ton	4df1043032	update	2026-08-07 15:18:42 +07:00
ton	7b465baddd	update	2026-08-07 14:31:33 +07:00
ton	9e637f92bd	update	2026-08-07 14:07:40 +07:00
ton	197a7a0cb7	update	2026-08-07 13:59:13 +07:00
ton	14bac755ea	update	2026-08-07 13:28:09 +07:00
ton	2de4fa07c1	update	2026-08-07 10:19:55 +07:00
ton	b08121ee50	update	2026-08-07 09:37:27 +07:00
ton	d63ddd3d27	update	2026-08-07 09:24:41 +07:00
ton	0a6af36b34	update	2026-08-07 08:46:48 +07:00
ton	d0bacbb538	update	2026-08-06 19:39:42 +07:00
ton	51fca0aa8c	update	2026-08-06 19:30:59 +07:00
ton	ebd79aa686	update	2026-08-06 18:59:39 +07:00
ton	dde9b019dd	update	2026-08-06 18:44:28 +07:00
ton	e592441387	update	2026-08-06 18:40:09 +07:00
ton	8232946e91	update	2026-08-06 18:29:31 +07:00
ton	8a2da0f5c3	update	2026-08-06 15:32:56 +07:00
ton	4cb01c71bb	update	2026-08-05 19:33:40 +07:00
ton	ec28e0ff54	update	2026-08-05 19:09:56 +07:00
ton	afc866a3a5	update	2026-08-05 10:27:17 +07:00
ton	a3f9e39249	update	2026-08-05 04:21:44 +07:00
ton	ba13ccc4b9	update	2026-08-04 23:17:13 +07:00
ton	a90203dc0a	update	2026-08-04 23:04:44 +07:00
ton	2c0f25189b	update	2026-08-04 22:17:58 +07:00
ton	c6f97be63a	update	2026-08-04 22:03:56 +07:00
ton	af0fa30107	update	2026-08-04 21:50:58 +07:00
ton	6f971abe5d	update	2026-08-04 21:46:49 +07:00
ton	e622a522dc	update	2026-08-04 21:45:00 +07:00
ton	96ff9f0924	update	2026-08-04 21:42:42 +07:00
ton	2072ebe554	update	2026-08-04 19:45:08 +07:00
ton	52eaeab5fc	update	2026-08-04 14:51:53 +07:00
ton	937d52053f	update	2026-08-04 12:13:22 +07:00
ton	569f85333f	update	2026-08-03 18:03:51 +07:00
ton	92eee07168	update	2026-08-03 11:23:45 +07:00
ton	c626d2cec5	update	2026-08-03 07:27:06 +07:00
ton	1ab97b3972	add yiemAgent	2026-08-02 20:57:53 +07:00
ton	d2081333f6	update	2026-08-02 17:58:57 +07:00
ton	1cb9ca106b	update	2026-08-01 23:01:52 +07:00
ton	94dd44236d	update	2026-08-01 21:47:30 +07:00
ton	be42fc4738	update	2026-08-01 21:46:38 +07:00
ton	7c4e84ba93	update	2026-08-01 21:38:31 +07:00
ton	368307742e	update	2026-08-01 17:56:16 +07:00
ton	b7658af76b	up version	2026-07-28 00:14:01 +00:00
		`@@ -0,0 +1 @@`
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