Merge pull request 'V0.9.0 agent status' (#50 ) from v0.9.0-agent_status into v0.9.0

Reviewed-on: #50
update
2026-09-12 13:04:59 +00:00 · 2026-09-12 17:04:00 +07:00 · 2026-09-12 16:12:56 +07:00 · 2026-09-12 16:09:00 +07:00 · 2026-07-28 00:16:48 +00:00 · 2026-07-27 10:43:41 +00:00
33 changed files with 5916 additions and 5929 deletions
@@ -1,8 +1,20 @@
 # This file is machine-generated - editing it directly is not advised
-julia_version = "1.12.6"
+julia_version = "1.12.7"
 manifest_format = "2.0"
-project_hash = "1c1379a2cec320abc347f3acb5ee815ba9855aa6"
+project_hash = "43252cf86e6030e836ede74c563b58ab2f0f0548"
 [[deps.AWS]]
 deps = ["Base64", "Compat", "Dates", "Downloads", "GitHub", "HTTP", "IniFile", "JSON", "MD5", "Mocking", "OrderedCollections", "Random", "SHA", "ScopedValues", "Sockets", "URIs", "UUIDs", "XMLDict"]
 git-tree-sha1 = "842075b12a7f763c5f815a04037a99b6ac346df9"
 uuid = "fbe9abb3-538b-5e4e-ba9e-bc94f4f92ebc"
 version = "1.99.0"
 [[deps.AWSS3]]
 deps = ["AWS", "ArrowTypes", "Base64", "Compat", "Dates", "EzXML", "FilePathsBase", "HTTP", "MbedTLS", "Mocking", "OrderedCollections", "Retry", "SymDict", "URIs", "UUIDs", "XMLDict"]
 git-tree-sha1 = "af99cea71364c8c4483405a6b03697d197ab4d3b"
 uuid = "1c724243-ef5b-51ab-93f4-b0a88ac62a95"
 version = "0.11.6"
 [[deps.Accessors]]
 deps = ["CompositionsBase", "ConstructionBase", "Dates", "InverseFunctions", "MacroTools"]
@@ -44,6 +56,12 @@ git-tree-sha1 = "d57bd3762d308bded22c3b82d033bff85f6195c6"
 uuid = "ec485272-7323-5ecc-a04f-4719b315124d"
 version = "0.4.0"
 [[deps.Arrow]]
 deps = ["ArrowTypes", "BitIntegers", "CodecLz4", "CodecZstd", "ConcurrentUtilities", "DataAPI", "Dates", "EnumX", "Mmap", "PooledArrays", "SentinelArrays", "StringViews", "Tables", "TimeZones", "TranscodingStreams", "UUIDs"]
 git-tree-sha1 = "4a69a3eadc1f7da78d950d1ef270c3a62c1f7e01"
 uuid = "69666777-d1a9-59fb-9406-91d4454c9d45"
 version = "2.8.1"
 [[deps.ArrowTypes]]
 deps = ["Sockets", "UUIDs"]
 git-tree-sha1 = "404265cd8128a2515a81d5eae16de90fdef05101"
@@ -58,6 +76,12 @@ version = "1.11.0"
 uuid = "2a0f44e3-6c83-55bd-87e4-b1978d98bd5f"
 version = "1.11.0"
 [[deps.BitIntegers]]
 deps = ["Random"]
 git-tree-sha1 = "091d591a060e43df1dd35faab3ca284925c48e46"
 uuid = "c3b6d118-76ef-56ca-8cc7-ebb389d030a1"
 version = "0.3.7"
 [[deps.BufferedStreams]]
 git-tree-sha1 = "6863c5b7fc997eadcabdbaf6c5f201dc30032643"
 uuid = "e1450e63-4bb3-523b-b2a4-4ffa8c0fd77d"
@@ -74,9 +98,9 @@ version = "1.11.0"
 [[deps.CSV]]
 deps = ["CodecZlib", "Dates", "FilePathsBase", "InlineStrings", "Mmap", "Parsers", "PooledArrays", "PrecompileTools", "SentinelArrays", "Tables", "Unicode", "WeakRefStrings", "WorkerUtilities"]
-git-tree-sha1 = "8d8e0b0f350b8e1c91420b5e64e5de774c2f0f4d"
+git-tree-sha1 = "abed1e735dd4152f48c90cf0767e1790e25f332f"
 uuid = "336ed68f-0bac-5ca0-87d4-7b16caf5d00b"
-version = "0.10.16"
+version = "0.10.17"
 [[deps.CodeTracking]]
 deps = ["InteractiveUtils", "REPL", "UUIDs"]
@@ -90,16 +114,29 @@ git-tree-sha1 = "40956acdbef3d8c7cc38cba42b56034af8f8581a"
 uuid = "6c391c72-fb7b-5838-ba82-7cfb1bcfecbf"
 version = "0.3.4"
 [[deps.CodecLz4]]
 deps = ["Lz4_jll", "TranscodingStreams"]
 git-tree-sha1 = "d58afcd2833601636b48ee8cbeb2edcb086522c2"
 uuid = "5ba52731-8f18-5e0d-9241-30f10d1ec561"
 version = "0.4.6"
 [[deps.CodecZlib]]
 deps = ["TranscodingStreams", "Zlib_jll"]
-git-tree-sha1 = "962834c22b66e32aa10f7611c08c8ca4e20749a9"
+git-tree-sha1 = "970758a3d591a2a5c2a907c53f2e2f8c1b1d3537"
 uuid = "944b1d66-785c-5afd-91f1-9de20f533193"
-version = "0.7.8"
+version = "0.7.9"
 [[deps.CodecZstd]]
 deps = ["TranscodingStreams", "Zstd_jll"]
 git-tree-sha1 = "da54a6cd93c54950c15adf1d336cfd7d71f51a56"
 uuid = "6b39b394-51ab-5f42-8807-6242bab2b4c2"
 version = "0.8.7"
 [[deps.CommonSolve]]
-git-tree-sha1 = "eeaad7cef88554c2fa56b5a3f71cfd5cb708c662"
+deps = ["PrecompileTools"]
 git-tree-sha1 = "6c389fa857f6ca5a95474b52a52023fd77f24cb7"
 uuid = "38540f10-b2f7-11e9-35d8-d573e4eb0ff2"
-version = "0.2.11"
+version = "0.2.14"
 [[deps.Compat]]
 deps = ["TOML", "UUIDs"]
@@ -119,7 +156,7 @@ version = "0.1.1"
 [[deps.CompilerSupportLibraries_jll]]
 deps = ["Artifacts", "Libdl"]
 uuid = "e66e0078-7015-5450-92f7-15fbd957f2ae"
-version = "1.3.0+1"
+version = "1.3.1+2"
 [[deps.CompositionsBase]]
 git-tree-sha1 = "802bb88cd69dfd1509f6670416bd4434015693ad"
@@ -130,6 +167,12 @@ weakdeps = ["InverseFunctions"]
    [deps.CompositionsBase.extensions]
    CompositionsBaseInverseFunctionsExt = "InverseFunctions"
 [[deps.ConcurrentUtilities]]
 deps = ["Serialization", "Sockets"]
 git-tree-sha1 = "3c9be947934c38475bafe822c6d61aaed17f0738"
 uuid = "f0e56b4a-5159-44fe-b623-3e5288b988bb"
 version = "2.6.0"
 [[deps.ConstructionBase]]
 git-tree-sha1 = "b4b092499347b18a015186eae3042f72267106cb"
 uuid = "187b0558-2788-49d3-abe0-74a17ed4e7c9"
@@ -146,14 +189,14 @@ version = "1.6.0"
    StaticArrays = "90137ffa-7385-5640-81b9-e52037218182"
 [[deps.Crayons]]
-git-tree-sha1 = "249fe38abf76d48563e2f4556bebd215aa317e15"
+git-tree-sha1 = "54b76cbb40d9a0f5368c880725b2f141da77c94f"
 uuid = "a8cc5b0e-0ffa-5ad4-8c14-923d3ee1735f"
-version = "4.1.1"
+version = "4.2.0"
 [[deps.DBInterface]]
-git-tree-sha1 = "a444404b3f94deaa43ca2a58e18153a82695282b"
+git-tree-sha1 = "265d7f87f329c063cbde41920f9d00713a43cedd"
 uuid = "a10d1c49-ce27-4219-8d33-6db1a4562965"
-version = "2.6.1"
+version = "2.7.0"
 [[deps.DataAPI]]
 git-tree-sha1 = "abe83f3a2f1b857aac70ef8b269080af17764bbe"
@@ -168,9 +211,9 @@ version = "1.8.2"
 [[deps.DataStructures]]
 deps = ["OrderedCollections"]
-git-tree-sha1 = "6fb53a69613a0b2b68a0d12671717d307ab8b24e"
+git-tree-sha1 = "b0bc6d2cad1fed8b7fd59a1551a991cb3d2809e6"
 uuid = "864edb3b-99cc-5e75-8d2d-829cb0a9cfe8"
-version = "0.19.5"
+version = "0.19.6"
 [[deps.DataValueInterfaces]]
 git-tree-sha1 = "bfc1187b79289637fa0ef6d4436ebdfe6905cbd6"
@@ -208,9 +251,9 @@ version = "1.11.0"
 [[deps.Distributions]]
 deps = ["AliasTables", "FillArrays", "LinearAlgebra", "PDMats", "Printf", "QuadGK", "Random", "Roots", "SpecialFunctions", "Statistics", "StatsAPI", "StatsBase", "StatsFuns"]
-git-tree-sha1 = "cd3c5ac74cd3923c8945c6a81518c46abd0e73a3"
+git-tree-sha1 = "a958ab3a40c755563f5e1405c0846cb0446bf19d"
 uuid = "31c24e10-a181-5473-b8eb-7969acd0382f"
-version = "0.25.129"
+version = "0.25.131"
    [deps.Distributions.extensions]
    DistributionsChainRulesCoreExt = "ChainRulesCore"
@@ -240,9 +283,9 @@ uuid = "4e289a0a-7415-4d19-859d-a7e5c4648b56"
 version = "1.0.7"
 [[deps.ExprTools]]
-git-tree-sha1 = "27415f162e6028e81c72b82ef756bf321213b6ec"
+git-tree-sha1 = "d2e49e7efd29719d6f28b891b0e0e159daa9d2b4"
 uuid = "e2ba6199-217a-4e67-a87a-7c52f15ade04"
-version = "0.1.10"
+version = "0.1.11"
 [[deps.EzXML]]
 deps = ["Printf", "XML2_jll"]
@@ -250,16 +293,6 @@ git-tree-sha1 = "7ea1aa5869e2626ccae84480e4f37185bc6f41d3"
 uuid = "8f5d6c58-4d21-5cfd-889c-e3ad7ee6a615"
 version = "1.2.3"
 [[deps.FileIO]]
 deps = ["Pkg", "Requires", "UUIDs"]
 git-tree-sha1 = "6621fef488e496356c9c9625d0562c12a6070819"
 uuid = "5789e2e9-d7fb-5bc7-8068-2c6fae9b9549"
 version = "1.20.0"
 weakdeps = ["HTTP"]
    [deps.FileIO.extensions]
    HTTPExt = "HTTP"
 [[deps.FilePathsBase]]
 deps = ["Compat", "Dates"]
 git-tree-sha1 = "3bab2c5aa25e7840a4b065805c0cdfc01f3068d2"
@@ -277,9 +310,9 @@ version = "1.11.0"
 [[deps.FillArrays]]
 deps = ["LinearAlgebra"]
-git-tree-sha1 = "2f979084d1e13948a3352cf64a25df6bd3b4dca3"
+git-tree-sha1 = "5bad39456d9f0166184fce2248783dd9862645c1"
 uuid = "1a297f60-69ca-5386-bcde-b61e274b549b"
-version = "1.16.0"
+version = "1.17.0"
 weakdeps = ["PDMats", "SparseArrays", "StaticArrays", "Statistics"]
    [deps.FillArrays.extensions]
@@ -294,17 +327,24 @@ uuid = "9fa8497b-333b-5362-9e8d-4d0656e87820"
 version = "1.11.0"
 [[deps.Gamma]]
-git-tree-sha1 = "86f86b6168a016ed88e4ae4e64577b98c3b59e8e"
+deps = ["LogExpFunctions"]
 git-tree-sha1 = "becc397f7cfb06e343496ae6ffb04818a851da51"
 uuid = "a0844989-3bd2-4988-8bea-c9407ab0941b"
-version = "1.1.0"
+version = "1.2.0"
 [[deps.GeneralUtils]]
-deps = ["CSV", "DataFrames", "DataStructures", "Dates", "Distributions", "Graphs", "HTTP", "JSON", "LibPQ", "NATS", "PrettyPrinting", "Random", "Revise", "SHA", "StringDistances", "UUIDs"]
+deps = ["AWS", "AWSS3", "CSV", "DataFrames", "DataStructures", "Dates", "Distributions", "Graphs", "HTTP", "JSON", "LibPQ", "NATS", "OrderedCollections", "PrettyPrinting", "Random", "Revise", "SHA", "StringDistances", "URIs", "UUIDs"]
-git-tree-sha1 = "93293126d24d3929ef6a5067f347bc28c6582c71"
+git-tree-sha1 = "4d7ff3b02a9cbd6d4f65fea43551b0bca6a83a22"
 repo-rev = "main"
 repo-url = "https://git.yiem.cc/ton/GeneralUtils"
 uuid = "c6c72f09-b708-4ac8-ac7c-2084d70108fe"
-version = "0.5.10"
+version = "0.6.9"
 [[deps.GitHub]]
 deps = ["Base64", "Dates", "HTTP", "JSON", "MbedTLS", "Sockets", "SodiumSeal", "URIs"]
 git-tree-sha1 = "33de1bc5665494f0a9669fd6027e28180709d874"
 uuid = "bc5e4493-9b4d-5f90-b8aa-2b2bcaad7a26"
 version = "5.13.0"
 [[deps.Graphs]]
 deps = ["ArnoldiMethod", "DataStructures", "Inflate", "LinearAlgebra", "Random", "SimpleTraits", "SparseArrays", "Statistics"]
@@ -321,9 +361,9 @@ version = "1.14.0"
 [[deps.HTTP]]
 deps = ["Base64", "CodecZlib", "Dates", "EnumX", "PrecompileTools", "Random", "Reseau", "SHA", "URIs", "UUIDs", "Zlib_jll"]
-git-tree-sha1 = "c2c808326222b6dc4bec295a83b55f79aeec98e0"
+git-tree-sha1 = "49a90cd540681d5b79bf5c8ca13c92677e37005e"
 uuid = "cd3eb016-35fb-5094-929b-558a96fad6f3"
-version = "2.5.5"
+version = "2.6.6"
 [[deps.HashArrayMappedTries]]
 git-tree-sha1 = "2eaa69a7cab70a52b9687c8bf950a5a93ec895ae"
@@ -332,9 +372,9 @@ version = "0.2.0"
 [[deps.HypergeometricFunctions]]
 deps = ["Gamma", "LinearAlgebra"]
-git-tree-sha1 = "18d7deab5fb0440dc6a7b6993c5c27b25420de10"
+git-tree-sha1 = "31bb6c92405c084617facc1d7ed9eb6c402d061e"
 uuid = "34004b35-14d8-5ef3-9330-4cdb6864b03a"
-version = "0.3.29"
+version = "0.3.30"
 [[deps.ICU_jll]]
 deps = ["Artifacts", "JLLWrappers", "Libdl"]
@@ -353,6 +393,11 @@ 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"
@@ -412,9 +457,9 @@ version = "1.8.0"
 [[deps.JSON]]
 deps = ["Dates", "Logging", "Parsers", "PrecompileTools", "StructUtils", "UUIDs", "Unicode"]
-git-tree-sha1 = "c89d196f5ffb64bfbf80985b699ea913b0d2c211"
+git-tree-sha1 = "c7345ab1a7ca4dc8a02c9f6510da0d9857bbe513"
 uuid = "682c06a0-de6a-54ab-a142-c8b1cf79cde6"
-version = "1.6.1"
+version = "1.7.1"
 weakdeps = ["ArrowTypes"]
    [deps.JSON.extensions]
@@ -432,9 +477,9 @@ weakdeps = ["ArrowTypes"]
 [[deps.JuliaInterpreter]]
 deps = ["CodeTracking", "InteractiveUtils", "Random", "UUIDs"]
-git-tree-sha1 = "58927c485919bf17ea308d9d82156de1adf4b006"
+git-tree-sha1 = "c3d401f110454b4ea24a76be33f6ee0d7d385103"
 uuid = "aa1ae85d-cabe-5617-a682-6adf51b2e16a"
-version = "0.10.12"
+version = "0.11.4"
 [[deps.JuliaSyntaxHighlighting]]
 deps = ["StyledStrings"]
@@ -447,18 +492,10 @@ 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 = "dda21b8cbd6a6c40d9d02a73230f9d70fed6918c"
+git-tree-sha1 = "f88f3ccef05a6a72a0cf0ed417c8fd68530f4ab2"
 uuid = "b964fa9f-0449-5b57-a5c2-d3ea65f4040f"
-version = "1.4.0"
+version = "1.4.1"
 [[deps.LayerDicts]]
 git-tree-sha1 = "6087ad3521d6278ebe5c27ae55e7bbb15ca312cb"
@@ -539,9 +576,21 @@ version = "1.11.0"
 [[deps.LoweredCodeUtils]]
 deps = ["CodeTracking", "Compiler", "JuliaInterpreter"]
-git-tree-sha1 = "3733419e9a71156b389f3e331672d2e95436783f"
+git-tree-sha1 = "1d4c737ab26f51ceed52ab2019c09b7660eb7440"
 uuid = "6f1432cf-f94c-5a45-995e-cdbf5db27b0b"
-version = "3.6.2"
+version = "3.8.0"
 [[deps.Lz4_jll]]
 deps = ["Artifacts", "JLLWrappers", "Libdl"]
 git-tree-sha1 = "191686b1ac1ea9c89fc52e996ad15d1d241d1e33"
 uuid = "5ced341a-0733-55b8-9ab6-a4889d929147"
 version = "1.10.1+0"
 [[deps.MD5]]
 deps = ["Random", "SHA"]
 git-tree-sha1 = "1576f756617d31eb397a4a517b68562fd28dc2b4"
 uuid = "6ac74813-4b46-53a4-afec-0b5dc9d7885c"
 version = "0.2.3"
 [[deps.MacroTools]]
 git-tree-sha1 = "1e0228a030642014fe5cfe68c2c0a818f9e3f522"
@@ -599,9 +648,9 @@ version = "0.1.1"
 [[deps.NanoDates]]
 deps = ["Dates", "Parsers"]
-git-tree-sha1 = "850a0557ae5934f6e67ac0dc5ca13d0328422d1f"
+git-tree-sha1 = "77c7e98ca39aefb481f9b97a2f4f5c5471c08a1d"
 uuid = "46f1a544-deae-4307-8689-c12aa3c955c6"
-version = "1.0.3"
+version = "1.1.0"
 [[deps.NetworkOptions]]
 uuid = "ca575930-c2e3-43a9-ace4-1e988b2c1908"
@@ -631,7 +680,7 @@ version = "0.8.7+0"
 [[deps.OpenSSL_jll]]
 deps = ["Artifacts", "Libdl"]
 uuid = "458c3c95-2e84-50aa-8efc-19380b2a3a95"
-version = "3.5.4+0"
+version = "3.5.6+0"
 [[deps.OpenSpecFun_jll]]
 deps = ["Artifacts", "CompilerSupportLibraries_jll", "JLLWrappers", "Libdl"]
@@ -640,15 +689,15 @@ uuid = "efe28fd5-8261-553b-a9e1-b2916fc3738e"
 version = "0.5.6+0"
 [[deps.OrderedCollections]]
-git-tree-sha1 = "94ba93778373a53bfd5a0caaf7d809c445292ff4"
+git-tree-sha1 = "05f45c2e0de6259db764adbfd2f1dc6d3f8de13c"
 uuid = "bac558e1-5e72-5ebc-8fee-abe8a469f55d"
-version = "1.8.2"
+version = "2.0.1"
 [[deps.PDMats]]
 deps = ["LinearAlgebra", "SparseArrays", "SuiteSparse"]
-git-tree-sha1 = "26766d4b5f1a410c218a19b85a672c6edb693c65"
+git-tree-sha1 = "123266c25174ef6c8d4718920abc206452cf8de6"
 uuid = "90014a1f-27ba-587c-ab20-58faa44d9150"
-version = "0.11.40"
+version = "0.11.41"
 weakdeps = ["StatsBase"]
    [deps.PDMats.extensions]
@@ -656,18 +705,9 @@ weakdeps = ["StatsBase"]
 [[deps.Parsers]]
 deps = ["Dates", "PrecompileTools", "UUIDs"]
-git-tree-sha1 = "32a4e09c5f29402573d673901778a0e03b0807b9"
+git-tree-sha1 = "3de8f5e6e90ebfa8d6d1f86997d6cdcd6a912ff3"
 uuid = "69de0a69-1ddd-5017-9359-2bf0b02dc9f0"
-version = "2.8.6"
+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"
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 weakdeps = ["REPL"]
    [deps.Pkg.extensions]
    REPLExt = "REPL"
 [[deps.PooledArrays]]
 deps = ["DataAPI", "Future"]
@@ -688,15 +728,15 @@ uuid = "21216c6a-2e73-6563-6e65-726566657250"
 version = "1.5.2"
 [[deps.PrettyPrinting]]
-git-tree-sha1 = "142ee93724a9c5d04d78df7006670a93ed1b244e"
+git-tree-sha1 = "0b7f4ad437e31c51cf5b91fb103579b04025170a"
 uuid = "54e16d92-306c-5ea0-a30b-337be88ac337"
-version = "0.4.2"
+version = "0.4.3"
 [[deps.PrettyTables]]
 deps = ["Crayons", "LaTeXStrings", "Markdown", "PrecompileTools", "Printf", "REPL", "Reexport", "StringManipulation", "Tables"]
-git-tree-sha1 = "ebf455bb866ee6737030e3d3816bb6a0683c4325"
+git-tree-sha1 = "1b8aa19f229b1cea7fc93874a52e49db6a854450"
 uuid = "08abe8d2-0d0c-5749-adfa-8a2ac140af0d"
-version = "3.4.0"
+version = "3.4.8"
    [deps.PrettyTables.extensions]
    PrettyTablesExcelExt = "XLSX"
@@ -757,15 +797,20 @@ version = "1.3.1"
 [[deps.Reseau]]
 deps = ["NetworkOptions", "OpenSSL_jll", "PrecompileTools", "Random", "SHA"]
-git-tree-sha1 = "0eab6d95ed40c2ef3992255c1c71e4f9748932b5"
+git-tree-sha1 = "aaee74fab545e9af4b21d10c2203c93c636c4ae0"
 uuid = "802f3686-a58f-41ce-bb0c-3c43c75bba36"
-version = "1.3.1"
+version = "1.4.1"
 [[deps.Retry]]
 git-tree-sha1 = "41ac127cd281bb33e42aba46a9d3b25cd35fc6d5"
 uuid = "20febd7b-183b-5ae2-ac4a-720e7ce64774"
 version = "0.4.1"
 [[deps.Revise]]
-deps = ["CRC32c", "CodeTracking", "FileWatching", "InteractiveUtils", "JuliaInterpreter", "LibGit2", "LoweredCodeUtils", "OrderedCollections", "Preferences", "REPL", "UUIDs"]
+deps = ["CRC32c", "CodeTracking", "FileWatching", "JuliaInterpreter", "LibGit2", "LoweredCodeUtils", "OrderedCollections", "Preferences", "REPL", "UUIDs"]
-git-tree-sha1 = "27e3ee13fc8739a59b380d6163d6a82f52c03bd7"
+git-tree-sha1 = "ab0f5630e37968bc49b8c4cedd3279a7d30b9486"
 uuid = "295af30f-e4ad-537b-8983-00126c2a3abe"
-version = "3.15.1"
+version = "3.17.0"
 weakdeps = ["Distributed"]
    [deps.Revise.extensions]
@@ -779,15 +824,15 @@ version = "0.9.0"
 [[deps.Rmath_jll]]
 deps = ["Artifacts", "JLLWrappers", "Libdl"]
-git-tree-sha1 = "58cdd8fb2201a6267e1db87ff148dd6c1dbd8ad8"
+git-tree-sha1 = "6d40b2fe70437b01397d2a4d5b020008da4e7019"
 uuid = "f50d1b31-88e8-58de-be2c-1cc44531875f"
-version = "0.5.1+0"
+version = "0.5.2+0"
 [[deps.Roots]]
 deps = ["Accessors", "CommonSolve", "Printf"]
-git-tree-sha1 = "a7caaf7ba8cf307112ca443784d1b56b4a591455"
+git-tree-sha1 = "13a9e0164267bb9ebc55c1c5d72fceea8f09555b"
 uuid = "f2b01f46-fcfa-551c-844a-d8ac1e96c665"
-version = "3.0.5"
+version = "3.0.7"
    [deps.Roots.extensions]
    RootsChainRulesCoreExt = "ChainRulesCore"
@@ -809,14 +854,6 @@ version = "3.0.5"
 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"
@@ -840,12 +877,6 @@ git-tree-sha1 = "084c47c7c5ce5cfecefa0a98dff69eb3646b5a80"
 uuid = "91c51154-3ec4-41a3-a24f-3f23e20d615c"
 version = "1.4.10"
 [[deps.Serde]]
 deps = ["CSV", "Dates", "EzXML", "JSON", "TOML", "UUIDs", "YAML"]
 git-tree-sha1 = "f397fc8779cc53e4677c2708f3802c6996f28d00"
 uuid = "db9b398d-9517-45f8-9a95-92af99003e0e"
 version = "3.7.2"
 [[deps.Serialization]]
 uuid = "9e88b42a-f829-5b0c-bbe9-9e923198166b"
 version = "1.11.0"
@@ -866,6 +897,12 @@ 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"
@@ -879,9 +916,9 @@ version = "1.12.0"
 [[deps.SpecialFunctions]]
 deps = ["IrrationalConstants", "LogExpFunctions", "OpenLibm_jll", "OpenSpecFun_jll"]
-git-tree-sha1 = "6547cbdd8ce32efba0d21c5a40fa96d1a3548f9f"
+git-tree-sha1 = "429071b23f4c9a13fb6582f807cc2ef454082408"
 uuid = "276daf66-3868-5448-9aa4-cd146d93841b"
-version = "2.8.0"
+version = "2.9.0"
    [deps.SpecialFunctions.extensions]
    SpecialFunctionsChainRulesCoreExt = "ChainRulesCore"
@@ -891,9 +928,9 @@ version = "2.8.0"
 [[deps.StaticArrays]]
 deps = ["LinearAlgebra", "PrecompileTools", "Random", "StaticArraysCore"]
-git-tree-sha1 = "246a8bb2e6667f832eea063c3a56aef96429a3db"
+git-tree-sha1 = "fac51faf3bb96e8bc0bf6f9f39ca4955652776bb"
 uuid = "90137ffa-7385-5640-81b9-e52037218182"
-version = "1.9.18"
+version = "1.9.19"
    [deps.StaticArrays.extensions]
    StaticArraysChainRulesCoreExt = "ChainRulesCore"
@@ -910,9 +947,9 @@ version = "1.4.4"
 [[deps.Statistics]]
 deps = ["LinearAlgebra"]
-git-tree-sha1 = "ae3bb1eb3bba077cd276bc5cfc337cc65c3075c0"
+git-tree-sha1 = "389592b8592e5fb48f498ff60dc6acb4a0e62953"
 uuid = "10745b16-79ce-11e8-11f9-7d13ad32a3b2"
-version = "1.11.1"
+version = "1.11.4"
 weakdeps = ["SparseArrays"]
    [deps.Statistics.extensions]
@@ -926,15 +963,15 @@ version = "1.8.0"
 [[deps.StatsBase]]
 deps = ["AliasTables", "DataAPI", "DataStructures", "IrrationalConstants", "LinearAlgebra", "LogExpFunctions", "Missings", "Printf", "Random", "SortingAlgorithms", "SparseArrays", "Statistics", "StatsAPI"]
-git-tree-sha1 = "e4d7a1a0edc20af42689ea6f4f3587a2175d50ee"
+git-tree-sha1 = "adb9da019510162e67a4493fc235c23203d8b09e"
 uuid = "2913bbd2-ae8a-5f71-8c99-4fb6c76f3a91"
-version = "0.34.12"
+version = "0.34.13"
 [[deps.StatsFuns]]
 deps = ["HypergeometricFunctions", "IrrationalConstants", "LogExpFunctions", "Reexport", "Rmath", "SpecialFunctions"]
-git-tree-sha1 = "770240df9a3b8888065046948f7a09b4e0f997d5"
+git-tree-sha1 = "91a5737baed20ee31f3faea0e51f57461f6a689e"
 uuid = "4c63d2b9-4356-54db-8cca-17b64c39e42c"
-version = "2.2.0"
+version = "2.2.1"
    [deps.StatsFuns.extensions]
    StatsFunsChainRulesCoreExt = "ChainRulesCore"
@@ -946,21 +983,20 @@ version = "2.2.0"
 [[deps.StringDistances]]
 deps = ["Distances", "StatsAPI"]
-git-tree-sha1 = "cd83a04baf746e3b43b83c61b7de77ab0409b80a"
+git-tree-sha1 = "456f343c7a6c8f52d8b501c0da2a05538ab1b907"
 uuid = "88034a9c-02f8-509d-84a9-84ec65e18404"
-version = "1.0.0"
+version = "1.0.1"
 [[deps.StringEncodings]]
 deps = ["Libiconv_jll"]
 git-tree-sha1 = "b765e46ba27ecf6b44faf70df40c57aa3a547dcb"
 uuid = "69024149-9ee7-55f6-a4c4-859efe599b68"
 version = "0.3.7"
 [[deps.StringManipulation]]
 deps = ["PrecompileTools"]
-git-tree-sha1 = "d05693d339e37d6ab134c5ab53c29fce5ee5d7d5"
+git-tree-sha1 = "773065c6e0e903924a9d838259be74338422aef2"
 uuid = "892a3eda-7b42-436c-8928-eab12a02cf0e"
-version = "0.4.4"
+version = "0.5.0"
 [[deps.StringViews]]
 git-tree-sha1 = "f2dcb92855b31ad92fe8f079d4f75ac57c93e4b8"
 uuid = "354b36f9-a18e-4713-926e-db85100087ba"
 version = "1.3.7"
 [[deps.StructTypes]]
 deps = ["Dates", "UUIDs"]
@@ -970,9 +1006,9 @@ version = "1.11.0"
 [[deps.StructUtils]]
 deps = ["Dates", "UUIDs"]
-git-tree-sha1 = "82bee338d650aa515f31866c460cb7e3bcef90b8"
+git-tree-sha1 = "2d0fc55c61321ba245c47be599570d11bac50303"
 uuid = "ec057cc2-7a8d-4b58-b3b3-92acb9f63b42"
-version = "2.8.2"
+version = "2.8.5"
    [deps.StructUtils.extensions]
    StructUtilsMeasurementsExt = ["Measurements"]
@@ -997,6 +1033,12 @@ 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"
@@ -1004,9 +1046,9 @@ version = "1.0.3"
 [[deps.TZJData]]
 deps = ["Artifacts"]
-git-tree-sha1 = "72df96b3a595b7aab1e101eb07d2a435963a97e2"
+git-tree-sha1 = "d3b30a9f29a898e21fb4bdf280101b7a12e1f39c"
 uuid = "dc5dba14-91b3-4cab-a142-028a31da12f7"
-version = "1.5.0+2025b"
+version = "1.5.1+2025b"
 [[deps.TableTraits]]
 deps = ["IteratorInterfaceExtensions"]
@@ -1016,14 +1058,9 @@ version = "1.0.1"
 [[deps.Tables]]
 deps = ["DataAPI", "DataValueInterfaces", "IteratorInterfaceExtensions", "OrderedCollections", "TableTraits"]
-git-tree-sha1 = "0f38a06c83f0007bbab3cf911262841c9a0f07e0"
+git-tree-sha1 = "a94d9bdda1b7bed0046cea645639ab3f62196fac"
 uuid = "bd369af6-aec1-5ad0-b16a-f7cc5008161c"
-version = "1.13.0"
+version = "1.14.0"
 [[deps.Tar]]
 deps = ["ArgTools", "SHA"]
 uuid = "a4e569a6-e804-4fa4-b0f3-eef7a1d5b13e"
 version = "1.10.0"
 [[deps.Test]]
 deps = ["InteractiveUtils", "Logging", "Random", "Serialization"]
@@ -1046,9 +1083,9 @@ uuid = "3bb67fe8-82b1-5028-8e26-92a6c54297fa"
 version = "0.11.3"
 [[deps.URIs]]
-git-tree-sha1 = "bef26fb046d031353ef97a82e3fdb6afe7f21b1a"
+git-tree-sha1 = "908fec9df6c5de98548ead82a468c95ccf6cd263"
 uuid = "5c2747f8-b7ea-4ff2-ba2e-563bfd36b1d4"
-version = "1.6.1"
+version = "1.7.0"
 [[deps.UTCDateTimes]]
 deps = ["Dates", "TimeZones"]
@@ -1082,17 +1119,17 @@ git-tree-sha1 = "3f3315d89fc954a28f5b471bce698ed6e27481be"
 uuid = "02c8fc9c-b97f-50b9-bbe4-9be30ff0a78a"
 version = "2.15.3+0"
-[[deps.YAML]]
+[[deps.XMLDict]]
-deps = ["Base64", "Dates", "Printf", "StringEncodings"]
+deps = ["EzXML", "IterTools", "OrderedCollections"]
-git-tree-sha1 = "a1c0c7585346251353cddede21f180b96388c403"
+git-tree-sha1 = "8e8cdc9c93e1f442eba471b56db6f86ffeb1a33a"
-uuid = "ddb6d928-2868-570f-bddf-ab3f9cf99eb6"
+uuid = "228000da-037f-5747-90a9-8195ccbf91a5"
-version = "0.4.16"
+version = "0.4.2"
 [[deps.YiemAgent]]
-deps = ["Base64", "CSV", "DataFrames", "DataStructures", "Dates", "HTTP", "JSON", "LLMMCTS", "LibPQ", "NATS", "PrettyPrinting", "Random", "Revise", "SQLLLM", "Serde", "Serialization", "URIs", "UUIDs"]
+deps = ["Base64", "CSV", "DataFrames", "DataStructures", "Dates", "HTTP", "JSON", "LibPQ", "NATS", "PrettyPrinting", "Random", "Revise", "Serialization", "URIs", "UUIDs", "msghandler"]
 path = "."
 uuid = "e012c34b-7f78-48e0-971c-7abb83b6f0a2"
-version = "0.7.4"
+version = "0.8.0"
 [[deps.Zlib_jll]]
 deps = ["Libdl"]
@@ -1116,6 +1153,14 @@ git-tree-sha1 = "011b0a7331b41c25524b64dc42afc9683ee89026"
 uuid = "a9144af2-ca23-56d9-984f-0d03f7b5ccf8"
 version = "1.0.21+0"
 [[deps.msghandler]]
 deps = ["Arrow", "Base64", "DataFrames", "Dates", "GeneralUtils", "HTTP", "JSON", "NATS", "PrettyPrinting", "UUIDs"]
 git-tree-sha1 = "e7889a120327db84cdf1b1acf4a2c7caba6e8ef7"
 repo-rev = "main"
 repo-url = "https://git.yiem.cc/ton/msghandler"
 uuid = "f2724d33-f338-4a57-b9f8-1be882570d10"
 version = "1.4.0"
 [[deps.nghttp2_jll]]
 deps = ["Artifacts", "Libdl"]
 uuid = "8e850ede-7688-5339-a07c-302acd2aaf8d"
@@ -1,6 +1,6 @@
 name = "YiemAgent"
 uuid = "e012c34b-7f78-48e0-971c-7abb83b6f0a2"
-version = "0.8.0"
+version = "0.9.0"
 authors = ["narawat lamaiin <narawat@outlook.com>"]
 [deps]
@@ -12,26 +12,22 @@ Dates = "ade2ca70-3891-5945-98fb-dc099432e06a"
 GeneralUtils = "c6c72f09-b708-4ac8-ac7c-2084d70108fe"
 HTTP = "cd3eb016-35fb-5094-929b-558a96fad6f3"
 JSON = "682c06a0-de6a-54ab-a142-c8b1cf79cde6"
 LLMMCTS = "d76c5a4d-449e-4835-8cc4-dd86ec44f241"
 LibPQ = "194296ae-ab2e-5f79-8cd4-7183a0a5a0d1"
 NATS = "55e73f9c-eeeb-467f-b4cc-a633fde63d2a"
 PrettyPrinting = "54e16d92-306c-5ea0-a30b-337be88ac337"
 Random = "9a3f8284-a2c9-5f02-9a11-845980a1fd5c"
 Revise = "295af30f-e4ad-537b-8983-00126c2a3abe"
 SQLLLM = "2ebc79c7-cc10-4a3a-9665-d2e1d61e63d3"
 Serde = "db9b398d-9517-45f8-9a95-92af99003e0e"
 Serialization = "9e88b42a-f829-5b0c-bbe9-9e923198166b"
 URIs = "5c2747f8-b7ea-4ff2-ba2e-563bfd36b1d4"
 UUIDs = "cf7118a7-6976-5b1a-9a39-7adc72f591a4"
 msghandler = "f2724d33-f338-4a57-b9f8-1be882570d10"
 [compat]
 Base64 = "1.11.0"
 CSV = "0.10.15"
 DataFrames = "1.7.0"
-GeneralUtils = "0.5.10"
+GeneralUtils = "0.6.9"
 HTTP = "2.4.0"
 JSON = "1.6.1"
 LLMMCTS = "0.1.5"
 NATS = "0.1.0"
-SQLLLM = "0.2.8"
+msghandler = "1.4.0"
 Serde = "3.7.2"
@@ -1,23 +1,311 @@
-# YiemAgent
+# YiemAgent
-
+
-## TODO
+Julia framework for building agents with tool use and MCP (Model Context Protocol) support.
- [WORKING] build prompt()
+
- [ ] build agent runLoop()
+## Quick Start
- [ ] build MCP server connector
+
- [ ] executeplan() to execute the plan
+```julia
- [ ] add comprehensive tests
+using YiemAgent
-
+
-## Changelog
+# 1. Create an LLM callable (see "Custom LLM Callable" below)
-
+llmCall = ...
-### Version 0.8.0
+
- Converted snake_case fields to camelCase:
+# 2. Create a callable MCP server (see "MCP Server Integration" below)
-  - `llmModel`: `base_url` → `baseUrl`, `context_window` → `contextWindow`, `max_tokens` → `maxTokens`
+mcpServer = ...
- Converted PascalCase type references to camelCase:
+
-  - `AgentState` → `agentState`
+# 3. Create the agent
-  - `AgentTool` → `agentTool`
+agent = yiemAgent(llmCall; mcpServer=mcpServer, eventSink=println)
-  - `AgentMessage` → `agentMessage`
+
-  - `PendingMessageQueue` → `pendingMessageQueue`
+# 4. Interact
-  - `ActiveRun` → `activeRun`
+promptAgent(agent, "What tools do you have?")
-  - `StreamFn` → `streamFn`
+response = takeResponse(agent)
-  - `ThinkingLevel` → `thinkingLevel`
+
-  - `ToolExecutionMode` → `toolExecutionMode`
+# 5. Stop when done
 stopAgent(agent)
 ```
 ## Agent API
 | Function | Description |
 |----------|-------------|
 | `promptAgent(agent, text)` | Send plain text to the agent's input channel |
 | `promptAgent(agent, msg)` | Send a structured message (OpenAI format dict) to the input channel |
 | `takeResponse(agent)` | Block and take the agent's response from output channel |
 | `followUp(agent, text)` | Send plain text follow-up while agent is still processing |
 | `followUp(agent, msg)` | Send a structured follow-up (OpenAI format dict) while agent is processing |
 | `stopAgent(agent)` | Gracefully stop the agent and close channels |
 ## Creating a yiemAgent
 ```julia
 yiemAgent(llmCall;
    systemPrompt::String = "You are a helpful assistant.",
    model = nothing,
    messages::Vector{agentMessage} = agentMessage[],
    prepareContext::Function = prepareContext,
    formatMsgForLLM::Function = formatMsgForLLM,
    beforeToolCall::Function = beforeToolCall,
    afterToolCall::Function = afterToolCall,
    sessionId::Union{String, Nothing} = nothing,
    maxRetryDelayMs::Union{Int64, Nothing} = nothing,
    parallelToolExecute::Bool = false,
    eventSink::Function = eventSink,
    mcpServer = nothing
 )
 ```
 | Parameter | Description |
 |-----------|-------------|
 | `llmCall` | **(required)** Function that receives formatted messages and returns an LLM response |
 | `systemPrompt` | System prompt for the agent |
 | `model` | LLM model configuration (`llmModel` struct) |
 | `messages` | Initial conversation messages |
 | `prepareContext` | Hook to transform messages/context before each LLM call |
 | `formatMsgForLLM` | Hook to convert agent context to LLM API format |
 | `beforeToolCall` | Hook to block/allow tool execution |
 | `afterToolCall` | Hook to post-process tool results |
 | `sessionId` | Optional session identifier for MCP server |
 | `maxRetryDelayMs` | Maximum retry delay in milliseconds |
 | `parallelToolExecute` | Run multiple tool calls in parallel |
 | `eventSink` | Callback for agent events/debug messages |
 | `mcpServer` | Callable struct for MCP server communication |
 ## Usage Examples
 ### Text Messages
 ```julia
 # Send plain text
 promptAgent(agent, "What tools do you have?")
 response = takeResponse(agent)
 # Follow-up while agent is processing
 promptAgent(agent, "Tell me about wine")
 followUp(agent, "Also include prices")
 response = takeResponse(agent)
 ```
 ### Structured Messages (OpenAI Format)
 ```julia
 promptAgent(agent, Dict("role" => "user", "content" => "Hello"))
 response = takeResponse(agent)
 ```
 ### Output Format
 `takeResponse()` returns a `Dict{String, Any}` in OpenAI message format:
 ```julia
 Dict(
    "role" => "assistant",
    "content" => [
        Dict("type" => "text", "text" => "The weather in Tokyo is Sunny, 22°C."),
        # ... more content blocks
    ]
 )
 ```
 Content block types:
 | Block Type | Fields | Description |
 |------------|--------|-------------|
 | `text` | `"type" => "text"`, `"text" => "..."` | Plain text response from the LLM |
 | `image_url` | `"type" => "image_url"`, `"image_url" => Dict("url" => "...")` | Image content (rare, used by multimodal models) |
 Example — extracting the text response:
 ```julia
 response = takeResponse(agent)
 text_response = first(c["text"] for c in response["content"] if c["type"] == "text")
 ```
 ### Multimodal Messages (Text + Image)
 ```julia
 # Encode images to data URIs
 image_path = "test/image.png"
 image_bytes = read(image_path)
 base64_string = base64encode(image_bytes)
 data_uri = "data:image/png;base64,$(base64_string)"
 # Build message with text and image content blocks
 message = Dict(
    "role" => "user",
    "content" => [
        Dict("type" => "text", "text" => "Do you have Brunello di Montalcino?"),
        Dict("type" => "image_url", "image_url" => Dict("url" => data_uri))
    ]
 )
 promptAgent(agent, message)
 response = takeResponse(agent)
 ```
 ### Custom Hooks
 ```julia
 # beforeToolCall — request user approval for destructive tools
 function my_before_tool_call(context::beforeToolCallContext, signal::abortSignal)::beforeToolCallResult
    if context.toolCall.name == "deleteFile"
        return beforeToolCallResult(true, "User must approve file deletion")
    end
    return beforeToolCallResult(false, "N/A")
 end
 # afterToolCall — mask sensitive data from results
 function my_after_tool_call(context::afterToolCallContext, signal::abortSignal)::Union{agentToolResult, Nothing}
    if context.isError
        return agentToolResult(
            [textContent("The tool encountered an error. Please try again.")],
            context.result.details, context.result.usage, context.result.terminate
        )
    end
    return nothing
 end
 agent = yiemAgent(llmCall;
    mcpServer=mcpServer,
    beforeToolCall=my_before_tool_call,
    afterToolCall=my_after_tool_call,
    eventSink=println
 )
 ```
 ### Custom LLM Callable
 ```julia
 function my_llm_call(messages_dict::Dict{String, Any})
    # messages_dict contains "messages" key with OpenAI-format message array,
    # plus optional "tools", "temperature", etc.
    # Call your LLM provider (OpenAI, Anthropic, local model, etc.)
    raw_response = call_my_llm_provider(messages_dict)
    # Return a dict with these fields:
    return Dict(
        "message" => Dict("role" => "assistant", "content" => raw_response["content"]),
        "finish_reason" => raw_response["finish_reason"],
        "api" => "my_provider",
        "provider" => "my_provider",
        "model" => "my-model-name",
        "usage" => llmUsage(100, 50)
    )
 end
 ```
 ### Custom Context Preparation
 ```julia
 function my_prepare_context(state::agentState, eventSink)::agentContext
    sysPrompt = state.systemPrompt * "\nCurrent time: $(now())"
    msgs = deepcopy(state.messages)
    # Prune old messages to fit context window
    max_messages = 20
    if length(msgs) > max_messages
        msgs = msgs[end-max_messages+1:end]
    end
    return agentContext(sysPrompt, msgs, state.tools)
 end
 agent = yiemAgent(llmCall; prepareContext=my_prepare_context, ...)
 ```
 ## MCP Server Integration
 YiemAgent uses JSON-RPC 2.0 for MCP communication. The `mcpServer` callable struct must implement `tools/list` and `tools/call` methods.
 ### Protocol
 ```julia
 # tools/list — returns tool definitions
 mcpServer("tools/list")
 # → Dict("jsonrpc"=>"2.0", "id"=>1, "result"=>Dict("tools"=>[...], "nextCursor"=>...))
 # tools/call — executes a tool
 mcpServer("tools/call", toolName, arguments)
 # → Dict("jsonrpc"=>"2.0", "id"=>2, "result"=>Dict("content"=>[...], "isError"=>...))
 # Protocol error responses include "error" instead of "result":
 Dict("jsonrpc"=>"2.0", "id"=>2, "error"=>Dict("code"=>-32602, "message"=>"..."))
 ```
 ### Example: NATS-based MCP Server Client
 ```julia
 struct mcpServer
    natsConn::NATS.Connection
    topic::String
    senderID::String
    fileserver_url::String
 end
 # tools/list implementation
 function (c::mcpServer)(method::String)
    payload = Dict("jsonrpc" => "2.0", "id" => 1, "method" => method, "params" => Dict{String, Any}())
    payloads = [("payload", payload, "dictionary")]
    _, msg_envelope = msghandler.smartpack(c.topic, payloads;
        sender_id=c.senderID, msg_purpose="mcp_tools_list", fileserver_url=c.fileserver_url)
    reply = NATS.request(c.natsConn, c.topic, msg_envelope, timeout=180)
    incoming = msghandler.smartunpack(String(reply.payload))
    return incoming["payloads"][1][2]
 end
 # tools/call implementation
 function (c::mcpServer)(method::String, toolName::String, arguments::Dict{String, Any})
    payload = Dict("jsonrpc" => "2.0", "id" => 2, "method" => method,
        "params" => Dict("name" => toolName, "arguments" => arguments))
    payloads = [("payload", payload, "dictionary")]
    _, msg_envelope = msghandler.smartpack(c.topic, payloads;
        sender_id=c.senderID, msg_purpose="mcp_tool_call", fileserver_url=c.fileserver_url)
    reply = NATS.request(c.natsConn, c.topic, msg_envelope, timeout=180)
    incoming = msghandler.smartunpack(String(reply.payload))
    return incoming["payloads"][1][2]
 end
 ```
 ## Tool Discovery
 Tools are discovered dynamically via the MCP server:
 1. `listTools` is the only pre-registered tool at agent creation
 2. When the LLM calls `listTools()`, tools are discovered from the MCP server and registered at runtime
 3. Supports pagination via `nextCursor` for large tool sets
 4. New tools become immediately available without restarting the agent
 ## Hooks
 Hooks allow customization of the agent's behavior at key points in the execution pipeline.
 | Hook | Signature | Purpose |
 |------|-----------|---------|
 | `prepareContext` | `(state::agentState, eventSink) -> agentContext` | Transform messages/context before LLM call (filter tools, inject context, prune messages) |
 | `formatMsgForLLM` | `(ctx::agentContext, eventSink) -> Dict` | Convert agent context to LLM API message format |
 | `beforeToolCall` | `(context::beforeToolCallContext, signal::abortSignal) -> beforeToolCallResult` | Block/allow tool execution; request user approval |
 | `afterToolCall` | `(context::afterToolCallContext, signal::abortSignal) -> Union{agentToolResult, Nothing}` | Post-process tool results; mask sensitive data; set `terminate` flag |
 | `eventSink` | `(msg) -> nothing` | Callback for agent events/debug messages throughout the pipeline |
 ## Agent Lifecycle
 1. **Creation** — `yiemAgent()` spawns a background task (`_agentLoop`) listening on `inputChannel` and `followUpChannel`
 2. **Input** — User messages enter via `promptAgent()` (immediate) or `followUp()` (queued while agent is busy)
 3. **Processing** — `_processMessage()` handles LLM calls, tool execution, and conversation history in a loop
 4. **Tool Execution Pipeline** — each tool call follows three phases:
   - `prepareToolCall` — resolve tool, validate arguments, run `beforeToolCall` hook
   - `executePreparedToolCall` — invoke `tool.execute()`
   - `finalizeExecutedToolCall` — run `afterToolCall` hook, construct `toolResultMessage`
 5. **Termination** — tool `execute` functions can set `terminate=true` to stop the agent loop
 6. **Shutdown** — `stopAgent()` sends a `:shutdown` signal, drains channels, and closes the agent
 ## 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)
 ```
@@ -0,0 +1,155 @@
 # 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
@@ -0,0 +1,119 @@
 # 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 |
@@ -0,0 +1,318 @@
 # 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
@@ -0,0 +1,518 @@
 # 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
@@ -0,0 +1,192 @@
 # 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.
@@ -0,0 +1,217 @@
 # 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 |
@@ -0,0 +1,445 @@
 # 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
@@ -1,92 +0,0 @@
 # Dynamic Tool Loading
 Tools can be loaded dynamically from `.jl` files in the `src/tools/` directory without hardcoding filenames in the main module.
 ## How It Works
 1. `src/tools/registry.jl` defines a `loadTools(dir::String)` function that scans a directory for `.jl` files
 2. Each tool file must define a single function: `getTool()::agentTool`
 3. `loadTools()` sorts files alphabetically, includes each one, calls `getTool()`, and registers the result
 4. Loaded tools are returned as `Vector{agentTool}` for use when constructing a `yiemAgent`
 ## Directory Structure
 ```
 src/
 ├── tools/
 │   ├── registry.jl      # Tool loader (do not edit)
 │   ├── getWeather.jl   # Your tool
 │   └── query_db.jl      # Another tool
 ├── type.jl
 ├── utils.jl
 ├── agentCore.jl
 ├── api.jl
 └── YiemAgent.jl
 ```
 ## Creating a Tool
 Each `.jl` file in `src/tools/` must define `getTool()` returning an `agentTool`:
 ```julia
 # src/tools/getWeather.jl
 function getTool()::agentTool
    return agentTool(
        name = "getWeather",
        label = "Weather Lookup",
        description = "Fetch current weather and forecast for a given city.",
        inputSchema = Dict{String,Any}(
            "type" => "object",
            "properties" => Dict(
                "city" => Dict("type" => "string", "description" => "City and country"),
                "units" => Dict("type" => "string", "enum" => ["celsius", "fahrenheit"], "default" => "celsius")
            ),
            "required" => ["city"]
        ),
        execute = (toolCallId, args, signal, onPartialResult) -> begin
            city = args["city"]
            return agentToolResult(
                [textContent("Weather in $(city): Sunny, 22C")],
                Dict{Any,Any}(), nothing, false
            )
        end,
        prepareArguments = nothing,
        parallelToolExecute = false
    )
 end
 ```
 No `module` wrapper needed — the registry includes each file in the current module scope so all types (`agentTool`, `textContent`, `agentToolResult`, etc.) resolve correctly.
 ## Loading Tools
 ```julia
 using .YiemAgent
 using .YiemAgent: toolRegistry
 # Load all tool files from src/tools/
 tools = YiemAgent.loadTools(joinpath(@__DIR__, "src", "tools"))
 # Create agent with loaded tools
 agent = yiemAgent(
    systemPrompt = "You are a helpful assistant.",
    model = my_model,
    tools = tools,
    llmCall = my_llm_call,
    agentEventSink = my_event_sink
 )
 ```
 ## Available Functions
 | Function | Description |
 |----------|-------------|
 | `loadTools(dir::String)` | Scan directory and load all `.jl` tool files |
 | `registerTool(tool::agentTool)` | Register a single tool into the global registry |
 | `getTools()` | Get deep copy of all registered tools |
 | `listTools()` | List all registered tools as `(name, label)` pairs |
 | `clearTools()` | Clear the global registry |
 ## File Loading Order
 Files are sorted alphabetically before loading, so `01_database.jl` loads before `02_weather.jl`. This ensures deterministic registration order.
@@ -1,2 +1,10 @@
-# ── executeToolCalls() Julia pseudo code ──────────────────────────
+# check if this column has vector embedding. if there is one, seach vector version instead
-# Full call stack from runLoop → executeToolCalls → prepare → execute → finalize → emit
+              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 +1,585 @@
 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)
@@ -0,0 +1,76 @@
 {
  "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"
    }
  }
 }
@@ -0,0 +1,706 @@
 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()
@@ -1 +0,0 @@
 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
@@ -1,53 +1,30 @@
-module YiemAgent
+module YiemAgent
-
+
-  # export agent
+  """Order by dependencies of each file. The 1st included file must not depend on any other
-
+  files and each file can only depend on the file included before it."""
-
+
-  """ Order by dependencies of each file. The 1st included file must not depend on any other
+  include("type.jl")
-  files and each file can only depend on the file included before it. 
+  using .type
-  """
+
-
+  include("utils.jl")
-  include("type.jl")
+  using .utils
-  using .type
+
-
+  include("toolRegistry.jl")
-  include("utils.jl")
+  using .toolRegistry
-  using .utils
+
-
+  include("agentCore.jl")
-  include("tools/registry.jl")
+  using .agentCore
-  using .toolRegistry
+
-
+  include("api.jl")
-  # include("llmfunction.jl")
+  using .api
-  # using .llmfunction
+
-
+  export promptAgent, takeResponse, followUp, stopAgent
-  include("agentCore.jl")
+  
-  using .agentCore
+  
-
+
-  include("api.jl")
+# ---------------------------------------------- 100 --------------------------------------------- #
-  using .api
+
-  
+
-  
+
-# ---------------------------------------------- 100 --------------------------------------------- #
+
-
+end # end module
 end # module YiemAgent_v1
@@ -1,16 +1,15 @@
 module api
-export prompt
+export promptAgent, takeResponse, followUp, stopAgent
 using JSON, DataStructures, Dates, UUIDs, HTTP, Random, PrettyPrinting, Serialization,
-      DataFrames, Serde
+      DataFrames
 using GeneralUtils
-using ..type, ..utils
+using ..type, ..utils, ..agentCore, ..toolRegistry
 # ---------------------------------------------- 100 --------------------------------------------- #
 """
 Send a message to the agent's input channel.
@@ -19,26 +18,31 @@ The agent processes messages from `inputChannel` in the background task.
 # Arguments
 - `agent::yiemAgent`: The agent instance to send a message to
- `msg`: The message to send (any type accepted by the agent's processing pipeline)
+- `msg`: The message to send, in OpenAI message format (e.g. `Dict("role" => "user", "content" => "Hello!")`)
 # Returns
 - The same `agent` instance for chaining
 # Notes
- Use `take_response(agent)` to receive the agent's response after sending a message.
+- Use `takeResponse(agent)` to receive the agent's response after sending a message.
- Use `follow_up(agent, msg)` to send messages while the agent is still processing.
+- Use `followUp(agent, msg)` to send messages while the agent is still processing.
 # Examples
 ```jldoctest
-julia> run_agent(agent, "Hello!")
+julia> promptAgent(agent, Dict("role" => "user", "content" => "Hello!"))
 yiemAgent(...)
 ```
 """
-function run_agent(agent::yiemAgent, msg)
+function promptAgent(agent::yiemAgent, msg::AbstractDict{String, Any})
  put!(agent.inputChannel, msg)
  return agent
 end
 function promptAgent(agent::yiemAgent, text::String)
  put!(agent.inputChannel, Dict("role" => "user", "content" => [Dict("type" => "text", "text" => text)]))
  return agent
 end
 """
 Take a response from the agent's output channel.
@@ -51,15 +55,15 @@ Blocks until the agent sends a response.
 - An `assistantMessage` instance representing the agent's response
 # Notes
- Use `run_agent(agent, msg)` to send a message before calling this function.
+- Use `promptAgent(agent, msg)` to send a message before calling this function.
 # Examples
 ```jldoctest
-julia> response = take_response(agent)
+julia> response = takeResponse(agent)
 assistantMessage(...)
 ```
 """
-function take_response(agent::yiemAgent)
+function takeResponse(agent::yiemAgent)
  return take!(agent.outputChannel)
 end
@@ -71,27 +75,32 @@ and before any tool call results are sent.
 # Arguments
 - `agent::yiemAgent`: The agent instance to send a follow-up message to
- `msg`: The follow-up message to send
+- `msg`: The follow-up message to send, in OpenAI message format (e.g. `Dict("role" => "user", "content" => "Also consider red wines")`)
 # Returns
 - The same `agent` instance for chaining
 # Notes
- Use `run_agent(agent, msg)` for the primary message and `follow_up(agent, msg)` for additional
+- Use `promptAgent(agent, msg)` for the primary message and `followUp(agent, msg)` for additional
  messages while the agent is processing.
 - Follow-up messages are buffered in a separate channel (capacity 32 by default).
 # Examples
 ```jldoctest
-julia> follow_up(agent, "Also consider red wines")
+julia> followUp(agent, Dict("role" => "user", "content" => "Also consider red wines"))
 yiemAgent(...)
 ```
 """
-function follow_up(agent::yiemAgent, msg)
+function followUp(agent::yiemAgent, msg::AbstractDict{String, Any})
  put!(agent.followUpChannel, msg)
  return agent
 end
 function followUp(agent::yiemAgent, text::String)
  put!(agent.followUpChannel, Dict("role" => "user", "content" => [Dict("type" => "text", "text" => text)]))
  return agent
 end
 """
 Gracefully stop the agent.
@@ -105,19 +114,19 @@ then closes all channels (`inputChannel`, `outputChannel`, `followUpChannel`).
 - `nothing`
 # Notes
- After calling `stop_agent`, the agent is no longer usable. A new agent must be created
+- After calling `stopAgent`, the agent is no longer usable. A new agent must be created
  for further interaction.
 - If the background task throws a `TaskFailedException`, it is rethrown.
 # Examples
 ```jldoctest
-julia> stop_agent(agent)
+julia> stopAgent(agent)
 ```
 """
-function stop_agent(agent::yiemAgent)
+function stopAgent(agent::yiemAgent)
  put!(agent.inputChannel, :shutdown)
  try
-    fetch(agent._agent_loop)
+    fetch(agent._agentLoop)
  catch e
    if e isa TaskFailedException
      rethrow(e)
@@ -0,0 +1,288 @@
 module toolRegistry
 export registerTool, registerAllTools, clearTools, listTool
 using Dates
 using JSON, DataStructures
 using ..type
 # ── MCP helper functions ────────────────────────────────────────────
 """
 Extract text from MCP tool result content array.
 Handles JSON-RPC 2.0 result content format:
    {"content": [{"type": "text", "text": "..."}], "isError": false}
 """
 function _extract_text_content(result::AbstractDict)::String
    content = get(result, "content", Any[])
    if content isa Vector && !isempty(content)
        lines = String[]
        for block in content
            if block isa Dict && get(block, "type", "") == "text"
                push!(lines, string(get(block, "text", "")))
            end
        end
        if !isempty(lines)
            return join(lines, "\n")
        end
    end
    return JSON.json(result)
 end
 """
 Wrap an MCP tool definition as an `agentTool`.
 The returned tool's `execute` function calls the MCP server's "tools/call"
 method with the validated arguments.
 """
 function _wrap_mcp_tool(mcpserver, tool_def::AbstractDict{String, Any}; eventSink=nothing, sessionId=nothing)::agentTool
    name = tool_def["name"]
    title = get(tool_def, "title", get(tool_def, "label", name))
    desc = get(tool_def, "description", "")
    input_schema = get(tool_def, "inputSchema", Dict{String,Any}())
    # Normalize inputSchema to OpenAI function format
    if haskey(input_schema, "properties") && input_schema["type"] == "object"
        params = Dict(
            "type" => "object",
            "properties" => input_schema["properties"],
            "required" => get(input_schema, "required", Any[]),
        )
    else
        params = Dict(
            "type" => "object",
            "properties" => Dict{String,Any}(),
            "required" => Any[],
        )
    end
    return agentTool(
      name=name,
      label=title,
      description=desc,
      inputSchema=params,
      execute=(toolCallId::String, args::AbstractDict{String, Any},
                signal::Union{Nothing,abortSignal},
                eventSink) -> begin
        try
          response = mcpserver("tools/call", name, args, sessionId; eventSink=eventSink)
          # Parse JSON-RPC 2.0 response envelope
          if haskey(response, "error")
            rpc_error = response["error"]
            err_msg = get(rpc_error, "message", "Unknown MCP error")
            return agentToolResult(
                [textContent("MCP error: $err_msg")],
                Dict{Any,Any}("error" => err_msg),
                nothing, false
            )
          end
          result_data = get(response, "result", response)
          content_text = _extract_text_content(result_data)
          is_error = get(result_data, "isError", false)
          return agentToolResult(
              [textContent(content_text)],
              Dict{Any,Any}("isError" => is_error),
              nothing, false
          )
        catch e
          errMsg = sprint(showerror, e)
          return agentToolResult(
              [textContent("MCP call error: $errMsg")],
              Dict{Any,Any}("error" => errMsg),
              nothing, false
          )
        end
      end,
      prepareArguments=nothing,
      validateRequiredArgs=nothing,
      parallelToolExecute=false,
    )
 end
 """
 Discover and register MCP tools into `store.tools`.
 Queries the MCP server via `mcpserver("tools/list")` (JSON-RPC 2.0 format),
 parses the response envelope, and registers each discovered tool.
 Handles pagination via `nextCursor`. Skips tools already registered.
 # Returns
 - `Int`: number of new tools registered
 """
 function register_mcp_tools(tools::OrderedDict{String, agentTool}, mcpserver; eventSink=nothing, sessionId=nothing)::Int
  if mcpserver === nothing
      return 0
  end
  new_count = 0
  try
    response = mcpserver("tools/list", sessionId; eventSink=eventSink)
    # Parse JSON-RPC 2.0 response envelope
    if haskey(response, "error")
        rpc_error = response["error"]
        err_msg = get(rpc_error, "message", "Unknown MCP error")
        println("[toolRegistry] MCP tools/list failed: $err_msg")
        return 0
    end
    if haskey(response, "result")
        response = response["result"]
    end
    tools_array = response["tools"]
    cursor = get(response, "nextCursor", nothing)
    for tool_def in tools_array
      name = tool_def["name"]
      if haskey(tools, name)
        continue
      end
      wrapped = _wrap_mcp_tool(mcpserver, tool_def, eventSink=eventSink, sessionId=sessionId)
      tools[name] = wrapped
      new_count += 1
    end
    # Paginate: fetch remaining tools if nextCursor is present
    while cursor !== nothing && cursor !== ""
      response = sessionId !== nothing ? mcpserver("tools/list", sessionId; eventSink=eventSink) : mcpserver("tools/list"; eventSink=eventSink)
      if haskey(response, "result")
          response = response["result"]
      end
      tools_array = get(response, "tools", Any[])
      cursor = get(response, "nextCursor", nothing)
      for tool_def in tools_array
        name = tool_def["name"]
        if haskey(tools, name)
            continue
        end
        wrapped = _wrap_mcp_tool(mcpserver, tool_def, eventSink=eventSink, sessionId=sessionId)
        tools[name] = wrapped
        new_count += 1
      end
    end
    println("[toolRegistry] Discovered $new_count MCP tools. Total: $(length(tools))")
  catch e
    bt = catch_backtrace()
    err_msg = sprint() do io
      showerror(io, e, bt)
      println(io)
    end
    eventSink(err_msg)
    errMsg = sprint(showerror, e)
    println("[toolRegistry] MCP tools/list failed: $errMsg")
  end
  return new_count
 end
 """
    registerTool(tools::OrderedDict{String, agentTool}, tool::agentTool) -> OrderedDict{String, agentTool}
 Add `tool` to `tools`, overwriting any existing tool with the same name.
 # Arguments
 - `tools`: Tool dict to modify
 - `tool`: The `agentTool` to register
 # Returns
 - The same `tools` dict (modified in place)
 """
 function registerTool(tools::OrderedDict{String, agentTool}, tool::agentTool; eventSink=nothing
  )::OrderedDict{String, agentTool}
    tools[tool.name] = tool
    println("[toolRegistry] Registered tool: $(tool.name)")
    return tools
 end
 """
 Remove all tools from the dict.
 # Arguments
 - `tools`: Tool dict to clear
 # Returns
 - `nothing`
 """
 function clearTools(tools::OrderedDict{String, agentTool})::Nothing
    empty!(tools)
    println("[toolRegistry] Registry cleared")
    return nothing
 end
 # ── listTools tool (auto-discover new MCP tools at runtime) ─────────
 """
    listTool(tools::OrderedDict{String, agentTool}, mcpserver) -> agentTool
 MCP-aware listTools tool (JSON-RPC 2.0 protocol).
 First call: queries the MCP server via `mcpserver("tools/list")`, registers
 all discovered tools into `tools` (in-place mutation, with
 pagination via nextCursor), then returns the full tool list.
 Subsequent calls: returns the current list (tools remain registered).
 """
 function listTool(tools::OrderedDict{String, agentTool}, mcpserver; eventSink=nothing, sessionId=nothing)::agentTool
    return agentTool(
        name="listTools",
        label="List Tools",
        description="List all available tools. First call discovers and registers all tools from the MCP server. After discovery, new tools become immediately available for use.",
        inputSchema=Dict{String,Any}(
            "type" => "object",
            "properties" => Dict{String,Any}(),
            "required" => Any[]
        ),
        execute=(toolCallId::String, args::AbstractDict{String, Any},
                 signal::Union{Nothing,abortSignal},
                 eventSink) -> begin
            # Discover and register MCP tools (idempotent — skips already registered)
            new_count = register_mcp_tools(tools, mcpserver; eventSink=eventSink, sessionId=sessionId)
            # Always include listTools itself in the count
            total = length(tools)
            lines = String[
                "- $(t.name): $(t.label) — $(t.description)"
                for (k, t) in tools
            ]
            result_text = "Available tools ($total):\n" * join(lines, "\n")
            return agentToolResult(
                [textContent(result_text)],
                Dict{Any,Any}("count" => total),
                nothing, false
            )
        end,
        prepareArguments=nothing,
        validateRequiredArgs=nothing,
        parallelToolExecute=false,
    )
 end
 # ── High-level API ──────────────────────────────────────────────────
 """
    registerAllTools(tools::OrderedDict{String, agentTool}, mcpserver)
 Register all tools for an agent:
  1. Auto-discover existing tools from the MCP server
  2. Register the `listTools` tool so the agent can discover new tools at runtime
 # Arguments
 - `tools`: The tool dict to populate
 - `mcpserver`: A callable struct that communicates with the MCP server
 """
 function registerAllTools(tools::OrderedDict{String, agentTool}, mcpserver=nothing; eventSink=nothing, sessionId=nothing)
    list_t = listTool(tools, mcpserver; eventSink=eventSink, sessionId=sessionId)
    registerTool(tools, list_t; eventSink=eventSink)
 end
 end # module
@@ -1,568 +0,0 @@
 # Tools
 Tools allow the agent to perform actions and fetch data. Each tool defines a **schema** (what arguments it accepts) and an **execution function** (what it does).
 ## Quick Start
 Add a new tool by creating a `.jl` file in `src/tools/`. The file must define a `getTool()` function that returns an `agentTool`:
 ```julia
 # src/tools/my_tool.jl
 function executeTool(toolCallId::String, args::Dict{String,Any}, signal::Union{Nothing,abortSignal}, onPartialResult::Function)::agentToolResult
    city = args["city"]
    return agentToolResult(
        [textContent("Hello from $(city)!")],
        Dict{Any,Any}(), nothing, false
    )
 end
 function getTool()::agentTool
    return agentTool(
        name = "my_tool",
        label = "My Tool",
        description = "Says hello to a city.",
        inputSchema = Dict{String,Any}(
            "type" => "object",
            "properties" => Dict(
                "city" => Dict("type" => "string", "description" => "City name")
            ),
            "required" => ["city"]
        ),
        execute = executeTool,
        prepareArguments = nothing,
        validateRequiredArgs = nothing,
        parallelToolExecute = false
    )
 end
 ```
 When `loadTools()` or `registerTool()` is called, the tool becomes available to the agent.
 ## Tool Anatomy
 Each tool has 3 main parts:
 ### 1. Schema (`inputSchema`)
 JSON Schema (MCP format) describing the tool's arguments. The `"required"` array lists mandatory fields:
 ```julia
 inputSchema = Dict{String,Any}(
    "type" => "object",
    "properties" => Dict(
        "city" => Dict("type" => "string", "description" => "City name"),
        "units" => Dict("type" => "string", "enum" => ["celsius", "fahrenheit"], "default" => "celsius")
    ),
    "required" => ["city"]
 )
 ```
 ### 2. Execution Function (`execute`)
 A function with the signature:
 ```julia
 execute(toolCallId::String, args::Dict{String,Any}, signal::Union{Nothing,abortSignal}, onPartialResult::Function)::agentToolResult
 ```
 - **`toolCallId`** — unique ID for this invocation (from the LLM's tool call)
 - **`args`** — validated arguments provided by the LLM
 - **`signal`** — abort signal for cancellable operations
 - **`onPartialResult`** — callback for streaming progress updates
 - **Returns** — `agentToolResult` with content, details, usage, and termination flag
 ```julia
 function executeTool(toolCallId::String, args::Dict{String,Any}, signal::Union{Nothing,abortSignal}, onPartialResult::Function)::agentToolResult
    # Optional: stream progress updates
    onPartialResult(Dict("status" => "Fetching data..."))
    # Do work
    result = "Weather in $(args["city"]): Sunny, 22°C"
    # Return result
    return agentToolResult(
        [textContent(result)],
        Dict{Any,Any}(),  # details
        nothing,           # usage
        false              # terminate (true to stop agent loop)
    )
 end
 ```
 ### 3. Tool Definition (`getTool()`)
 Returns an `agentTool` struct:
 | Field | Type | Description |
 |---|---|---|
 | `name` | `String` | Unique identifier (e.g. `"getWeather"`) |
 | `label` | `String` | Human-readable name (e.g. `"Weather Lookup"`) |
 | `description` | `String` | What the tool does (shown to the LLM) |
 | `inputSchema` | `Any` | JSON Schema (MCP format) |
 | `execute` | `Function` | The execution function |
 | `prepareArguments` | `Union{Function,Nothing}` | Optional argument transform before validation |
 | `validateRequiredArgs` | `Union{Function,Nothing}` | Optional custom validation |
 | `parallelToolExecute` | `Bool` | Run this tool in parallel with others |
 ## Argument Validation
 Validation happens **before** tool execution, in the `prepareToolCall` phase. Invalid calls return an error immediately without invoking `execute`, `beforeToolCall`, or logging `toolExecutionStart`.
 ### Default: JSON Schema Required Fields
 Set `validateRequiredArgs = nothing` to use the default validator, which checks that all fields in `inputSchema["required"]` are present:
 ```julia
 # src/tools/getWeather.jl — uses default validation
 function getTool()::agentTool
    return agentTool(
        name = "getWeather",
        # ...
        validateRequiredArgs = nothing,  # uses default
    )
 end
 ```
 ### Custom Validation Hook
 Override `validateRequiredArgs` when you need:
 - **Cross-field constraints** (e.g. "at least one of X or Y")
 - **Format validation** (e.g. regex patterns, date parsing)
 - **Domain rules** (e.g. value ranges, business logic)
 The hook signature takes only `args`:
 ```julia
 function validateRequiredArgs(args::Dict{String,Any})::Union{Nothing,String}
    tz = get(args, "timezone", nothing)
    city = get(args, "city", "")
    if !haskey(args, "timezone") && isempty(city)
        return "Missing required argument: provide at least one of 'timezone' or 'city'"
    end
    if tz !== nothing
        tz_str = string(tz)
        if !occursin(r"^[A-Za-z]+\/[A-Za-z]+(/[A-Za-z]+)*$", tz_str)
            return "Invalid timezone format: '$tz_str'. Use IANA format, e.g. 'America/New_York'"
        end
    end
    return nothing
 end
 ```
 Return `nothing` to pass, or an error `String` to fail. The error is fed back to the LLM so it can retry with corrected arguments.
 ## Tool Lifecycle — Framework Internals
 This section traces the full code path from the moment the LLM returns tool calls to the final result being fed back into the conversation. All code references are to `agentCore.jl`.
 ### Phase 1: Detect Tool Calls in LLM Response
 After the LLM returns an `assistantMessage`, the loop at `agentCore.jl:220-244` inspects each `content` block:
 ```julia
 # agentCore.jl:217-244
 has_tool_calls = false
 tool_call_list = agentToolCall[]
 for content_block in response.content
  if content_block isa Dict
    # OpenAI-style: type == "tool_calls" with array of tool calls
    if get(content_block, :type, "") == "tool_calls"
      for tc_data in get(content_block, :tool_calls, [])
        tc = agentToolCall(
          type="function",
          id=get(tc_data, :id, string(uuid4())),
          name=get(tc_data, :function, Dict{String,Any}())[:name],
          arguments=get(tc_data, :function, Dict{String,Any}())[:arguments],
        )
        push!(tool_call_list, tc)
      end
    # Alternative style: type == "tool_call" single dict per block
    elseif get(content_block, :type, "") == "tool_call"
      tc = agentToolCall(
        type="function",
        id=get(tc_data, :id, string(uuid4())),
        name=get(tc_data, :name, ""),
        arguments=get(tc_data, :arguments, Dict{String,Any}()),
      )
      push!(tool_call_list, tc)
    end
  end
 end
 ```
 Each content block with `type == "tool_calls"` or `type == "tool_call"` extracts an `agentToolCall` (id, name, arguments dict) and collects them into a `Vector{agentToolCall}`.
 ### Phase 2: Dispatch to Sequential or Parallel Execution
 At `agentCore.jl:247`, the framework checks if any tool calls exist and decides execution mode:
 ```julia
 # agentCore.jl:247-265
 context = agentContext(agent._state.systemPrompt, agent._state.messages, agent._state.tools)
 config = agentLoopConfig(
  agent._state.tools,
  agent.beforeToolCall,
  agent.afterToolCall,
  agent.parallelToolExecute ? "parallel" : "sequential",
 )
 batch = executeToolCalls(context, response, tool_call_list, config, signal, emit)
 ```
 `executeToolCalls` (`agentCore.jl:988-1015`) checks:
 - `config.toolExecution == "sequential"` → sequential mode
 - Any tool has `parallelToolExecute == false` → sequential mode
 - Otherwise → parallel mode
 ### Phase 3: Per-Call Preparation (`prepareToolCall`)
 Each tool call goes through `prepareToolCall` (`agentCore.jl:511-547`):
 ```
 1. Look up tool by name: find(t -> t.name == tc.name, context.tools)
 2. If not found → immediateOutcome("Tool X not found", true)
 3. Run tool.prepareArguments (if defined) → transforms raw LLM args
 4. Run validateToolArguments → validateRequiredArgs (hook or default)
   → if fails → throws ArgumentError → caught below
 5. Run beforeToolCall hook (if defined) → can block execution
   → if blocked → immediateOutcome("Tool execution was blocked", true)
 6. Return preparedToolCall(tool, tc, validatedArgs)
 ```
 If any step throws (validation, prepareArguments, beforeToolCall), the catch block at `agentCore.jl:545` converts it to an `immediateOutcome`:
 ```julia
 catch err
  return immediateOutcome(createErrorToolResult(sprint(showerror, err)), true)
 end
 ```
 ### Phase 4: Execution (`executePreparedToolCall`)
 For each `preparedToolCall`, `executePreparedToolCall` (`agentCore.jl:589-617`) runs:
 ```julia
 function executePreparedToolCall(prep::preparedToolCall, signal, emit)::executedOutcome
  updateEvents = promise[]
  accepting = true
  try
    result = prep.tool.execute(
      prep.toolCall.id, prep.args, signal,
      partialResult -> begin
        if accepting
          push!(updateEvents, emit(toolExecUpdateEvent(..., partialResult)))
        end
      end
    )
    accepting = false
    wait.(updateEvents)
    return executedOutcome(result, false)
  catch err
    accepting = false
    wait.(updateEvents)
    return executedOutcome(createErrorToolResult(sprint(showerror, err)), true)
  end
 end
 ```
 Key behaviors:
 - Calls `tool.execute(id, args, signal, onPartialResult)` — your tool's `executeTool` function
 - `signal` can be checked inside `executeTool` for cancellation
 - `onPartialResult` is called for streaming updates, which are emitted as `toolExecutionUpdate` events
 - `accepting` guard prevents emitting updates after the result is already captured
 - `wait.(updateEvents)` ensures all streaming updates are delivered before returning
 - Execution errors are caught and returned as `executedOutcome(isError=true)` — never thrown
 ### Phase 5: Finalization (`finalizeExecutedToolCall`)
 After execution, `finalizeExecutedToolCall` (`agentCore.jl:675-706`) runs the `afterToolCall` hook:
 ```julia
 function finalizeExecutedToolCall(context, assistantMsg, prep, executed, config, signal)::finalizedOutcome
  result = executed.result
  isError = executed.isError
  if config.afterToolCall !== nothing
    try
      after = config.afterToolCall(afterCtx(assistantMsg, prep.toolCall, prep.args, result, isError, context), signal)
      if after !== nothing
        # Hook can mutate: content, details, usage, terminate, isError
        result = merge(result, dict(...))
        isError = get(after, :isError, isError)
      end
    catch err
      result = createErrorToolResult(sprint(showerror, err))
      isError = true
    end
  end
  return finalizedOutcome(prep.toolCall, result, isError)
 end
 ```
 The hook can:
 - Mask sensitive data from result content
 - Normalize usage tracking
 - Flip `terminate: true` based on business logic
 - Wrap errors in friendlier messages for the LLM
 If the hook itself throws, the error is caught and converted to an error outcome.
 ### Phase 6: Emit Events and Create Result Message
 Each call emits `toolExecutionEnd`:
 ```julia
 function emitToolExecutionEnd(finalized::finalizedOutcome, emit::Function)
  emit(toolExecEndEvent(finalized.toolCall.id, finalized.toolCall.name, finalized.result, finalized.isError))
 end
 ```
 Then creates the `toolResultMessage` for conversation history (`agentCore.jl:373-379`):
 ```julia
 function createToolResultMessage(f::finalizedOutcome)::toolResultMessage
  return toolResultMessage(
    "toolResult", f.toolCall.id, f.toolCall.name,
    f.result.content, f.result.details, f.result.usage,
    get(f.result, :addedToolNames, string[]), f.isError, nowMillis()
  )
 end
 ```
 ### Phase 7: Batch Assembly and Loop Control
 In `executeToolCallsSequential` (`agentCore.jl:795-829`) or `executeToolCallsParallel` (`agentCore.jl:888-936`), all results are collected:
 ```julia
 messages = toolResultMessage[]
 for finalized in finalizedCalls
  push!(messages, createToolResultMessage(finalized))
 end
 return agentToolCallBatch(messages, shouldTerminate(finalizedCalls))
 ```
 `shouldTerminate` (`agentCore.jl:409`) returns `true` only if ALL tools in the batch set `result.terminate == true`. If `false`, the agent loop at `agentCore.jl:176-308` feeds the tool results back to the LLM for another turn.
 ### Data Flow Summary
 ```
 response.content (Vector{Any})
  └── phase 1: parse content blocks
       └── tool_call_list :: Vector{agentToolCall}
            └── phase 2: dispatch to sequential/parallel
                 └── phase 3: prepareToolCall
                      └── preparedToolCall or immediateOutcome
                           └── phase 4: executePreparedToolCall
                                └── executedOutcome
                                     └── phase 5: finalizeExecutedToolCall
                                          └── finalizedOutcome
                                               └── phase 6: createToolResultMessage
                                                    └── toolResultMessage
                                                         └── phase 7: agentToolCallBatch
                                                              └── pushed to agent._state.messages
                                                                   └── loop back to LLM
 ```
 ## Execution Modes
 ### Sequential
 Tools execute one at a time in order. Required when:
 - Tools have implicit dependencies
 - Tools share state (e.g. writing to the same file)
 - Tools have `parallelToolExecute = false`
 Set globally via `agentLoopConfig.toolExecution = "sequential"`, or per-tool via `parallelToolExecute = false`.
 ### Parallel
 Tools execute concurrently when all are independent. Reduces wall-clock time. Set `parallelToolExecute = true` on individual tools, or set `agentLoopConfig.toolExecution = "parallel"`.
 ## Streaming Partial Results
 For long-running tools (API calls, file uploads, training), use `onPartialResult` to stream progress:
 ```julia
 function executeTool(toolCallId::String, args::Dict{String,Any}, signal::Union{Nothing,abortSignal}, onPartialResult::Function)::agentToolResult
    onPartialResult(Dict("status" => "Step 1: Fetching data..."))
    sleep(1)
    onPartialResult(Dict("status" => "Step 2: Processing..."))
    sleep(1)
    return agentToolResult(
        [textContent("Done!")],
        Dict{Any,Any}(), nothing, false
    )
 end
 ```
 UI listeners and the TUI consume these events in real time via `toolExecutionUpdate`.
 ## Loading Tools
 ### Auto-load from Directory
 ```julia
 using .toolRegistry
 tools = loadTools("src/tools")  # scans for *.jl files with getTool()
 ```
 Files are loaded alphabetically for deterministic registration order.
 ### Manual Registration
 ```julia
 tool = getTool()  # from your tool module
 registerTool(tool)
 ```
 ## Using Tools with an Agent
 Loading tools only registers them — you must pass them to the `yiemAgent` and provide an `llmCall` function. Here is the complete flow:
 ```julia
 using .YiemAgent
 using .toolRegistry
 # 1. Load tools from the tools directory
 tools = loadTools("src/tools")
 # [toolRegistry] Loading tool from: src/tools/getTime.jl
 # [toolRegistry] Loaded tool: getTime — Time Lookup
 # [toolRegistry] Loading tool from: src/tools/getWeather.jl
 # [toolRegistry] Loaded tool: getWeather — Weather Lookup
 # 2. Define your LLM call function
 function my_llm_call(messages::Dict)::assistantMessage
    # Call your LLM API here (OpenAI, Anthropic, local model, etc.)
    # Return an assistantMessage with the response content
    # If the LLM wants to call a tool, include tool_call content blocks
    ...
 end
 # 3. Define your event sink (optional, for logging/debugging)
 function my_event_sink(event)
    if event isa toolExecStartEvent
        println("[EVENT] Tool start: $(event.toolName)")
    elseif event isa toolExecEndEvent
        status = event.isError ? "ERROR" : "OK"
        println("[EVENT] Tool end: $(event.toolName) — $status")
    end
 end
 # 4. Create the agent with tools
 agent = yiemAgent(
    systemPrompt = "You are a helpful assistant that can check weather and time.",
    model = my_model,
    tools = tools,                        # pass loaded tools
    llmCall = my_llm_call,                # your LLM function
    agentEventSink = my_event_sink,       # event handler
 )
 # 5. Send a message and get a response
 run_agent(agent, "What's the weather in Tokyo?")
 response = take_response(agent)
 # response.content contains the LLM's reply (with tool results if applicable)
 println(response.content)
 # 6. When done, stop the agent
 stop_agent(agent)
 ```
 ### How It Works
 1. **User sends a message** via `run_agent(agent, "What's the weather in Tokyo?")`. The message goes into `inputChannel`.
 2. **Agent loop** (`_agent_loop`) picks it up, converts it to a `userMessage`, and adds it to `agent._state.messages`.
 3. **LLM is called** via `agent.llmCall(formatted_messages)`. The LLM sees the system prompt, conversation history, and the tool definitions in the prompt (via `formatMsgForLLM`).
 4. **If the LLM uses a tool**, it returns a response with `tool_call` content blocks. The agent:
   - Extracts each tool call (name, arguments)
   - Runs validation (`validateRequiredArgs` or default)
   - Executes the tool (or returns an error if validation fails)
   - Feeds the result back as a `toolResultMessage` in the conversation
 5. **LLM is called again** with the tool results. This repeats until the LLM returns a text response with no tool calls.
 6. **Final response** is sent to `outputChannel` — retrieve it with `take_response(agent)`.
 ### Minimal Working Example
 ```julia
 using .YiemAgent
 using .toolRegistry
 # Load tools
 tools = loadTools("src/tools")
 # Mock LLM that echoes back a tool call, then a text response
 call_count = 0
 function mock_llm_call(messages::Dict)::assistantMessage
    global call_count += 1
    if call_count == 1
        # First call: LLM decides to use getWeather
        return assistantMessage(
            content=[
                Dict("type" => "tool_calls", 
                     "tool_calls" => [Dict("id" => "call_1", "name" => "getWeather",
                                           "arguments" => Dict("city" => "Tokyo"))])
            ],
            model = "mock",
            usage = llmUsage(0, 0)
        )
    else
        # Second call: LLM returns text (after tool result)
        return assistantMessage(
            content = [textContent("The weather in Tokyo is sunny, 22°C.")],
            model = "mock",
            usage = llmUsage(0, 0)
        )
    end
 end
 # Create agent
 agent = yiemAgent(
    systemPrompt = "You are a helpful assistant.",
    tools = tools,
    llmCall = mock_llm_call,
    agentEventSink = e -> nothing,  # no events
 )
 # Run
 run_agent(agent, "What's the weather in Tokyo?")
 response = take_response(agent)
 stop_agent(agent)
 ```
 ## Available Tools
 | Tool | Description | Validation |
 |---|---|---|
 | `getWeather` | Fetch weather for a city | Default (JSON Schema required) |
 | `getTime` | Get current time for a timezone or city | Custom (cross-field + format) |
 ## Example: Error Flow
 When the LLM calls a tool with invalid arguments:
 ```
 User: "What's the weather?"
  └── LLM: call getWeather() with no arguments
       └── prepareToolCall → validateRequiredArgs → "Missing required arguments: city"
            └── immediateOutcome → error tool result
                 └── LLM sees: "Missing required arguments: city"
                      └── LLM retries: call getWeather(city="Tokyo")
                           └── executeTool → "Weather in Tokyo: Sunny, 22°C"
 ```
 The agent feeds the error back to the LLM as a tool result message, allowing it to self-correct.
@@ -1,89 +0,0 @@
 """
 Validate required arguments for the getTime tool.
 Demonstrates custom validation beyond simple required-field checking:
 - Ensures at least one time source (timezone or city) is provided
 - Validates timezone is in IANA format if specified
 - Validates city name is not empty if specified
 # Arguments
 - `args::Dict{String,Any}`: Arguments from the LLM
 # Returns
 - `nothing` if validation passes
 - `String` error message if validation fails
 """
 function validateRequiredArgs(args::Dict{String,Any})::Union{Nothing,String}
    tz = get(args, "timezone", nothing)
    city = get(args, "city", "")
    hasTz = tz !== nothing && !isempty(tz)
    hasCity = !isempty(city)
    # At least one of timezone or city is required
    if !hasTz && !hasCity
        return "Missing required argument: provide at least one of 'timezone' or 'city'"
    end
    # Validate timezone format (IANA tz database: "Continent/City" or "Continent/City/SubCity")
    if hasTz
        tz_str = string(tz)
        if !occursin(r"^[A-Za-z]+\/[A-Za-z]+(/[A-Za-z]+)*$", tz_str)
            return "Invalid timezone format: '$tz_str'. Use IANA format, e.g. 'America/New_York' or 'Asia/Tokyo'"
        end
    end
    return nothing
 end
 """
 Execute the getTime tool.
 # Arguments
 - `toolCallId::String`: Unique identifier for this tool call
 - `args::Dict{String,Any}`: Parsed arguments from the LLM
 - `signal::Union{Nothing,abortSignal}`: Optional abort signal
 - `onPartialResult::Function`: Callback for streaming partial results
 # Returns
 - `agentToolResult`: Result content with current time data
 """
 function executeTool(toolCallId::String, args::Dict{String,Any}, signal::Union{Nothing,abortSignal}, onPartialResult::Function)::agentToolResult
    tz = get(args, "timezone", nothing)
    city = get(args, "city", "")
    # Simulate time lookup — replace with actual timezone API call
    if tz !== nothing
        result = "Current time in $(tz): $(now())"
    else
        result = "Current time in $(city): $(now())"
    end
    return agentToolResult(
        [textContent(result)],
        Dict{Any,Any}(), nothing, false
    )
 end
 """
 Define and return the getTime agentTool.
 """
 function getTool()::agentTool
    return agentTool(
        name = "getTime",
        label = "Time Lookup",
        description = "Get current local time for a timezone or city.",
        inputSchema = Dict{String,Any}(
            "type" => "object",
            "properties" => Dict(
                "timezone" => Dict("type" => "string", "description", "IANA timezone, e.g. 'America/New_York'"),
                "city" => Dict("type" => "string", "description", "City name as fallback")
            ),
            "required" => []
        ),
        execute = executeTool,
        prepareArguments = nothing,
        validateRequiredArgs = validateRequiredArgs,
        parallelToolExecute = false
    )
 end
@@ -1,52 +0,0 @@
 """
 Execute the getWeather tool.
 # Arguments
 - `toolCallId::String`: Unique identifier for this tool call
 - `args::Dict{String,Any}`: Parsed arguments from the LLM
 - `signal::Union{Nothing,abortSignal}`: Optional abort signal
 - `onPartialResult::Function`: Callback for streaming partial results
 # Returns
 - `agentToolResult`: Result content with weather data
 """
 function executeTool(toolCallId::String, args::Dict{String,Any}, signal::Union{Nothing,abortSignal}, onPartialResult::Function)::agentToolResult
    city = get(args, "city", "")
    units = get(args, "units", "celsius")
    # Simulate weather fetch — replace with actual API call
    # You can call onPartialResult() here for streaming progress updates:
    #   onPartialResult(Dict("status" => "Fetching weather data..."))
    #   onPartialResult(Dict("status" => "Processing..."))
    temp = units == "fahrenheit" ? "72" : "22"
    unit_symbol = units == "celsius" ? "°C" : "°F"
    return agentToolResult(
        [textContent("Weather in $(city): Sunny, $(temp)$(unit_symbol)")],
        Dict{Any,Any}(), nothing, false
    )
 end
 """
 Define and return the getWeather agentTool.
 """
 function getTool()::agentTool
    return agentTool(
        name = "getWeather",
        label = "Weather Lookup",
        description = "Fetch current weather and forecast for a given city.",
        inputSchema = Dict{String,Any}(
            "type" => "object",
            "properties" => Dict(
                "city" => Dict("type" => "string", "description" => "City and country, e.g., 'San Francisco, CA' or 'Tokyo, Japan'"),
                "units" => Dict("type" => "string", "enum" => ["celsius", "fahrenheit"], "default" => "celsius", "description" => "Temperature scale")
            ),
            "required" => ["city"]
        ),
        execute = executeTool,  # reference the function defined above
        prepareArguments = nothing,
        validateRequiredArgs = nothing,
        parallelToolExecute = false
    )
 end
@@ -1,142 +0,0 @@
 module toolRegistry
 export loadTools, registerTool, getTools, listTools, clearTools
 using ..type
 # Global registry — populated at runtime by loadTools() or registerTool()
 const _registry = Vector{agentTool}()
 """
 Load all tool modules from a directory.
 Scans `dir` for `.jl` files. Each file must define a function named
 `getTool()::agentTool`.  Files are sorted alphabetically so tool
 registration order is deterministic.
 # Tool file format
 Each `.jl` file defines one function `getTool()` that returns an `agentTool`:
 ```julia
 # src/tools/getWeather.jl
 function getTool()::agentTool
    return agentTool(
        name = "getWeather",
        label = "Weather Lookup",
        description = "Fetch current weather and forecast for a given city.",
        inputSchema = Dict{String,Any}(
            "type" => "object",
            "properties" => Dict(
                "city" => Dict("type" => "string", "description" => "City and country"),
                "units" => Dict("type" => "string", "enum" => ["celsius", "fahrenheit"], "default" => "celsius")
            ),
            "required" => ["city"]
        ),
        execute = (toolCallId, args, signal, onPartialResult) -> begin
            city = args["city"]
            return agentToolResult(
                [textContent("Sunny, 22C in $(city)")],
                Dict{Any,Any}(), nothing, false
            )
        end,
        prepareArguments = nothing,
        parallelToolExecute = false
    )
 end
 ```
 # Arguments
 - `dir::String`: Directory path to scan for `.jl` tool files
 # Returns
 - `Vector{agentTool}`: All loaded tools
 # Errors
 - Throws `ArgumentError` if a tool file does not define a `getTool` function
 """
 function loadTools(dir::String)::Vector{agentTool}
    if !isdir(dir)
        throw(ArgumentError("Tool directory does not exist: $dir"))
    end
    tools = agentTool[]
    jl_files = filter(f -> endswith(f, ".jl"), readdir(dir))
    sort!(jl_files)
    for filename in jl_files
        filepath = joinpath(dir, filename)
        println("[toolRegistry] Loading tool from: $filepath")
        # Include the file in the current module scope so all types resolve
        # (agentTool, textContent, agentToolResult, etc. are all available)
        include(filepath)
        # Validate that getTool was defined (include() places it in current module scope)
        if !isdefined(@__MODULE__, :getTool)
            throw(ArgumentError(
                "Tool file $(filepath) does not define a `getTool()` function. " *
                "Each tool file must define: function getTool()::agentTool ... end"
            ))
        end
        # Call getTool() — it runs in current scope where types are visible
        tool = getTool()
        if !(tool isa agentTool)
            throw(ArgumentError(
                "getTool() in $(filepath) did not return an agentTool instance, got: $(typeof(tool))"
            ))
        end
        push!(_registry, tool)
        push!(tools, tool)
        println("[toolRegistry] Loaded tool: $(tool.name) — $(tool.label)")
    end
    return tools
 end
 """
 Register a single agentTool into the global registry.
 # Arguments
 - `tool::agentTool`: The tool to register
 # Returns
 - `Vector{agentTool}`: Updated registry
 """
 function registerTool(tool::agentTool)::Vector{agentTool}
    push!(_registry, tool)
    println("[toolRegistry] Registered tool: $(tool.name)")
    return _registry
 end
 """
 Get all registered tools.
 # Returns
 - `Vector{agentTool}`: Copy of the registry
 """
 function getTools()::Vector{agentTool}
    return deepcopy(_registry)
 end
 """
 List all registered tool names and labels.
 # Returns
 - `Vector{Tuple{String,String}}`: Pairs of (name, label)
 """
 function listTools()::Vector{Tuple{String,String}}
    return [(t.name, t.label) for t in _registry]
 end
 """
 Clear all registered tools from the global registry.
 """
 function clearTools()::Nothing
    empty!(_registry)
    println("[toolRegistry] Registry cleared")
    return nothing
 end
 end # module
@@ -4,17 +4,17 @@
           messageContent, agentMessage, agent,
           # Model types
           modelCost, llmModel, llmUsage,
-           # Message content types
+            # Message content types
-           textContent, imageContent,
+            textContent, imageContent, reasoningContent,
-           # Message types
+            # Message types
-           userMessage, assistantMessage, toolResultMessage,
+            userMessage, assistantMessageToolCall, assistantMessage, toolResultMessage,
            # Tool types
-            agentTool, validateRequiredArgs
+             agentTool, validateRequiredArgs,
           # Context types
           agentContext, agentState, agentToolCall, prepareNextTurnContext,
           # Loop & execution types
-           agentLoopConfig, abortSignal, agentToolResult,
+           agentLoopConfig, abortSignal, agentToolResult,beforeToolCallContext, 
-           assistantMsgCtx, afterCtx,
+           beforeToolCallResult, afterToolCallContext,
           # Event types
           toolExecStartEvent, toolExecUpdateEvent, toolExecEndEvent,
           # Agent
@@ -23,7 +23,7 @@
           preparedToolCall, immediateOutcome, executedOutcome, finalizedOutcome,
           agentToolCallBatch,
           # Functions (defined elsewhere)
-           run_agent, take_response, follow_up, stop_agent
+           promptAgent, takeResponse, followUp, stopAgent
 using Dates, UUIDs, DataStructures, JSON, NATS, Base.Threads
@@ -31,6 +31,13 @@ using GeneralUtils
 const Timestamp = DateTime
 struct agentToolCall               # A tool invocation from the LLM
  type::String                              # Always "function"
  id::String                                # Unique tool call identifier
  name::String                              # Tool name
  arguments::Dict{String, Any}              # Parsed tool arguments
 end
 # ------------------------------------------------------------------------------------------------ #
 #                                          LLM model info                                          #
 # ------------------------------------------------------------------------------------------------ #
@@ -75,6 +82,10 @@ struct imageContent <: messageContent         # Image message content
    mimeType::String                          # MIME type (e.g., "image/png")
 end
 struct reasoningContent <: messageContent    # LLM reasoning/thinking content
    text::String                              # The reasoning text
 end
 # ------------------------------------------------------------------------------------------------ #
 #                                           Message types                                          #
@@ -108,6 +119,52 @@ function userMessage(; role="user", content=Vector{messageContent}(), timestamp=
    return userMessage(role, content, timestamp)
 end
 struct assistantMessageToolCall <: agentMessage   # Assistant message containing tool calls
    role::String                            # Always "assistant"
    toolCalls::Vector{agentToolCall}        # Tool calls to execute
    content::Vector{messageContent}         # Reasoning/thinking content blocks
    api::String                             # API name used (e.g., "openai")
    provider::String                        # Provider name (e.g., "anthropic")
    model::String                           # Model identifier
    usage::llmUsage                         # Token usage for this message
    stopReason::String                      # Why generation stopped (e.g., "tool_calls")
    errorMessage::Union{String, Nothing}    # Error if generation failed
    timestamp::Timestamp                    # When the message was received
 end
 """
 Create a new assistant message containing tool calls.
 # Arguments
 - `role::String`: Always "assistant"
 - `toolCalls::Vector{agentToolCall}`: Tool calls to execute
 - `content::Vector{messageContent}`: Reasoning/thinking content blocks
 - `api::String`: API name used
 - `provider::String`: Provider name
 - `model::String`: Model identifier
 - `usage::llmUsage`: Token usage
 - `stopReason::String`: Why generation stopped
 - `errorMessage::Union{String, Nothing}`: Error if generation failed
 - `timestamp::Timestamp`: When the message was received
 # Returns
 - A new `assistantMessageToolCall` instance
 # Examples
 ```julia
 julia> tc = agentToolCall("function", "call_1", "getWeather", Dict("city" => "Tokyo"))
 julia> msg = assistantMessageToolCall(toolCalls=[tc], stopReason="tool_calls")
 assistantMessageToolCall("assistant", [agentToolCall(...)], messageContent[], "", "", "", llmUsage(0, 0), "tool_calls", nothing, DateTime(...))
 ```
 """
 function assistantMessageToolCall(; role="assistant", toolCalls=agentToolCall[],
    content=Vector{messageContent}(), api="", provider="", model=nothing, usage=llmUsage(0, 0),
    stopReason="tool_calls", errorMessage=nothing, timestamp=now())
    model_str = model isa AbstractString ? String(model) : ""
    return assistantMessageToolCall(role, toolCalls, content, api, provider, model_str,
                                     usage, stopReason, errorMessage, timestamp)
 end
 struct assistantMessage <: agentMessage     # Message from the AI assistant
    role::String                            # Always "assistant"
    content::Vector{messageContent}         # Text and/or image content
@@ -144,9 +201,10 @@ assistantMessage("assistant", [textContent("Hello!")], "", "", "gpt-4", ..., "en
 ```
 """
 function assistantMessage(; role="assistant", content=Vector{messageContent}(),
-    api="", provider="", model="", usage=llmUsage(0, 0), stopReason="end_turn",
+    api="", provider="", model=nothing, usage=llmUsage(0, 0), stopReason="end_turn",
    errorMessage=nothing, timestamp=now())
-    return assistantMessage(role, content, api, provider, model, usage, stopReason, errorMessage, timestamp)
+    model_str = model isa AbstractString ? String(model) : ""
    return assistantMessage(role, content, api, provider, model_str, usage, stopReason, errorMessage, timestamp)
 end
 struct toolResultMessage <: agentMessage    # Result returned from a tool execution
@@ -248,7 +306,7 @@ tool = agentTool(
    execute=(toolCallId, args, signal, onPartialResult) -> begin
        city = args["city"]
        return agentToolResult(
-            [textContent("Sunny, 22C in $(city)")],
+            [textContent("Sunny, 22C in Bangkok")],
            Dict{Any,Any}(), nothing, false
        )
    end,
@@ -263,12 +321,23 @@ struct agentTool                              # A tool available to the agent
    label::String                             # Human-readable tool name
    description::String                       # What the tool does
    inputSchema::Any                          # Tool parameters schema (JSON schema, MCP inputSchema format)
-    execute::Function                         # Tool execution function
+    execute                         # Tool execution function
    prepareArguments::Union{Function, Nothing}  # Optional argument preparation callback
    validateRequiredArgs::Union{Function, Nothing}  # Optional validation hook for required args
    parallelToolExecute::Bool  # Override: run tool calls sequentially or in parallel
 end
 """
 Keyword constructor for agentTool — allows `agentTool(name=..., label=..., ...)`.
 """
 function agentTool(; name::String, label::String, description::String, inputSchema::Any,
                     execute, prepareArguments::Union{Function, Nothing}=nothing,
                     validateRequiredArgs::Union{Function, Nothing}=nothing,
                     parallelToolExecute::Bool=false)
    return agentTool(name, label, description, inputSchema, execute,
                     prepareArguments, validateRequiredArgs, parallelToolExecute)
 end
 # ------------------------------------------------------------------------------------------------ #
 #                                           Agent context                                          #
@@ -280,7 +349,7 @@ Snapshot of the agent's conversation context.
 # Arguments
 - `systemPrompt::String`: System prompt for the agent
 - `messages::Vector{agentMessage}`: Conversation messages
- `tools::Union{Vector{agentTool}, Nothing}`: Available tools
+- `tools::Union{OrderedDict{String, agentTool}, Nothing}`: Available tools keyed by name for O(1) lookup
 # Returns
 - A new `agentContext` instance
@@ -288,7 +357,7 @@ Snapshot of the agent's conversation context.
 struct agentContext                           # Snapshot of the agent's conversation context
    systemPrompt::String                      # System prompt for the agent
    messages::Vector{agentMessage}            # Conversation messages
-    tools::Union{Vector{agentTool}, Nothing}  # Available tools
+    tools::Union{OrderedDict{String, agentTool}, Nothing}  # Available tools keyed by name
 end
@@ -297,15 +366,14 @@ end
 # ------------------------------------------------------------------------------------------------ #
 mutable struct agentState                     # Mutable runtime state of an agent
-    systemPrompt::String                      # System prompt text
+    systemPrompt::String                      # System prompt for the agent
-    model::llmModel                           # LLM model to use
+    model::Union{llmModel, Nothing}                           # LLM model to use
-    tools::Vector{agentTool}                  # Available tools
+    tools::OrderedDict{String, agentTool}     # Available tools keyed by name, insertion-ordered
    # messages history includes userMessage, assistantMessage, toolResultMessage. NO system prompt
    messages::Vector{agentMessage}            
    pendingToolCalls::Vector{String}          # Tool call IDs waiting for results
    activeRun::Bool                             # is agent processing user message?
    errorMessage::Union{String, Nothing}      # Last error message
 end
@@ -318,7 +386,7 @@ new state from external references.
 # Arguments
 - `systemPrompt::String`: System prompt text
 - `model::llmModel`: LLM model to use (defaults to an unknown model)
- `tools::Vector{agentTool}`: Available tools (deep copied)
+- `tools::OrderedDict{String, agentTool}`: Available tools keyed by name (deep copied)
 - `messages::Vector{agentMessage}`: Conversation messages (deep copied)
 # Returns
@@ -327,32 +395,23 @@ new state from external references.
 # Examples
 ```julia
 julia> state = agentState(systemPrompt="You are a helpful assistant")
-agentState("You are a helpful assistant", ..., agentTool[], agentMessage[], String[], nothing)
+agentState("You are a helpful assistant", OrderedDict{String, agentTool}(), agentMessage[], String[], nothing)
 ```
 """
 function agentState(
-    systemPrompt::String="",
+  systemPrompt::String="",
-    model::llmModel=llmModel{String}("", "", "unknown", "unknown", "", false, String[], modelCost(0.0, 0.0, 0.0, 0.0), 0, 0),
+  model=llmModel("model_1", "unknown", "unknown", "", false, String[], 
-    tools::Vector{agentTool}=agentTool[],
+                  modelCost(0.0, 0.0, 0.0, 0.0), 0, 0),
-    messages::Vector{agentMessage}=agentMessage[],
+  tools::OrderedDict{String, agentTool}=OrderedDict{String, agentTool}(),
  messages::Vector{agentMessage}=agentMessage[],
 )
-    agentState(
+  agentState(
-      systemPrompt,
+    systemPrompt,
-      model,
+    model,
-      deepcopy(tools),
+    tools,
-      deepcopy(messages),
+    deepcopy(messages),
-      Vector{String}(),
+    Vector{String}(),
-      false,
+    nothing,
-      nothing,
+  )
    )
 end
 struct agentToolCall               # A tool invocation from the LLM
    type::String                              # Always "function"
    id::String                                # Unique tool call identifier
    name::String                              # Tool name
    arguments::Dict{String, Any}              # Parsed tool arguments
 end
@@ -383,14 +442,12 @@ end
 """
 Configuration for the agent tool execution loop.
-# Arguments
+# Fields
 - `tools::Vector{agentTool}`: Available tools
 - `beforeToolCall::Union{Function, Nothing}`: Callback before tool execution
 - `afterToolCall::Union{Function, Nothing}`: Callback after tool execution
 - `toolExecution::String`: Execution mode — "sequential" or "parallel"
 """
 struct agentLoopConfig
    tools::Vector{agentTool}
    beforeToolCall::Union{Function, Nothing}
    afterToolCall::Union{Function, Nothing}
    toolExecution::String
@@ -426,33 +483,38 @@ end
 Context passed to the `beforeToolCall` hook.
 # Arguments
- `message::assistantMessage`: The assistant message containing the tool call
+- `message::assistantMessageToolCall`: The assistant message containing the tool call
 - `toolCall::agentToolCall`: The tool call being prepared
- `args::Dict{String,Any}`: Validated tool arguments
+- `args::AbstractDict{String, Any}`: Validated tool arguments
 - `context::agentContext`: Current conversation context
 """
-struct assistantMsgCtx
+struct beforeToolCallContext
-    message::assistantMessage
+    message::assistantMessageToolCall
    toolCall::agentToolCall
-    args::Dict{String,Any}
+    args::AbstractDict{String, Any}
    context::agentContext
 end
 struct beforeToolCallResult
  block::Bool
  reason::String
 end
 """
 Context passed to the `afterToolCall` hook.
 # Arguments
- `message::assistantMessage`: The assistant message containing the tool call
+- `message::assistantMessageToolCall`: The assistant message containing the tool call
 - `toolCall::agentToolCall`: The tool call that was executed
- `args::Dict{String,Any}`: Tool arguments
+- `args::AbstractDict{String, Any}`: Tool arguments
 - `result::agentToolResult`: The raw tool result
 - `isError::Bool`: Whether execution resulted in an error
 - `context::agentContext`: Current conversation context
 """
-struct afterCtx
+struct afterToolCallContext
-    message::assistantMessage
+    message::assistantMessageToolCall
    toolCall::agentToolCall
-    args::Dict{String,Any}
+    args::AbstractDict{String, Any}
    result::agentToolResult
    isError::Bool
    context::agentContext
@@ -464,12 +526,12 @@ Event emitted when a tool call execution starts.
 # Arguments
 - `toolCallId::String`: ID of the tool call
 - `toolName::String`: Name of the tool
- `arguments::Dict{String,Any}`: Tool arguments
+- `arguments::AbstractDict{String, Any}`: Tool arguments
 """
 struct toolExecStartEvent
    toolCallId::String
    toolName::String
-    arguments::Dict{String,Any}
+    arguments::AbstractDict{String, Any}
 end
 """
@@ -478,13 +540,13 @@ Event emitted with partial results during tool execution.
 # Arguments
 - `toolCallId::String`: ID of the tool call
 - `toolName::String`: Name of the tool
- `arguments::Dict{String,Any}`: Tool arguments
+- `arguments::AbstractDict{String, Any}`: Tool arguments
 - `partialResult::Any`: The partial result data
 """
 struct toolExecUpdateEvent
    toolCallId::String
    toolName::String
-    arguments::Dict{String,Any}
+    arguments::AbstractDict{String, Any}
    partialResult::Any
 end
@@ -510,138 +572,6 @@ end
 abstract type agent end 
 """
 docstring
 """
 mutable struct yiemAgent <: agent             # High-level agent wrapper
  _state::agentState                          # Current state (prompt, model, messages, tools, etc.)
  # user sends prompt message to agent. if agent is idle, it process user message right away. 
  # if agent is running, it process user message after  the current tool call finished.
  inputChannel::Channel                        
  # Buffers messages the user sends while the agent is busy. Processed after all inputChannel 
  # messages are handled and the agent is idle (not using a tool call).
  followUpChannel::Channel                    
  # agent sends response message to user after processing all user messages in inputChannel 
  # and all followUp messages.
  outputChannel::Channel                     
  _agent_loop::Union{Task, Nothing}                 # agent loop running in the background
  # Preprocess/transform messages and context (modify, filter, prune, inject context from memory,  
  # reorder, ...) for a single LLM call in _process_message()'s loop.
  # returns new Vector{agentMessage}
  prepareContext ::Union{Function, Nothing}   
  # Convert prepareContext()'s new Vector{agentMessage} to LLM message format
  formatMsgForLLM::Function    
  # Actually invoke the LLM to get a completion response. The LLM response comes back as an 
  # assistantMessage whose content is an array of content blocks. 
  # Each block has a type — "text", "thinking", or "toolCall". 
  # The code filters for type === "toolCall" blocks, then passes them to executeToolCalls().
  llmCall::Function                           
  # Callback invoked before executing a tool call (ask for user permission/confirmation/abort, etc..)
  beforeToolCall::Union{Function, Nothing}    
  executeToolCalls::Function                  # execute tool calls ()
  # Callback invoked after executing a tool call to sanitize tools output so the output is ready 
  # to be converted into toolResults message
  afterToolCall::Union{Function, Nothing}     
  # prepareNextTurn::Union{Function, Nothing}   # Callback to prepare the next conversation turn
  # prepareNextTurnWithContext::Union{Function, Nothing}  # Same but receives context
  sessionId::Union{String, Nothing}           # Optional session identifier
  maxRetryDelayMs::Union{Int64, Nothing}      # Maximum delay between retries (ms)
  parallelToolExecute::Bool                   # Default: false
  agentEventSink::Function                    # agent emits its status via this function
 end
 """
 Create a new yiemAgent instance with a background loop task.
 Spawns a background `@spawn` task that runs the agent loop, listening
 on `inputChannel` and `followUpChannel` channels concurrently.
 # Keyword Arguments
 - `systemPrompt::String`: System prompt for the agent
 - `model`: LLM model to use
 - `tools::Vector{agentTool}`: Available tools (default: empty)
 - `messages::Vector{agentMessage}`: Initial conversation messages (default: empty)
 - `formatMsgForLLM::Function`: Convert agent messages to LLM message format (default: `defaultformatMsgForLLM`)
 - `llmCall::Function`: Function to invoke the LLM (required)
 - `prepareContext::Union{Function, Nothing}`: Preprocess/transform messages before sending to LLM (default: `nothing`)
 - `beforeToolCall::Union{Function, Nothing}`: Callback invoked before executing a tool call (default: `nothing`)
 - `afterToolCall::Union{Function, Nothing}`: Callback invoked after executing a tool call (default: `nothing`)
 - `prepareNextTurn::Union{Function, Nothing}`: Callback to prepare the next conversation turn (default: `nothing`)
 - `prepareNextTurnWithContext::Union{Function, Nothing}`: Same but receives context (default: `nothing`)
 - `sessionId::Union{String, Nothing}`: Optional session identifier (default: `nothing`)
 - `maxRetryDelayMs::Union{Int64, Nothing}`: Maximum delay between retries in milliseconds (default: `nothing`)
 - `parallelToolExecute::Bool`: Run tool calls in parallel (default: `false`)
 - `agentEventSink::Function`: Callback to receive agent events
 # Returns
 - A new `yiemAgent` instance with an active background task
 # Examples
 ```julia
 julia> agent = yiemAgent(systemPrompt="You are a helpful assistant", model=my_model)
 yiemAgent(agentState(...), Channel(...), Channel(...), Channel(...), ..., ...)
 ```
 """
 function yiemAgent(
  ; systemPrompt::String="You are helpful assistant.",
    model=nothing,
    tools::Vector{agentTool}=agentTool[],
    messages::Vector{agentMessage}=agentMessage[],
    prepareContext::Union{Function, Nothing}=nothing,
    formatMsgForLLM::Function=defaultformatMsgForLLM,
    llmCall::Function,
    beforeToolCall::Union{Function, Nothing}=nothing,
    afterToolCall::Union{Function, Nothing}=nothing,
    # prepareNextTurn::Union{Function, Nothing}=nothing,
    # prepareNextTurnWithContext::Union{Function, Nothing}=nothing,
    sessionId::Union{String, Nothing}=nothing,
    maxRetryDelayMs::Union{Int64, Nothing}=nothing,
    parallelToolExecute::Bool=false,
    agentEventSink::Function,
  )
  # Create channels: input (user -> agent), followUp (async queue), output (agent -> user)
  inputChannel    = Channel(16)
  followUp    = Channel(32)
  outputChannel   = Channel(16)
  # Create struct with a placeholder task, then spawn and replace it
  agent = yiemAgent(
    agentState(systemPrompt, model, tools, messages),
    inputChannel,
    followUp,
    outputChannel,
    nothing,  # placeholder — replaced below
    prepareContext,
    formatMsgForLLM,
    llmCall,
    beforeToolCall,
    afterToolCall,
    # prepareNextTurn,
    # prepareNextTurnWithContext,
    sessionId,
    maxRetryDelayMs,
    parallelToolExecute,
    agentEventSink,
  )
  # Spawn the background loop and attach it
  agent._agent_loop = @spawn _agent_loop(agent)
  return agent
 end
 """
    preparedToolCall(tool, toolCall, args)
@@ -1,9 +1,11 @@
 module utils
-export clearhistory, availableWineToText, prepareContext, formatMsgForLLM, validateRequiredArgs, validateToolArguments, _userMessageToOpenAI, 
+export clearhistory, availableWineToText, prepareContext, formatMsgForLLM, validateRequiredArgs, 
-        _assistantMessageToOpenAI, _toolResultMessageToOpenAI, _messageContentToBlocks
+      validateToolArguments, _userMessageToOpenAI, 
      _assistantMessageToOpenAI, _toolResultMessageToOpenAI, _messageContentToBlocks,
      _toolsToOpenAI, beforeToolCall, afterToolCall
-using UUIDs, Dates, DataStructures, HTTP, JSON
+using UUIDs, Dates, DataStructures, HTTP, JSON, NATS
 using GeneralUtils
 using ..type
@@ -73,7 +75,6 @@ function availableWineToText(vecd::Vector)::String
 end
 """
    prepareContext(state::agentState) -> agentContext
@@ -95,7 +96,7 @@ or pruning and reordering messages before formatting and calling the LLM.
  messages, and tools to be sent to the LLM
 # Examples
-```julia
+```jldoctest
 # Default: returns an agentContext with deep copies of system prompt, messages, and tools
 prepareContext(state).messages == deepcopy(state.messages)
@@ -108,14 +109,14 @@ prepareContext(state).messages == deepcopy(state.messages)
 # end
 ```
 """
-function prepareContext(state::agentState)::agentContext
+function prepareContext(state::agentState, eventSink)::agentContext
-  
+
  #TODO filter tools from state.tools based on user intend in user message and tool description
  filteredTools = state.tools
-  #TODO add tools to current system prompt
+  #TODO add filtered tools to the current system prompt / modify systemPrompt here
  preparedSystemPrompt = state.systemPrompt
-  
+
  #TODO add system prompt, adjust/modify and inject additional context into messages
  preparedMessages = deepcopy(state.messages) # messages that will be send to LLM
@@ -145,7 +146,7 @@ LLM message formats for different APIs/providers.
  an array of OpenAI-format message dicts
 # Examples
-```julia
+```jldoctest
 # Default output:
 formatMsgForLLm(ctx) == Dict("messages" => [
  Dict("role" => "system", "content" => [...]),
@@ -155,7 +156,7 @@ formatMsgForLLm(ctx) == Dict("messages" => [
 ])
 ```
 """
-function formatMsgForLLM(ctx::agentContext)::Dict{String, Any}
+function formatMsgForLLM(ctx::agentContext, eventSink)::Dict{String, Any}
  """ openai message format example
  msg = Dict(
@@ -183,19 +184,30 @@ function formatMsgForLLM(ctx::agentContext)::Dict{String, Any}
          Dict("type" => "text", "text" => "let me check."),
        ]
      ),
    ],
    "tools"=> [
      Dict(
-        "role" => "toolResult",
+        "type" => "function",
-        "content" => [
+        "function" => Dict(
-          Dict("type" => "text", "text" => "name: Chateau Montelena ..."),
+          "name" => "getWeather",
-        ]
+          "description" => "Get current weather",
-      ),
+          "parameters" => Dict(
            "type" => "object",
            "properties" => Dict(
              "city" => Dict("type" => "string")
            ),
            "required" => ["city"]
          )
        )
      )
    ],
    "temperature" => 0.7
  )
  """
  openaiReadyMsg = Dict{String, Any}()
  # openaiReadyMsg["model"] = "gemma-4-E4B-it-UD-Q4_K_XL"
  messages = Vector{Dict{String, Any}}()
  # System prompt as system message
  if !isempty(ctx.systemPrompt)
    push!(messages, Dict(
@@ -203,21 +215,101 @@ function formatMsgForLLM(ctx::agentContext)::Dict{String, Any}
      "content" => [Dict("type" => "text", "text" => ctx.systemPrompt)]
    ))
  end
  # Conversation messages
  for msg in ctx.messages
    if msg isa userMessage
      push!(messages, _userMessageToOpenAI(msg))
    elseif msg isa assistantMessageToolCall
      push!(messages, _assistantMessageToolCallToOpenAI(msg))
    elseif msg isa assistantMessage
      push!(messages, _assistantMessageToOpenAI(msg))
    elseif msg isa toolResultMessage
      push!(messages, _toolResultMessageToOpenAI(msg))
    end
  end
  # Convert ctx.tools into OpenAI tools format
  tools_array = _toolsToOpenAI(ctx.tools, eventSink)
  openaiReadyMsg["messages"] = messages
  openaiReadyMsg["temperature"] = 0.7
-  return Dict("messages" => messages)
+  if !isempty(tools_array)
    openaiReadyMsg["tools"] = tools_array
  end
  return openaiReadyMsg
 end
 """
    beforeToolCall(context::beforeToolCallContext, signal::abortSignal) -> beforeToolCallResult
 Callback invoked before executing a tool call. Use this hook to inspect
 the tool call and decide whether to allow, block, or modify it.
 Common use cases:
  - Request user approval via UI before running destructive tools.
  - Validate business rules that cannot be expressed in the JSON schema.
  - Check final context (e.g. session state, rate limits, permissions).
 # Arguments
 - `context::beforeToolCallContext`: Contains the assistant message, tool call,
  validated arguments, and current conversation context.
 - `signal::abortSignal`: Signal that may be set to abort the operation.
 # Returns
 - `beforeToolCallResult(false, "N/A")` to allow the call to proceed.
 - `beforeToolCallResult(true, "Reason")` to block the call with a reason.
 - `nothing` is treated as allow (equivalent to `beforeToolCallResult(false, "N/A")`).
 # Example
 ```jldoctest
 function beforeToolCall(context::beforeToolCallContext, signal::abortSignal)
    if context.toolCall.name == "deleteFile"
        # Block file deletion unless explicitly approved
        return beforeToolCallResult(true, "User must approve file deletion")
    end
    return beforeToolCallResult(false, "N/A")
 end
 ```
 """
 function beforeToolCall(context::beforeToolCallContext, signal::abortSignal, eventSink
  )::beforeToolCallResult
  # eventSink("beforeToolCall: $(context.toolCall.name) args=$(context.args)")
  return beforeToolCallResult(false, "N/A")
 end
 """
    afterToolCall(context::afterToolCallContext, signal::abortSignal) -> Union{agentToolResult, Nothing}
 Callback invoked after a tool call finishes executing (before and after errors).
 Use this hook to post-process the tool result before it is fed back to the LLM.
 Common use cases:
  - Mask sensitive data (API keys, tokens) from result content.
  - Normalize usage tracking data into a consistent format.
  - Inspect the result and set `terminate: true` based on business logic
    (e.g. "if deployment failed, stop the agent rather than retrying").
  - Wrap error results in friendlier messages for the LLM to understand.
 # Arguments
 - `context::afterToolCallContext`: Contains the assistant message, tool call,
  arguments, raw result, error status, and current conversation context.
 - `signal::abortSignal`: Signal that may be set to abort the operation.
 # Returns
 - `nothing` to pass the result through unchanged.
 - `agentToolResult(...)` to return a modified result (content, details, usage,
  terminate flag can all be overridden).
 """   # arrange result from searchWine tool
 function afterToolCall(context::afterToolCallContext, signal::abortSignal, eventSink
  )::Union{agentToolResult, Nothing}
  # eventSink("afterToolCall: $(context.toolCall.name) result=$(context.result) error=$(context.isError)")
  return nothing
 end
 """
 Convert a userMessage to OpenAI message format.
@@ -230,6 +322,44 @@ function _userMessageToOpenAI(msg::userMessage)::Dict{String, Any}
 end
 """
 Convert an assistantMessageToolCall to OpenAI message format.
 Produces a message with role="assistant", content=null, and a tool_calls array:
 {
  "role": "assistant",
  "content": null,
  "tool_calls": [
    {
      "id": "call_1",
      "type": "function",
      "function": {
        "name": "get_weather",
        "arguments": "{\"location\": \"San Francisco, CA\"}"
      }
    }
  ]
 }
 """
 function _assistantMessageToolCallToOpenAI(msg::assistantMessageToolCall)::Dict{String, Any}
  tool_calls = Dict{String, Any}[]
  for tc in msg.toolCalls
    push!(tool_calls, Dict(
      "id" => tc.id,
      "type" => tc.type,
      "function" => Dict(
        "name" => tc.name,
        "arguments" => JSON.json(tc.arguments)
      )
    ))
  end
  return Dict(
    "role" => "assistant",
    "content" => nothing,
    "tool_calls" => tool_calls
  )
 end
 """
 Convert an assistantMessage to OpenAI message format.
 """
@@ -280,7 +410,40 @@ end
 """
-    validateRequiredArgs(args::Dict{String,Any}, inputSchema::Dict{String,Any}) -> Union{Nothing,String}
+    _toolsToOpenAI(tools::Union{OrderedDict{String, agentTool}, Nothing}) -> Vector{Dict{String, Any}}
 Convert an OrderedDict of agentTool definitions into OpenAI function tool format.
 Returns an empty vector when `tools` is `nothing` or empty.
 # Examples
 ```jldoctest
 _toolsToOpenAI(nothing)  # => Dict{String, Any}[]
 _toolsToOpenAI(tools)    # => [Dict("type" => "function", "function" => Dict("name" => "getWeather", ...))]
 ```
 """
 function _toolsToOpenAI(tools::Union{OrderedDict{String, agentTool}, Nothing}, eventSink)::Vector{Dict{String, Any}}
  tools_array = Vector{Dict{String, Any}}()
  if tools !== nothing
    for (_, tool) in tools
      push!(tools_array, Dict(
        "type" => "function",
        "function" => Dict(
          "name" => tool.name,
          "description" => tool.description,
          "parameters" => tool.inputSchema
        )
      ))
    end
  end
  return tools_array
 end
 """
    validateRequiredArgs(args::AbstractDict{String, Any}, inputSchema::AbstractDict{String, Any}) -> Union{Nothing,String}
 Validates that all required fields listed in the tool's JSON Schema are present
 in `args`. Returns `nothing` if validation passes, or a descriptive error string
@@ -291,15 +454,15 @@ with a custom validation function that performs additional checks (e.g. type
 coercion, format validation, cross-field constraints).
 # Arguments
- `args::Dict{String,Any}`: The arguments provided by the LLM
+- `args::AbstractDict{String, Any}`: The arguments provided by the LLM
- `inputSchema::Dict{String,Any}`: The tool's `inputSchema` (JSON Schema format)
+- `inputSchema::AbstractDict{String, Any}`: The tool's `inputSchema` (JSON Schema format)
 # Returns
 - `nothing` if all required args are present
 - `String` error message listing missing fields otherwise
 # Examples
-```julia
+```jldoctest
 schema = Dict("required" => ["city"])
 args = Dict{String,Any}()
 validateRequiredArgs(args, schema)  # => "Missing required arguments: city"
@@ -308,7 +471,7 @@ args2 = Dict("city" => "Tokyo")
 validateRequiredArgs(args2, schema)  # => nothing
 ```
 """
-function validateRequiredArgs(args::Dict{String,Any}, inputSchema::Dict{String,Any})::Union{Nothing,String}
+function validateRequiredArgs(args::AbstractDict{String, Any}, inputSchema::AbstractDict{String, Any})::Union{Nothing,String}
    required = get(inputSchema, "required", Any[])
    if isempty(required)
        return nothing
@@ -354,7 +517,7 @@ false `toolExecutionStart` events.
  and converted to an `immediateOutcome`
 # Examples
-```julia
+```jldoctest
 # With validateRequiredArgs hook set on the tool
 validateToolArguments(toolWithHook, tc)  # => validated args or throws
@@ -362,7 +525,7 @@ validateToolArguments(toolWithHook, tc)  # => validated args or throws
 validateToolArguments(toolDefault, tc)  # => args or throws
 ```
 """
-function validateToolArguments(tool::agentTool, prepared::agentToolCall)::Dict{String,Any}
+function validateToolArguments(tool::agentTool, prepared::agentToolCall)::AbstractDict{String, Any}
    # Use default (2-arg: args + schema) or tool-specific hook (1-arg: args only)
    if isnothing(tool.validateRequiredArgs)
        result = validateRequiredArgs(prepared.arguments, tool.inputSchema)
@@ -375,7 +538,7 @@ function validateToolArguments(tool::agentTool, prepared::agentToolCall)::Dict{S
    end
    return prepared.arguments
-end
+end
@@ -1,375 +0,0 @@
 module type
 export agent, sommelier, companion, virtualcustomer, agentcontext
 using Dates, UUIDs, DataStructures, JSON, NATS
 using GeneralUtils
 # ---------------------------------------------- 100 --------------------------------------------- #
 mutable struct agentcontext
  text2textInstructLLM::Function
  getTextEmbedding::Function
  executeSQL::Function
  similarSQLVectorDB::Function
  insertSQLVectorDB::Function
  similarSommelierDecision::Function
  insertSommelierDecision::Function
  find_related_tables_for_user_question::Function
  pg_conn_str::String
  agentconfig::AbstractDict
 end
 abstract type agent end
 mutable struct sommelier <: agent
  name::String  # agent name
  id::String    # agent id
  retailername::String
  retailerid::String
  tools::Dict   
  maxHistoryMsg::Integer  # e.g. 21th and earlier messages will get summarized
  chathistory::Vector{Dict{String, Any}}
  memory::Dict{String, Any}
  context::agentcontext 
  llmFormatName::String
 end
 """ A sommelier agent.
 # Arguments
  - `context::agentcontext`
    Application context containing shared functions for LLM, SQL, and vector database operations.
 # Keyword Arguments
  - `name::String`
    Agent's name. Default: `"Assistant"`
  - `id::String`
    Agent's ID. Default: generated UUID string.
  - `retailername::String`
    Retailer name associated with the sommelier. Default: `"retailer_name"`
  - `maxHistoryMsg::Integer`
    Maximum history messages. Default: `20`
  - `chathistory::Vector{Dict{String, String}}`
    Chat history. Default: empty vector.
  - `llmFormatName::String`
    LLM format name. Default: `"granite3"`
 # Return
  - `sommelier`: An instantiated sommelier agent.
 # Example
 ```julia
 julia> using YiemAgent
 julia> context = agentcontext(
           text2textInstructLLM,
           getTextEmbedding,
           executeSQL,
           similarSQLVectorDB,
           insertSQLVectorDB,
           similarSommelierDecision,
           insertSommelierDecision
       )
 julia> agent = sommelier(context, name="WineExpert", id="123", retailername="MyWineShop")
 ```
 """
 function sommelier(
  context::agentcontext,  # agent functions, db connect and other context
  ;
  name::String= "Assistant",
  id::String= string(uuid4()),
  retailername::String= "not specified",
  retailerid::String= "not specified",
  maxHistoryMsg::Integer= 20,
  chathistory::Vector{Dict{String, Any}} = Vector{Dict{String, Any}}(),
  llmFormatName::String= "granite3"
  )
  tools = Dict( # update input format
      "chatbox"=> Dict(
        "description" => "<askbox tool description>Useful for when you need to ask the user for more context. Do not ask the user their own question.</askbox tool description>",
        "input" => """<input>Input is a text in JSON format.</input><input example>{\"Q1\": \"How are you doing?\", \"Q2\": \"How may I help you?\"}</input example>""",
        "output" => "" ,
      ),
      "winestock"=> Dict(
        "description" => "<winestock tool description>A handy tool for searching wine in your inventory that match the user preferences.</winestock tool description>",
        "input" => """<input>Input is a JSON-formatted string that contains a detailed and precise search query.</input><input example>{\"wine type\": \"rose\", \"price\": \"max 35\", \"sweetness level\": \"sweet\", \"intensity level\": \"light bodied\", \"Tannin level\": \"low\", \"Acidity level\": \"low\"}</input example>""",
        "output" => """<output>Output are wines that match the search query in JSON format.""",
      ),
    )
  """ Memory
    Chat history use openai format as follow:
    image1_path = "test/large_image.png"                           ---
    image1_bytes = read(image1_path)                                 | this part must be done
    image1_base64_string = base64encode(image1_bytes)                | in frontend 
    mime_type = "image/png"                                          | not in agent code
    data1_uri = "data:<mime_type>;base64,<image1_base64_string>"   ---
    chathistory= [
      Dict(
        "role" => "system",
        "content" => [
          Dict("type" => "text", "text" => "You are a helpful assistant"),
        ]
      ),
      Dict(
        "role" => "user",
        "content" => [
          Dict("type" => "text", "text" => "<internal_context_for_assistant> 
                                            LLM context here...
                                            </internal_context_for_assistant>
                                            Do you know this wine? Just give me brief intro."
          ),
          Dict(
            "type" => "image_url",
            "image_url" => Dict("url" => data1_uri)
          ),
        ]
      ),
    ]
  shortmem = Dict(
    "1"=> Dict("plan"=> "...", "action_name"=> "...", "action_input"=> "...", "action_result"=> "..."),
    "2"=> Dict("plan"=> "...", "action_name"=> "...", "action_input"=> "...", "action_result"=> "..."),
    ...
    )
  """
  memory = Dict{String, Any}(
            "shortmem"=> OrderedDict{String, Any}(),
            "scratchpad"=> "",
            "recap"=> OrderedDict{String, Any}(),
          )
  newAgent = sommelier(
    name,
    id,
    retailername,
    retailerid,
    tools,
    maxHistoryMsg,
    chathistory,
    memory,
    context,
    llmFormatName
    )
  systemmsg = 
    """
    # store_policy
      - Generally speaking, the store inventory has some wines from France, the United States, Australia, Spain, and Italy, but you won't know exactly until you check your inventory.
      - If you found wines in the store's database, they are in stock.
      - You can only recommend wines that are currently in our inventory
      - Before searching the database for wine, ensure you have at least the following information: 1) budget, 2) wine type, and 3) occasion. Additional details are always helpful. If the user is unsure, provide relevant information and gather insights to make reasonable inferences.
      - Ask the user one question at a time.
      - Once the user has selected their wine, if you haven't already, ask the user whether they need any further assistance. Do not offer any additional services.
      - Only end the conversation when the user explicitly intends to do so. When ending, ensure a polite farewell and an invitation to return in the future.
      - Spicy foods should be paired only with light red wines.
      - We do not sell organic, sustainable, gluten-free, and sulfite-free wine. Inform the user imediately if they are looking for these types of wines. Do not sell our wines as such.
      - Gift box, gift card, and custom messages are available. Inform the user to contact our sales team.
    # store_guidelines
      - Greeting the customer warmly by ask them how could you help. Do not ask any other questions during this greeting.
      - Customer may provide images for you to look up.
      - Encourage the customer to explore different options and try new things.
      - If you are unable to locate the desired item in the database after 2 attempts, it may not be available in your inventory. In such cases, inform the user that the item is unavailable and suggest an alternative instead.
      - Your store carries only wine.
      - Vintage 0 means non-vintage.
      - Start searching the database as broadly as possible within the given information boundary to maximize the chances of finding. Avoid unnecessary parameters unless specified by the user. Refine the search subsequently.
      - User usually ask for something similar. This means you should use the search term based on the profile they like.
    # situation
      You are having conversation with a customer.
    # your role
      Your name is $(newAgent.name). You are a helpful sommelier for website-based $(newAgent.retailername)'s wine store.
    # objective
      - Establish a connection with the customer by talking to them politely and showing your enthusiasm for their wine preferences.
      - Provide relevant information and guide them to select the best wines only from your store's inventory that align with their preferences.
    # your responsibility includes
      - According to the store's policy and guidelines, and make an informed decision about what available_actions you need to use to achieve the objective.
      - Keep the conversation with the customer going smoothly
    # your responsibility does NOT includes
      - Requesting the user to place an order, make a purchase, or confirm the order. These are the job of our sales team at the store.
      - Processing sales orders or engaging in any other sales-related activities. These are the job of our sales team at the store.
      - Answering questions or offering additional services beyond those related to your store's wine recommendations such as discounts, quantity, rewards programs, promotions, delivery options, shipping, boxes, gift wrapping, packaging, personalized messages or something similar. These are the job of our sales team at the store.
    # you should then respond to the user with interleaving plan, action_name, action_input in JSON format
      1) "plan", Based on the current situation, state a complete action plan to complete the task and rationale. Be specific.
      2) "action_name", (Typically corresponds to the execution of the first step in your plan) Can be one of the available_actions name
      3) "action_input", The input to the action you are about to perform according to your plan.
      After the action is executed you gets "action_result". It is the output from the action you selected.
    # available actions
      "CHAT_BOX", which you can use to talk with the user. The input is dialogue you want to chat with the user according to your plan.
      "SEARCH_WINE_DATABASE", allows you to search information about wines you want in your inventory's database. The input is strictly supported search term including: retailer_name, wine price, winery, name, vintage, region, country, type of wine, grape varietal, tasting notes, occasion, food pairing, intensity, tannin, sweetness, and acidity.
        Example query 1: "Dry, full-bodied red wine from Burgundy, France. Grape varietal could be Merlot or Syrah. price 100 to 1000 USD."
        Example query 2: "Red or white wine, medium tannin, price under 700 USD"
        Example query 3: "white wine from Tuscany, Italy or Bordeaux, France
      "WINE_PRESENTATION_GUIDELINE", which you can use to check the store guidelines about how to present wines you have found to the user. The input is "nothing" keyword. The output is the guidelines that you can follow.
      "END_CONVER_GUIDELINE", which you can use to check the store guidelines about how to end the conversation with the user. The input is "nothing" keyword. The output is the guidelines that you can follow.
    """
  system_msg = Dict(
      "role" => "system",
      "content" => [
        Dict("type" => "text", "text" => systemmsg),
      ]
    )
  push!(newAgent.chathistory, system_msg)
  return newAgent
 end
 mutable struct virtualcustomer <: agent
  name::String  # agent name
  id::String    # agent id
  systemmsg::String # system message
  tools::Dict   
  maxHistoryMsg::Integer  # e.g. 21th and earlier messages will get summarized
  chathistory::Vector{Dict{String, Any}}
  memory::Dict{String, Any}
  context  # NamedTuple of functions 
  llmFormatName::String
 end
 function virtualcustomer(
  context,  # NamedTuple of functions
  ;
  name::String= "Assistant",
  id::String= string(uuid4()),
  maxHistoryMsg::Integer= 20,
  chathistory::Vector{Dict{String, String}} = Vector{Dict{String, String}}(),
  llmFormatName::String= "granite3",
  systemmsg::String=
    """
      Your name: $name
      Your sex: Female
      Your role: You are a helpful assistant.
      You should follow the following guidelines:
      - Focus on the latest conversation.
      - Your like to be short and concise.
      Let's begin!
      """,
  )
  tools = Dict( # update input format
      "chatbox"=> Dict(
        "description" => "<askbox tool description>Useful for when you need to ask the user for more context. Do not ask the user their own question.</askbox tool description>",
        "input" => """<input>Input is a text in JSON format.</input><input example>{\"Q1\": \"How are you doing?\", \"Q2\": \"How may I help you?\"}</input example>""",
        "output" => "" ,
      ),
    )
  """ Memory
    Ref: Chat prompt format is openai
    chathistory = [
      Dict(
        "role" => "system",
        "content" => [
          Dict("type" => "text", "text" => system_msg),
        ]
      ),
      Dict(
        "role" => "user",
        "content" => [
          Dict("type" => "text", "text" => "Do you know this wine? Just give me brief intro."),
          Dict(
            "type" => "image_url",
            "image_url" => Dict("url" => data1_uri)
          )
        ]
      )
    ]
  """
  memory = Dict{String, Any}(
            "shortmem"=> OrderedDict{String, Any}(
            ),
            "scratchpad"=> "",
            "events"=> Vector{Dict{String, Any}}(),
            "state"=> Dict{String, Any}(
            ),
            "recap"=> OrderedDict{String, Any}(),
          )
  newAgent = virtualcustomer(
    name,
    id,
    systemmsg,
    tools,
    maxHistoryMsg,
    chathistory,
    memory,
    context,
    llmFormatName
    )
  return newAgent
 end
 end # module type
@@ -1,41 +0,0 @@
 # This file is machine-generated - editing it directly is not advised
 julia_version = "1.11.4"
 manifest_format = "2.0"
 project_hash = "71d91126b5a1fb1020e1098d9d492de2a4438fd2"
 [[deps.Base64]]
 uuid = "2a0f44e3-6c83-55bd-87e4-b1978d98bd5f"
 version = "1.11.0"
 [[deps.InteractiveUtils]]
 deps = ["Markdown"]
 uuid = "b77e0a4c-d291-57a0-90e8-8db25a27a240"
 version = "1.11.0"
 [[deps.Logging]]
 uuid = "56ddb016-857b-54e1-b83d-db4d58db5568"
 version = "1.11.0"
 [[deps.Markdown]]
 deps = ["Base64"]
 uuid = "d6f4376e-aef5-505a-96c1-9c027394607a"
 version = "1.11.0"
 [[deps.Random]]
 deps = ["SHA"]
 uuid = "9a3f8284-a2c9-5f02-9a11-845980a1fd5c"
 version = "1.11.0"
 [[deps.SHA]]
 uuid = "ea8e919c-243c-51af-8825-aaa63cd721ce"
 version = "0.7.0"
 [[deps.Serialization]]
 uuid = "9e88b42a-f829-5b0c-bbe9-9e923198166b"
 version = "1.11.0"
 [[deps.Test]]
 deps = ["InteractiveUtils", "Logging", "Random", "Serialization"]
 uuid = "8dfed614-e22c-5e08-85e1-65c5234f0b40"
 version = "1.11.0"
@@ -1,2 +0,0 @@
 [deps]
 Test = "8dfed614-e22c-5e08-85e1-65c5234f0b40"
@@ -0,0 +1,642 @@
 using Test
 using YiemAgent
 using YiemAgent.agentCore
 using YiemAgent.type
 using JSON
 # Import the function from the private module scope
 import YiemAgent.agentCore: _extractToolCalls
@testset "_extractToolCalls" begin
    # ------------------------------------------------------------------ #
    # Format 1: response["message"]["tool_calls"] (LMStudio.jl style)     #
    # ------------------------------------------------------------------ #
    @testset "single tool call via message format" begin
        response = Dict{String,Any}(
            "finish_reason" => "tool_calls",
            "index" => 0,
            "message" => Dict{String,Any}(
                "role" => "assistant",
                "content" => "",
                "reasoning_content" => "Let me check the weather.",
                "tool_calls" => Any[
                    Dict{String,Any}(
                        "type" => "function",
                        "function" => Dict{String,Any}(
                            "name" => "getWeather",
                            "arguments" => "{\"city\":\"Bangkok, Thailand\"}",
                        ),
                        "id" => "tc_001",
                    )
                ],
            ),
        )
        has_toolcalls, tc_list, assistant_msg = _extractToolCalls(response)
        @test has_toolcalls == true
        @test length(tc_list) == 1
        @test tc_list[1].name == "getWeather"
        @test tc_list[1].id == "tc_001"
        @test tc_list[1].type == "function"
        @test tc_list[1].arguments["city"] == "Bangkok, Thailand"
        @test assistant_msg isa assistantMessage
        @test assistant_msg.role == "assistant"
        @test assistant_msg.stopReason == "tool_calls"
        @test length(assistant_msg.content) == 1
        @test assistant_msg.content[1] isa reasoningContent
        @test assistant_msg.content[1].text == "Let me check the weather."
    end
    @testset "multiple tool calls via message format" begin
        response = Dict{String,Any}(
            "message" => Dict{String,Any}(
                "role" => "assistant",
                "content" => "",
                "tool_calls" => Any[
                    Dict{String,Any}(
                        "type" => "function",
                        "function" => Dict{String,Any}(
                            "name" => "getWeather",
                            "arguments" => "{\"city\":\"Tokyo, Japan\"}",
                        ),
                        "id" => "tc_001",
                    ),
                    Dict{String,Any}(
                        "type" => "function",
                        "function" => Dict{String,Any}(
                            "name" => "getTime",
                            "arguments" => "{\"timezone\":\"Asia/Tokyo\"}",
                        ),
                        "id" => "tc_002",
                    ),
                ],
            ),
        )
        has_toolcalls, tc_list, assistant_msg = _extractToolCalls(response)
        @test has_toolcalls == true
        @test length(tc_list) == 2
        @test tc_list[1].name == "getWeather"
        @test tc_list[1].arguments["city"] == "Tokyo, Japan"
        @test tc_list[2].name == "getTime"
        @test tc_list[2].arguments["timezone"] == "Asia/Tokyo"
        @test assistant_msg.role == "assistant"
    end
    @testset "tool call with empty arguments string" begin
        response = Dict{String,Any}(
            "message" => Dict{String,Any}(
                "role" => "assistant",
                "tool_calls" => Any[
                    Dict{String,Any}(
                        "type" => "function",
                        "function" => Dict{String,Any}(
                            "name" => "listTools",
                            "arguments" => "{}",
                        ),
                        "id" => "tc_empty",
                    ),
                ],
            ),
        )
        has_toolcalls, tc_list, assistant_msg = _extractToolCalls(response)
        @test has_toolcalls == true
        @test length(tc_list) == 1
        @test tc_list[1].name == "listTools"
        @test tc_list[1].arguments == Dict{String,Any}()
        @test assistant_msg.stopReason == "end_turn"
    end
    @testset "tool call with missing id falls back to uuid" begin
        response = Dict{String,Any}(
            "message" => Dict{String,Any}(
                "role" => "assistant",
                "tool_calls" => Any[
                    Dict{String,Any}(
                        "type" => "function",
                        "function" => Dict{String,Any}(
                            "name" => "getTime",
                            "arguments" => "{\"city\":\"NYC\"}",
                        ),
                    ),
                ],
            ),
        )
        has_toolcalls, tc_list, assistant_msg = _extractToolCalls(response)
        @test has_toolcalls == true
        @test length(tc_list) == 1
        @test !isempty(tc_list[1].id)
        @test tc_list[1].name == "getTime"
    end
    @testset "tool call with non-string arguments (pre-parsed dict)" begin
        response = Dict{String,Any}(
            "message" => Dict{String,Any}(
                "role" => "assistant",
                "tool_calls" => Any[
                    Dict{String,Any}(
                        "type" => "function",
                        "function" => Dict{String,Any}(
                            "name" => "getWeather",
                            "arguments" => Dict{String,Any}("city" => "London", "units" => "fahrenheit"),
                        ),
                        "id" => "tc_parsed",
                    ),
                ],
            ),
        )
        has_toolcalls, tc_list, assistant_msg = _extractToolCalls(response)
        @test has_toolcalls == true
        @test length(tc_list) == 1
        @test tc_list[1].arguments["city"] == "London"
        @test tc_list[1].arguments["units"] == "fahrenheit"
    end
    @testset "tool call with api/provider/model/usage metadata" begin
        response = Dict{String,Any}(
            "api" => "openai",
            "provider" => "anthropic",
            "model" => "claude-3-opus",
            "message" => Dict{String,Any}(
                "role" => "assistant",
                "tool_calls" => Any[
                    Dict{String,Any}(
                        "type" => "function",
                        "function" => Dict{String,Any}(
                            "name" => "getTime",
                            "arguments" => "{}",
                        ),
                        "id" => "tc_meta",
                    ),
                ],
            ),
        )
        has_toolcalls, tc_list, assistant_msg = _extractToolCalls(response)
        @test has_toolcalls == true
        @test assistant_msg.api == "openai"
        @test assistant_msg.provider == "anthropic"
        @test assistant_msg.model == "claude-3-opus"
    end
    # --------------------------------------------------------------- #
    # Format 2: response.content blocks (OpenAI API style)            #
    # --------------------------------------------------------------- #
    @testset "content blocks with tool_calls" begin
        response = Dict{String,Any}(
            "content" => Any[
                Dict{String,Any}("type" => "text", "text" => "Let me check."),
                Dict{String,Any}(
                    "type" => "tool_calls",
                    "tool_calls" => Any[
                        Dict{String,Any}(
                            "type" => "function",
                            "function" => Dict{String,Any}(
                                "name" => "getWeather",
                                "arguments" => "{\"city\":\"Paris\"}",
                            ),
                            "id" => "tc_block_1",
                        ),
                    ],
                ),
            ],
        )
        has_toolcalls, tc_list, assistant_msg = _extractToolCalls(response)
        @test has_toolcalls == true
        @test length(tc_list) == 1
        @test tc_list[1].name == "getWeather"
        @test tc_list[1].arguments["city"] == "Paris"
        # text block before tool_calls should be included in content
        @test length(assistant_msg.content) == 1
        @test assistant_msg.content[1].text == "Let me check."
    end
    @testset "content blocks with tool_call (single-call format)" begin
        response = Dict{String,Any}(
            "content" => Any[
                Dict{String,Any}(
                    "type" => "tool_call",
                    "id" => "tc_single",
                    "name" => "getTime",
                    "arguments" => Dict{String,Any}("timezone" => "Europe/London"),
                ),
            ],
        )
        has_toolcalls, tc_list, assistant_msg = _extractToolCalls(response)
        @test has_toolcalls == true
        @test length(tc_list) == 1
        @test tc_list[1].name == "getTime"
        @test tc_list[1].id == "tc_single"
        @test tc_list[1].arguments["timezone"] == "Europe/London"
    end
    @testset "content blocks with reasoning and text" begin
        response = Dict{String,Any}(
            "content" => Any[
                Dict{String,Any}("type" => "reasoning", "text" => "Thinking..."),
                Dict{String,Any}("type" => "text", "text" => "Here's the answer."),
            ],
        )
        has_toolcalls, tc_list, assistant_msg = _extractToolCalls(response)
        @test has_toolcalls == false
        @test length(tc_list) == 0
        @test length(assistant_msg.content) == 2
        @test assistant_msg.content[1] isa reasoningContent
        @test assistant_msg.content[1].text == "Thinking..."
        @test assistant_msg.content[2] isa textContent
        @test assistant_msg.content[2].text == "Here's the answer."
    end
    @testset "content blocks with text and tool_call (tool_call not in content)" begin
        response = Dict{String,Any}(
            "content" => Any[
                Dict{String,Any}("type" => "text", "text" => "Sure, I'll check."),
                Dict{String,Any}(
                    "type" => "tool_call",
                    "id" => "tc_mix",
                    "name" => "getWeather",
                    "arguments" => Dict{String,Any}("city" => "London"),
                ),
            ],
        )
        has_toolcalls, tc_list, assistant_msg = _extractToolCalls(response)
        @test has_toolcalls == true
        @test length(tc_list) == 1
        @test tc_list[1].name == "getWeather"
        # text block included, tool_call block excluded from content
        @test length(assistant_msg.content) == 1
        @test assistant_msg.content[1].text == "Sure, I'll check."
    end
    # ------------------------------------------------------------------ #
    # assistantMessage construction                                       #
    # ------------------------------------------------------------------ #
    @testset "assistantMessage with error_message and errorMessage fallback" begin
        response = Dict{String,Any}(
            "error_message" => "rate limit",
            "content" => Any[Dict{String,Any}("type" => "text", "text" => "fail")],
        )
        has_toolcalls, tc_list, assistant_msg = _extractToolCalls(response)
        @test has_toolcalls == false
        @test assistant_msg.errorMessage == "rate limit"
    end
    @testset "assistantMessage with usage tracking" begin
        response = Dict{String,Any}(
            "content" => Any[Dict{String,Any}("type" => "text", "text" => "hi")],
            "usage" => llmUsage(100, 50),
        )
        has_toolcalls, tc_list, assistant_msg = _extractToolCalls(response)
        @test assistant_msg.usage.inputTokens == 100
        @test assistant_msg.usage.outputTokens == 50
    end
    @testset "assistantMessage with invalid usage defaults to zero" begin
        response = Dict{String,Any}(
            "content" => Any[Dict{String,Any}("type" => "text", "text" => "hi")],
            "usage" => "invalid",
        )
        has_toolcalls, tc_list, assistant_msg = _extractToolCalls(response)
        @test assistant_msg.usage.inputTokens == 0
        @test assistant_msg.usage.outputTokens == 0
    end
    @testset "reasoning_content as textContent" begin
        response = Dict{String,Any}(
            "reasoning_content" => textContent("internal thought"),
            "content" => Any[Dict{String,Any}("type" => "text", "text" => "output")],
        )
        has_toolcalls, tc_list, assistant_msg = _extractToolCalls(response)
        @test length(assistant_msg.content) == 2
        @test assistant_msg.content[1] isa reasoningContent
        @test assistant_msg.content[1].text == "internal thought"
        @test assistant_msg.content[2] isa textContent
        @test assistant_msg.content[2].text == "output"
    end
    # ------------------------------------------------------------------ #
    # No tool call cases                                                  #
    # ------------------------------------------------------------------ #
    @testset "no tool calls found" begin
        response = Dict{String,Any}(
            "content" => Any[
                Dict{String,Any}("type" => "text", "text" => "Hello world."),
            ],
        )
        has_toolcalls, tc_list, assistant_msg = _extractToolCalls(response)
        @test has_toolcalls == false
        @test length(tc_list) == 0
        @test assistant_msg.stopReason == "end_turn"
    end
    @testset "empty message" begin
        response = Dict{String,Any}()
        has_toolcalls, tc_list, assistant_msg = _extractToolCalls(response)
        @test has_toolcalls == false
        @test length(tc_list) == 0
        @test assistant_msg.role == "assistant"
        @test assistant_msg.stopReason == "end_turn"
        @test length(assistant_msg.content) == 0
    end
    @testset "message with empty tool_calls array" begin
        response = Dict{String,Any}(
            "message" => Dict{String,Any}(
                "role" => "assistant",
                "tool_calls" => Any[],
            ),
        )
        has_toolcalls, tc_list, assistant_msg = _extractToolCalls(response)
        @test has_toolcalls == false
        @test length(tc_list) == 0
        @test assistant_msg.role == "assistant"
    end
    @testset "message field is not a Dict" begin
        response = Dict{String,Any}(
            "message" => "not a dict",
            "content" => Any[Dict{String,Any}("type" => "text", "text" => "fallback")],
        )
        has_toolcalls, tc_list, assistant_msg = _extractToolCalls(response)
        @test has_toolcalls == false
        @test length(tc_list) == 0
        @test length(assistant_msg.content) == 1
    end
    @testset "Format 1 takes priority over Format 2" begin
        response = Dict{String,Any}(
            "message" => Dict{String,Any}(
                "role" => "assistant",
                "tool_calls" => Any[
                    Dict{String,Any}(
                        "type" => "function",
                        "function" => Dict{String,Any}(
                            "name" => "getWeather",
                            "arguments" => "{\"city\":\"Format1\"}",
                        ),
                        "id" => "tc_fmt1",
                    ),
                ],
            ),
            "content" => Any[
                Dict{String,Any}(
                    "type" => "tool_call",
                    "id" => "tc_fmt2",
                    "name" => "getTime",
                    "arguments" => Dict{String,Any}("city" => "Format2"),
                ),
            ],
        )
        has_toolcalls, tc_list, assistant_msg = _extractToolCalls(response)
        @test has_toolcalls == true
        @test length(tc_list) == 1
        @test tc_list[1].name == "getWeather"
    end
    # ------------------------------------------------------------------ #
    # Edge cases                                                          #
    # ------------------------------------------------------------------ #
    @testset "tool call with null arguments" begin
        response = Dict{String,Any}(
            "message" => Dict{String,Any}(
                "role" => "assistant",
                "tool_calls" => Any[
                    Dict{String,Any}(
                        "type" => "function",
                        "function" => Dict{String,Any}(
                            "name" => "getTime",
                            "arguments" => nothing,
                        ),
                        "id" => "tc_null",
                    ),
                ],
            ),
        )
        has_toolcalls, tc_list, assistant_msg = _extractToolCalls(response)
        @test has_toolcalls == true
        @test length(tc_list) == 1
        @test tc_list[1].name == "getTime"
    end
    @testset "tool call with missing function key" begin
        response = Dict{String,Any}(
            "message" => Dict{String,Any}(
                "role" => "assistant",
                "tool_calls" => Any[
                    Dict{String,Any}(
                        "type" => "function",
                        "id" => "tc_nofunc",
                    ),
                ],
            ),
        )
        has_toolcalls, tc_list, assistant_msg = _extractToolCalls(response)
        @test has_toolcalls == true
        @test length(tc_list) == 1
        @test tc_list[1].name == ""
    end
    @testset "tool call with missing name in function block" begin
        response = Dict{String,Any}(
            "message" => Dict{String,Any}(
                "role" => "assistant",
                "tool_calls" => Any[
                    Dict{String,Any}(
                        "type" => "function",
                        "function" => Dict{String,Any}("arguments" => "{}"),
                        "id" => "tc_noname",
                    ),
                ],
            ),
        )
        has_toolcalls, tc_list, assistant_msg = _extractToolCalls(response)
        @test has_toolcalls == true
        @test length(tc_list) == 1
        @test tc_list[1].name == ""
    end
    @testset "message format with JSON.Object (JSON.parse result)" begin
        json_str = JSON.json(Dict(
            "message" => Dict(
                "role" => "assistant",
                "tool_calls" => [
                    Dict(
                        "type" => "function",
                        "function" => Dict("name" => "getWeather", "arguments" => "{\"city\":\"Test\"}"),
                        "id" => "tc_jsonobj",
                    ),
                ],
            ),
        ))
        parsed = JSON.parse(json_str)
        has_toolcalls, tc_list, assistant_msg = _extractToolCalls(parsed)
        @test has_toolcalls == true
        @test length(tc_list) == 1
        @test tc_list[1].name == "getWeather"
        @test tc_list[1].arguments["city"] == "Test"
    end
    @testset "tool_calls block with mixed content types (text + tool_calls)" begin
        response = Dict{String,Any}(
            "content" => Any[
                Dict{String,Any}("type" => "text", "text" => "I'll check both."),
                Dict{String,Any}(
                    "type" => "tool_calls",
                    "tool_calls" => Any[
                        Dict{String,Any}(
                            "type" => "function",
                            "function" => Dict{String,Any}(
                                "name" => "getWeather",
                                "arguments" => "{\"city\":\"London\"}",
                            ),
                            "id" => "tc_mix1",
                        ),
                        Dict{String,Any}(
                            "type" => "function",
                            "function" => Dict{String,Any}(
                                "name" => "getTime",
                                "arguments" => "{\"timezone\":\"UTC\"}",
                            ),
                            "id" => "tc_mix2",
                        ),
                    ],
                ),
            ],
        )
        has_toolcalls, tc_list, assistant_msg = _extractToolCalls(response)
        @test has_toolcalls == true
        @test length(tc_list) == 2
        @test tc_list[1].name == "getWeather"
        @test tc_list[2].name == "getTime"
        @test length(assistant_msg.content) == 1
        @test assistant_msg.content[1].text == "I'll check both."
    end
    @testset "tool_call block without arguments field" begin
        response = Dict{String,Any}(
            "content" => Any[
                Dict{String,Any}(
                    "type" => "tool_call",
                    "id" => "tc_noargs",
                    "name" => "getTime",
                ),
            ],
        )
        has_toolcalls, tc_list, assistant_msg = _extractToolCalls(response)
        @test has_toolcalls == true
        @test length(tc_list) == 1
        @test tc_list[1].arguments == Dict{String,Any}()
    end
    @testset "tool_calls block with empty tool_calls array" begin
        response = Dict{String,Any}(
            "content" => Any[
                Dict{String,Any}(
                    "type" => "tool_calls",
                    "tool_calls" => Any[],
                ),
            ],
        )
        has_toolcalls, tc_list, assistant_msg = _extractToolCalls(response)
        @test has_toolcalls == false
        @test length(tc_list) == 0
    end
    @testset "tool_calls block with non-AbstractDict elements" begin
        response = Dict{String,Any}(
            "content" => Any[
                Dict{String,Any}(
                    "type" => "tool_calls",
                    "tool_calls" => Any["not a dict", 42, nothing],
                ),
            ],
        )
        has_toolcalls, tc_list, assistant_msg = _extractToolCalls(response)
        @test has_toolcalls == false
        @test length(tc_list) == 0
    end
    @testset "content field is not a Vector" begin
        response = Dict{String,Any}(
            "content" => "not a vector",
        )
        has_toolcalls, tc_list, assistant_msg = _extractToolCalls(response)
        @test has_toolcalls == false
        @test length(tc_list) == 0
        @test length(assistant_msg.content) == 0
    end
    @testset "Dict-based response with all metadata fields" begin
        response = Dict{String,Any}(
            "api" => "openai",
            "provider" => "anthropic",
            "model" => "claude-3-sonnet",
            "content" => Any[
                Dict{String,Any}(
                    "type" => "tool_call",
                    "id" => "tc_meta",
                    "name" => "getTime",
                    "arguments" => Dict{String,Any}("city" => "Seoul"),
                ),
            ],
        )
        has_toolcalls, tc_list, assistant_msg = _extractToolCalls(response)
        @test has_toolcalls == true
        @test length(tc_list) == 1
        @test tc_list[1].name == "getTime"
        @test tc_list[1].arguments["city"] == "Seoul"
        @test assistant_msg.api == "openai"
        @test assistant_msg.provider == "anthropic"
        @test assistant_msg.model == "claude-3-sonnet"
    end
    @testset "default role is assistant" begin
        response = Dict{String,Any}(
            "content" => Any[Dict{String,Any}("type" => "text", "text" => "no role specified")],
        )
        has_toolcalls, tc_list, assistant_msg = _extractToolCalls(response)
        @test assistant_msg.role == "assistant"
    end
    @testset "tool call with custom role in message format" begin
        response = Dict{String,Any}(
            "message" => Dict{String,Any}(
                "role" => "custom_role",
                "tool_calls" => Any[
                    Dict{String,Any}(
                        "type" => "function",
                        "function" => Dict{String,Any}(
                            "name" => "getWeather",
                            "arguments" => "{}",
                        ),
                        "id" => "tc_role",
                    ),
                ],
            ),
        )
        has_toolcalls, tc_list, assistant_msg = _extractToolCalls(response)
        @test assistant_msg.role == "custom_role"
    end
    @testset "image content block handling" begin
        response = Dict{String,Any}(
            "content" => Any[
                Dict{String,Any}(
                    "type" => "image_url",
                    "image_url" => Dict("url" => "data:image/png;base64,abc123"),
                ),
                Dict{String,Any}("type" => "text", "text" => "What is this?"),
            ],
        )
        has_toolcalls, tc_list, assistant_msg = _extractToolCalls(response)
        @test has_toolcalls == false
        @test length(assistant_msg.content) == 2
        @test assistant_msg.content[1] isa textContent
        @test assistant_msg.content[1].text == ""
        @test assistant_msg.content[2].text == "What is this?"
    end
 end
@@ -1,401 +1,5 @@
-using JSON, Dates, UUIDs, PrettyPrinting, LibPQ, Base64, DataFrames, DataStructures, HTTP, Base64,
+using Test
-      NATS, Base.Threads
+using YiemAgent
 using YiemAgent, GeneralUtils, msghandler
  function text2text_instruct_llm(sender_id::String, openai_msg::Dict{String, Any})
    payloads = [("msg", openai_msg, "dictionary")]  # List of tuples
    _, msg_envelope_json_str = msghandler.smartpack(
          config["externalservice"]["servicesloadbalancer"]["nats"], 
          payloads;
          sender_id=sender_id,
          msg_purpose="text2text",  
          broker_url=config["nats_server_info"]["url"],
          fileserver_url=config["externalservice"]["fileserver"]["url"])
    reply = NATS.request(agent_conn, 
                        config["externalservice"]["servicesloadbalancer"]["nats"], 
                        msg_envelope_json_str, timeout=120)
    incoming_env_json_str = String(reply.payload)
    incoming_env = msghandler.smartunpack(incoming_env_json_str)
    _llm_response = incoming_env["payloads"][1][2]
    llm_response = _llm_response["choices"][1]["message"]["content"]
    return llm_response
  end
  """ get a single text embedding from a LLM service
  Example
  text = ["hello"]
  embedding = get_embedding(text)
  """
  function get_embedding(text::AbstractArray{String})
    documents_dict = Dict("documents" => text)
    payloads = [("documents", documents_dict, "dictionary")]
    _, msg_envelope_json_str = msghandler.smartpack(
      config["externalservice"]["servicesloadbalancer"]["nats"], 
      payloads;   
      msg_purpose="embedding",  
      broker_url=config["nats_server_info"]["url"],
      fileserver_url=config["externalservice"]["fileserver"]["url"])
    reply = NATS.request(agent_conn, 
                        config["externalservice"]["servicesloadbalancer"]["nats"], 
                        msg_envelope_json_str, timeout=120)
    incoming_env_json_str = String(reply.payload)
    incoming_env = msghandler.smartunpack(incoming_env_json_str)
    embedding_response = incoming_env["payloads"][1][2]
    return embedding_response
  end
  """ sql = "SELECT * FROM wine;"
  result = execute_sql_winedb(sql)
  """
  function execute_sql_winedb(sql::T) where {T<:AbstractString}
    host_url, _port = split(config["externalservice"]["sommpanion_db"]["url"], ':')
    port = parse(Int, _port)
    dbname = "winedb"
    user = config["externalservice"]["sommpanion_db"]["user"]
    password = config["externalservice"]["sommpanion_db"]["password"]
    db_connection = LibPQ.Connection("host=$host_url port=$port dbname=$dbname user=$user password=$password")
    result = nothing
    try
      result = LibPQ.execute(db_connection, sql)  
    catch e
      LibPQ.close(db_connection)
    end
    LibPQ.close(db_connection)
    return result
  end
  """ find similar sql from vector database
  sql = "SELECT * FROM wine;"
  result, distance = similar_sql_vectordb(sql)
  """
  function similar_sql_vectordb(sql::T; maxdistance::Number=0.2) where {T<:AbstractString}
    tablename = "sqlllm_decision_repository"
    # get embedding of the query
    df = find_similar_text_from_vectordb(sql, tablename, 
          "function_input_embedding", execute_sql_vectordb)
    # println(df[1, [:id, :function_output]])
    row, col = size(df)
    distance = row == 0 ? Inf : df[1, :distance]  
    if row != 0 && distance < maxdistance
      # if there is usable SQL, return it.
      output_b64 = df[1, :function_output_base64]   # pick the closest match
      output_str = String(base64decode(output_b64))
      rowid = df[1, :id]
      println("\n--| similar sql found. row id $rowid, distance $distance ", @__FILE__, ":", @__LINE__, " $(Dates.now())")
      pprintln(output_str)
      return (result=output_str, distance=distance)
    else
      println("\n--| similar sql not found, max distance $maxdistance ", @__FILE__, ":", @__LINE__, " $(Dates.now())")
      return (result=nothing, distance=nothing)
    end
  end
  """ insert query and sql into vector database
  query = "get all wines from wine table"
  sql = "SELECT * FROM wine;"
  insert_sql_vectordb(query, sql)
  """
  function insert_sql_vectordb(query::T1, SQL::T2; maxdistance::Number=3
    ) where {T1<:AbstractString, T2<:AbstractString}
    tablename = "sqlllm_decision_repository"
    # get embedding of the query
    # query = state[:thoughtHistory][:question]
    df = find_similar_text_from_vectordb(query, tablename, 
                                    "function_input_embedding", execute_sql_vectordb)
    row, col = size(df)
    distance = row == 0 ? Inf : df[1, :distance]
    if row == 0 || distance > maxdistance # no close enough SQL stored in the database
      _query_embedding = get_embedding([query])
      _query_embedding = GeneralUtils.dictify(_query_embedding)
      # println("\n--- _query_embedding() ", @__FILE__, ":", @__LINE__, " $(Dates.now())")
      # println(_query_embedding)
      # println("---\n")
      query_embedding = _query_embedding["data"][1]["embedding"]
      query = replace(query, "'" => "")
      sql_base64 = base64encode(SQL)
      sql_ = replace(SQL, "'" => "")
      sql = 
        """
        INSERT INTO $tablename (function_input, function_output, function_output_base64, function_input_embedding) VALUES ('$query', '$sql_', '$sql_base64', '$query_embedding');
        """
      # println("\n--| added new decision to vectorDB ", @__FILE__, ":", @__LINE__, " $(Dates.now())")
      # println(sql)
      _ = execute_sql_vectordb(sql)
    end
  end
  """ execute sql against vectordb
  sql = "SELECT * FROM wine;"
  result = execute_sql_vectordb(sql)
  """
  function execute_sql_vectordb(sql::T) where {T<:AbstractString}
    host_url, _port = split(config["externalservice"]["sommpanion_vectordb"]["url"], ':')
    port = parse(Int, _port)
    dbname = config["externalservice"]["sommpanion_vectordb"]["dbname"]
    user = config["externalservice"]["sommpanion_vectordb"]["user"]
    password = config["externalservice"]["sommpanion_vectordb"]["password"]
    DBconnection = LibPQ.Connection("host=$host_url port=$port dbname=$dbname user=$user password=$password")
    result = LibPQ.execute(DBconnection, sql)
    close(DBconnection)
    return result
  end
  """ search similar decision llm made from vectordb
  """
  function similar_sommelier_decision(recentevents::T1; maxdistance::Integer=3
    )::Union{AbstractDict, Nothing} where {T1<:AbstractString}
    tablename = "sommelier_decision_repository"
    # find similar
    df = find_similar_text_from_vectordb(recentevents, tablename, 
                                    "function_input_embedding", execute_sql_vectordb)
    row, col = size(df)
    distance = row == 0 ? Inf : df[1, :distance]
    if row != 0 && distance < maxdistance
      # if there is usable decision, return it.
      rowid = df[1, :id]
      println("\n--| found similar decision. row id $rowid, distance $distance ", @__FILE__, " ", @__LINE__)
      output_b64 = df[1, :function_output_base64]   # pick the closest match
      _output_str = String(base64decode(output_b64))
      output = copy(JSON.read(_output_str))
      return output
    else
      println("\n--| similar decision not found, max distance $maxdistance ", @__FILE__, " ", @__LINE__)
      return nothing
    end
  end
  """ search similar text from vectordb
  """
  function find_similar_text_from_vectordb(text::T1, tablename::T2, embeddingColumnName::T3,
    vectorDB::Function; limit::Integer=1
    )::DataFrame where {T1<:AbstractString, T2<:AbstractString, T3<:AbstractString}
    # get embedding from LLM service
    _embedding = get_embedding([text])
    _embedding = _embedding["data"][1]["embedding"]
    _embedding = "$_embedding"
    embedding = _embedding[4:end]
    # check whether there is close enough vector already store in vectorDB. if no, add, else skip 
    sql = """
          SELECT *, $embeddingColumnName <-> '$embedding' as distance
          FROM $tablename
          ORDER BY distance LIMIT $limit;
          """
    response = vectorDB(sql)
    df = DataFrame(response)
    return df
  end
  """ insert decision llm made to vectordb
  """
  function insert_sommelier_decision(recentevents::T1, decision::T2; maxdistance::Integer=5
    ) where {T1<:AbstractString, T2<:AbstractDict}
    tablename = "sommelier_decision_repository"
    # find similar
    df = find_similar_text_from_vectordb(recentevents, tablename, 
                                    "function_input_embedding", execute_sql_vectordb)
    row, col = size(df)
    distance = row == 0 ? Inf : df[1, :distance]
    if row == 0 || distance > maxdistance # no close enough SQL stored in the database
      _embedding = get_embedding([recentevents])[1]
      recentevents_embedding = _embedding["data"][1]["embedding"]
      recentevents = replace(recentevents, "'" => "")
      decision_json = JSON.json(decision)
      decision_base64 = base64encode(decision_json)
      decision = replace(decision_json, "'" => "")
      sql = 
      """
      INSERT INTO $tablename (function_input, function_output, function_output_base64, function_input_embedding) VALUES ('$recentevents', '$decision', '$decision_base64', '$recentevents_embedding');
      """
      println("\n--| added new decision to vectorDB ", @__FILE__, " ", @__LINE__)
      println(sql)
      _ = execute_sql_vectordb(sql)
    else
      println("--| similar decision previously cached, distance $distance ", @__FILE__, " ", @__LINE__)
    end
  end
 config = JSON.parsefile("./appconfig.json")
 sessionId = "0"
 backend_session_topic = "sommpanion.testsubject"
 agent_ch = Channel(8)
 agent_conn  = NATS.connect(config["nats_server_info"]["url"])
 sub2 = NATS.subscribe(agent_conn, backend_session_topic) do msg
  put!(agent_ch, msg)
 end
 agent_context = YiemAgent.agentcontext(
    text2text_instruct_llm,
    get_embedding,
    execute_sql_winedb,
    similar_sql_vectordb,
    insert_sql_vectordb,
    similar_sommelier_decision,
    insert_sommelier_decision
  )
  # can't instantiate
  agent = YiemAgent.sommelier(
    agent_context;
    name="Janie",
    id=sessionId,  # agent instance id
    retailername="Yiem Wine Ltd.",
    llmFormatName=""
  )
 image1_path = "test/large_image.png"
 image1_bytes = read(image1_path)
 image1_base64_string = base64encode(image1_bytes)
 mime_type = "image/png" 
 data1_uri = "data:$(mime_type);base64,$(image1_base64_string)"
 # 1. Read local file and encode to base64 string
 image2_path = "test/small_image.png"
 image2_bytes = read(image2_path)
 image2_base64_string = base64encode(image2_bytes)
 mime_type = "image/png" 
 data2_uri = "data:$(mime_type);base64,$(image2_base64_string)"
 # 3. Construct payload with the Data URI
 message = Dict(
    "role" => "user",
    "content" => [
      Dict("type" => "text", "text" => "Do you know type of wine in the image?"),
      Dict(
        "type" => "image_url",
        "image_url" => Dict("url" => data1_uri)
      )
    ]
  )
 result = YiemAgent.conversation(agent; userinput=message)
 println("\n$result")
 # message = Dict(
 #     "role" => "user",
 #     "content" => [
 #       Dict("type" => "text", "text" => 
 #       "
 #       เป็นงานเลี้ยงทั่วไป
 #       "),
 #     ]
 #   )
 # result = YiemAgent.conversation(agent; userinput=message)
 # println("\n$result")
 # message = Dict(
 #     "role" => "user",
 #     "content" => [
 #       Dict("type" => "text", "text" => "no thanks. that's all"),
 #     ]
 #   )
 # result = YiemAgent.conversation(agent; userinput=message)
 # println("\n$result")
 # message = Dict(
 #     "role" => "user",
 #     "content" => [
 #       Dict("type" => "text", "text" => "What about this wine?"),
 #       Dict(
 #         "type" => "image_url",
 #         "image_url" => Dict("url" => data2_uri)
 #       )
 #     ]
 #   )
 # result = YiemAgent.conversation(agent; userinput=message)
 # println("\n$result")
 include("toolTest.jl")
 include("_extractToolCalls.jl")
Author	SHA1	Message	Date
ton	af81503f8b	Merge pull request 'V0.9.0 agent status' (#50 ) from v0.9.0-agent_status into v0.9.0 Reviewed-on: #50	2026-09-12 13:04:59 +00:00
ton	4624ad99ab	update	2026-09-12 17:04:00 +07:00
ton	2fd363d9ef	bump version	2026-09-12 16:12:56 +07:00
ton	ea6012b2fa	update	2026-09-12 16:09:00 +07:00
ton	6e53f57707	Merge pull request 'v0.7.4' (#39 ) from v0.7.4 into main Reviewed-on: #39	2026-07-28 00:16:48 +00:00
ton	1eea6c66b6	Merge pull request 'update' (#38 ) from v0.7.4-add_vector_search into v0.7.4 Reviewed-on: #38	2026-07-27 10:43:41 +00:00
ton	37ff68c963	update	2026-07-27 17:43:11 +07:00
ton	e5fc800c83	Merge pull request 'update' (#37 ) from v0.7.4-add_vector_search into v0.7.4 Reviewed-on: #37	2026-07-27 09:55:11 +00:00
ton	4397ec5fb3	update	2026-07-27 16:54:36 +07:00
		`@@ -1 +0,0 @@`
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		`@@ -1,2 +0,0 @@`
			`[deps]`
			`Test = "8dfed614-e22c-5e08-85e1-65c5234f0b40"`