Stéphane Tailland
3c44cc63ea
One entry in the registry and a realm grant, which was the claim the design made about adding a specialist. Nothing in the graph changed. What the two specialists have in common, from the orchestrator's side, is nothing: one is PydanticAI over MCP, the other n8n workflows behind an adapter. It cannot tell them apart, which is what the protocol is for. Co-Authored-By: Claude Opus 5 <noreply@anthropic.com>
37 lines
1.2 KiB
TOML
37 lines
1.2 KiB
TOML
[project]
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name = "assistant-agent"
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version = "0.2.0"
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description = "Assistant — the orchestrating agent for the classic-models estate"
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readme = "README.md"
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requires-python = ">=3.12"
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dependencies = [
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# The A2A facade, and the client for calling specialists. The official SDK
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# rather than a framework's own: this estate exists to prove A2A
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# interoperability across five runtimes, so the reference implementation is
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# the safer base for the others to interoperate with.
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"a2a-sdk[http-server]>=1.1.2,<2",
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# The graph. The orchestrator is where routing is worth making explicit —
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# which specialist is consulted when, and where the answers are reconciled.
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"langgraph>=1.2.11,<2",
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# The model client. The inference endpoint speaks the OpenAI protocol.
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"langchain-openai>=1.6.0,<2",
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"langchain-core>=1.6.1,<2",
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"httpx>=0.28.1,<1",
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"pydantic-settings>=2.7",
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"uvicorn>=0.52.4",
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]
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[project.optional-dependencies]
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dev = ["pytest>=8", "pytest-asyncio>=0.24"]
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[build-system]
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requires = ["hatchling"]
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build-backend = "hatchling.build"
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[tool.hatch.build.targets.wheel]
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packages = ["src/assistant_agent"]
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[tool.pytest.ini_options]
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pythonpath = ["src"]
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asyncio_mode = "auto"
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