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agents-cli-langchain

Use when working in this project — adding tools, editing the agent, running, evaluating, serving or deploying it — or when guidance mentions ADK, LlmAgent, google.adk, adk web, or an ADK runner. This project is LangChain/LangGraph scaffolded by agents-cli, so ADK-specific instructions do not apply.

インストール方法を見る

含まれるファイル(3)

  • SKILL.md3.9 KB
  • references/langchain.md5.9 KB
  • references/samples.md3.5 KB

SKILL.md(原文)

インストールする前に、エージェントに与えられる指示の中身を確認できます。

LangChain project (agents-cli)

The agent is a compiled LangGraph graph exported as root_agent from app/agent.py. There is no google.adk dependency and no ADK runner. Other google-agents-cli-* skills assume ADK; where they describe the agent itself, this skill wins.

Experimental, and Agent Runtime is degraded. Deploy to cloud_run or gke. On agent_runtime the app serves, but publish gemini-enterprise is refused, the Console playground cannot invoke it, and Console sessions/traces stay empty: all three want the ADK reasoning_engine routes this project does not serve. Say so before recommending it.

What ADK guidance maps to here

ADK guidanceThis project
LlmAgent, Agent, google.adk.toolslangchain.agents.create_agent, plain Python functions as tools, or any compiled StateGraph
adk web, adk runagents-cli playground (runs langgraph dev)
ADK runner behind agents-cli runagents-cli run invokes the graph in-process
agents-cli eval dataset synthesize, eval optimizeUnavailable: both drive the agent through ADK. The command says so and exits
Add an LlmAgent in app/agent.pyChange the graph in app/agent.py; keep the name root_agent

The contract

Keep these two, whatever you build inside them:

  • app/agent.py exports root_agent, a compiled graph with messages state. Callers only use root_agent.invoke({"messages": [...]}) and root_agent.astream(stream_mode="messages").
  • app/fast_api_app.py exposes app. Every deployment target runs uvicorn app.fast_api_app:app.

Adding a tool means writing a typed function with a docstring and passing it in tools=[...]. Switching frameworks (LangGraph StateGraph, deepagents.create_deep_agent) means rewriting app/agent.py only. Pre-1.0 LangChain (LCEL chains, AgentExecutor) is not supported: not compiled graphs.

Commands

agents-cli install                  # uv sync
agents-cli playground               # langgraph dev, port 8080
agents-cli run "hello"              # invoke the graph in-process
agents-cli eval generate --dataset tests/eval/datasets/basic-dataset.json -o tests/eval/output/
agents-cli eval grade --traces tests/eval/output/<dataset>.json --config tests/eval/eval_config.yaml
agents-cli deploy                   # unchanged
agents-cli scaffold enhance -d cloud_run --cicd-runner github_actions   # add infra later

playground, run and eval generate are overridden by agents-cli-extension.yaml at the project root. Prefix any command with AGENTS_CLI_DISABLE_OVERRIDES=1 to reach the built-in instead.

Serving

A2A only: JSON-RPC at POST /a2a/app, card at /a2a/app/.well-known/agent-card.json, health at /health. Token streaming comes from astream(stream_mode="messages").

Common mistakes

  • Renaming root_agent or app, which breaks run, eval and deploy.
  • Reaching for eval dataset synthesize or eval optimize: they need ADK. Write cases into tests/eval/datasets/ and use eval generate + eval grade.
  • Expecting /run_sse or ADK session routes; this server serves A2A.
  • Running agents-cli run --url ... against a deployed agent without AGENTS_CLI_DISABLE_OVERRIDES=1, which invokes the local graph instead.
  • run and eval generate call Gemini through Vertex AI with ADC, so they need GOOGLE_CLOUD_PROJECT and credentials, or GOOGLE_API_KEY / GEMINI_API_KEY in .env.

References

  • references/langchain.md — framework contract and per-command detail.
  • references/samples.md — agents worth copying from, by shape.

レビュー

まだレビューはありません。使ってみた感想をお寄せください。

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