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 guidance	This project
`LlmAgent`, `Agent`, `google.adk.tools`	`langchain.agents.create_agent`, plain Python functions as tools, or any compiled `StateGraph`
`adk web`, `adk run`	`agents-cli playground` (runs `langgraph dev`)
ADK runner behind `agents-cli run`	`agents-cli run` invokes the graph in-process
`agents-cli eval dataset synthesize`, `eval optimize`	Unavailable: both drive the agent through ADK. The command says so and exits
Add an `LlmAgent` in `app/agent.py`	Change 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.

/agents-cli-langchain

SKILL.md