First-party frameworks¶

A framework is delivered as a template you scaffold from, not as an extension you install. The template carries its own agents-cli-extension.yaml, so the project gets the framework's command overrides with nothing installed machine-wide.

Framework	What it does
LangChain	Run a LangChain agent through the `agents-cli` lifecycle.

LangChain¶

The LangChain template runs a LangChain agent through the agents-cli lifecycle. It overrides only the framework-coupled commands and leaves everything else — deploy, infra, eval grade — running natively.

Experimental: deploy to Cloud Run or GKE

On Agent Runtime the container serves and traces normally, but the integrations that expect an ADK app do not work, because this project serves A2A and no reasoning_engine routes: publish gemini-enterprise is refused (registration invokes :streamQuery), the Console playground cannot invoke the agent, and the Console's session and trace views stay empty. Traces still reach Cloud Trace.

Create a project¶

agents-cli create my-agent \
    --agent google/agents-cli/extensions/langchain/template@v1.5.0 \
    -d cloud_run
cd my-agent && agents-cli install

The scaffolded project contains agents-cli-extension.yaml at its root. That file is auto-loaded at project scope, so the overrides below are active in this project and nowhere else. Commit it.

What it overrides¶

Command	Behavior in a LangChain project
`playground`	`uv run langgraph dev` — the local LangGraph dev server (also serves A2A).
`publish gemini-enterprise`	Runs the built-in, except on Agent Runtime, where it refuses with the reason.
`run`	Invokes the compiled graph in-process.
`eval generate`	Produces the standard `EvaluationDataset` shape, so `eval grade` is unchanged.
`eval dataset synthesize`, `eval optimize`	Refused with an explanation: both drive the agent through ADK.

Everything else stays native — do not override deploy (the target-appropriate native deploy, e.g. gcloud run deploy --source . on Cloud Run), eval grade / compare / analyze (framework-agnostic), infra, or publish.

The scaffolded agent¶

The default app/agent.py is a Gemini ReAct agent built with langchain.agents.create_agent and a sample get_weather tool. It calls Gemini via Vertex AI using Application Default Credentials; set GOOGLE_API_KEY or GEMINI_API_KEY in .env to use AI Studio instead (the scaffolded .env.example names the latter).

Because the project rides the framework-neutral empty_py substrate, the generated code contains no ADK dependency, and the template ships its own coding-agent skill.

Serving over A2A¶

The deployed agent is served over the Agent2Agent (A2A) protocol — the same contract the rest of the toolchain expects — so it works unchanged:

Entrypoint: uvicorn app.fast_api_app:app (the scaffold Dockerfile CMD).
Endpoints: JSON-RPC at POST /a2a/app; Agent Card at /a2a/app/.well-known/agent-card.json.
Streaming: the executor streams LLM token chunks as incremental A2A task artifacts (capabilities.streaming=True), so a real chat model streams token-by-token. Graphs whose nodes don't stream tokens fall back to a single final artifact.

Query a deployed (or locally served) agent over A2A. Because the project overrides run with in-process graph invocation, set AGENTS_CLI_DISABLE_OVERRIDES=1 to reach the built-in A2A client:

AGENTS_CLI_DISABLE_OVERRIDES=1 agents-cli run --url https://<service-url> --mode a2a --app-name app "hello"

Full journey¶

agents-cli create my-agent --agent google/agents-cli/extensions/langchain/template@v1.5.0 -d cloud_run
cd my-agent && agents-cli install
agents-cli run "what's the weather in San Francisco?"       # in-process graph
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                                            # native Cloud Run deploy

Notes¶

Credentials: run and eval call Gemini, so they need credentials — GOOGLE_CLOUD_PROJECT plus ADC, or GOOGLE_API_KEY / GEMINI_API_KEY for AI Studio.
Run the built-in instead of an override: prefix with AGENTS_CLI_DISABLE_OVERRIDES=1 (e.g. AGENTS_CLI_DISABLE_OVERRIDES=1 agents-cli run "hi").
Deploy contract: app/fast_api_app.py must keep exposing app. If you restructure the agent, keep that import working.
Compatibility: the template's manifest declares no requires range, so its overrides apply on any CLI version. See Authoring → Compatibility for declaring one in your own.