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agents-cli-claude

Work on an agent built with the Claude Agent SDK: add tools, run it, generate and grade evaluations, serve it, or deploy it. The guide explains which ADK workflows do not apply and how this scaffold is configured.

OriginalShow the original description

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 a Claude Agent SDK agent scaffolded by agents-cli, so ADK-specific instructions do not apply.

google/agents-cli6,222 stars(GitHub 6,222 + cccskills 0)
Works withGemini

Run score Not tested

See how to install

Good for

  • Adding a tool to a Claude Agent SDK agent
  • Running an agent and continuing a session
  • Generating and grading evaluation cases
  • Serving or deploying the agent

Files included (4)

  • SKILL.md5.2 KB
  • references/claude.md18.7 KB
  • references/migrate-claude-agent.md20.9 KB
  • references/migrate-from-aws.md7.8 KB

SKILL.md (original)

Review the instructions the agent will receive before you install.

Claude Agent SDK project (agents-cli)

The agent is a ClaudeAgentOptions 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. Deploy to agent_runtime (limits: references/claude.md) or cloud_run. The model runs on Claude on Gemini platform and must be enabled in Model Garden.

What ADK guidance maps to here

ADK guidanceThis project
LlmAgent, AgentClaudeAgentOptions(model=..., system_prompt=..., ...) assigned to root_agent
Agent(name=...)AGENT_NAME in app/app_utils/settings.py (ClaudeAgentOptions has no name); keep it equal to create_params.root_agent_name in agents-cli-manifest.yaml
google.adk.tools, FunctionTool@tool(...) async functions, served by create_sdk_mcp_server and passed in mcp_servers
adk web, agents-cli playgroundUnavailable (no SDK dev UI). Use agents-cli run, or uv run uvicorn app.fast_api_app:app --port 8080
ADK runner behind agents-cli runagents-cli run runs root_agent in process
agents-cli eval dataset synthesize, eval optimizeUnavailable: both drive the agent through ADK. The command says so and exits
agents-cli eval generate --url (traces from a served agent)Unavailable: the server speaks A2A, not ADK's /run_sse. eval generate runs root_agent in process; the command refuses --url
ADK sessionsClaude sessions, one per A2A conversation; scaffold with --session-type cloud_sql to keep them across instances

The contract

  • app/agent.py exports root_agent: ClaudeAgentOptions. Only it changes when you change the agent.
  • app/app_utils/claude.py runs it. Leave app/app_utils/ alone.
  • The served agent's own CLAUDE.md, .claude/ and .mcp.json go in agent_config/.
  • app/fast_api_app.py exposes app. Every deployment target runs uvicorn app.fast_api_app:app.

Adding a tool

@tool("lookup_order", "Look up an order by id.", {"order_id": str})
async def lookup_order(args: dict[str, Any]) -> dict[str, Any]:
    return {"content": [{"type": "text", "text": f"Order {args['order_id']}: shipped"}]}


server = create_sdk_mcp_server(name="orders", tools=[lookup_order])

root_agent = ClaudeAgentOptions(
    ...,
    mcp_servers={"orders": server},
    tools=[],  # no built-in Claude Code tools
    allowed_tools=["mcp__orders__lookup_order"],  # mcp__<server>__<tool>
)

A tool missing from allowed_tools is not available to the agent. Subagents (agents= or agent_config/.claude/agents/) run through the built-in Agent tool: add "Agent" to both tools and allowed_tools. Built-in tools (WebFetch, WebSearch, Read, Bash, ...) go in both tools and allowed_tools; keep Bash and file-editing tools out of a served agent unless the user asks.

Commands

agents-cli install                  # uv sync
agents-cli run "hello"              # run root_agent in process; prints a session id
agents-cli run "and?" --session-id <id>   # continue that conversation
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
agents-cli scaffold enhance -d cloud_run --cicd-runner github_actions   # add infra later

run, eval generate, publish gemini, playground, eval dataset and eval optimize 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: JSON-RPC at POST /a2a/app, card at /a2a/app/.well-known/agent-card.json, health at /health; over A2A, text streams as it is generated. On Agent Runtime, also :query and :streamQuery (new sessions and turns), which agents-cli run --mode adk and an ingress Agent Gateway use. See references/claude.md for sessions, telemetry and deployment.

Common mistakes

  • Renaming root_agent or app, which breaks run, eval and deploy.
  • Leaving tools unset: the agent then gets every built-in Claude Code tool.
  • Using a model id that is not enabled on Gemini platform for the project.
  • 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's REST session routes; this server serves A2A and Agent Runtime's :query / :streamQuery.

References

  • references/claude.md — how a turn runs, model access, project settings, sessions, calling other agents, telemetry and logs.
  • references/migrate-claude-agent.md — moving an existing Claude Agent SDK agent into this project and onto GCP.
  • references/migrate-from-aws.md — extra migration steps for an agent running on AWS.

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