agents-cli is a command-line tool and collection of skills that guides coding assistants through building, evaluating, and deploying AI agents on Google's Gemini Enterprise Agent Platform. Developers use it with assistants such as Claude Code, Codex, and Antigravity CLI to work with Google Cloud agent services. The catalogue entries provide skills and a plugin for that development workflow.
Getting it into your agent
One page per mod, every tool's command on it. A separate URL per tool would split the same page into five that compete with each other.
npx agentmods add skills/google/agents-cli/agents-cli-langchainnpx skills add google/agents-cli --skill agents-cli-langchaingit clone --depth 1 https://github.com/google/agents-cliWrote this? Show the measurements
A badge with what this costs and how it scanned, read live from this page, so it follows the numbers instead of freezing them. Markdown for a README, HTML for a documentation site or a project page.
[](https://agentmods.dev/skills/google/agents-cli/agents-cli-langchain)<a href="https://agentmods.dev/skills/google/agents-cli/agents-cli-langchain"><img src="https://agentmods.dev/badge/skills/google/agents-cli/agents-cli-langchain.svg" alt="Measured on agentmods" height="20"></a>What it costs to keep this loaded
Counted locally with the o200k_base tokenizer, which is exact for GPT models; Claude uses its own tokenizer and its counts differ. Treat this as one consistent yardstick across the catalogue rather than a bill. Prices are per million input tokens.
| Model | Per session | Once invoked |
|---|---|---|
| Fable 5.1 | $0.00076 | $0.01013 |
| Opus 5 | $0.00038 | $0.00507 |
| Sonnet 5 | $0.00015 | $0.00203 |
| Haiku 4.5 | $0.00008 | $0.00101 |
Grade A, and why
agents-cli-langchain scanned grade A with 0 findings against 26 rules in 11 categories — prompt injection, anti-refusal, data exfiltration, privilege escalation, supply chain, agent snooping, system-prompt leakage, SSRF and excessive agency — measured 4d ago.
A static scan of the body, not an audit. Every finding is printed with the line that produced it so you can judge whether it matters here. A mod is markdown that instructs an agent; that is exactly why what it instructs is worth reading.
Nothing flagged
None of the 26 patterns this scan looks for appear in this file: no shell pipes, no recursive deletes, no credential paths, no hidden text, no instruction-override or anti-refusal phrasing, no agent-config snooping. That is not a guarantee, it is the absence of the things that are checkable.
How it starts
The opening of the file, as written. The whole thing — 85 lines — stays where its author put it; the contents beside it link to each section on GitHub.
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.pyexportsroot_agent, a compiled graph withmessagesstate. Callers only useroot_agent.invoke({"messages": [...]})androot_agent.astream(stream_mode="messages").app/fast_api_app.pyexposesapp. Every deployment target runsuvicorn 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
What ships with it
2 files beside SKILL.md in the same directory: the scripts, references and assets a skill reads on demand. Not counted in the per-session cost; read them before you install if any of them is executable.
What this file has done since we first saw it
Hashed on every crawl. A supply-chain change to an agent config is a question of when, not whether, so the history is kept rather than the latest state alone.
- 4d ago First seen · 85 lines · 76 tokens per session scan A b8035ffe8051
agents-cli-langchain is a skill published in the GitHub repository google/agents-cli (5,825 stars, last pushed 3d ago), licensed Apache-2.0. It adds 76 tokens to every session and 1,013 once invoked, about $0.0004 per session on Opus 5. A static security scan graded it A with 0 findings. No closer match exists in the catalogue, so it is treated as the original; first seen 2026-09-02.
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