Borrowing it
Nothing to install: this file belongs to mrgoonie/a2a-langgraph-boilerplate. Take a copy, put it at the same path in your own repository, and replace the rules that are about this project with yours.
curl -O https://raw.githubusercontent.com/mrgoonie/a2a-langgraph-boilerplate/main/GEMINI.mdgit clone --depth 1 https://github.com/mrgoonie/a2a-langgraph-boilerplateWrote 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/instructions/mrgoonie/a2a-langgraph-boilerplate/gemini-md)<a href="https://agentmods.dev/instructions/mrgoonie/a2a-langgraph-boilerplate/gemini-md"><img src="https://agentmods.dev/badge/instructions/mrgoonie/a2a-langgraph-boilerplate/gemini-md/github.svg" alt="Measured on agentmods" height="20"></a>Or the 80×15 button, for a site that already has a row of RSS and ATOM ones. Only the verdict fits; the numbers stay here.
<a href="https://agentmods.dev/instructions/mrgoonie/a2a-langgraph-boilerplate/gemini-md"><img src="https://agentmods.dev/badge/instructions/mrgoonie/a2a-langgraph-boilerplate/gemini-md.svg" alt="Reviewed on agentmods" width="80" 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 | $0.00746 | $0.00746 |
| Opus 5 | $0.00373 | $0.00373 |
| Sonnet 5 | $0.00149 | $0.00149 |
| Haiku 4.5 | $0.00075 | $0.00075 |
Grade A, and why
a2a-langgraph-boilerplate GEMINI.md 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 9d 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 — 67 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Overview
This project is a boilerplate for developers who want to start building an AI agent cluster faster and more efficient.
Concept
- Each AI agent cluster can have multiple AI agent crews (AI Crews)
- Each AI crew can have multiple AI agent, leaded by a superviser (a default AI agent of an AI crew)
- Each AI agent can call tools via MCP servers integration
How it works
- A supervisor agent will receive input (prompt) from a user via API call, then create a detailed plan with its current capabilities (AI agents underneat and their tools)
- Then request the AI agents to perform tasks via A2A protocol
- Wait for all AI agents finish given tasks
- Grab all the results, analyze and respond to user based on the original input prompt.
Core Features
- Create & manage AI crews easily (with a default supervisor agent, add/remove AI agents)
- Create & manage AI agents easily (add/remove MCP tools)
- Create & manage MCP servers easily (supports Streamable HTTP transport only)
- Create & manage conversations with AI crews / AI agents easily
- Able to monitor all the activity logs of AI crews and AI agents easily
- Expose API for frontend (nextjs) interaction (support streaming request)
- Expose Swagger API Docs for frontend integration instructions
Technical Requirements
- Programming language: Python
- Store variables in
.envfile - AI framework: LangGraph (with OpenRouter AI API)
- Supports Agent-to-Agent (A2A) protocol for AI agents to communicate with each others ("Supervisor" architecture)
- Supports Model Context Protocol (MCP) servers integration (for AI agents to use tool call)
- Expose API for frontend (nextjs) interaction (support streaming request)
- Database: PostgreSQL
- Cloud storage: Cloudflare R2 bucket
Environment Variables (Development Environment / localhost)
DATABASE_URL=""
OPENROUTER_API_KEY=""
...
Documentations & References
- https://langchain-ai.github.io/langgraph/concepts/multi_agent/
- https://github.com/langchain-ai/langgraph
- https://github.com/a2aproject/A2A/tree/main
- https://openrouter.ai/docs/quickstart
- https://www.relari.ai/blog/ai-agent-framework-comparison-langgraph-crewai-openai-swarm
- https://langchain-ai.github.io/langgraph/agents/mcp/
- https://modelcontextprotocol.io/introduction
- https://github.com/modelcontextprotocol/python-sdk
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.
- 9d ago First seen · 67 lines · 746 tokens per session scan A 48d5e3ad4aad
a2a-langgraph-boilerplate GEMINI.md is an instructions file published in the GitHub repository mrgoonie/a2a-langgraph-boilerplate (40 stars, last pushed 1y ago), licensed MIT. It adds 746 tokens to every session, about $0.0037 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 2026-08-30.
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