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/CLAUDE.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/claude-md)<a href="https://agentmods.dev/instructions/mrgoonie/a2a-langgraph-boilerplate/claude-md"><img src="https://agentmods.dev/badge/instructions/mrgoonie/a2a-langgraph-boilerplate/claude-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/claude-md"><img src="https://agentmods.dev/badge/instructions/mrgoonie/a2a-langgraph-boilerplate/claude-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 | $0.00980 | $0.00980 |
| Opus 5 | $0.00490 | $0.00490 |
| Sonnet 5 | $0.00196 | $0.00196 |
| Haiku 4.5 | $0.00098 | $0.00098 |
Grade A, and why
a2a-langgraph-boilerplate CLAUDE.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 — 80 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 decide to assign more tasks to AI agents, or finish the plan
- Synthesize the results and respond to user based on the original input prompt.
Example workflow:
- Case 1: Simple Direct Response - User asks "hello" and supervisor decides to answer directly with a simple response. This requires no agent delegation and completes in a single workflow step.
- Case 2: Multi-Agent Collaboration with Termination Control - User asks for travel advice about Nha Trang beach (Vietnam):
- Supervisor receives query and creates a task plan with clear termination conditions
- Supervisor delegates to agent 1 (connected to Search API MCP server) to find top attractions
- Supervisor delegates to agent 2 (connected to Search API MCP server) to research local cuisine
- Each agent responds with its findings in a single message back to supervisor
- Supervisor synthesizes all information into a final response
- Workflow terminates after supervisor's final response (enforced by message depth limit)
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
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 · 80 lines · 980 tokens per session scan A 2a085ea3a295
a2a-langgraph-boilerplate CLAUDE.md is an instructions file published in the GitHub repository mrgoonie/a2a-langgraph-boilerplate (40 stars, last pushed 1y ago), licensed MIT. It adds 980 tokens to every session, about $0.0049 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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