What is MultiGen CLAUDE.md?

A repository guide for MultiGen, a privately deployable AI-agent system. It describes the web interface, API, databases, task queue, isolated Docker sandbox, and two-agent process that plans and carries out requests.

Use it when developing MultiGen's frontend, API, agent execution flow, event streaming, or Docker-based sandbox.

It gives coding agents a map of how messages move through the system and where each service or agent fits.

How lingyuanli/MultiGen configures Claude Code

Reuse

Borrowing it

Nothing to install: this file belongs to lingyuanli/MultiGen. Take a copy, put it at the same path in your own repository, and replace the rules that are about this project with yours.

Copy the file

curl -O https://raw.githubusercontent.com/lingyuanli/MultiGen/master/CLAUDE.md

Clone the repo

git clone --depth 1 https://github.com/lingyuanli/MultiGen

Made for: Claude Code.

Wrote 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.

README.md

[![agentmods](https://agentmods.dev/badge/instructions/lingyuanli/multigen/claude-md/github.svg)](https://agentmods.dev/instructions/lingyuanli/multigen/claude-md)

Your own site

<a href="https://agentmods.dev/instructions/lingyuanli/multigen/claude-md"><img src="https://agentmods.dev/badge/instructions/lingyuanli/multigen/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.

Your own site · 80×15

<a href="https://agentmods.dev/instructions/lingyuanli/multigen/claude-md"><img src="https://agentmods.dev/badge/instructions/lingyuanli/multigen/claude-md.svg" alt="Reviewed on agentmods" width="80" height="20"></a>

Per session 1,388 This file is loaded in full into every session.

When invoked 1,388 The same file — it is already loaded in full.

Security scan A 0 findings. A grade says what 26 rules found in the file — not that it is safe.

Origin original No closer match found in the catalogue.

Token cost

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	$0.01388	$0.01388
Opus 5.5	$0.00555	$0.00555
Sonnet 5.5	$0.00278	$0.00278
Haiku 4.5	$0.00139	$0.00139

Measured 2mo ago against content hash 11c14d2c1000, method: parsed. Prices are Anthropic first-party input rates as of 2026-10-07, from the pricing page.

Security

Grade A, and why

MultiGen 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 2mo 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.

CLAUDE.md · 127 lines

How it starts

The opening of the file, as written. The whole thing — 127 lines — stays where its author put it; the contents beside it link to each section on GitHub.

CLAUDE.md

This file provides guidance to Claude Code (claude.ai/code) when working with code in this repository.

Overview

MultiGen is a general-purpose AI Agent system for fully private deployments. It uses a Planner + ReAct multi-agent architecture with A2A (Agent-to-Agent) and MCP (Model Context Protocol) tool connectivity, and executes operations inside an isolated Docker sandbox.

Architecture

Next.js UI (3000) → FastAPI API (8000) → PostgreSQL / Redis / Tencent COS
                                        ↓
                               Docker Sandbox (8080)
                               Ubuntu + Chrome + VNC

Agent execution flow:

AgentService (application layer) receives a chat message and dispatches it to an AgentTaskRunner via a Redis Stream task queue.
AgentTaskRunner runs PlannerReActFlow, which coordinates two agents:
- PlannerAgent — breaks the user request into sub-steps (JSON output, no tool calls)
- ReActAgent — iteratively executes each step using tools, then summarizes
Events are streamed back to the frontend via SSE (/api/sessions/{id}/chat).

Event types (discriminated union in app/domain/models/event.py): plan, title, step, message, tool, wait, error, done.

Tools available to agents (app/domain/services/tools/): file, shell, browser, search, message (ask_user), image_generation, volcano_image, volcano_video, video_concatenation, model_3d, virtual_anchor, qwen_tts, audio_mixing, mcp, a2a.

LLM abstraction: app/domain/external/llm.py defines a Protocol. The only implementation is OpenAILLM (app/infrastructure/external/llm/openai_llm.py), which supports any OpenAI-compatible endpoint (DeepSeek, Volcengine, SiliconFlow, etc.).

DeepSeek thinking models: Models whose names start with deepseek-v4 are treated as reasoning models. When tool calls are present in a turn, reasoning_content must be included in subsequent history; when there are no tool calls, it is passthrough-safe. The codebase handles both cases with a compatibility guard in BaseAgent.

Read the full file on GitHub · 127 lines

Changes

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.

2mo ago First seen · 127 lines · 1,388 tokens per session scan A 11c14d2c1000

Subscribe to this mod's changes

MultiGen CLAUDE.md is an instructions file published in the GitHub repository lingyuanli/MultiGen (394 stars, last pushed 3mo ago), licensed MIT. It adds 1,388 tokens to every session, about $0.0056 per session on Opus 5.5. 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.

Building agents Containers DevOps and cloud a2a-agent agents docker multi-agent-systems sandbox vnc

MultiGen CLAUDE.md next to 6 instructions from other repositories
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MultiGen CLAUDE.mdlingyuanli/MultiGen	Instructions file	394	3mo ago	MIT	A	1,388	Claude Code
vscode buildNext.instructions.mdmicrosoft/vscode	Instructions file	195k	yesterday	MIT	A	9,719	GitHub Copilot
next.js AGENTS.mdvercel/next.js	Instructions file	143k	today	MIT	A	7,461	Codex, OpenCode
langchain AGENTS.mdlangchain-ai/langchain	Instructions file	149k	yesterday	MIT	A	4,680	Codex, OpenCode
vscode oss-third-party-notices.instructions.mdmicrosoft/vscode	Instructions file	195k	yesterday	MIT	A	5,516	GitHub Copilot
vscode copilot-instructions.mdmicrosoft/vscode	Instructions file	195k	yesterday	MIT	A	3,093	GitHub Copilot
deepseek-harness AGENTS.mddeepseek-ai/deepseek-harness	Instructions file	274k	2d ago	MIT	A	4,313	Codex, OpenCode

MultiGen: Instructions file for Claude Code