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- Lead with "Production-ready" (recruiter keyword)
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Co-Authored-By: Claude <noreply@anthropic.com>
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# MCP Multiagent Bridge # MCP Multiagent Bridge
### Secure, rate-limited coordination for multiple LLM agents
> *Because even AI agents need traffic lights.*
Multi-agent systems are already here: backend agents debugging frontend agents, compliance bots reviewing security agents, and specialized models coordinating prod deployments. Production-ready Python MCP server for secure multi-agent coordination with comprehensive safeguards.
But nobody's built the safety layer that keeps them from trampling each other.
**MCP** is the protocol. **This** is the traffic control system. ## Overview
Enables multiple LLM agents (Claude, Codex, GPT, etc.) to collaborate safely through the Model Context Protocol without sharing workspaces or credentials. Built with security-first architecture and production-grade safeguards.
**Use cases:**
- Backend agent coordinating with frontend agent on different codebases
- Security review agent validating changes from development agent
- Specialized agents collaborating on complex multi-step workflows
- Any scenario requiring isolated agents to communicate securely
--- ---
## Why it exists ## Key Features
Multi-agent execution is both powerful and horrifying. ### 🔒 Security Architecture
So this bridge adds layered safeguards:
- Environment gate (explicit opt-in)
- Typed confirmation phrase
- One-time validation codes
- Expiring approval tokens (because regret has a TTL)
> ⚠️ **Beta Software**: Built for development/testing environments with human supervision. See [Security Policy](SECURITY.md) before production use. **Authentication & Authorization:**
- HMAC-SHA256 session token authentication
- Automatic secret redaction (API keys, passwords, tokens, private keys)
- 3-hour session expiration with automatic cleanup
- SQLite WAL mode for atomic, race-condition-free operations
--- **4-Stage YOLO Guard™:**
Command execution (optional) requires multiple confirmation layers:
## Under the hood 1. Environment gate - explicit `YOLO_MODE=1` opt-in
2. Interactive typed confirmation phrase
**Security:** 3. One-time validation code (prevents automation)
- HMAC-SHA256 session authentication 4. Time-limited approval tokens (5-minute TTL, single-use)
- Automatic secret redaction (API keys, passwords, tokens)
- SQLite WAL mode for atomic operations
- Comprehensive audit trail (JSONL format)
- 3-hour conversation expiration
**YOLO Guard™ (4-stage confirmation):**
- Environment gate (`YOLO_MODE=1`)
- Interactive typed confirmation
- One-time validation codes
- Time-limited approval tokens (5-min TTL, single-use)
- Dry-run by default
**Rate Limiting:** **Rate Limiting:**
- Token bucket algorithm - Token bucket algorithm with configurable windows
- 10 requests/minute, 100/hour, 500/day - Default: 10 requests/minute, 100/hour, 500/day
- Per-session tracking with automatic reset - Per-session tracking with automatic reset
- Prevents abuse while allowing legitimate bursts
--- **Audit Trail:**
- Comprehensive JSONL logging of all operations
- Timestamps, session IDs, actions, results
- Tamper-evident sequential logging
- Supports compliance and forensic analysis
## Paperwork ### 🏗️ Production-Ready Architecture
All the boring-but-necessary stuff is here: - **Message-only bridge** - No auto-execution, returns proposals only
- **[LICENSE](LICENSE)** - MIT (do what you want) - **Schema validation** - Strict JSON schemas for all MCP tools
- **[SECURITY.md](SECURITY.md)** - Threat model + responsible disclosure - **Command validation** - Configurable whitelist/blacklist patterns
- **[CONTRIBUTING.md](CONTRIBUTING.md)** - How to help - **Comprehensive error handling** - Graceful degradation, informative errors
- **Policy compliance** - Anthropic & OpenAI friendly - **Extensible design** - Plugin architecture for future backends
--- ### 📦 Platform Support
## Works with **Works with any MCP-compatible LLM:**
- Claude Code, Claude Desktop, Claude API
Any MCP-compatible LLM: - OpenAI models (via MCP adapters)
- Claude (Code, Desktop, API)
- OpenAI models via MCP adapters
- Anthropic API models - Anthropic API models
- Future: Codex, GPT, custom models - Custom/future models (not tied to specific backend)
Not tied to any specific backend. Build once, swap models freely. ---
## Installation ## Installation
```bash ```bash
# Clone repository
git clone https://github.com/dannystocker/mcp-multiagent-bridge.git
cd mcp-multiagent-bridge
# Install dependencies # Install dependencies
pip install mcp pip install mcp>=1.0.0
# Make scripts executable # Run tests
chmod +x claude_bridge_secure.py bridge_cli.py python test_security.py
# Test the bridge
python3 claude_bridge_secure.py --help
``` ```
## Quick Start Full setup: See [QUICKSTART.md](QUICKSTART.md)
### 1. Configure MCP Server ---
Add to `~/.claude.json`: ## Documentation
```json **Getting Started:**
{ - [QUICKSTART.md](QUICKSTART.md) - 5-minute setup guide
"mcpServers": { - [EXAMPLE_WORKFLOW.md](EXAMPLE_WORKFLOW.md) - Real-world collaboration scenarios
"bridge": {
"command": "python3",
"args": ["/absolute/path/to/claude_bridge_secure.py"],
"env": {}
}
}
}
```
Or use project-scoped config in `.mcp.json` at your project root. **Security & Compliance:**
- [SECURITY.md](SECURITY.md) - Threat model, responsible disclosure policy
- [YOLO_MODE.md](YOLO_MODE.md) - Command execution safety guide
- Policy compliance: Anthropic AUP, OpenAI Usage Policies
### 2. Start Session A (Backend Developer) **Contributing:**
- [CONTRIBUTING.md](CONTRIBUTING.md) - Development setup, PR workflow
- [LICENSE](LICENSE) - MIT License
---
## Technical Stack
- **Python 3.11+** - Modern Python with type hints
- **SQLite** - Atomic operations with WAL mode
- **MCP Protocol** - Model Context Protocol integration
- **pytest** - Comprehensive test suite
- **CI/CD** - GitHub Actions (tests, security scanning, linting)
---
## Project Statistics
- **Lines of Code:** ~5,200 (including tests + documentation)
- **Test Coverage:** Core security components verified
- **Documentation:** 2,000+ lines across 7 markdown files
- **Dependencies:** 1 (mcp, pinned for reproducibility)
- **License:** MIT
---
## Development
```bash ```bash
cd ~/projects/backend # Install dev dependencies
pip install -r requirements.txt
claude-code --prompt " # Install pre-commit hooks
You are Session A in a multi-agent collaboration. pip install pre-commit
pre-commit install
Role: Backend API Developer # Run test suite
pytest
Instructions: # Run security tests
1. Use create_conversation tool with: python test_security.py
- my_role: 'backend_developer'
- partner_role: 'frontend_developer'
2. Save your conversation_id and token (keep token secret!)
3. Communicate using:
- send_to_partner (to send messages)
- check_messages (poll every 30 seconds)
- update_my_status (keep partner informed)
4. IMPORTANT: Include your token in every tool call for authentication
Task: Design and implement REST API for a todo application.
Coordinate with Session B on API contract before implementing.
Poll for messages regularly with: check_messages
"
``` ```
### 3. Start Session B (Frontend Developer) See [CONTRIBUTING.md](CONTRIBUTING.md) for complete development workflow.
```bash ---
cd ~/projects/frontend
claude-code --prompt " ## Security Notice
You are Session B in a multi-agent collaboration.
Role: Frontend React Developer ⚠️ **Beta Software**: Designed for development/testing environments with human supervision.
Instructions: **Recommended for:**
1. Get conversation_id and your token from Session A - Development and testing workflows
(They should share these securely) - Isolated workspaces
- Human-supervised operations
- Prototype multi-agent systems
2. Check for messages from Session A: **Not recommended for:**
check_messages with conversation_id and your token - Production systems without additional safeguards
- Unattended automation
- Critical infrastructure
- Environments with untrusted agents
3. Reply using send_to_partner See [SECURITY.md](SECURITY.md) for complete security considerations and threat model.
4. Poll for new messages every 30 seconds ---
Task: Build React frontend for todo application. ## Support
Coordinate with Session A on API requirements before implementing.
"
```
## Tool Reference - **Issues:** [GitHub Issues](https://github.com/dannystocker/mcp-multiagent-bridge/issues)
- **Discussions:** [GitHub Discussions](https://github.com/dannystocker/mcp-multiagent-bridge/discussions)
- **Security:** See [SECURITY.md](SECURITY.md) for responsible disclosure
### create_conversation ---
Initializes a secure conversation and returns tokens.
```json
{
"my_role": "backend_developer",
"partner_role": "frontend_developer"
}
```
**Returns:**
```json
{
"conversation_id": "conv_a1b2c3d4e5f6g7h8",
"session_a_token": "64-char-hex-token",
"session_b_token": "64-char-hex-token",
"expires_at": "2025-10-26T17:00:00Z"
}
```
### send_to_partner
Send authenticated, redacted message to partner.
```json
{
"conversation_id": "conv_...",
"session_id": "a",
"token": "your-session-token",
"message": "Proposed API endpoint: POST /todos",
"action_type": "proposal",
"files_involved": ["api/routes.py"]
}
```
### check_messages
Atomically read and mark messages as read.
```json
{
"conversation_id": "conv_...",
"session_id": "b",
"token": "your-session-token"
}
```
### update_my_status
Heartbeat mechanism to show liveness.
```json
{
"conversation_id": "conv_...",
"session_id": "a",
"token": "your-session-token",
"status": "working"
}
```
Status values: `working`, `waiting`, `blocked`, `complete`
### check_partner_status
See if partner is alive and what they're doing.
```json
{
"conversation_id": "conv_...",
"session_id": "a",
"token": "your-session-token"
}
```
## Management CLI
```bash
# List all conversations
python3 bridge_cli.py list
# Show conversation details and messages
python3 bridge_cli.py show conv_a1b2c3d4e5f6g7h8
# Get tokens (use carefully!)
python3 bridge_cli.py tokens conv_a1b2c3d4e5f6g7h8
# View audit log
python3 bridge_cli.py audit
python3 bridge_cli.py audit conv_a1b2c3d4e5f6g7h8 100
# Clean up expired conversations
python3 bridge_cli.py cleanup
```
## Secret Redaction
The bridge automatically redacts:
- AWS keys (AKIA...)
- Private keys (-----BEGIN...PRIVATE KEY-----)
- Bearer tokens
- API keys
- Passwords
- GitHub tokens (ghp_...)
- OpenAI keys (sk-...)
Redacted content is replaced with placeholders like `AWS_KEY_REDACTED`.
## Security Best Practices
### DO ✅
- Keep session tokens secret
- Use separate workspaces for each session
- Poll for messages regularly (every 30s)
- Update status frequently so partner knows you're alive
- Use `action_type` to clarify message intent
- Review redaction before sending sensitive info
### DON'T ❌
- Share tokens in chat messages
- Commit tokens to version control
- Use expired conversations
- Send unrestricted command execution requests
- Assume messages are end-to-end encrypted (local only)
## Architecture
```
Session A (claude-code) Session B (claude-code)
| |
|--- MCP Tool Calls ---| |
| ↓ |
| Bridge Server |
| (Python + SQLite)
| ↓ |
|--- Authenticated, ---|------|
Redacted Messages
```
### Data Flow
1. Session A calls `create_conversation` → Gets conv_id + token_a + token_b
2. Session A shares conv_id + token_b with Session B
3. Session A calls `send_to_partner` → Message redacted → Stored in DB
4. Session B calls `check_messages` → Retrieves + marks read atomically
5. Session B replies via `send_to_partner`
6. Both sessions update status periodically
### Database Schema
- **conversations**: Conv ID, roles, tokens, expiration
- **messages**: From/to sessions, redacted content, read status
- **session_status**: Current status + heartbeat timestamp
- **audit_log**: All actions for forensics
## Limitations & Safeguards
- **No command execution**: Bridge only passes messages, never executes code
- **3-hour expiration**: Conversations auto-expire
- **50KB message limit**: Prevents token bloat
- **Interactive only**: Human must review all proposed actions
- **No file sharing**: Sessions must use shared workspace or Git
- **Local-only**: No network transport, Unix socket or stdio only
## Testing
```bash
# Basic connectivity test
python3 claude_bridge_secure.py /tmp/test.db &
BRIDGE_PID=$!
# Test tool calls (requires MCP client)
# ... test scenarios ...
kill $BRIDGE_PID
rm /tmp/test.db
```
## Troubleshooting
**"Invalid session token"**
- Check token hasn't expired (3 hours)
- Verify you're using correct token for your session
- Use `bridge_cli.py tokens` to retrieve if lost
**"No MCP servers connected"**
- Verify `~/.claude.json` has correct absolute path
- Restart Claude Code after config changes
- Check MCP server logs: `claude-code --mcp-debug`
**Messages not appearing**
- Confirm both sessions use same conversation_id
- Check token authentication with `bridge_cli.py show`
- Verify partner sent messages (check audit log)
**Redaction too aggressive**
- Review redaction patterns in `SecretRedactor.PATTERNS`
- Consider adding custom patterns if needed
- False positives are safer than leaking secrets
## Use Cases
### 1. API-First Development
- Session A: Backend - designs API, implements endpoints
- Session B: Frontend - consumes API, provides feedback
- **Benefit**: Contract-first design with real-time feedback
### 2. Security Review
- Session A: Feature developer - implements functionality
- Session B: Security auditor - reviews for vulnerabilities
- **Benefit**: Continuous security assessment
### 3. Specialized Expertise
- Session A: Python expert - backend services
- Session B: TypeScript expert - React frontend
- **Benefit**: Each operates in domain of strength
### 4. Parallel Problem-Solving
- Session A: Investigates bug in module X
- Session B: Implements workaround in module Y
- **Benefit**: Non-blocking progress on related tasks
## Advanced Configuration
### Custom Database Location
```bash
python3 claude_bridge_secure.py /path/to/custom.db
```
### Adjust Expiration Time
Edit `create_conversation` method:
```python
expires_at = datetime.utcnow() + timedelta(hours=6) # 6 hours instead of 3
```
### Add Custom Redaction Patterns
Edit `SecretRedactor.PATTERNS`:
```python
PATTERNS = [
# ... existing patterns ...
(r'my_secret_format_[A-Z0-9]{10}', 'CUSTOM_SECRET_REDACTED'),
]
```
## Production Hardening (Future)
Current MVP is designed for local development. For production:
- [ ] Add TLS for network transport
- [ ] Implement rate limiting per session
- [ ] Add message size quotas
- [ ] Enable sandboxed command execution (Docker)
- [ ] Add Redis pub/sub for real-time notifications
- [ ] Implement message encryption at rest
- [ ] Add role-based access control
- [ ] Enable multi-conversation per session
- [ ] Add conversation export/import
- [ ] Implement backup/restore
## License ## License
MIT - Use responsibly. Not liable for data loss or security issues. MIT License - Copyright © 2025 Danny Stocker
## Credits See [LICENSE](LICENSE) for full terms.
Inspired by Zen MCP Server's multi-model orchestration concepts. ---
Built for secure local multi-agent coordination without external dependencies.
## Acknowledgments
Built with [Claude Code](https://docs.claude.com/claude-code) and [Model Context Protocol](https://modelcontextprotocol.io/).

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pyproject.toml
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@ -5,7 +5,7 @@ build-backend = "setuptools.build_meta"
[project] [project]
name = "mcp-multiagent-bridge" name = "mcp-multiagent-bridge"
version = "1.0.0-beta" version = "1.0.0-beta"
description = "Secure multi-agent coordination for LLMs — because even AI agents need traffic lights" description = "Production-ready Python MCP server for secure multi-agent coordination with 4-stage safeguards and rate limiting"
readme = "README.md" readme = "README.md"
license = {text = "MIT"} license = {text = "MIT"}
authors = [ authors = [