MCP Agent Memory

MCP Agent Memory

A production-ready MCP server that enables multiple AI agents to collaborate through a shared, concurrency-safe memory space. It supports advanced search, full CRUD operations, and automatic backups to facilitate asynchronous communication between agents.

Category
Visit Server

README

MCP Agent Memory - v2.0

Python 3.10+ License: MIT Tests

Production-ready MCP server providing shared memory for multi-agent collaboration.


Overview

MCP Agent Memory is an enhanced Model Context Protocol (MCP) server that enables multiple AI agents (like Claude Code instances) to communicate asynchronously through a shared memory space. Think of it as a sophisticated shared notepad where AI agents can leave messages, search for information, and coordinate their work.

Key Features

  • ๐Ÿ”’ Concurrency Safe - File locking for shared environments
  • ๐Ÿ“ Full CRUD - Create, Read, Update, Delete operations
  • ๐Ÿ” Advanced Search - Full-text search across all fields
  • ๐Ÿท๏ธ Organization - Tags, priority levels, metadata
  • ๐Ÿ“Š Analytics - Comprehensive memory statistics
  • ๐Ÿ’พ Reliable - Automatic backups and corruption recovery
  • ๐Ÿ“‹ Structured Logging - Complete operation visibility
  • ๐Ÿ›ก๏ธ Health Monitoring - Built-in health check system

Quick Start

Installation

  1. Clone or download this repository
  2. Install dependencies:
    pip install mcp pydantic
    
  3. Run the server:
    python3 shared_memory_mcp.py
    

Basic Usage

# Add a memory entry
await add_memory(
    agent_name="claude-alpha",
    content="Analysis complete. Found 3 key insights.",
    tags=["analysis", "complete"],
    priority="high"
)

# Search for entries
results = await search_memory(query="analysis")

# Get statistics
stats = await get_memory_stats()

Configuration

Add to your Claude Code config (~/.claudeCode/config.json):

{
  "mcpServers": {
    "shared-memory": {
      "command": "python3",
      "args": ["/path/to/shared_memory_mcp.py"]
    }
  }
}

What's New in v2.0

New Tools (6 total)

  • โœ… update_memory - Modify existing entries
  • โœ… delete_memory - Remove specific entries
  • โœ… get_memory - Retrieve single entry by ID
  • โœ… search_memory - Full-text search
  • โœ… get_memory_stats - Memory analytics
  • โœ… health_check - System health monitoring

Enhanced Tools

  • โšก add_memory - Now supports tags, priority, metadata
  • โšก read_memory - Advanced filtering and sorting
  • โšก clear_memory - Auto-backup before clearing

Core Improvements

  • ๐Ÿ”’ Thread-safe file locking
  • ๐Ÿ’พ Automatic backups (keeps 10)
  • ๐Ÿ“ Structured logging
  • ๐Ÿ”„ Auto-rotation at 1000 entries
  • ๐Ÿ›ก๏ธ Corruption recovery
  • ๐Ÿ†” Unique entry IDs (UUID)

Zero breaking changes! All v1 code works without modification.


Architecture

MCP Agent Memory v2.0
โ”œโ”€โ”€ shared_memory_mcp.py      # Main server (9 MCP tools)
โ”œโ”€โ”€ utils/
โ”‚   โ”œโ”€โ”€ file_lock.py           # Concurrency safety
โ”‚   โ””โ”€โ”€ logger.py              # Structured logging
โ”œโ”€โ”€ tests/
โ”‚   โ”œโ”€โ”€ test_memory_operations.py
โ”‚   โ””โ”€โ”€ test_concurrency.py
โ””โ”€โ”€ docs/
    โ”œโ”€โ”€ API_REFERENCE_V2.md    # Complete API docs
    โ”œโ”€โ”€ CHANGELOG_V2.md        # Version history
    โ”œโ”€โ”€ UPGRADE_GUIDE.md       # v1โ†’v2 migration
    โ””โ”€โ”€ IMPLEMENTATION_SUMMARY.md

Storage

~/.shared_memory_mcp/
โ”œโ”€โ”€ memory.json              # Main storage
โ”œโ”€โ”€ mcp_memory.log          # Rotating logs
โ””โ”€โ”€ backups/                # Auto-backups (10 kept)
    โ””โ”€โ”€ memory_backup_*.json

Documentation

Getting Started

Reference

Developer


API Overview

Memory Operations

Tool Description Type
add_memory Create new entry with tags/priority Write
read_memory Read with advanced filtering Read
update_memory Modify existing entry Write
delete_memory Remove specific entry Write
get_memory Retrieve single entry by ID Read
search_memory Full-text search Read
get_memory_stats Memory analytics Read
clear_memory Clear all entries Write
health_check System health status Read

See API Reference for detailed documentation.


Testing

Run Basic Tests

python3 run_basic_tests.py

Run Full Test Suite (requires pytest)

pip install pytest pytest-cov
pytest tests/ -v --cov

Test Coverage

  • โœ… 70+ test cases
  • โœ… Unit tests (operations, filtering, search)
  • โœ… Concurrency tests (locking, atomic writes)
  • โœ… Integration tests

Development

Setup Development Environment

# Install dev dependencies
pip install -r requirements-dev.txt

# Install pre-commit hooks
pre-commit install

# Run tests
python3 run_basic_tests.py

# Type checking
mypy shared_memory_mcp.py

# Linting
ruff check .

Code Quality Tools

  • โœ… pytest - Testing framework
  • โœ… mypy - Type checking
  • โœ… ruff - Linting and formatting
  • โœ… pre-commit - Git hooks

Performance

Typical Operations

  • Add entry: 5-15ms (includes backup)
  • Read entries: 2-10ms
  • Search (100 entries): 1-5ms
  • Update/Delete: 5-15ms (includes backup)

Limits

  • Max words per entry: 200
  • Max tags per entry: 10
  • Max entries before rotation: 1000
  • File lock timeout: 10 seconds
  • Backup retention: 10 backups

Use Cases

Multi-Agent Collaboration

# Agent A: Data collector
await add_memory(
    agent_name="data-collector",
    content="Collected 10,000 data points",
    tags=["data", "ready-for-analysis"],
    priority="high"
)

# Agent B: Analyzer picks up work
pending = await read_memory(tags=["ready-for-analysis"])

Task Tracking

# Start task
result = await add_memory(
    agent_name="worker",
    content="Starting analysis...",
    tags=["analysis", "in-progress"],
    priority="high"
)

# Update on completion
await update_memory(
    entry_id=result.entry_id,
    tags=["analysis", "complete"],
    priority="low"
)

Knowledge Base

# Search for information
results = await search_memory(
    query="user behavior insights",
    limit=10
)

# Get statistics
stats = await get_memory_stats()
print(f"Total knowledge: {stats.total_entries} entries")

FAQ

Q: Is v2 compatible with v1 code? A: Yes! 100% backward compatible. All v1 code works without changes.

Q: How does migration work? A: Automatic. v2 detects v1 format and migrates on first write.

Q: Can multiple agents write simultaneously? A: Yes! File locking ensures safe concurrent access.

Q: What happens if storage gets corrupted? A: Automatic recovery from the most recent valid backup.

Q: How much disk space does it use? A: ~500-1000 bytes per entry. 1000 entries โ‰ˆ 500KB-1MB.

Q: Can I use it for production? A: Yes! v2 is production-ready with reliability features.

See UPGRADE_GUIDE.md for more details.


Troubleshooting

Common Issues

File lock timeout

# Check for other running instances
ps aux | grep shared_memory_mcp

# Increase timeout in code if needed

JSON parse error

# Restore from backup
cp ~/.shared_memory_mcp/backups/memory_backup_*.json \
   ~/.shared_memory_mcp/memory.json

Check system health

health = await health_check({})
print(health.message)  # "All systems operational"

Logs

# View logs
tail -f ~/.shared_memory_mcp/mcp_memory.log

# Check for errors
grep ERROR ~/.shared_memory_mcp/mcp_memory.log

Contributing

Contributions welcome! Please:

  1. Fork the repository
  2. Create a feature branch
  3. Add tests for new features
  4. Ensure all tests pass
  5. Submit a pull request

Code Style

  • Use type hints
  • Follow existing patterns
  • Add docstrings
  • Run pre-commit hooks

License

MIT License - See LICENSE file for details


Changelog

v2.0.0 (2025-10-30)

  • โœ… Added concurrency safety (file locking)
  • โœ… Added structured logging
  • โœ… Added 6 new MCP tools
  • โœ… Enhanced data model (tags, priority, metadata)
  • โœ… Added automatic backups and recovery
  • โœ… Added comprehensive test suite (70+ tests)
  • โœ… Added complete documentation
  • โœ… Zero breaking changes

See CHANGELOG_V2.md for detailed history.


Acknowledgments

Built on the Model Context Protocol (MCP) by Anthropic.

Enhanced with production-ready features while maintaining the simplicity and elegance of the original design.


Links


Made with โค๏ธ for multi-agent collaboration

Version 2.0.0 - Production Ready ๐Ÿš€

Recommended Servers

playwright-mcp

playwright-mcp

A Model Context Protocol server that enables LLMs to interact with web pages through structured accessibility snapshots without requiring vision models or screenshots.

Official
Featured
TypeScript
Magic Component Platform (MCP)

Magic Component Platform (MCP)

An AI-powered tool that generates modern UI components from natural language descriptions, integrating with popular IDEs to streamline UI development workflow.

Official
Featured
Local
TypeScript
Audiense Insights MCP Server

Audiense Insights MCP Server

Enables interaction with Audiense Insights accounts via the Model Context Protocol, facilitating the extraction and analysis of marketing insights and audience data including demographics, behavior, and influencer engagement.

Official
Featured
Local
TypeScript
VeyraX MCP

VeyraX MCP

Single MCP tool to connect all your favorite tools: Gmail, Calendar and 40 more.

Official
Featured
Local
graphlit-mcp-server

graphlit-mcp-server

The Model Context Protocol (MCP) Server enables integration between MCP clients and the Graphlit service. Ingest anything from Slack to Gmail to podcast feeds, in addition to web crawling, into a Graphlit project - and then retrieve relevant contents from the MCP client.

Official
Featured
TypeScript
Kagi MCP Server

Kagi MCP Server

An MCP server that integrates Kagi search capabilities with Claude AI, enabling Claude to perform real-time web searches when answering questions that require up-to-date information.

Official
Featured
Python
E2B

E2B

Using MCP to run code via e2b.

Official
Featured
Neon Database

Neon Database

MCP server for interacting with Neon Management API and databases

Official
Featured
Exa Search

Exa Search

A Model Context Protocol (MCP) server lets AI assistants like Claude use the Exa AI Search API for web searches. This setup allows AI models to get real-time web information in a safe and controlled way.

Official
Featured
Qdrant Server

Qdrant Server

This repository is an example of how to create a MCP server for Qdrant, a vector search engine.

Official
Featured