MemMachine is an open-source long-term memory layer for AI agents and LLM-powered applications. It enables your AI to learn, store, and recall information from past sessions—transforming stateless chatbots into personalized, context-aware assistants.

• Episodic Memory: Graph-based conversational context that persists across sessions
• Profile Memory: Long-term user facts and preferences stored in SQL
• Working Memory: Short-term context for the current session
• Agent Memory Persistence: Memory that survives restarts, sessions, and even model changes

Get up and running in under 5 minutes:

Prerequisites: This code requires a running MemMachine Server.
Start a server locally or create a free account on the MemMachine Platform.

pip install memmachine-client

from memmachine import MemMachineClient

# Initialize the client
client = MemMachineClient(base_url="http://localhost:8080")

# Get or create a project
project = client.get_or_create_project(org_id="my_org", project_id="my_project")

# Create a memory instance for a user session
memory = project.memory(
    group_id="default",
    agent_id="travel_agent",
    user_id="alice",
    session_id="session_001"
)

# Add a memory
memory.add("I prefer aisle seats on flights", metadata={"category": "travel"})
# => [AddMemoryResult(uid='...')]

# Search memories
results = memory.search("What are my flight preferences?")
print(results.content.episodic_memory.long_term_memory.episodes[0].content)
# => "I prefer aisle seats on flights"

For full installation options (Docker, self-hosted, cloud), visit the Quick Start Guide.

MemMachine works seamlessly with your favorite AI frameworks:

MemMachine includes a native Model Context Protocol (MCP) server for seamless integration with Claude Desktop, Cursor, and other MCP-compatible clients:

# Stdio mode (for Claude Desktop)
memmachine-mcp-stdio

# HTTP mode (for web clients)
memmachine-mcp-http

See the MCP documentation for setup instructions.

• Developers building AI agents, assistants, or autonomous workflows
• Researchers experimenting with agent architectures and cognitive models
• Teams who need persistent, cross-session memory for their LLM applications

• Multiple Memory Types: Working (short-term), Episodic (long-term conversational), and Profile (user facts) memory
• Developer-Friendly APIs: Python SDK, RESTful API, TypeScript SDK, and MCP server interfaces
• Flexible Storage: Graph database (Neo4j) for episodic memory, SQL for profiles
• LLM Agnostic: Works with OpenAI, Anthropic, Bedrock, Ollama, and any LLM provider
• Self-Hosted or Cloud: Run locally, in Docker, or use our managed service

For more information, refer to the API Reference Guide.

1. Agents interact via the API Layer: Users interact with an agent, which connects to MemMachine through a RESTful API, Python SDK, or MCP Server.
2. MemMachine manages memory: Processes interactions and stores them as Episodic Memory (conversational context) and Profile Memory (long-term user facts).
3. Data is persisted: Episodic memory is stored in a graph database; profile memory is stored in SQL.

MemMachine's versatile memory architecture can be applied across any domain. Explore our examples to see memory-powered agents in action:

Are you using MemMachine in your project? We'd love to feature you!

• Share your project in GitHub Discussions → Showcase
• Drop a message in our Discord #showcase channel

MemMachine is a growing community of builders and developers. Help us grow by clicking the ⭐ Star button above!

• Main Website – Learn about MemMachine
• Docs & API Reference – Full documentation
• Quick Start Guide – Get started in minutes

• Discord: Join our community for support, updates, and discussions: https://discord.gg/usydANvKqD
• Issues & Feature Requests: Use GitHub Issues

We welcome contributions! Please see our CONTRIBUTING.md for guidelines.

MemMachine is released under the Apache 2.0 License.