# memmachine/memmachine

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4,607 stars · 138 forks · Python · apache-2.0

## Links

- GitHub: https://github.com/MemMachine/MemMachine
- Homepage: https://memmachine.ai
- awesome-repositories: https://awesome-repositories.com/repository/memmachine-memmachine.md

## Topics

`agent` `agentic-ai` `agents` `agents-sdk` `ai` `ai-agents` `chatbots` `conversational-agents` `conversational-ai` `genai` `knowledge-graph` `llm` `long-short-term-memory` `memory` `memory-management` `persistent-memory` `personalization` `python`

## Description

MemMachine is a centralized memory management server and model-agnostic memory layer for large language models. It functions as a persistence layer that stores user profiles and conversational context, providing a decoupled data store that prevents vendor lock-in by serving different AI models through a consistent API.

The system implements the Model Context Protocol to share persistent agent memories and session data with compatible AI clients. It utilizes a multi-tiered memory hierarchy, combining a graph-based conversation store for episodic interactions with a vector knowledge base for searchable long-term memory.

The platform covers state management for AI agents, including the creation of individual user profiles and the maintenance of short-term working memory. It provides capabilities for natural language memory search, interaction recall, and profile-based data partitioning to ensure personalized AI behavior across multiple sessions.

Connectivity is provided through a REST API gateway and language-specific SDKs to integrate the memory layer with external agent frameworks and AI models.

## Tags

### Artificial Intelligence & ML

- [Agent State Management](https://awesome-repositories.com/f/artificial-intelligence-ml/agent-state-management.md) — Manages short-term working memory and long-term knowledge for autonomous agents to ensure coherent workflows.
- [AI Memory Layers](https://awesome-repositories.com/f/artificial-intelligence-ml/ai-memory-layers.md) — Provides a decoupled architectural layer for indexing and storing long-term context for AI agents to prevent vendor lock-in.
- [Hybrid Short-and-Long Term Memory](https://awesome-repositories.com/f/artificial-intelligence-ml/agent-architectures/memory-management-systems/long-term-memory-stores/hybrid-short-and-long-term-memory.md) — Implements a memory hierarchy separating immediate session context from long-term persistent profiles.
- [Context Memory Management](https://awesome-repositories.com/f/artificial-intelligence-ml/context-memory-management.md) — Manages short-term application state and interaction history to populate LLM context windows during active sessions. ([source](https://memmachine.ai/llms.txt#memmachine))
- [Conversation Memory Stores](https://awesome-repositories.com/f/artificial-intelligence-ml/conversation-memory-stores.md) — Maintains a graph-based store of interaction history to provide continuity and track evolving goals in agentic workflows. ([source](https://memmachine.ai/))
- [Short-term Memory](https://awesome-repositories.com/f/artificial-intelligence-ml/language-model-orchestration/ai-memory-systems/short-term-memory.md) — Handles short-term conversational context within active interaction threads to ensure coherent responses. ([source](https://cdn.jsdelivr.net/gh/memmachine/memmachine@main/README.md))
- [MCP Servers](https://awesome-repositories.com/f/artificial-intelligence-ml/mcp-servers.md) — Implements an MCP server that shares persistent agent memories and session data with compatible AI clients.
- [Memory Persistence](https://awesome-repositories.com/f/artificial-intelligence-ml/memory-persistence.md) — Stores user preferences and conversational history across multiple sessions for consistent AI interactions.
- [Model-Agnostic Memory Layers](https://awesome-repositories.com/f/artificial-intelligence-ml/model-agnostic-memory-layers.md) — Mem0 enables linking a centralized memory layer to various AI models via API to prevent vendor lock-in and maintain data control. ([source](https://memmachine.ai/))
- [Model Context Protocol Implementations](https://awesome-repositories.com/f/artificial-intelligence-ml/model-context-protocol-implementations.md) — Implements the Model Context Protocol to expose memory state to language model clients.
- [Model Context Protocol Servers](https://awesome-repositories.com/f/artificial-intelligence-ml/model-context-protocol-servers.md) — Implements a Model Context Protocol server to expose memory state to compatible AI clients and editors. ([source](https://cdn.jsdelivr.net/gh/memmachine/memmachine@main/README.md))
- [Stateful LLM Application Servers](https://awesome-repositories.com/f/artificial-intelligence-ml/stateful-llm-application-servers.md) — Functions as a centralized stateful server storing user profiles and conversational context for LLMs via API.
- [Long-term Memory Stores](https://awesome-repositories.com/f/artificial-intelligence-ml/agent-architectures/memory-management-systems/long-term-memory-stores.md) — Utilizes persistent storage mechanisms to retain user-scoped context and knowledge across multiple interactions. ([source](https://memmachine.ai/llms.txt#memmachine))
- [Agent Framework Integrations](https://awesome-repositories.com/f/artificial-intelligence-ml/agent-framework-integrations.md) — Provides adapters for integrating the persistent memory layer with external agent frameworks and no-code tools. ([source](https://cdn.jsdelivr.net/gh/memmachine/memmachine@main/README.md))
- [Agent Memory Persistence](https://awesome-repositories.com/f/artificial-intelligence-ml/agent-memory-persistence.md) — Provides architectures for maintaining long-term state and continuity for AI assistants across multiple sessions. ([source](https://docs.memmachine.ai/))
- [Agent Memory Storage](https://awesome-repositories.com/f/artificial-intelligence-ml/agent-memory-storage.md) — Implements direct database storage solutions for AI agent memory to ensure long-term data recall. ([source](https://console.memmachine.ai/))
- [Episodic Memory Stores](https://awesome-repositories.com/f/artificial-intelligence-ml/memory-persistence/episodic-memory-stores.md) — Maintains a graph-based history of conversational context as episodic memory that persists across sessions. ([source](https://cdn.jsdelivr.net/gh/memmachine/memmachine@main/README.md))

### Content Management & Publishing

- [Vector](https://awesome-repositories.com/f/content-management-publishing/documentation-knowledge-management/knowledge-bases/vector.md) — Implements a vector-based knowledge base for indexing document embeddings to enable semantic retrieval of facts and preferences.

### Data & Databases

- [Episodic Conversation Stores](https://awesome-repositories.com/f/data-databases/graph-databases/episodic-conversation-stores.md) — Maintains conversational history and evolving goals using a graph database to track interaction relationships.
- [Contextual Memory Recall](https://awesome-repositories.com/f/data-databases/indexing-and-search/recall-optimization/contextual-memory-recall.md) — Retrieves stored user preferences and past interaction data to provide personalized, context-aware AI responses. ([source](https://docs.memmachine.ai/))
- [Natural Language Memory Queries](https://awesome-repositories.com/f/data-databases/indexing-and-search/recall-optimization/conversation-memory-retrieval/natural-language-memory-queries.md) — Allows retrieving relevant factual and preference records from persistent memory using natural-language prompts. ([source](https://cdn.jsdelivr.net/gh/memmachine/memmachine@main/README.md))
- [Semantic Knowledge Base Search](https://awesome-repositories.com/f/data-databases/search-indexing-technologies/search-indexing/search-and-indexing/local-knowledge-base-indexers/semantic-knowledge-base-search.md) — Retrieves long-term knowledge using vector embeddings and semantic similarity between queries and stored data.
- [Graph Databases](https://awesome-repositories.com/f/data-databases/graph-databases.md) — Uses a graph database to maintain episodic interactions and track evolving goals across sessions.

### Security & Cryptography

- [AI Behavioral Profiling](https://awesome-repositories.com/f/security-cryptography/identity-access-management/identity-management/user-management/user-profile-management/ai-behavioral-profiling.md) — Creates and manages individual data profiles to tailor AI responses based on user identities and past behaviors.

### Software Engineering & Architecture

- [Model Context Protocol Integrations](https://awesome-repositories.com/f/software-engineering-architecture/integration-extensibility/programmatic-interfaces/model-context-protocol-integrations.md) — Connects AI models and editors to a centralized memory layer using the standardized Model Context Protocol.
- [Cross-Model Memory Protocols](https://awesome-repositories.com/f/software-engineering-architecture/shared-memory-management/shared-knowledge-graph-memory/shared-filesystem-memory/cross-model-memory-protocols.md) — Utilizes a language-agnostic protocol to control memory operations across diverse AI models and microservices. ([source](https://docs.memmachine.ai/api_reference))
- [Cross-Model Memory Sharing](https://awesome-repositories.com/f/software-engineering-architecture/shared-memory-management/shared-knowledge-graph-memory/shared-filesystem-memory/cross-model-memory-sharing.md) — Links a single memory layer to different LLM providers to avoid vendor lock-in using portable protocols.
- [User Profile Isolations](https://awesome-repositories.com/f/software-engineering-architecture/user-profile-isolations.md) — Isolates memory stores by user identity to ensure personalized agent behavior and strict data separation.

### Web Development

- [AI Memory Profiles](https://awesome-repositories.com/f/web-development/user-profiles/ai-memory-profiles.md) — Enables the creation of unique memory profiles for each user to personalize AI responses and adapt behavior. ([source](https://memmachine.ai/llms.txt#memmachine))
- [Agent Profile Stores](https://awesome-repositories.com/f/web-development/agent-profile-stores.md) — Saves long-term facts and personal preferences in a structured database for consistent AI recall. ([source](https://cdn.jsdelivr.net/gh/memmachine/memmachine@main/README.md))

### Development Tools & Productivity

- [AI Identity Persistence](https://awesome-repositories.com/f/development-tools-productivity/ai-identity-persistence.md) — Records key facts and preferences in a structured database to provide tailored responses based on long-term identity. ([source](https://memmachine.ai/))
- [REST APIs](https://awesome-repositories.com/f/development-tools-productivity/rest-apis.md) — Exposes a standard HTTP REST API for interacting with the memory engine from any platform. ([source](https://docs.memmachine.ai/api_reference))
- [SDK Integrations](https://awesome-repositories.com/f/development-tools-productivity/sdk-integrations.md) — Offers type-safe client libraries in multiple languages to connect applications to the persistent memory layer. ([source](https://docs.memmachine.ai/api_reference))

### Networking & Communication

- [REST API Interfaces](https://awesome-repositories.com/f/networking-communication/rest-api-interfaces.md) — Provides a stateless HTTP interface as a gateway for managing memory operations across platforms.