MemMachine/MemMachine

Health v2 · maintenance only

Adoption not part of the score

3201 stars · 207 forks observed · 2026-08-28

What it is AI-extracted, prompt v1, taxonomy v1, 2026-08-30, confidence not recorded

MemMachine is an open-source memory layer for AI agents and LLM applications, providing episodic, profile, and working memory that persists across sessions, agents, and models. It runs as a server (self-hosted or cloud) with a Python client SDK for storing and retrieving conversational context and user preferences.

Use cases

• give my ai agent persistent memory across sessions
• store and recall user preferences in a chatbot
• add long-term memory to llm applications
• personalize ai assistant responses based on past conversations
• manage agent state that survives restarts
• build context-aware conversational agents in python

When to choose

• you need drop-in persistent memory for AI agents with a simple client SDK
• you want episodic, profile, and working memory abstractions out of the box
• you need memory that persists across sessions, agents, and model changes
• you prefer self-hosting via Docker or using a managed platform

When to avoid

• you only need simple in-process state that a database or vector store already covers
• you require a memory solution in a language other than Python
• you cannot run an additional server component alongside your application
• you need fully offline operation without any server dependency

Facets

service · maturity active

rag agent-framework vector-database chatbot llm-inference artificial-intelligence large-language-models chatbots developer-tools python self-hosted cross-platform agent-memory long-term-memory episodic-memory knowledge-graph personalization memory-layer conversational-ai state-management ai-agents retrieval-augmented-generation docker web-server

Member repositories

For agents

markdown · JSON · MCP: product_card(name="MemMachine/MemMachine")