SuanmoSuanyangTechnology/MemoryBear

Health v2 · maintenance only

Adoption not part of the score

5088 stars · 396 forks observed · 2026-08-28

What it is AI-extracted, prompt v1, taxonomy v1, 2026-08-29, confidence not recorded

MemoryBear is an AI memory management system that gives LLM agents and chatbots human-like long-term memory across a full lifecycle of perception, extraction, association, and biologically-inspired forgetting. Built in Python with FastAPI and Neo4j, it stores memories as dynamic knowledge graphs, compresses chat history to reduce token usage, and exposes an API that integrates with any agent or workflow.

Use cases

• give my chatbot long-term memory across conversations
• stop my LLM agent forgetting user preferences
• store and retrieve personalized user context for AI assistants
• compress conversation history to save tokens
• share memory between multiple agents and workflows
• build a knowledge graph of user interactions
• add memory with forgetting to an AI companion app

When to choose

• you need persistent, evolving memory for LLM agents beyond the context window
• you want graph-based associative memory with consolidation and forgetting rather than plain vector lookup
• you need cross-agent memory sharing and token-efficient history compression
• you prefer a self-hostable Python service with an HTTP API

When to avoid

• you only need simple key-value session storage or a plain vector store
• your stack cannot run Python 3.12+ and Neo4j
• you need a battle-tested incumbent like Mem0 or Zep with a large ecosystem
• you require fully offline operation without LLM calls for memory extraction

Facets

service · maturity active

vector-database rag agent-framework llm-inference search-engine api-framework artificial-intelligence large-language-models chatbots databases python self-hosted cross-platform ai-memory long-term-memory memory-graph knowledge-graph neo4j fastapi forgetting-mechanism personalization agent-memory ai-agents retrieval-augmented-generation natural-language-processing docker

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