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redleaves/context-keeper

by Various

🧠 LLM-Driven Intelligent Memory & Context Management System (AI记忆管理与智能上下文感知平台) AI记忆管理平台 | 智能上下文感知 | RAG检索增强生成 | 向量检索引擎

R

MCP

redleaves/context-keeper

Added 1 June 2026

#ai-assistant #ai-assistants #ai-memory #context-awareness #go #golang #llm #programming-assistant

Overview

redleaves/context-keeper is an open-source memory and context management system for LLM-driven applications. It uses retrieval-augmented generation (RAG) and vector search to provide intelligent context awareness and memory capabilities. Written in Go, it helps AI agents retain and retrieve relevant information across sessions.

Best for

Best for
Developers building Go-based AI applications that require persistent memory and context management

Use cases

  • Manage long-term memory for AI chatbots and virtual assistants
  • Retrieve relevant context from vector stores to ground LLM responses
  • Build persistent, context-aware AI agents in Go-based projects

How to use

Tools exposed

  • Go

Notes

redleaves/context-keeper is an open-source memory and context management system for LLM-driven applications. It uses retrieval-augmented generation (RAG) and vector search to provide intelligent context awareness and memory capabilities. Written in Go, it helps AI agents retain and retrieve relevant information across sessions.

148 stars on GitHub. Last updated 2026-01-13. Licensed MIT.

Use cases

  • Manage long-term memory for AI chatbots and virtual assistants
  • Retrieve relevant context from vector stores to ground LLM responses
  • Build persistent, context-aware AI agents in Go-based projects

Pros

  • Open source with a permissive license
  • Lightweight and efficient due to Go implementation
  • Integrates RAG and vector search out of the box

Cons

  • Limited community and documentation due to low star count
  • Primarily useful only for Go-based projects
  • Not yet battle-tested in large-scale production environments

Indexed from awesome-mcp-servers-punkpeye and enriched against its public facts.

Pros

  • Open source with a permissive license
  • Lightweight and efficient due to Go implementation
  • Integrates RAG and vector search out of the box

Cons

  • Limited community and documentation due to low star count
  • Primarily useful only for Go-based projects
  • Not yet battle-tested in large-scale production environments
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