TeamMemory
TeamMemory turns your AI assistant into something that actually learns from past conversations instead of forgetting everything between sessions. It exposes seven MCP tools including memory_save for extracting structured knowledge from chat logs, memory_recall for semantic search across your team's experience database, and memory_context for pulling relevant history based on file locations. The system automatically scores experiences based on usage and lets them decay over time, so frequently referenced bug fixes and architecture decisions bubble up while outdated info fades. It runs on Postgres with Ollama embeddings for vector search, supports multi-user teams with RBAC, and includes a web UI for reviewing AI-extracted drafts before they get published. Useful when you want your AI pair programmer to remember that tricky API gotcha from three weeks ago.
- GitHub stars
- 1
- tools
- 7
- auth
- Secret needed
- transport
- STDIO
- env vars
- 2
- last updated
- Feb 26, 2026
Tools
7 toolsPublic tool metadata for what this MCP can expose to an agent.
memory_context任务开始拉上下文 + 用户画像摘要 + 相关经验
任务开始拉上下文 + 用户画像摘要 + 相关经验
No parameter schema in public metadata yet.
memory_recall统一检索：problem / query / file_path 等
统一检索：problem / query / file_path 等
No parameter schema in public metadata yet.
memory_get_archive档案 L2 全文
档案 L2 全文
No parameter schema in public metadata yet.
memory_archive_upsert创建/更新档案馆（与 POST /api/v1/archives 一致）
创建/更新档案馆（与 POST /api/v1/archives 一致）
No parameter schema in public metadata yet.
memory_save保存或长文解析保存
保存或长文解析保存
No parameter schema in public metadata yet.
memory_feedback对结果评分
对结果评分
No parameter schema in public metadata yet.
memory_submit_response提交回复用于 Faithfulness 评估
提交回复用于 Faithfulness 评估
No parameter schema in public metadata yet.
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DATABASE_URL*secretPostgreSQL connection string (with pgvector extension)
TEAM_MEMORY_CONFIGPath to config.yaml (optional)
Readme
TeamMemory
mcp-name: io.github.ysydhc/team-memory
让 AI 拥有团队记忆 — 跨会话积累经验，像资深成员一样理解你的项目。
这是我学 AI 时萌生的一个想法。市面上已有类似产品，但总觉得不太贴合自己的使用习惯。做这个项目，既想通过和 AI 一起写代码来加深对大模型的理解，也希望能按自己的工作流，打磨出真正顺手的功能。
给 Agent / 贡献者：AGENTS.md · docs/README.md · MCP 实现 src/team_memory/server.py · 分层约束见 scripts/harness_import_check.py（LAYER_MAP）
为什么需要 TeamMemory？
用 Cursor、Claude 等 AI 助手写代码时，往往会遇到三个问题：
| 盲区 | 现象 |
|---|---|
| 无记忆 | 上周刚帮你修过的 Bug，这周遇到类似的，它完全不记得 |
| 只见代码，不懂决策 | 能看懂「是什么」，却不知道「为什么这么写」「上次踩过什么坑」 |
| 静态知识不够用 | Rules、Skills 管得了规范，管不住每天冒出来的隐性经验（接口坑、故障根因、被否掉的方案） |
TeamMemory 就是冲着这三个问题来的。 通过 MCP 把语义可搜索的经验库接进 AI：遇到问题自动查历史方案，解决后自动提炼并存下来，下次谁遇到同类问题，直接就能命中。既适合 3–10 人的技术团队共享，也适合部署在本地个人使用，配合 Cursor / Claude Desktop。
快速开始（4 条命令 + 1 项配置）
环境：Docker Desktop、Python 3.11+、Make
# 1. 初始化（Docker + 依赖 + 数据库）
make setup
Details
- Repository
- github.com/ysydhc/team_memory
- Categories
- Databases, AI & LLM Tools, Documents & Knowledge
- Package
team_memory- Transport
- STDIO
- Auth
- Required
- Registry
- MCP registry, active
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