Structured AI memory layer. A single source of truth for what your agent knows across conversations.
devmcp-context is a Model Context Protocol (MCP) server that provides persistent, organized memory for AI agents. Your agent's memory is now visible, editable, and searchable — without retraining.
Published on PyPI: https://pypi.org/project/devmcp-context/
AI agents are black boxes. You can't see what they remember. When they forget something important or remember something wrong, you're stuck.
devmcp-context changes this. Your agent's memory is now:
- Visible — Plain text files in your project folder
- Editable — Change any entry, agent sees it immediately
- Structured — Organized into 5 categories with automatic cleanup
- Persistent — Survives across agent sessions and restarts
pip install devmcp-contextOr with uv:
uv add devmcp-contextRegister in your agent's MCP config (Claude, Node.js, Python, Docker), then:
devmcp-contextYour agent will auto-discover the memory server. Start asking questions, and memories are saved to ai-context/ folder.
- 5 memory categories (project, decisions, errors, tasks, ephemeral)
- Automatic expiration (TTL) — errors expire in 30 days, tasks in 14
- Full-text search across all entries
- Tagging system for organization
- Persistent file-based storage (no database)
- MCP-compliant server
Full docs: https://kushal1o1.github.io/devmcp-context/
- Getting Started guide
- Installation instructions
- API Reference (6 tools)
- Memory categories explained
- Architecture diagrams (Mermaid)
- Deployment guide
- Development guide
In your agent prompt:
Use context_save to remember insights:
- save("decisions", "auth_strategy", "Use JWT with refresh tokens", tags=["security"])
Use context_load to retrieve memories:
- load("decisions")
Use context_search to find specific memories:
- search("JWT")
MIT — See LICENSE for details.
Contributions welcome! See CONTRIBUTING.md for guidelines.