10 MCP-compatible memory frameworks that actually make Cursor agents useful

One of the biggest gaps in most AI coding setups today is persistent memory. By default, session history gets reset, which kills continuity and prevents Cursor from adapting to your project or codebase over time. That means you end up re-explaining the same context and instructions, which hurts productivity.

I’ve been experimenting with different MCP-compatible memory layers to extend Cursor agents. Here are some standouts and their best-fit use cases:

1. File-based memory (claude.md, Cursor configs)

- Best for personalization and lightweight assistants. Simple, transparent, but doesn’t scale.

- MCP compatibility: Not built-in. Needs custom connectors to be useful in agent systems.

2. Vector DBs (Pinecone, Weaviate, Chroma, FAISS, pgvector, Milvus)

- Best for large-scale semantic search across docs, logs, or knowledge bases.

- MCP compatibility: No native MCP, requires wrappers.

3. Byterover

- Best for team collaboration with Git-like system for AI memories. Support episodic and semantic memory, plus agent tools and workflows to help agents build and use context effectively in tasks like debugging, planning, and code generation.

- MCP compatibility: Natively designed for MCP servers and works smoothly with Cursor across IDEs and CLIs.

4. Zep

- Best for production-grade assistants on large, evolving codebases. Hybrid search and summarization keep memory consistent.

- MCP compatibility: Partial. Some connectors exist, but setup is not always straightforward.

5. Letta

- Best for structured, policy-driven long-term memory. Useful in projects that evolve frequently and need strict update rules.

- MCP compatibility: Limited. Requires integration work for MCP.

6. Mem0

- Best for experimentation and custom pipelines. Backend-agnostic, good for testing retrieval and storage strategies.

- MCP compatibility: Not native, but some community connectors exist.

7. Serena

- Best for personal or small projects where polished UX and easy setup matter more than depth.

- MCP compatibility: No out-of-the-box MCP support.

8. LangChain Memories

- Best for quick prototyping of conversational memory. Easy to use but limited for long-term use.

- MCP compatibility: Some LangChain components can be wrapped, but not MCP-native.

9. LlamaIndex Memory Modules

- Best for pluggable and flexible memory experiments on top of retrieval engines.

- MCP compatibility: Similar to LangChain, integration requires wrappers.

Curious what everyone else is using. Are there any memory frameworks you’ve had good luck with, especially for MCP setups? Any hidden gems I should try? (with specific use cases)