memsearch
Markdown-first memory system for AI agents that stores memories as human-readable .md files with vector-based semantic retrieval via Milvus. Features smart deduplication, live file sync, hybrid search (dense + BM25), and a Claude Code plugin.
Score Breakdown
⚙ Agent Friendliness
🔒 Security
Local-first architecture. API keys for embedding providers stored in config. Markdown files are plaintext (no encryption at rest). Milvus connection may or may not use TLS depending on deployment.
⚡ Reliability
Best When
You want persistent, searchable memory for AI agents that stores everything as human-readable markdown files you can version control.
Avoid When
You need a production-scale vector database (use Milvus directly), don't want to manage Milvus, or need non-markdown storage.
Use Cases
- • Persistent memory for AI coding agents across sessions
- • Semantic search over project documentation and notes
- • RAG pipeline with git-friendly markdown storage
- • Claude Code plugin for automatic memory persistence
Not For
- • High-throughput real-time search (latency-sensitive production systems)
- • Teams not comfortable running Milvus infrastructure
- • Simple key-value storage needs
- • Non-AI applications without embedding requirements
Interface
Authentication
Requires API key for chosen embedding provider (OpenAI, Google, Voyage, etc.). Milvus connection string for remote instances. No auth for local usage with local embeddings.
Pricing
Open source (MIT). Free to use. Embedding API costs depend on chosen provider. Milvus can run locally for free or via Zilliz Cloud (paid).
Agent Metadata
Known Gotchas
- ⚠ Requires Milvus running (local or remote) -- adds infrastructure dependency
- ⚠ Python 3.10+ required
- ⚠ Embedding provider costs can add up with large memory stores
- ⚠ New project (Feb 2026) -- API surface may still be evolving
- ⚠ File watcher must be running for live sync to work
Alternatives
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Scores are editorial opinions as of 2026-03-08.