AgentVec
The SQLite for mutable agent memory. A lightweight, serverless, embedded vector database written in pure Rust — built for AI agents that need to remember, forget, and update knowledge in real time.
No server. No cloud. No external dependencies. Just point it at a directory.
pip install agentvec
import agentvec
db = agentvec.AgentVec("./agent_memory.avdb")
memories = db.collection("episodic", dim=384, metric="cosine")
# Store a memory that expires in one hour
memories.add([0.1, 0.2, ...], {"event": "user said hello"}, ttl=3600)
# Recall the most similar memories
for r in memories.search(query_embedding, k=10):
print(r.id, r.score, r.metadata)
Why AgentVec?
Most vector databases are built for search over static datasets — you index once and query forever. Agent memory is the opposite workload: it changes constantly. Agents add new observations every turn, update what they already know, and let stale context decay away.
AgentVec is designed for that mutable, transactional, row-oriented pattern instead of bulk analytics.
| AgentVec | LanceDB | ChromaDB | FAISS | |
|---|---|---|---|---|
| Primary use | Agent memory | RAG / analytics | Static search | Batch search |
| Architecture | Row-oriented | Columnar | Memory-first | Index-only |
| Dependencies | Zero (pure Rust) | Arrow / C++ | Python / C++ | C++ / BLAS |
| In-place updates | ACID | Copy-on-write | Variable | Rebuild |
| TTL / expiry | Native | — | — | — |
| Incremental index | Yes | Rebuild | Variable | Rebuild |
Built for
- AI agents — conversational memory, working memory, long-term knowledge
- Local-first apps — no server, no network, just a directory on disk
- Mutable workloads — frequent upserts, deletes, and TTL-based expiry
- Embedded use — single library, low memory footprint, instant startup
Not built for
- Billion-vector or distributed search (use Qdrant, Milvus, or Pinecone)
- Heavy analytical queries over vectors (use LanceDB or DuckDB)
- Static datasets you never update (use FAISS or usearch)
- Multi-process concurrent access (AgentVec is single-process, multi-threaded)
Features
- Memory decay (TTL) — give any record a time-to-live; expired memories are skipped on read and reclaimed on
compact(). - In-place upserts —
upsert(id, ...)is idempotent and ACID-safe; update a memory without rebuilding anything. - HNSW approximate search — auto-enabled past a threshold, built in parallel across all cores, with incremental inserts (no full rebuilds) and on-disk persistence.
- Metadata filtering — filter by metadata with
$eq,$ne,$gt,$gte,$lt,$lte,$in,$nin. - Multiple distance metrics —
cosine,dot, andl2(Euclidean). - Crash-safe storage — vectors live in memory-mapped files for zero-copy reads; metadata lives in an ACID key-value store with checksums and a recovery path.
- Product quantization — optional compression for larger collections.
- Export / import — snapshot a collection to a file for backup or transfer.
Quick start
import agentvec
db = agentvec.AgentVec("./my_agent.avdb")
# Different memory types can use different embedding dimensions
episodic = db.collection("episodic", dim=384, metric="cosine") # conversations
semantic = db.collection("semantic", dim=1536, metric="cosine") # knowledge
working = db.collection("working", dim=384, metric="cosine") # scratchpad
# Add a memory (auto-generated id, expires in 24h)
mem_id = episodic.add(vector, {"user": "alice", "type": "chat"}, ttl=86400)
# Update it later — same id, no rebuild
episodic.upsert("conv_turn_42", vector, {"summary": "discussed timeline"})
# Bulk insert is 10-100x faster than one-at-a-time
episodic.add_batch(vectors=[...], metadatas=[...])
# Search, filtered by metadata
results = episodic.search(query_vector, k=10, where_={"user": "alice"})
# Reclaim expired memories and flush to disk
episodic.compact()
db.sync()
Companion packages
AgentVec is the storage engine. Higher-level libraries build on top of it:
agentvec-memory— tiered agent memory (working / session / project / user) with pluggable embedders, so you store text and let it handle vectors and TTLs.agentvec-mcp— a Model Context Protocol server that exposes AgentVec memory to MCP-compatible AI tools.
Documentation & source
- Repository: https://github.com/Akuming/agentvec
- Issues: https://github.com/Akuming/agentvec/issues
- Full API reference, architecture notes, and benchmarks are in the repository README.
License
Dual-licensed under either of MIT or Apache-2.0, at your option.
1 maintainer
Release files for agentvec 0.2.1
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Built distributions (wheels)
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