Question 1

How do I install Semvec?

Accepted Answer

pip install semvec for the core. pip install "semvec[cortex,coding,api]" for the full suite (multi-agent, coding-agent MCP server, FastAPI REST). Pre-built wheels ship for Linux (glibc + musl), macOS (Intel and Apple Silicon), and Windows x86-64. Python 3.10 through 3.14, stable ABI.

Question 2

How does Semvec compare to mem0?

Accepted Answer

On LongMemEval-S, Semvec scored 42.8% vs mem0 v1.0.11 at 36.2% (p = 0.020, McNemar). Full run took 2.77 hours vs 47.04 hours — roughly 17× faster. The architectural reason: Semvec ingests with zero LLM calls per turn (sub-millisecond updates on a recent x86_64 CPU); mem0 fires ~50 internal extraction calls per turn.

Question 3

How does Semvec compare to Letta (formerly MemGPT)?

Accepted Answer

Letta runs as a server with its own database and uses LLM-driven page-in/page-out. Semvec is a pip-install library with a Rust core, no server required, and zero LLM calls on ingest. Letta is more flexible for free-form agentic memory; Semvec is faster, cheaper, and deterministic, with a fixed-size state and built-in multi-agent coordination via Cortex.

Question 4

Why is the Semvec core in Rust if the API is Python?

Accepted Answer

Determinism, throughput, and footprint. The semantic state update runs sub-millisecond on a single core. Bit-exact replay across runs comes for free — you can reproduce any session from a saved state. The Python layer stays familiar; the hot path stays cheap.

Question 5

Which embedders does Semvec work with?

Accepted Answer

Embedder-agnostic by design. Bring your own — sentence-transformers, OpenAI embeddings, ONNX models, anything that emits a fixed-dimension vector. The default examples use all-MiniLM-L6-v2 (384-d) on CPU; swap in whatever fits your latency budget.

Question 6

Does Semvec integrate with Claude Code and Cursor?

Accepted Answer

Yes. Semvec ships an MCP (Model Context Protocol) server. The SessionStart hook loads prior decisions, invariants, and error patterns into the prompt automatically. The PreCompact hook ingests the transcript and saves state before context compaction. The agent never has to be told to call save() — persistence is automatic across sessions.

Question 7

Does Semvec collect telemetry?

Accepted Answer

No. Anonymous pings were removed in v0.5.3. License verification happens locally via Ed25519-signed JWTs — no runtime phone-home. The library runs entirely on your infrastructure. Conversation content, embeddings, and state vectors never leave your machines.

Question 8

How does Semvec handle GDPR data deletion?

Accepted Answer

The compliance pack ships GDPR Article 17 (right to erasure) as async deletion with Ed25519-signed certificates, audit logging, HMAC signing, and RS256 JWTs. Verbatim cache uses Decimal precision so exact values (IBANs, dates, totals) are never lossy-rounded by cosine similarity.

Question 9

What if I want to remove Semvec later?

Accepted Answer

Zero lock-in. The chat proxy is a thin wrapper around your existing OpenAI-compatible client — drop the wrapper, your code keeps working. The library is a normal pip dependency; uninstall and the Python code stops importing it. State files in .semvec/ are JSON-shaped artefacts you can keep or delete.

Why constant-size memory beats vector stores for LLM agents