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Show HN: Memv – Memory for AI Agents
memv is an open-source Python library that gives AI agents persistent memory. Feed it conversations; it extracts knowledge.
The extraction mechanism is predict-calibrate (Nemori paper): given existing knowledge, it predicts what a new conversation should contain, then extracts only what the prediction missed.
v0.1.2 adds the production path:
- PostgreSQL backend (pgvector for vectors, tsvector for text search, asyncpg pooling). Single db_url parameter — file path for SQLite, connection string for Postgres.
- Embedding adapters: OpenAI, Voyage, Cohere, fastembed (local ONNX).
Other things it does:
- Bi-temporal validity: event time (when was the fact true) + transaction time (when did we learn it), following Graphiti's model.
- Hybrid retrieval: vector similarity + BM25 merged with Reciprocal Rank Fusion.
- Episode segmentation: groups messages before extraction.
- Contradiction handling: new facts invalidate old ones, with full audit trail.
Procedural memory (agents learning from past runs) is next, deferred until there's usage data.
- peaklineops 6mo ago[dead]
- brgsk 6mo agoInstall with ``` uv add "memvee[postgres]" ``` - Links: - GitHub: https://github.com/vstorm-co/memv - Docs: https://vstorm-co.github.io/memv - PyPI: https://pypi.org/project/memvee/ - Quickstart: ```python from memv import Memory from memv.embeddings import OpenAIEmbedAdapter from memv.llm import PydanticAIAdapter memory = Memory( db_url="postgresql://user:pass@host/db", embedding_client=OpenAIEmbedAdapter(), llm_client=PydanticAIAdapter("openai:gpt-4o-mini"), ) ```
- JaredCampbell 6mo agoI wish I was less dumb, to take advantage of this
- rakeshd 6mo agoI'm curious about how it handles context window limitations and retrieval for very long histories.
- jghiglia 6mo ago[flagged]
- jghiglia 6mo ago[flagged]