AI Agents
zep
Examples and integrations for Zep — the memory layer for AI agents.
★ 5.0k starsApache-2.0
View guide →A serverless, single-file memory layer for AI agents - replace complex RAG pipelines
memvid is an open-source ai agents project by memvid (memvid/memvid on GitHub). It has 16,584 GitHub stars and 1,421 forks, is released under the Apache-2.0 license, and was last updated on GitHub on 14 July 2026.
Use the Rust core directly, or the Python, Node or CLI packages.
Add memvid-core to a Rust project, or install the SDK for your language.
Create a .mv2 file and put your first documents into it with metadata.
Run a semantic search and hand the results to your agent as context.
Your whole knowledge base lives in one portable .mv2 file - no server to deploy or manage.
Combines vector similarity search (HNSW with local ONNX embeddings) and BM25 full-text search.
Feature flags add PDF text extraction and repair, CLIP image search and Whisper audio transcription.
Natural-language date parsing understands expressions like 'last Tuesday' in searches.
Optional password-based encryption produces .mv2e memory files.
Rust core with Python, Node.js and CLI packages sharing the same format.
Get the source code from GitHub and enter the project directory.
git clone https://github.com/memvid/memvid.git
cd memvidAdd memvid-core to your Cargo project (feature flags enable lex, vec and more).
cargo add memvid-coreInstall the Python SDK and create your first memory file per the docs.
pip install memvid-sdkIt is maintained by the Memvid project (memvid) on GitHub.
Yes - Memvid is Apache-2.0 licensed and free to use.
No key is required for local embeddings and search; an optional api_embed feature can use cloud embeddings (for example OpenAI) if you choose it.
Compared with a hosted vector database, Memvid is an embedded single file - zero operations and offline-first - in exchange for single-node scale.
Run cargo add memvid-core (or pip install memvid-sdk), create a .mv2 file, put a document in, and search it.
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