{"repo":"sachinsharma9780/memweave","free":true,"listed":false,"github":"https://github.com/sachinsharma9780/memweave","clone":"git clone https://github.com/sachinsharma9780/memweave.git","description":"memweave is a zero-infrastructure, async-first Python library that gives AI agents persistent, searchable memory — stored as plain Markdown files","language":"Python","stars":53,"topics":["agentic-memory","agents","chatbot","embeddings","genai","hybrid-search","long-term-memory","markdown-memory","memory","multi-agent-systems"],"license":"MIT","category":"ai-agents","readme_excerpt":"memweave Agent memory you can read, search, and git diff . memweave is a zero-infrastructure, async-first Python library that gives AI agents persistent, searchable memory — stored as plain Markdown files and indexed by SQLite. No external services. No black-box databases. Every memory is a file you can open, edit, grep, and version-control. --- 📊 Benchmark — LongMemEval-S Evaluated on LongMemEval-S — a 500-question benchmark covering multi-session memory, temporal reasoning, knowledge updates, and user preferences. Primary metric: retrieval recall i.e. Recall@k (correct session in the top-k results). Embedding model used: all-MiniLM-L6-v2 via Ollama (local), same as mempalace. No LLM, no API key, no cloud at any stage. Comparison with mempalace — held-out split (450 questions) System R@5 R@10 NDCG@5 100% recall at -------- ----- ------ -------- ---------------- memweave (ECR + IDF + CAATB) 98.00% 99.11% 93.75% R@23 mempalace Hybrid v4 98.44% 99.78% — R@30 ECR — confidence-adaptive entity boost · IDF — corpus-relative keyword boost · CAATB — additive confidence-adaptive temporal boost. Three lightweight heuristic post-processors, zero neural inference. Implemented as custom plugins via mem.register postprocessor() . Details and source in benchmarks/ . memweave achieves 100% recall at R@23 — 7 ranks earlier than mempalace (R@30). For any downstream re-ranker or LLM pass operating on a fixed top-K window, a smaller context window guarantees full coverage. Note: mempalace Hybri","default_branch":null,"files":null,"tree":[],"storefront":"/r/sachinsharma9780","claimed":false,"request_supported":{"post":"https://gitbuyer.com/r/sachinsharma9780/memweave/request-supported","requests":0},"note":"indexed from public GitHub; nothing is for sale on this page. Clone it from GitHub. Paid listings live at /search."}