vishalbanwari26/mnemos

Persist assistant memory as a lightweight knowledge graph across sessions.

2 tools · create_entities, search_nodes

Sponsored by <a href="https://oiya.ai/?utm_source=github&amp;utm_medium=referral&amp;utm_campaign=openlore&amp;utm_content=sponsor_logo"><img src="assets/oiya-logo.svg" alt="Oiya" height="24" align="absmiddle"></a> OpenLore is a minimal, extensible, agent-native knowledge base that keeps shared context current and inspectable. AI coding agents already know how to explore files with ls, cat, grep, find, pipes, and shell loops. OpenLore gives them that same interface over SSH, backed by your documentation instead of a real machine.

5 tools · Manage identity-scoped access, publish

Self-improving skill memory for coding agents: skills that help get reinforced, unused ones fade on an Ebbinghaus decay curve. $0 write path (SQLite FTS5 + local ONNX embeddings, no LLM calls), bilingual EN/RU hybrid search, SHA256 tamper-evident history, deterministic LongMemEval benchmark in-repo (hit@5 0.871). 8 mem_* tools; deep Claude Code hook integration optional. `pip install skillmem`

Shared, git-tracked memory for AI agents on the same codebase. Stores facts/decisions/gotchas in a `.ai-memory/` directory you commit to the repo — sync is git, no DB, no daemon. What one Claude/Cursor/Cline learns, the next one picks up automatically across users and machines. 5 MCP tools (`get_repo_memory`, `add_fact`, `list_facts`, `add_decision`, `add_gotcha`). Install: `uvx repo-memory-mcp --repo /path/to/repo`.

Memory the human governs: vaults separated by domain (code, notes, journal) on your own machine, hybrid retrieval (BM25 fused with vectors by RRF), and a declared vault list, so a vault left out cannot be read by mistake. Also reads what the OTHER coding agents on this machine wrote to disk, and flags when two are live in the same git worktree. Needs the Mnemosyne OS desktop app running. `npx -y @mnemosyne_os/mcp`

Persistent coding memory across sessions, machines, and teammates. Provides MCP tools for searching and managing memories, optional cross-machine sync, and project sharing. OpenCode, Claude Code, and Codex integrations add automatic capture and context injection.

Cache expensive LLM results — HTTP-style freshness (TTL / stale-while-revalidate) plus semantic recall of related past answers

Permission-aware enterprise search and RAG that gives agents context from your business apps (Google Workspace, Microsoft 365, Slack, Jira, and others). Streamable HTTP or local stdio (`npx @pipeshub-ai/mcp`). Official registry: `io.github.pipeshub-ai/mcp`.