anythingllm-rag
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- AlicenseAqualityBmaintenanceEnables per-project, traceable access to a RAG knowledge base, with tools for searching and adding knowledge chunks.4MIT
- AlicenseNot gradedqualityBmaintenanceGives AI coding agents on-demand retrieval of workspace context, documentation, lessons, decisions and handoffs from a five-level (GLOBAL → WORKSPACE → PROJECT → FEATURE → SESSION) store, so they start with a small rule set instead of re-reading bloated docs. Exposes tools for dependency routing, knowledge search, doc reading and health checks backed by a zero-dependency SQLite + FTS5 memory with cross-project sharing of global-scoped knowledge.MIT
- AlicenseNot gradedqualityAmaintenanceEnables AI coding agents to persist and retrieve project knowledge using three MCP tools with local vector and full-text search.MIT
- AlicenseAqualityAmaintenanceEnables coding agents to query local notes, decisions, docs, and code with hybrid retrieval (BM25 + embeddings + reranking) and get path:line citations. It provides tools like rag_query for full-corpus search and search_knowledge for project-scoped knowledge recall.224 PyPI1MIT
- AlicenseAqualityBmaintenanceEnables AI agents to maintain a persistent knowledge graph of a project, providing dependency context, impact analysis, side-effect discovery, and session recording for more informed coding decisions.5MIT
- AlicenseNot gradedqualityDmaintenanceEnables AI coding agents to retrieve and manage code context with hybrid search, project memory, and observability via MCP tools.29MIT
TDQS
Scored across 5 tools
Each tool targets a distinct operation: lookup, creation, semantic search, document injection, and document removal. The only minor overlap is that rag_write can remove stale embeddings via overwrite, but the intent (write vs. forget) is clearly separated in the descriptions.
All tools share a consistent rag_ prefix and use lowercase snake_case verbs. However, create_workspace includes an object while find, search, write, and forget are bare verbs, so the pattern is not perfectly uniform.
Five tools is a well-scoped set for a focused RAG/workspace utility. Each tool covers a distinct part of the workflow without redundancy or bloat.
The core RAG lifecycle is covered: find/create workspaces, write docs, semantic search, and forget docs. The main gap is the lack of a workspace deletion tool, which prevents full lifecycle cleanup.