anythingllm-rag
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Alternatives to 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.21MIT
- AlicenseNot gradedqualityCmaintenanceEnables AI coding agents to create, search, read, write, and manage persistent structured repository context such as facts, decisions, regressions, preferences, sessions, and changelog entries. This helps agents orient themselves, avoid repeating past mistakes, and retain knowledge across coding sessions.101 npm2MIT
- 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
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.