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anythingllm-rag

by smnspz

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      Enables 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.
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    TDQS

    A3.9/5.0

    Scored across 5 tools

    Disambiguation4/5

    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.

    Naming Consistency4/5

    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.

    Tool Count5/5

    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.

    Completeness4/5

    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.

    Maintenance

    ActivitySlowing
    ResponsivenessNo issues