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stevepridemore

Graph-Memory

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  • A
    license
    A
    quality
    B
    maintenance
    Gives your AI persistent memory across conversations. Stores facts automatically, finds them by meaning using hybrid search with query expansion, and organizes everything into topics without manual tagging.
    18
    1
    MIT

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  • F
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    Enables Claude Code to persist, semantically search, update, and delete memories across sessions, plus explore knowledge-graph entities and their relationships, backed by self-hosted Qdrant, Neo4j, and Ollama. Session hooks and prompts automate context injection and summarization, with fact extraction handled by either the Claude Code session or a fully local Ollama model.
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  • F
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    quality
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    Enables Claude Code to add, search, update, delete, and organize persistent memories across sessions using self-hosted Qdrant, Neo4j, and Ollama, with optional Anthropic or local LLM support. It also exposes tools for knowledge-graph entity relationship search and retrieval.
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  • F
    license
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    quality
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    maintenance
    Enables Claude Code to persistently add, search, update, and delete memories backed by self-hosted Qdrant vector storage, an optional Neo4j knowledge graph, and Ollama embeddings, using either Anthropic or a fully local LLM for fact extraction. It also exposes graph entity tools and session hooks so relevant context is automatically injected at startup and summaries saved on exit.
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  • F
    license
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    quality
    A
    maintenance
    Enables Claude Code to maintain persistent memory across sessions through semantic search, memory CRUD, entity listing, and knowledge graph queries against self-hosted Qdrant, Neo4j, and Ollama, while supporting Anthropic or fully local LLM providers.
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  • F
    license
    Not graded
    quality
    A
    maintenance
    Enables Claude Code to store and recall persistent cross-session memory backed by self-hosted Qdrant, Neo4j, and Ollama, using Anthropic or fully local Ollama as the LLM. Exposes tools for adding, searching, listing, updating, and deleting memories plus querying knowledge-graph entities and relationships, with optional session hooks for automatic context injection and saving.
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TDQS

A4.3/5.0

Scored across 24 tools

Disambiguation4/5

The retrieval tools (graph_query vs graph_search vs graph_entities) and the dashboard tools (graph_stats vs graph_build_context) overlap, but descriptions explicitly state when to prefer each (name-based vs semantic vs catalog; build_context explicitly bundles stats/query/contradictions). Other pairs like boost/weaken, merge/unmerge, decay/prune are cleanly opposing and distinct.

Naming Consistency5/5

Every tool uses the graph_ prefix followed by a lowercase verb or noun in snake_case (graph_query, graph_relate, graph_merge, graph_build_context), with no camelCase or style deviations. The convention is fully predictable across all 24 tools.

Tool Count4/5

24 tools is on the heavy side, but the domain (graph memory with CRUD, semantic search, entity resolution, decay/prune maintenance, ingestion, audit, and backup) genuinely justifies breadth. Almost every tool has a distinct role rather than being redundant.

Completeness4/5

Coverage is broad: entity/edge CRUD, aliasing, merge/unmerge, semantic retrieval, communities, decay/prune, validation, re-embedding, ingest, export, and audit. The main gap is no direct tool to edit an existing entity's properties or type in place (only aliases/relations are mutable).

Maintenance

ActivityMaintained
ResponsivenessNo issues