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Alternatives to sqlite-local-ai-memory

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  • A
    license
    Not graded
    quality
    D
    maintenance
    Lightweight persistent memory for AI agents using a single SQLite file with hybrid search (keywords + semantics). Zero to 12MB install, no cloud or server required.
    7
    MIT
  • -
    license
    Not graded
    quality
    Not graded
    maintenance
    Provides persistent local memory functionality for AI assistants, enabling them to store, retrieve, and search contextual information across conversations with SQLite-based full-text search. All data stays private on your machine while dramatically improving context retention and personalized assistance.
    3
    -
  • A
    license
    A
    quality
    C
    maintenance
    Provides persistent local memory for any MCP-compatible AI agent, enabling agents to store, search, recall, forget, export, and manage preferences, rules, and facts with zero dependencies, no cloud, and no API keys.
    8
    MIT
  • A
    license
    A
    quality
    B
    maintenance
    Persistent long-term memory for AI coding assistants. Local SQLite, no cloud - 16 MCP tools to store, search, relate, and consolidate typed memories with a confidence lifecycle, hybrid BM25 + semantic search, namespaces, a knowledge graph, and a built-in OS keychain credential vault.
    23
    7
    MIT
  • A
    license
    Not graded
    quality
    B
    maintenance
    Persistent memory for any AI assistant. Zero token cost until recall. Stores memories in local SQLite, ranks by 6-factor scoring, returns results 79% smaller than JSON. Works with Claude, ChatGPT, Grok, Cursor, Windsurf, and any MCP client.
    56
    Apache 2.0
  • A
    license
    Not graded
    quality
    A
    maintenance
    Enables AI agents to keep durable long-term memory on the user's own machine, writing sessions and messages into a local SQLite database and retrieving them via keyword, exact, or optional semantic search with surrounding context. Runs entirely offline over stdio, so no cloud service or API key is required to give an agent recall across sessions.
    1
    MIT

TDQS

A4.4/5.0

Scored across 5 tools

Disambiguation5/5

Each tool has a clearly distinct purpose: two targeted reads (get_fact_lineage, get_current_state), a broad search (search_memory), an atomic write (save_fact), and a batch maintenance write (consolidate). The descriptions explicitly cross-reference each other with 'use X instead of Y' guidance, eliminating selection ambiguity.

Naming Consistency4/5

Four tools follow a clean verb_noun or verb_noun-ish pattern (get_fact_lineage, get_current_state, save_fact, search_memory), while 'consolidate' is a bare verb. This is a minor deviation that remains readable and predictable.

Tool Count5/5

Five tools is well-scoped for a local memory server, with each earning its place across read, search, write, audit, and maintenance roles. No redundant or trivially fragmented tools.

Completeness4/5

The surface covers search, atomic persistence, lineage auditing, dashboard state, and batch consolidation, which spans the core memory lifecycle. An explicit forget/delete or update operation is absent, though append-only supersede via save_fact mostly compensates.

Maintenance

ActivityMaintained
ResponsivenessNo recent community issues