llmslim
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- AlicenseNot gradedqualityAmaintenanceEnables 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.1MIT
- AlicenseNot gradedqualityAmaintenanceProvides long-term memory for LLMs via local SQLite storage with hybrid search (BM25, vectors, recency decay), enabling AI coding agents to persist and recall memories across sessions without cloud or API keys.53MIT
- AlicenseBqualityAmaintenanceProvides AI agents with a persistent long-term memory over a single SQLite file, letting them write, screen, consolidate, and recall notes across sessions without any generative LLM in the loop. Recall judges relevance, follows links between notes, and signals when memory is insufficient, so agents get one to three useful facts per question instead of a generic top-k list.111MIT
- FlicenseNot gradedqualityBmaintenanceEnables agents to store conversational turns in a single SQLite store and recall relevant context through lexical, entity, dense, and recent views without an LLM in the recall loop. It exposes MCP tools for remembering, bulk ingest, recall, stats, and session deletion, with persistent indexes and zero-token maintenance.-
- FlicenseNot gradedqualityAmaintenanceLocal memory for AI agents in one SQLite file on your own machine. Facts carry a validity window and a confidence score, so an outdated one is superseded and downweighted rather than deleted, and retrieval fuses BM25, vector similarity and a knowledge graph.2-
- AlicenseNot gradedqualityDmaintenanceProvides persistent long-term memory for LLMs via local SQLite storage and semantic search, enabling recall across sessions without external APIs.16 npm5MIT
TDQS
Scored across 5 tools
Each tool targets a distinct operation: remember stores, list_memories lists recent ids, recall searches by query, forget deletes by id, and fit_context compresses arbitrary text. Minimal overlap exists (recall/list_memories both surface stored messages; recall/fit_context both use query+budget), but descriptions clarify boundaries.
All names use lowercase snake_case, but the pattern mixes single verbs (recall, remember, forget) with verb_noun compounds (list_memories, fit_context). This is still readable and largely predictable, with only minor inconsistency.
Five tools is well-scoped for a memory/context server. Each tool earns its place: store, list, search, delete, and compress, with no redundant or missing core operations.
Core memory lifecycle is covered: create (remember), read/list/search (list_memories, recall), delete (forget), plus fit_context. Minor gaps exist: no direct get_memory by id and no update (though messages are immutable), which may force workarounds for id-based retrieval.