llmslim
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- AlicenseNot gradedqualityBmaintenanceEnables coding agents to store and retrieve long-term project memory in a local SQLite database, with no LLM calls and a hard token budget, so they can recall decisions, commands, and fixes across sessions while injecting only the context needed for the current step.1Apache 2.0
- AlicenseNot gradedqualityBmaintenanceEnables 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 gradedqualityBmaintenanceProvides 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.52MIT
- AlicenseAqualityBmaintenanceProvides 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.1165 PyPI4MIT
- AlicenseNot gradedqualityBmaintenanceGives an AI agent persistent memory across sessions: it wakes each session with what it previously chose to keep in its own words, recalls relevant memories as conversations unfold, and lets memories fade, link, and consolidate into lessons between sessions. Everything is stored locally in a single SQLite file on the user's machine, with tools for keeping, correcting, and recalling memories plus a journal and notes to the user.1Apache 2.0
- 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.-
TDQS
Scored across 5 tools
The tools mostly target distinct actions: remember stores, forget deletes, list_memories lists, recall searches stored memories, and fit_context shrinks arbitrary text. recall and list_memories both retrieve stored memories, but their descriptions distinguish query-based search from chronological listing with ids.
All names are snake_case and verb-first, giving a predictable overall pattern. The minor deviation is that remember, recall, and forget are bare verbs while fit_context and list_memories use verb_noun forms, but this remains readable and consistent enough.
Five tools is well-scoped for a memory/context server: store, search, list, delete, and compress context. Each tool earns its place without redundancy or obvious padding.
The core memory lifecycle is covered: create with remember, read/search with recall and list_memories, delete with forget, plus fit_context for text compression. The main gap is the lack of an update/edit operation for stored messages, though forget plus remember provides a workaround.