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- FlicenseNot gradedqualityCmaintenanceLocal-first cross-agent memory for AI coding agents. Persistent, shared memory over MCP — what you tell one agent can be recalled by another — with all data stored in a single local SQLite file, no cloud and no API keys.-
- AlicenseBqualityAmaintenanceProvides local-first durable memory and session continuity for AI coding agents over MCP, enabling context across restarts without cloud services or telemetry.21MIT
- AlicenseAqualityAmaintenanceLocal-first persistent memory for coding agents and MCP clients. It keeps important project context across sessions and reduces wasted tokens by retrieving only relevant memories instead of replaying unnecessary history.8MIT
- AlicenseNot gradedqualityDmaintenanceA shared, local-first memory layer for AI CLIs, providing persistent, layered memory across Claude Code, Gemini CLI, and other MCP-aware clients.1MIT
- AlicenseAqualityBmaintenanceLocal-first, source-traceable memory for AI agents — no LLM at ingest, $0 per message, zero data egress. Gives Claude Code, Cursor, and any MCP client one shared persistent memory with semantic recall, belief revision, selective forgetting, and a provenance guard that blocks acting on stale or unconfirmed memories.2350 PyPI14MIT
- AlicenseNot gradedqualityAmaintenanceLocal-first, file-based memory layer for AI agents — one shared Markdown vault across Claude, Codex, Gemini, Cursor and any MCP client. Provides read/write memory tools with an audit trail, per-agent trust levels, and Git sync; no cloud and no lock-in.2MIT
TDQS
Scored across 25 tools
chat and ask_memory are nearly identical in purpose—both answer using approved conclusions first with raw fallback and return provenance plus a recallId—creating clear ambiguity. Additionally, search_memory, get_context, recall_memory, and ask_memory overlap in retrieval behavior, though their descriptions partially clarify output differences. The raw-evidence tools (remember, create_conclusion, consolidate_memory) are more distinct but still require careful reading.
Most tools follow a consistent verb_noun snake_case pattern such as create_conclusion, approve_conclusion, delete_memory, and export_memory. A few bare-verb or noun-like names like remember, chat, and session_trace deviate slightly, but the overall convention is recognizable and predictable.
At 25 tools, this sits at the heavy end of the borderline range and feels like more surface than most agents will need. The count is defensible for a full memory lifecycle system covering capture, recall, conclusions, audit, and maintenance, but it risks overwhelming users.
The tool set covers the core memory lifecycle well: capture raw evidence, propose and approve conclusions, search and recall, update/delete/supersede, audit, compact, backup, and export. Minor gaps exist—there is no import tool to complement export, and no direct get-by-id retrieval—but agents can work around these via search and export/backup workflows.