ctxkeeper
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Alternatives to ctxkeeper
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Related Servers
- AlicenseNot gradedqualityAmaintenanceEnables AI coding assistants to persistently save, search, and restore project context across sessions, including decisions, progress, file caches, checkpoints, and channel-based memory. It supports git-aware organization, batch operations, and shared memory across parallel sessions.228 npmMIT
- FlicenseNot gradedqualityAmaintenanceEnables AI coding agents to read and write persistent project-scoped and cross-project memory during sessions, using local semantic search to surface relevant decisions, preferences, error patterns, and guidelines across sessions. It also optionally exposes an autonomous task queue that draws on that same stored context.-
- AlicenseNot gradedqualityAmaintenanceEnables AI coding assistants to maintain persistent context across sessions by saving, searching, organizing, checkpointing, and restoring conversations, decisions, file content, and progress.228 npmMIT
- AlicenseAqualityDmaintenanceLong-term memory for AI coding assistants. Remembers context once and recalls it across sessions.724MIT
- FlicenseNot gradedqualityAmaintenanceProvides AI coding agents with a local-first persistent memory layer they can read and write during sessions, storing decisions, preferences, error patterns, and research per project or globally. Enables semantic and keyword search, formatted context recall, and cross-project guideline surfacing without API keys or cloud storage.-
- AlicenseNot gradedqualityBmaintenanceProvides persistent long-term memory (semantic RAG) for AI coding assistants, enabling them to store and semantically search code and documentation across chat sessions without token limits.5MIT
TDQS
Scored across 8 tools
Most tools have distinct roles: remember/recall/forget form a clear memory lifecycle, todo manages a separate list, and log_work and status are unambiguous. project_brief and context_pack both return token-budgeted context, but their scopes (session-start overview vs question-specific excerpts) are described well enough to distinguish them.
The set mixes simple verbs (remember, recall, forget), noun-like names (todo, status), and compound names (project_brief, log_work, context_pack). No single predictable convention is followed, though all names are readable and mostly snake_case.
Eight tools is well-scoped for a project-context memory server, with each tool covering a distinct operation. There is no obvious bloat or missing essential capability at this count.
The surface covers memory creation, search, archival with restore, todos, work logs, briefs, status, and context packing, providing solid lifecycle coverage. Minor gaps exist, such as no direct memory-edit operation or bulk cleanup beyond archiving by ID.