scoped_memory
Related Servers
Alternatives to scoped_memory
No user-submitted related servers found.
Related Servers
- AlicenseBqualityCmaintenanceProvides persistent local memory for AI coding sessions, enabling agents to save and retrieve project context across different tools and sessions.16MIT
- AlicenseNot gradedqualityBmaintenanceProvides durable project context for coding agents, including project maps, session history, and explicit memories, all stored locally.46 npm7MIT
- AlicenseNot gradedqualityCmaintenanceEnables MCP-compatible coding agents to persist and retrieve durable project context—such as decisions, preferences, issues, tasks, and notes—locally across sessions without cloud services.MIT
- AlicenseNot gradedqualityAmaintenanceProvides persistent project memory for AI coding agents, enabling context retention across sessions via event logging, briefing generation, and querying.MIT
- FlicenseNot gradedqualityCmaintenanceLocal-first deterministic project memory for AI coding agents, with context packs, decisions, gates, risks, scoped claims and explicit checkpoints in project-owned files.-
- AlicenseNot gradedqualityAmaintenanceProvides AI agents with persistent, local, and shareable project memory by storing decisions and code context in a searchable SQLite index, supporting keyword and semantic search via MCP.39 PyPI3MIT
TDQS
Scored across 8 tools
Each tool has a distinct responsibility: ingestion, recall, forgetting, status, checkpointing, and project init. However, memory_recall and memory_engineering_context both produce context packets, and memory_remember overlaps somewhat with memory_ingest_project and memory_checkpoint, requiring careful reading of descriptions.
All tools share the memory_ prefix, but the pattern after the prefix is inconsistent: verb_noun forms like memory_ingest_project and memory_init_project, bare verbs like memory_forget and memory_remember, and nouns like memory_engineering_context, memory_status, and memory_checkpoint. This is readable but not a single predictable convention.
Eight tools is well-scoped for a memory server covering project initialization, ingestion, remembering, checkpointing, recall, forgetting, and status. Each tool has a clear place in the workflow, and none feels redundant.
The memory lifecycle is largely covered: init, ingest, remember, checkpoint, recall, forget, and status. Minor gaps include no direct single-memory retrieval or search operation, and the relationship between memory_engineering_context and memory_recall is not fully clarified.