team-memory
Related Servers
Alternatives to team-memory
No user-submitted related servers found.
Related Servers
AlicenseNot gradedqualityCmaintenanceProvides persistent memory for AI coding assistants, storing and retrieving architectural decisions, patterns, and solutions across sessions using semantic search, while also offering git integration for commit messages and code expertise mapping.MIT- AlicenseNot gradedqualityAmaintenanceProvides 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
- AlicenseNot gradedqualityDmaintenanceProvides AI coding assistants with persistent, context-rich memory of a codebase, including documentation and git history, enabling recall across sessions.104Apache 2.0
- AlicenseBqualityCmaintenanceProvides AI assistants with persistent memory of your project architecture, development history, and technical decisions, allowing them to give context-aware coding help without needing repeated explanations.1661 npm2MIT
- AlicenseAqualityBmaintenanceEnables AI coding assistants to contribute to and query a team-shared project memory, storing human-confirmed decisions with source and scope so work can continue across machines.12Apache 2.0
- AlicenseNot gradedqualityCmaintenanceProvides persistent, searchable memory and knowledge capture for AI-assisted development, enabling agents to retain decisions, bugs, and patterns across sessions and projects.MIT
TDQS
Scored across 9 tools
Most tools have clearly distinct purposes: recall searches, save writes, get_archive fetches full archives, draft_save/publish manage pipeline drafts. The only ambiguous pair is memory_recall and memory_context, which both retrieve relevant knowledge but differ in trigger scenario, so an agent might occasionally choose the wrong one.
All tools share a consistent memory_ prefix and most use a verb_noun pattern (get_archive, save, recall, archive_upsert, draft_publish, submit_response). memory_context breaks the pattern by using only a noun, and memory_save is a bit vague without an object, but the overall convention is predictable.
Nine tools is well-scoped for a team memory server covering search, save, archives, drafts, feedback, context, and response submission. Each tool serves a distinct part of the memory lifecycle without excessive granularity or missing core operations.
The tool surface covers the main workflows: saving, recalling, retrieving full archives, managing archive upserts, pipeline draft handling, feedback, and context. Minor gaps exist—there is no explicit delete or update operation for saved knowledge experiences, and no direct get-by-id for non-archive entries—but agents can work around these via recall and archive upsert.