OpenReflex
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- AlicenseNot gradedqualityBmaintenanceEnables AI coding agents to store decisions, preferences, and lessons in a local SQLite store, then recall the most relevant ones on demand. Runs fully local — no cloud, API keys, or per-query cost — and also exposes forget and consolidate operations.2Apache 2.0
- FlicenseNot gradedqualityAmaintenanceEnables AI coding agents to read and write persistent memory directly during sessions, storing decisions, preferences, error patterns, research, and cross-project guidelines in local SQLite databases. Supports semantic search so relevant project and global context can be recalled by meaning, fully offline and without API keys.-
- FlicenseNot gradedqualityAmaintenanceEnables AI coding agents to store, search, and recall persistent project-specific and cross-project context — decisions, preferences, error patterns, and guidelines — from a local-first SQLite memory store with semantic search, so knowledge survives across sessions. An optional companion task server lets agents queue and autonomously execute work using that same memory.-
- FlicenseNot gradedqualityAmaintenanceGives AI coding agents a local-first persistent memory they can read and write directly during sessions, storing decisions, preferences, error patterns, and guidelines in per-project and global SQLite stores. Enables semantic and keyword retrieval of relevant context across sessions so agents start work already knowing a codebase's conventions and history.-
- AlicenseBqualityBmaintenancePersistent memory for AI coding agents, storing learned architecture decisions, patterns, and bug fixes in a local SQLite database with full-text search, enabling agents to recall information across sessions.6464 npm1MIT
TDQS
Scored across 14 tools
Each tool targets a distinct action or view, and close pairs like explain_decision vs get_reflex_score are clearly separated by output format and explicit 'Not for' cross-references. The tools are easy for an agent to tell apart despite several operating on the same recent-task context.
All 14 tools follow a consistent snake_case verb_noun pattern: get_, explain_, search_, check_, choose_, record_, approve_, forget_. No mixed casing or stylistic drift is present.
At 14 tools, the set is well within the ideal range and each tool has a clear role in the OpenReflex workflow. The count feels appropriate for covering planning, execution monitoring, explanation, memory management, and metrics without redundancy.
The core lifecycle is well covered: approval, planning, path selection, progress checks, outcome recording, searching experiences, and forgetting tasks. Minor gaps exist, such as no MCP tool to revoke project approval or perform a full memory wipe, both of which are delegated to CLI commands.