A
licenseNot graded
qualityC
maintenanceGives AI coding agents persistent memory by storing observations, decisions, and learnings in a local SQLite database with vector search, full-text search, and a rules engine.
4
MIT
OpenReflex
by vishnu-77
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- 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.-
- AlicenseBqualityAmaintenancePersistent 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.662 npm1MIT
- FlicenseNot gradedqualityAmaintenanceProvides a local-first persistent memory layer that AI coding agents can read and write during sessions, enabling semantic search and recall of project decisions, preferences, error patterns, research, and cross-project guidelines stored in local SQLite. It also exposes an optional task queue and orchestration tools for delegating autonomous coding work that reuses the same stored context.-
TDQS
A4.7/5.0
Scored across 14 tools
Disambiguation5/5
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.
Naming Consistency5/5
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.
Tool Count5/5
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.
Completeness4/5
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.
Maintenance
ActivityMaintained
ResponsivenessNo issues