Engram

A skill where the agent logs it's own findings for self-improvement

Helps users discover and install agent skills when they ask questions like "how do I do X", "find a skill for X", "is there a skill that can...", or express...

A fast Rust-based headless browser automation CLI with Node.js fallback that enables AI agents to navigate, click, type, and snapshot pages via structured co...

Self-reflection + Self-criticism + Self-learning + Self-organizing memory. Agent evaluates its own work, catches mistakes, and improves permanently. Use when...

Security-first skill vetting for AI agents. Use before installing any skill from ClawdHub, GitHub, or other sources. Checks for red flags, permission scope, and suspicious patterns.

Typed knowledge graph for structured agent memory and composable skills. Use when creating/querying entities (Person, Project, Task, Event, Document), linkin...

Transform AI agents from task-followers into proactive partners that anticipate needs and continuously improve. Now with WAL Protocol, Working Buffer, Autonomous Crons, and battle-tested patterns. Part of the Hal Stack 🦞

Headless browser automation CLI optimized for AI agents with accessibility tree snapshots and ref-based element selection

Persistent semantic memory layer for AI agents. Local-first storage (SQLite+LanceDB) with Ollama embeddings. Store and recall facts, decisions, preferences, events, relationships across sessions. Supports memory decay, deduplication, typed memories (5 types), memory relationships (7 graph relation types), agent/user scoping, semantic search, context-aware recall, auto-extraction from text (rules/LLM/hybrid), import/export, REST API, MCP protocol. Solves context window and compaction amnesia. Server at localhost:3400, dashboard at /dashboard. Install via npm (engram-memory), requires Ollama with nomic-embed-text model.

Automatically generates and saves alternative perspectives from a secondary LLM for every user message to enhance reasoning and response quality.