— · MIT
Correction-first persistent memory for AI agents. MCP server + SDK + CLI. Compounds across sessions.
— · Apache-2.0
Lightweight Long-Term Memory for LLM Agents.
v1.6.2 · MIT
Persistent project knowledge graph for coding agents. MCP server with semantic search, in-process embeddings, and web explorer.
— · no license
A persistent, Git-versioned map of your whole codebase and database schema that coding agents read before they touch anything. Local-first MCP server + CLI in Go: a governed repository index of code knowledge that gives Claude Code, Codex, Cursor, and opencode long-term context, memory, and code intelligence.
v0.5.1 · MIT
Deterministic, persistent 3D/2D spatial world model for AI agents with entity tracking, object permanence, movement simulation, and expected view frustum projection.
v0.20.0 · MIT
Cross-Code Organizer (formerly Claude Code Organizer): cross-harness config dashboard for Claude Code, Codex CLI, MCP servers, skills, memories, agents, sessions, security scanning, context budget, and backups.
v13.1.0 · MIT
Neo.mjs is a self-evolving software organism: a professional end-to-end AI engineering team whose cross-model swarm inhabits live apps via Neural Link, Active Hybrid GraphRAG, DreamService, and self-healing loops.
v1.0.11 · MIT
Verified npm launcher for the open-source ContextStream Rust MCP server
v0.10.169 · Apache-2.0
Your AI forgets everything between sessions. cachly fixes that permanently: 124 MCP tools for Claude Code, Cursor, Copilot & Windsurf. Learns from every bug fix, commit and CI run. Arrives pre-briefed. Free tier forever, EU servers, GDPR.
v0.3.0 · Apache-2.0
Your First LLM-Wiki Conversation Knowledge Base
— · MIT
Prismer Cloud
— · AGPL-3.0
AgentRQ: Human-in-loop realtime conversational task manager for AI Agents. Self-hosted! Control your own agents from wherever you want Mobile, Web, Desktop. Designed to work well with your own Claude subscriptions and any harness with ACP support.
— · Apache-2.0
vMLX - Use MLX models easily - JANGQ (GGUF for MLX) - Not dependant on mlx_vlm
v0.91.1 · Apache-2.0
A fast object-first data engine for applications and AI agents.
v0.5.1 · MIT
会话接力 SessionRelay — 属于项目、不属于任何厂商的本地记忆层(跨 Agent 会话记忆 / 中文检索 / MCP / HOP 交接协议)
— · MIT
One-stop handbook for building, deploying, and understanding LLM agents with 60+ skeletons, tutorials, ecosystem guides, and evaluation tools.
v0.3.8 · Apache-2.0
Local-first, self-hosted AI agent runtime and MCP bridge with sandboxed sessions, memory, credentials, audit/replay, and a local Console.
v0.3.1 · MIT
Strategic BDI reasoning engine for autonomous AI agents — goal decomposition, utility scoring, risk evaluation, and reactive replanning over PuterVision memory and world models.
v3.50.0 · MIT
🌊 The original agent harness. Deploy intelligent multi-player swarms, coordinate autonomous workflows, and build conversational AI systems. Features adaptive memory, self-learning intelligence, federation, vector RAG integration, and native Claude Code / Codex / Hermes and many more Integrated
v20.21.17 · Apache-2.0
Persistent session memory for AI coding agents that never leaves your machine — including the on-device model that reasons over it. Restores your prior decisions, open TODOs, and changed files across sessions; adds associative recall of related past work,
v2.0.2-alpha · MIT
The agentic meta-harness — freeze the model, evolve the harness. An open runtime that routes each query to the cost-optimal model, evolves its own harness (planner/context/reviewer/retry/tool/memory/score policy) and autonomously repairs code, then orches
v1.0.169 · no license
Context window optimization for AI coding agents. Sandboxes tool output (98% reduction), persists session memory, and enforces routing across 17 platforms via MCP + hooks.
v0.1.0 · MIT
🛠️ The meta-harness for AI agents — scaffold your own focused, branded agent harness with its own npx CLI, MCP server, memory, learning loop, and witness-signed releases. Works with Claude Code, Codex, pi.dev, Hermes, OpenClaw, and RVM (hardware-isolated sandbox).
v2026.9.24 · MIT
Reusable project context and specialist workflows for the AI tools you already use. AIWG places agents, skills, commands, and rules in provider-readable locations, with optional utilities for artifact memory, workflow orchestration, recovery, and discover