AI-powered cascading development framework. Decompose complex projects into parallel executable tasks with auto-generated PRDs, design docs, and multi-agent collaboration (Claude Code, Codex, Aider).

Nornicdb is a distributed low-latency, Graph+Vector, Temporal MVCC with all sub-ms HNSW search, graph traversal, and writes. Using Neo4j Bolt/Cypher and qdrant's gRPC means you can switch with no changes while adding intelligent features like schemas, managed embeddings, reranking+llm, GPU accel, Auto-TLP, Policy-based Memory Decay, and MCP server.

A cyber brain for your AI. It never forgets a detail, remembers exactly what you said, and learns how you work over time. Free, local, works with Cursor, Claude Code, Codex, OpenClaw, Hermes and more. MIT.

Email, SMS & phone-call infrastructure for AI agents — send and receive real email and text messages, and place agent-driven outbound voice calls, all programmatically

vMLX - Use MLX models easily - JANGQ (GGUF for MLX) - Not dependant on mlx_vlm

WebCite MCP Server - verify AI claims against authoritative sources, bind quotes back to their source passage, and extract document figures deterministically. Works with any MCP-compatible agent or tool.

dsh-sieve: context engineering & token optimization plugin for DeepSeek Harness (DSH) — tool output filtering, context pruning, progressive skill disclosure. 36% smaller payload in offline replay. DSH 上下文管理与 token 优化节省插件。

Enables multiple DSH plugin/session agents to share trust-labeled Markdown memory and query on-demand cross-plugin digests, with write access isolated per channel so untrusted third-party content cannot become instructions.

Turn any laptop, desktop, VM, or server into a secure AI-agent workspace via MCP. Let ChatGPT, Claude, Grok, Qwen and other MCP clients read and edit files, run commands, test code, inspect systems, and complete real-world tasks.

A unified Model Context Protocol server implementation that aggregates multiple MCP servers into one.

An official Qdrant Model Context Protocol (MCP) server implementation

Harness the power of local LLMs with this TUI MCP Client for Ollama. Featuring all core MCP primitives (tools, prompts, resources), agent mode, multi-server, model switching, streaming responses, human-in-the-loop, thinking mode, model params config, system prompts, and saved preferences.

Stores what AI agents learn — thoughts, decisions, session events and repo facts — in PostgreSQL with pgvector and serves it back through versioned MCP tools with hybrid vector-plus-full-text retrieval and budgeted context packs. Enables multiple agents to share one scoped, role-isolated long-term memory, with deliberate promotion of distilled candidates into durable memories.

SceneScout — exploratory UI testing for AI coding agents. An MCP server that gives any agent (Claude Code, Cursor, VS Code Copilot, Codex, Gemini CLI and others) a structured view of a running web app, always-on oracles, a network-level write policy, memo

Enables Claude Code to delegate tasks to persistent GitHub Copilot CLI sessions via MCP, inspect pending tool requests, and approve each operation once or deny it while continuing multi-turn context. It bridges ACP runtime sessions locally over stdio without a public service or background daemon.

Provides a shared, repo-local memory that lets coding agents recall relevant decisions, conventions, gotchas, and open threads, save new entries, coordinate file claims, and cache file summaries across tools and sessions.

Enables AI agents to debug Commodore software in a locally running VICE emulator through its text remote monitor, inspecting registers, memory and screens, disassembling code, setting breakpoints and watchpoints, controlling execution, and working with disk images, snapshots and exported graphics. Runs locally over stdio and includes safety guidance for state-changing operations.

Keeps large tool outputs out of an agent's context window by spilling them to disk and handing back a short pointer instead. Lets the agent pull only what it needs — exact numbered line ranges read directly from disk, or verbatim relevant lines returned by a small model answering a question about the stored output, plus tools to open, list, and close documents.

Enables AI agents to run Meta (Facebook and Instagram) advertising: pull spend, CPA, ROAS and breakdown reports, audit account health, build and boost campaigns, ad sets and ads, and pause or adjust budgets, all starting paused until the user approves going live. Also supports brand memory, change logs and an optional daily spend cap.

MCP server for JADX-AI Plugin

Enables an LLM to orchestrate multiple downstream MCP tools inside one isolated sandbox in a single turn, filtering intermediate results in memory and returning only the final output to cut context tokens.

It enables AI agents and browser-based users to inspect, edit, validate, reorder, diff, and safely persist SillyTavern Chat Completion presets through revision-safe live synchronization over stdio or HTTP.

Enables AI clients to drive local video editing projects through a CEP bridge, covering media ingest, sequence and timeline editing, effects, keyframes, captions, markers, audio, color, and exports across hundreds of discoverable tools. Ships context resources, guided prompts, and higher-level assembly workflows for agent-driven editing.

Enables AI agents to store and retrieve persistent memory across sessions and machines, and to pay any x402 API while keeping a signed, offline-verifiable ledger of purchases and spending.