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Gives coding agents symbol-level codebase access via language servers (LSP), turning cross-file renames, reference lookups, and edits into precise operations instead of fragile text search. Runs as an MCP server spanning 40+ languages.
Bridges IDA Pro with language-model MCP clients so LLMs can query, inspect, modify and annotate disassemblies through a typed MCP API. Provides batch-first analysis tools (decompile, xrefs, int_convert), headless idalib support, and integrations for many MCP clients; requires commercial IDA Pro and Python 3.11+.
Turns any GitHub repo into a remote MCP server, giving AI assistants live, searchable access to that project's docs and code so they stop hallucinating outdated APIs. No install: point your IDE at gitmcp.io/owner/repo.
Collects 40+ importable n8n workflows from the AI Agents A-Z YouTube channel, each tied to one video episode — spanning content generation, social-media posting, and short-video and narrated-story pipelines, plus companion Docker MCP/REST servers.
Lets an LLM read, search, and send your personal WhatsApp messages, contacts, and media through MCP. A Go bridge speaks to WhatsApp's web multidevice API and stores the full history in local SQLite, so data stays on your machine until a tool is invoked.
Twelve engineering principles for building production-grade LLM agents, modeled on the 12-Factor App. Argues the best agents are mostly deterministic software with a few well-placed LLM calls, not a prompt-and-tools loop.
Generates full-stack web apps with the backend included — database, auth, file uploads, real-time UIs, and background workflows — by writing code against Convex's reactive APIs. A fork of bolt.diy; bring your key for Claude, GPT, Gemini, or Grok.
JVM framework for authoring agentic flows that mixes LLM-driven prompts with strongly typed domain models and normal code to plan and execute goals. Key features: pluggable planners (GOAP, Utility AI), Spring integration, strong typing, testability, and support for local and cloud LLMs.
Turns natural-language requirements into a dependency-aware graph of atomic, testable dev tasks for AI coding agents. Adds cross-session memory and a plan-reflect loop that forces the agent to think through each step before writing code.
Lets LLM agents drive real Android and iOS devices from natural-language commands by turning each screen's accessibility tree into structured text the model reads directly, not just screenshots. LLM-agnostic; runs via CLI, Python, or Docker.
Clean-room, modular implementations of multi-object tracking algorithms — SORT, ByteTrack, OC-SORT, BoT-SORT, C-BIoU — behind one interface. Detector-agnostic: works with YOLO, DETR, or any bounding-box model via supervision.Detections.