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Creates human-directed teams of AI agents (via GitHub Copilot) that live in your repo, persist knowledge, and coordinate development work. Key features: repo-first persistence, watch/triage automation, and an extensible CLI/SDK — alpha software, Copilot-dependent.
A TypeScript framework for building programmable, headless autonomous agents with a harness-centric runtime. Includes an SDK and CLI, virtual sandboxes (just-bash) with optional full container sandboxes, provider-agnostic model settings, and connectors for CI/Daytona/MCP—suited for deployable agent runtimes.
Local integration layer that lets AI agents discover and securely call OpenAPI, MCP, GraphQL, or custom JavaScript functions. Centralizes a shared tool catalog, auth, and policy surface across multiple agents, with a local web UI and CLI for runtime control.
Provides a minimal web and desktop GUI for coding agents (Codex and Claude), letting you run LLM-driven code workflows through a lightweight interface. Emphasizes quick provider switching, desktop packaging, and an opinionated minimal UX; early-stage project, expect bugs.
A fast, local document parser that extracts spatial text with bounding boxes from PDFs and other formats. Bundles Tesseract OCR and supports HTTP OCR servers, multi-language bindings (Rust, Node, Python, WASM) and screenshot generation; best for lightweight local pipelines but less suited to very complex or heavily scanned documents.
Autonomous white-box AI pentester for web apps and APIs. It reads your source code, maps the running app, then runs specialized agents that fire real proof-of-concept exploits for injection, XSS, SSRF, and auth flaws — reporting only what it can exploit.
Acts as an OpenAI‑compatible local and cloud gateway that routes requests across 100+ LLM providers with smart routing, load balancing, retries and fallbacks. Adds policies, rate limits, semantic caching and observability for reliable, cost‑aware inference in Docker, Electron or npm installs.
Scans a React codebase and produces a 0–100 health score plus actionable diagnostics across state & effects, performance, architecture, security, accessibility, and dead code. Auto-adapts to framework and React version, supports Next.js/Vite/React Native, a CLI, GitHub Action, and agent integrations to teach coding agents.
Desktop app for managing markdown-based knowledge bases with a files-first, git-first workflow. Works offline, uses plain markdown + YAML frontmatter for portability, and includes AI-agent integrations and agent configuration to organize context, memory, and procedures for assistants.
Provides a workspace-first, Kanban-backed multi-agent coordination platform that routes goals through specialist lanes (Backlog→Todo→Dev→Review→Done), enforces evidence-based review gates and traces, and runs on both web and desktop runtimes.
A Chromium binary patched at the C++ level to evade bot-detection and serve as a drop-in Playwright/Puppeteer replacement. Notable features: source-level fingerprint patches, human-like input emulation, auto-updating binaries, and integrations for Python/Node.js and Docker — useful for scraping, agent-driven browsing, and stealth automation.
Dramatically reduces AI agents' context usage by sandboxing large tool outputs and indexing only relevant snippets into a searchable SQLite FTS5 (BM25) knowledge base, improving session continuity and privacy. Deploys cross-platform hooks and sandbox tools to cut context size by ~98% and avoid dumping raw logs into the model's window. ([github.com](https://github.com/mksglu/context-mode/blob/main/README.md?utm_source=openai))