LiveKit

Autonomous coding agents that ship while you sleep

Twill is an autonomous coding agent platform that implements features, fixes bugs, and ships pull requests without manual intervention. Uses structured workflow of research, planning, human review, implementation in isolated sandbox, AI code review, then merge. Supports custom agent configurations with multiple LLM providers, isolated dev environments for verification, and integrations with GitHub, Linear, Sentry, Notion, and cloud platforms for end-to-end engineering automation.

Agentic AI security posture management

Trent AI is a specialized security platform for agentic AI applications providing AI Security Posture Management that compounds with every development cycle. Scans, judges, mitigates, and evaluates AI agent security detecting threats traditional tools miss including prompt injection attacks, tool misuse, unintended autonomous actions, data exfiltration through agent chains, and privilege escalation. Offers continuous assessment with remediation plan execution through Claude Code.

Official MCP server for GitHub repo operations

GitHub MCP Server is the official Model Context Protocol server from GitHub that connects AI assistants to repositories, issues, pull requests, workflows, and code search. It exposes 100+ operations with toolset filtering, permission scoping, and audit logging, available in both remote-hosted and self-hosted Docker deployment modes.

Up-to-date docs for AI code editors via MCP

Context7 is an MCP server developed by Upstash that injects up-to-date, version-specific documentation directly into AI code editors and coding assistants. By typing 'use context7' in prompts, developers get accurate library documentation instead of hallucinated or outdated API references. It pulls from official source documentation and serves it through the Model Context Protocol, solving the common problem of LLMs generating code with incorrect or nonexistent API calls.

Apache-2.0 Python framework for repeatable LLM, RAG, agent, MCP, and safety evaluation workflows.

DeepEval is an Apache-2.0 Python framework for evaluating LLM apps, RAG systems, agents, MCP workflows, and safety behavior with repeatable test cases. It works locally and in CI/CD, then connects to Confident AI for hosted reports, observability, red teaming, and governance when teams need shared evidence instead of ad-hoc prompt reviews and manual QA.

Turn websites into LLM-ready structured data

Firecrawl is a Y Combinator-backed API that crawls websites and converts them into clean, LLM-ready Markdown or structured JSON. Handles JavaScript rendering, pagination, sitemaps, and anti-bot measures automatically. Designed for RAG pipelines, AI agents, and data extraction workflows. Features batch crawling, scheduled scraping, webhook notifications, and custom extraction schemas. Processes content for direct ingestion into vector databases and LLM context windows.

Virtual engineering team as Claude Code skills by YC CEO Garry Tan

GStack transforms Claude Code into a structured virtual engineering team through 23 opinionated slash command skills created by Y Combinator CEO Garry Tan. It assigns specialist roles including CEO product review, engineering manager architecture oversight, designer visual audit, QA lead with real browser testing, and release engineer deployment. Each skill enforces focused workflows with clear decision principles for running parallel coding sessions.

Zig-built headless browser engineered for AI agent workloads

Open-source headless browser written in Zig for AI agents, crawling, and automation. Lightpanda omits graphical rendering, keeps DOM and JavaScript execution, exposes CDP for Puppeteer/Playwright/chromedp, and adds Agent, PandaScript, and MCP workflows. Current public benchmarks claim about 9x faster execution and 16x less memory than Chrome.