Open-source platform for autonomous coding agents that work like developers: editing files, running shell commands, browsing the web, and calling APIs in an isolated sandbox. Model-agnostic, with GitHub, Slack, and CI/CD integration.
Developer framework for building AI agents that autonomously trade on Polymarket prediction markets. Bundles the Polymarket and Gamma APIs, a Chroma RAG layer that pulls in news, and a CLI to query markets, reason with an LLM, and execute trades.
Structured dataset for training and evaluating LLM agentic behavior: function-calling conversations, JSON-mode structured outputs, and extraction samples for teaching models to generate tool calls and strict structured responses. Includes single-turn and multi-turn scenarios across several configs.
Runs a native, extensible AI agent on desktop, CLI, or API to automate code, workflows, research, and writing. Built in Rust, supports 15+ LLM providers and 70+ extensions via the Model Context Protocol — designed for local-first automation and developer workflows.
Gives LLM agents self-editing memory that persists across sessions, so they keep learning about a user instead of resetting each chat. Model-agnostic: bring your own LLM while it handles the memory and agent state, run via API or open source.
Give an agent a goal and it plans, then executes each step using AI models and your everyday apps. Build agents via chat-driven AutoPilot, a drag-and-drop builder, or self-hosted code, then run them on a schedule across integrations.
Reviews code in the IDE, CLI, and pull requests, flagging bugs, logic gaps, security holes, and missing tests using context from the whole repo and its dependencies. Enforces team-specific rules learned from past PRs.
Runs untrusted workloads inside hardware-isolated local microVMs with Docker-like workflows; cross-platform and OCI-compatible, embeddable via SDKs and optimized for fast startups (typical guest boot <100 ms).
Enables agents to autonomously operate GUIs and complete complex computer tasks — includes the Agent S papers and the gui-agents SDK, grounding-model support, and runnable S3 agent implementations for Windows/macOS/Linux.
Agentive operating system for physical robots that lets developers compose agent-native modules in Python to connect perception, spatial memory, and control across humanoids, quadrupeds, drones, and simulators.
Reference architectures and microservices for building GPU-accelerated vision agents that enable natural-language video search, long-video summarization, visual Q&A, and alert verification. Integrates NVIDIA NIM models, embeddings, VLMs/LLMs, and agent workflows for deployable video-analytics stacks.
Turns any website into a structured, text-like interface that LLM agents can read and act on, handling clicks, forms, scraping, anti-detection and CAPTCHAs. Ships as an open-source Python library plus a hosted cloud API for running browser agents at scale.