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Autonomously proposes hypotheses, runs experiments, analyzes results, and drafts workshop-level papers via an agentic tree-search pipeline. Unlike template-driven predecessors, it explores open-ended ML research paths but requires GPU/PyTorch and careful sandboxing due to execution of LLM-written code.
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.
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.
Brings Gemini models into the terminal as an agent that reads files, runs shell commands, and edits code in place. Includes Google Search grounding, MCP server support, and a free OAuth tier (60 req/min, 1,000 req/day) with a 1M-token context window.
Provides MCP servers and agent skills that let AI assistants query, correlate and safely manage UniFi controllers (Network, Protect, Access). Includes a Cloud Relay for multi-location access, an independent REST/GraphQL API, secret redaction and preview-then-confirm mutation flows for safer automation.
Provides a modular full-stack reinforcement learning stack to train and evaluate long-horizon, multi-turn tool-use LLM agents, including a performant trainer, a Tinker-compatible backend, agent orchestration, and Gymnasium-style environments for task design.
Builds production-grade AI agents and multi-agent workflows in .NET and Python, with graph-based orchestration for sequential, concurrent, and handoff patterns. Unifies Microsoft's Semantic Kernel and AutoGen lineages, adding durable, checkpointed runs.
Official reference code for building browser-controlling agents on Google's Gemini computer-use models. The model sees a screenshot, proposes a UI action, and the loop executes its clicks, typing and scrolling via local Playwright or cloud Browserbase.
Orchestrates a lead agent, isolated parallel sub-agents, long-term memory, and sandboxes for long-horizon tasks — minutes to hours of deep research, coding, and content creation. LangChain/LangGraph-based with extensible skills; v2 is a full rewrite.
Transforms research papers, natural-language specs, and technical descriptions into runnable code via a multi-agent system. Covers Paper2Code, Text2Web, and Text2Backend; scores 75.9% on OpenAI's PaperBench, ahead of top ML PhDs.
Builds production AI agents around a model-driven loop with provider abstraction, tools, guardrails, streaming, MCP, tracing, and multi-agent patterns across Python and TypeScript SDKs.
Turns Chromium into a local-first AI browser with an embedded assistant that can summarise pages, extract structured data, automate web tasks, and run scheduled agents. Built as an open-source Chromium fork with 53+ built-in browser tools, 40+ app integrations, and support for BYO AI keys or fully local models (Ollama / LM Studio).