Agent Development Kit (ADK)
Compose Python agents with tools, delegation and a workflow runtime.
Explore model runners, AI frameworks and components for your own projects. Open-source code and downloadable weights have different licenses; model access, hardware and hosting may have separate costs.
Frameworks and model weights need integration, configuration or compute. Check the required runtime and supported models before treating a repository as a ready-to-use assistant.
Check the project license, model license and dependencies separately. A free client or repository does not guarantee free inference, commercial permission or managed hosting.
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Compose Python agents with tools, delegation and a workflow runtime.
Let a coding agent operate an authorized browser through CDP.
Run model-directed browser tasks from a Python library or CLI.
Expose Cloudflare API operations as an agent-friendly CLI with JSON output.
Store document embeddings and query matching records for AI applications.
Build queryable graph memory from documents and agent context.
Turn permitted web pages into Markdown for retrieval and AI pipelines.
Run a plugin-based agent harness with a local web interface.
Run DeepSeek's mixture-of-experts language model through a compatible inference stack.
Extract graph context from unstructured text for LLM question answering.
Run agent benchmarks in controlled environments and collect evaluation runs.
Expose multiple coding harnesses through one session and task API.
Route model requests and inspect LLM traces, latency and recorded costs.
Generate a 3D mesh and texture through the Hunyuan3D-2 pipeline.
Compose model calls, tools and retrieval components into AI applications.
Trace, evaluate and version prompts for an instrumented AI application.
Run a headless browser with native agent mode and replayable scripts.
Run supported language-model weights locally with a C++ inference runtime.
Select models for installed coding agents and route them through a local gateway.
Extract and retrieve persistent context for model-connected assistants.
Provide reference MCP servers that expose tools and resources to compatible clients.
Download supported models and serve local chat or model API responses.
Define tool-using agents with handoffs, sessions, guardrails and tracing in Python.
Organize agent knowledge, memory and skills in an inspectable virtual filesystem.
Extract recognized text and document structure from images and PDFs.
Store vectors with payloads and query them through a filtered similarity-search API.
Deploy Qwen3 language-model checkpoints for text generation and reasoning.
Process text, images and video through Qwen's vision-language checkpoints.
Run and fine-tune supported models with local desktop and training tooling.
Transcribe audio and perform supported speech translation locally.
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