Andrew Ng ships Open CoWorker desktop AI agent

Andrew Ng released Open Coworker, a desktop AI agent built on aisuite that can perform tasks like deep research, file reading, and messaging. The open-source tool supports multiple LLM providers and runs locally, with downloads available for macOS and Windows.

An AI agent that lives on your desktop, built on aisuite.OpenCoworker is a desktop AI agent that can not only chat, but also do deep research and carry out tasks for you on your computer. It can read files with permission to gain context, read/send messages slack, email, etc. , and create real deliverables like PDF reports, documents, spreadsheets. It also supports scheduled automations, such as providing you a daily news summary. Requires bringing your own API key OpenAI, Anthropic, Google or run fully local with Ollama. Your data stays on your machine. ⬇ Download for macOSmacOS 13+ Apple Silicon ⬇ Download for WindowsWindows 10/11 x64 · — install, connect a model, first tasks, automations. Quickstart:Its source lives in this repository under platform/ — a working reference for building your own agent harness on aisuite. aisuite is a lightweight Python library for building with LLMs, in two layers: a unified Chat Completions API across providers, and an Agents API with tools and toolkits on top. This repo is also home to OpenCoworker , a desktop AI coworker built using aisuite: ┌───────────────────────────────────────────────┐ │ OpenCoworker │ agent harness for doing everyday tasks ├───────────────────────────────────────────────┤ │ Agents API · Toolkits · MCP │ build agents across multiple LLMs ├───────────────────────────────────────────────┤ │ Chat Completions API │ one API across multiple LLM providers ├────────┬───────────┬────────┬────────┬────────┤ │ OpenAI │ Anthropic │ Google │ Ollama │ Others │ └────────┴───────────┴────────┴────────┴────────┘ — a unified, OpenAI-style interface for Chat Completions API chat-completions OpenAI, Anthropic, Google, Mistral, Hugging Face, AWS, Cohere, Ollama, OpenRouter , and more. Swap providers by changing one string.— give models real Python functions as tools, run multi-turn loops, attach ready-made toolkits files, git, shell or any MCP server, and govern it all with tool policies. Agents API · Toolkits · MCP agents — a desktop AI coworker built using aisuite, shipped as an app for everyday tasks. OpenCoworker /andrewyng/aisuite/blob/main/docs/opencoworker-quickstart.md Install the base package, or include the SDKs of the providers you plan to use: pip install aisuite base package, no provider SDKs pip install 'aisuite anthropic ' with a specific provider's SDK pip install 'aisuite all ' with all provider SDKs You'll also need API keys for the providers you call — the Chat Completions quickstart /andrewyng/aisuite/blob/main/docs/chat-completions-quickstart.md covers key setup and your first calls. Download the installer and bring your own API key or run local models with Ollama : ⬇ macOS Apple Silicon https://github.com/andrewyng/aisuite/releases/latest/download/OpenCoworker-macos-arm64.dmg · · https://github.com/andrewyng/aisuite/releases/latest/download/OpenCoworker-windows-setup.exe ⬇ Windows 10/11 x64 OpenCoworker quickstart /andrewyng/aisuite/blob/main/docs/opencoworker-quickstart.md The chat API provides a high-level abstraction for model interactions. It supports all core parameters temperature , max tokens , tools , etc. in a provider-agnostic way, and standardizes request and response structures so you can focus on logic rather than SDK differences. Model names use the format <provider :<model-name ; aisuite routes the call to the right provider with the right parameters: python import aisuite as ai client = ai.Client models = "openai:gpt-4o", "anthropic:claude-3-5-sonnet-20240620" messages = {"role": "system", "content": "Respond in Pirate English."}, {"role": "user", "content": "Tell me a joke."}, for model in models: response = client.chat.completions.create model=model, messages=messages, temperature=0.75 print response.choices 0 .message.content → Quickstart: docs/chat-completions-quickstart.md /andrewyng/aisuite/blob/main/docs/chat-completions-quickstart.md — install, key setup, local models, and more examples. aisuite turns tool calling into a one-liner: pass plain Python functions and it generates the schemas, executes the calls, and feeds results back to the model. python def will it rain location: str, time of day: str : """Check if it will rain in a location at a given time today. Args: location str : Name of the city time of day str : Time of the day in HH:MM format. """ return "YES" client = ai.Client response = client.chat.completions.create model="openai:gpt-4o", messages= { "role": "user", "content": "I live in San Francisco. Can you check for weather " "and plan an outdoor picnic for me at 2pm?" } , tools= will it rain , max turns=2 Maximum number of back-and-forth tool calls print response.choices 0 .message.content With max turns set, aisuite sends your message, executes any tool calls the model requests, returns the results to the model, and repeats until the conversation completes. response.choices 0 .intermediate messages carries the full tool interaction history if you want to continue the conversation. Prefer full manual control? Omit max turns and pass OpenAI-format JSON tool specs — aisuite returns the model's tool-call requests and you run the loop yourself. See examples/tool calling abstraction.ipynb for both styles. For longer-running, structured work there is a first-class Agents API: declare an agent once, run it with a Runner , and attach toolkits — prebuilt, sandboxed tool families for files, git, and shell: python import aisuite as ai from aisuite import Agent, Runner agent = Agent name="repo-helper", model="anthropic:claude-sonnet-4-6", instructions="You are a careful repo assistant. Use your tools to answer from the code.", tools= ai.toolkits.files root="." , ai.toolkits.git root="." , result = Runner.run agent, "What changed in the last commit? Summarize in 3 bullets." print result.final output The Agents API also gives you the pieces a production harness needs: Tool policies — RequireApprovalPolicy , allow/deny lists, or your own callable deciding which tool calls run. State stores — persist and resume runs in-memory, file, or Postgres and continue conversations across processes. Artifacts & tracing — capture what an agent produced and every step it took along the way. aisuite natively supports the Model Context Protocol https://modelcontextprotocol.io/docs/getting-started/intro , so any MCP server's tools can be handed to a model without boilerplate pip install 'aisuite mcp ' : client = ai.Client response = client.chat.completions.create model="openai:gpt-4o", messages= {"role": "user", "content": "List the files in the current directory"} , tools= { "type": "mcp", "name": "filesystem", "command": "npx", "args": "-y", "@modelcontextprotocol/server-filesystem", "/path/to/directory" } , max turns=3 print response.choices 0 .message.content For reusable connections, security filters, and tool prefixing, use the explicit MCPClient . → Quickstart: docs/agents-quickstart.md /andrewyng/aisuite/blob/main/docs/agents-quickstart.md — manual tool handling, the full Agents API, policies, state stores, and MCP in depth. New providers can be added by implementing a lightweight adapter. The system uses a naming convention for discovery: | Element | Convention | |---|---| Module file | <provider provider.py | Class name | <Provider Provider capitalized | Example: providers/openai provider.py class OpenaiProvider BaseProvider : ... This convention ensures consistency and enables automatic loading of new integrations. Contributions are welcome. Please review the Contributing Guide https://github.com/andrewyng/aisuite/blob/main/CONTRIBUTING.md and join our Discord https://discord.gg/T6Nvn8ExSb for discussions. Released under the MIT License — free for commercial and non-commercial use.