#OpenWorker

A desktop AI coworker, built on aisuite — now in its own repository: andrewyng/openworker.

OpenWorker chats, does deep research, and carries out real tasks on your computer — reading files with permission, connecting to Slack/email, producing PDFs, documents, and spreadsheets, and running scheduled automations. Bring your own API key (OpenAI, Anthropic, Google) or run fully local with Ollama; your data stays on your machine.

⬇ Download for macOS _{macOS 13+ (Apple Silicon)}  ·  ⬇ Download for Windows _{Windows 10/11 (x64)}  ·  Quickstart

OpenWorker development has moved to the new repo. A historical snapshot of its source remains in openworker-archive/.

#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. aisuite also powers OpenWorker, a desktop AI coworker developed in its own repository:

┌───────────────────────────────────────────────┐
│          OpenWorker  (separate repo)          │   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 │
└────────┴───────────┴────────┴────────┴────────┘

• Chat Completions API — a unified, OpenAI-style interface for OpenAI, Anthropic, Google, Mistral, Hugging Face, AWS, Cohere, Ollama, OpenRouter, Requesty, and more. Swap providers by changing one string.
• Agents API · Toolkits · MCP — 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.
• OpenWorker — a desktop AI coworker built using aisuite, shipped as an app for everyday tasks. Developed in its own repository.

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#Installation

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 covers key setup and your first calls.

Looking for the OpenWorker desktop app? Downloads are on its releases page.

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#Chat Completions — one API across providers

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:

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 — install, key setup, local models, and more examples.

#Streaming

Pass stream=True to get an iterator of OpenAI-shaped chunks from any supporting provider (OpenAI, Anthropic, Ollama, and OpenAI-compatible endpoints) — the same loop works across all of them:

for chunk in client.chat.completions.create(model=model, messages=messages, stream=True):
    print(chunk.choices[0].delta.content or "", end="", flush=True)

The async variant is await client.chat.completions.acreate(..., stream=True), iterated with async for. Tool calls stream too: schema dicts and callables are passed to the model as usual, and the chunks carry incremental delta.tool_calls fragments for you to assemble and execute (streaming is manual tool calling — it can't be combined with max_turns).

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#Agents — give models real tools

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.

#Tool calling with max_turns

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.

#The Agents API

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:

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.

#MCP tools

aisuite natively supports the Model Context Protocol, 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 — manual tool handling, the full Agents API, policies, state stores, and MCP in depth.

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#Extending aisuite: Adding a Provider

New providers can be added by implementing a lightweight adapter. The system uses a naming convention for discovery:

Example:

# providers/openai_provider.py
class OpenaiProvider(BaseProvider):
    ...

This convention ensures consistency and enables automatic loading of new integrations.

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#Contributing

Contributions are welcome. Please review the Contributing Guide and join our Discord for discussions.

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#License

Released under the MIT License — free for commercial and non-commercial use.

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