<div align="center">
  <div>
    <a href="https://strandsagents.com">
      <img src="https://strandsagents.com/latest/assets/logo-github.svg" alt="Strands Agents" width="55px" height="105px">
    </a>
  </div>

  <h1>
    Strands Agents
  </h1>

  <h2>
    A model-driven approach to building AI agents in just a few lines of code.
  </h2>

  <div align="center">
    <a href="https://github.com/strands-agents/harness-sdk/graphs/commit-activity"><img alt="GitHub commit activity" src="https://img.shields.io/github/commit-activity/m/strands-agents/harness-sdk"/></a>
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  </div>
  
  <p>
    <a href="https://strandsagents.com/">Documentation</a>
    ◆ <a href="https://github.com/strands-agents/samples">Samples</a>
    ◆ <a href="https://github.com/strands-agents/tools">Tools</a>
    ◆ <a href="https://github.com/strands-agents/agent-builder">Agent Builder</a>
    ◆ <a href="https://github.com/strands-agents/mcp-server">MCP Server</a>
  </p>
</div>

Strands Agents is a simple yet powerful SDK that takes a model-driven approach to building and running AI agents. From simple conversational assistants to complex autonomous workflows, from local development to production deployment, Strands Agents scales with your needs.

This monorepo contains the Python SDK, TypeScript SDK, documentation site, and supporting packages:

| Directory | Description |
|-----------|-------------|
| `strands-py/` | Python SDK — agent loop, model providers, tools ([PyPI](https://pypi.org/project/strands-agents/)) |
| `strands-ts/` | TypeScript SDK — agent loop, model providers, tools ([npm](https://www.npmjs.com/package/@strands/agent)) |
| `strands-wasm/` | WebAssembly bindings for running Python tools from TypeScript agents |
| `strands-py-wasm/` | Python host for WASM components (bridges WIT interfaces to Python) |
| `strandly/` | Developer CLI for local builds, codegen, and workspace tooling |
| `site/` | Documentation site built with Astro/Starlight ([strandsagents.com](https://strandsagents.com)) |
| `designs/` | Design proposals for significant features (RFC-style) |

## Feature Overview

- **Lightweight & Flexible**: Simple agent loop that just works and is fully customizable
- **Model Agnostic**: Support for Amazon Bedrock, Anthropic, Gemini, LiteLLM, Llama, Ollama, OpenAI, Writer, and custom providers
- **Advanced Capabilities**: Multi-agent systems, autonomous agents, and streaming support
- **Built-in MCP**: Native support for Model Context Protocol (MCP) servers, enabling access to thousands of pre-built tools

## Quick Start

```bash
# Install Strands Agents
pip install strands-agents strands-agents-tools
```

```python
from strands import Agent
from strands_tools import calculator
agent = Agent(tools=[calculator])
agent("What is the square root of 1764")
```

> **Note**: For the default Amazon Bedrock model provider, you'll need AWS credentials configured and model access enabled for Claude 4 Sonnet in the us-west-2 region. See the [Quickstart Guide](https://strandsagents.com/) for details on configuring other model providers.

## Installation

Ensure you have Python 3.10+ installed, then:

```bash
# Create and activate virtual environment
python -m venv .venv
source .venv/bin/activate  # On Windows use: .venv\Scripts\activate

# Install Strands and tools
pip install strands-agents strands-agents-tools
```

## Features at a Glance

### Python-Based Tools

Easily build tools using Python decorators:

```python
from strands import Agent, tool

@tool
def word_count(text: str) -> int:
    """Count words in text.

    This docstring is used by the LLM to understand the tool's purpose.
    """
    return len(text.split())

agent = Agent(tools=[word_count])
response = agent("How many words are in this sentence?")
```

**Hot Reloading from Directory:**
Enable automatic tool loading and reloading from the `./tools/` directory:

```python
from strands import Agent

# Agent will watch ./tools/ directory for changes
agent = Agent(load_tools_from_directory=True)
response = agent("Use any tools you find in the tools directory")
```

### MCP Support

Seamlessly integrate Model Context Protocol (MCP) servers:

```python
from strands import Agent
from strands.tools.mcp import MCPClient
from mcp import stdio_client, StdioServerParameters

aws_docs_client = MCPClient(
    lambda: stdio_client(StdioServerParameters(command="uvx", args=["awslabs.aws-documentation-mcp-server@latest"]))
)

with aws_docs_client:
   agent = Agent(tools=aws_docs_client.list_tools_sync())
   response = agent("Tell me about Amazon Bedrock and how to use it with Python")
```

### Multiple Model Providers

Support for various model providers:

```python
from strands import Agent
from strands.models import BedrockModel
from strands.models.ollama import OllamaModel
from strands.models.llamaapi import LlamaAPIModel
from strands.models.gemini import GeminiModel
from strands.models.llamacpp import LlamaCppModel

# Bedrock
bedrock_model = BedrockModel(
  model_id="us.amazon.nova-pro-v1:0",
  temperature=0.3,
  streaming=True, # Enable/disable streaming
)
agent = Agent(model=bedrock_model)
agent("Tell me about Agentic AI")

# Google Gemini
gemini_model = GeminiModel(
  client_args={
    "api_key": "your_gemini_api_key",
  },
  model_id="gemini-2.5-flash",
  params={"temperature": 0.7}
)
agent = Agent(model=gemini_model)
agent("Tell me about Agentic AI")

# Ollama
ollama_model = OllamaModel(
  host="http://localhost:11434",
  model_id="llama3"
)
agent = Agent(model=ollama_model)
agent("Tell me about Agentic AI")

# Llama API
llama_model = LlamaAPIModel(
    model_id="Llama-4-Maverick-17B-128E-Instruct-FP8",
)
agent = Agent(model=llama_model)
response = agent("Tell me about Agentic AI")
```

Built-in providers:
 - [Amazon Bedrock](https://strandsagents.com/docs/user-guide/concepts/model-providers/amazon-bedrock/)
 - [Anthropic](https://strandsagents.com/docs/user-guide/concepts/model-providers/anthropic/)
 - [Gemini](https://strandsagents.com/docs/user-guide/concepts/model-providers/gemini/)
 - [Cohere](https://strandsagents.com/docs/user-guide/concepts/model-providers/cohere/)
 - [LiteLLM](https://strandsagents.com/docs/user-guide/concepts/model-providers/litellm/)
 - [llama.cpp](https://strandsagents.com/docs/user-guide/concepts/model-providers/llamacpp/)
 - [LlamaAPI](https://strandsagents.com/docs/user-guide/concepts/model-providers/llamaapi/)
 - [MistralAI](https://strandsagents.com/docs/user-guide/concepts/model-providers/mistral/)
 - [Ollama](https://strandsagents.com/docs/user-guide/concepts/model-providers/ollama/)
 - [OpenAI](https://strandsagents.com/docs/user-guide/concepts/model-providers/openai/)
 - [OpenAI Responses API](https://strandsagents.com/docs/user-guide/concepts/model-providers/openai/)
 - [SageMaker](https://strandsagents.com/docs/user-guide/concepts/model-providers/sagemaker/)
 - [Writer](https://strandsagents.com/docs/user-guide/concepts/model-providers/writer/)

Custom providers can be implemented using [Custom Providers](https://strandsagents.com/docs/user-guide/concepts/model-providers/custom_model_provider/)

### Example tools

Strands offers an optional strands-agents-tools package with pre-built tools for quick experimentation:

```python
from strands import Agent
from strands_tools import calculator
agent = Agent(tools=[calculator])
agent("What is the square root of 1764")
```

It's also available on GitHub via [strands-agents/tools](https://github.com/strands-agents/tools).

### Bidirectional Streaming

> **⚠️ Experimental Feature**: Bidirectional streaming is currently in experimental status. APIs may change in future releases as we refine the feature based on user feedback and evolving model capabilities.

Build real-time voice and audio conversations with persistent streaming connections. Unlike traditional request-response patterns, bidirectional streaming maintains long-running conversations where users can interrupt, provide continuous input, and receive real-time audio responses. Get started with your first BidiAgent by following the [Quickstart](https://strandsagents.com/docs/user-guide/concepts/bidirectional-streaming/quickstart/) guide. 

**Supported Model Providers:**
- Amazon Nova Sonic (v1, v2)
- Google Gemini Live
- OpenAI Realtime API

**Installation:**

```bash
# Server-side only (no audio I/O dependencies)
pip install strands-agents[bidi]

# With audio I/O support (includes PyAudio dependency)
pip install strands-agents[bidi,bidi-io]
```

**Quick Example:**

```python
import asyncio
from strands.experimental.bidi import BidiAgent
from strands.experimental.bidi.models import BidiNovaSonicModel
from strands.experimental.bidi.io import BidiAudioIO, BidiTextIO
from strands.experimental.bidi.tools import stop_conversation
from strands_tools import calculator

async def main():
    # Create bidirectional agent with Nova Sonic v2
    model = BidiNovaSonicModel()
    agent = BidiAgent(model=model, tools=[calculator, stop_conversation])

    # Setup audio and text I/O (requires bidi-io extra)
    audio_io = BidiAudioIO()
    text_io = BidiTextIO()

    # Run with real-time audio streaming
    # Say "stop conversation" to gracefully end the conversation
    await agent.run(
        inputs=[audio_io.input()],
        outputs=[audio_io.output(), text_io.output()]
    )

if __name__ == "__main__":
    asyncio.run(main())
```

> **Note**: `BidiAudioIO` and `BidiTextIO` require the `bidi-io` extra. For server-side deployments where audio I/O is handled by clients (browsers, mobile apps), install only `strands-agents[bidi]` and implement custom input/output handlers using the `BidiInput` and `BidiOutput` protocols.

**Configuration Options:**

```python
from strands.experimental.bidi.models import BidiNovaSonicModel

# Configure audio settings and turn detection (v2 only)
model = BidiNovaSonicModel(
    provider_config={
        "audio": {
            "input_rate": 16000,
            "output_rate": 16000,
            "voice": "matthew"
        },
        "turn_detection": {
            "endpointingSensitivity": "MEDIUM"  # HIGH, MEDIUM, or LOW
        },
        "inference": {
            "max_tokens": 2048,
            "temperature": 0.7
        }
    }
)

# Configure I/O devices
audio_io = BidiAudioIO(
    input_device_index=0,  # Specific microphone
    output_device_index=1,  # Specific speaker
    input_buffer_size=10,
    output_buffer_size=10
)

# Text input mode (type messages instead of speaking)
text_io = BidiTextIO()
await agent.run(
    inputs=[text_io.input()],  # Use text input
    outputs=[audio_io.output(), text_io.output()]
)

# Multi-modal: Both audio and text input
await agent.run(
    inputs=[audio_io.input(), text_io.input()],  # Speak OR type
    outputs=[audio_io.output(), text_io.output()]
)
```

## Documentation

For detailed guidance & examples, explore our documentation:

- [User Guide](https://strandsagents.com/)
- [Quick Start Guide](https://strandsagents.com/docs/user-guide/quickstart/)
- [Agent Loop](https://strandsagents.com/docs/user-guide/concepts/agents/agent-loop/)
- [Examples](https://strandsagents.com/docs/examples/)
- [API Reference](https://strandsagents.com/docs/api/python/strands.agent.agent/)
- [Production & Deployment Guide](https://strandsagents.com/docs/user-guide/deploy/operating-agents-in-production/)

## Development

Git operations (commits, branches, PRs) are done from the repo root. Each package has its own toolchain:

**Python SDK** (`strands-py/`):
```bash
cd strands-py
pip install hatch
hatch test        # run unit tests
hatch fmt         # format & lint
```

**TypeScript SDK** (`strands-ts/`):
```bash
npm ci            # install from repo root
npm run build     # build
npm test          # run unit tests
```

**Documentation site** (`site/`):
```bash
cd site
npm install
npm run dev       # local dev server at http://localhost:4321/
```

## Contributing ❤️

We welcome contributions! See our [Contributing Guide](CONTRIBUTING.md) for details on:
- Reporting bugs & features
- Development setup
- Contributing via Pull Requests
- Code of Conduct
- Reporting of security issues

## Stay in touch with the team
Come meet the Strands team and other users on [**Discord**](https://discord.com/invite/strands)

## License

This project is licensed under the Apache License 2.0 - see the [LICENSE](LICENSE) file for details.

## Security

See [CONTRIBUTING](CONTRIBUTING.md#security-issue-notifications) for more information.

