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simbiot-harness/venv/lib/python3.12/site-packages/strands_agents-1.57.1.dist-info/METADATA
Simbiot Harness b60439152b Simbiot Harness v0.1: тактовая лестница на Strands + CQRS/ES-спина 
- harness/es.py: append-only event store + проекции (образец — кооп-портал)
- harness/levels.py: уровни 1с/1м/1ч/1д, контекст ∝ масштабу
- harness/scheduler.py: уровневые потоки, идемпотентные тики, absorbed-стабы
- harness/isolated_runner.py + _llm_worker.py: LLM-тик в сабпроцессе
  (глобальный asyncio-рантайм Strands не переживает 2+ create_harness)
- cli.py: init/task/tasks/status/events + requeue (написан самим минутным агентом)
- 9router: отдельный ключ sk-simbiot, модель auto (glm-5.3 первым)
2026-09-29 15:28:50 +00:00

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Metadata-Version: 2.5
Name: strands-agents
Version: 1.57.1
Summary: A model-driven approach to building AI agents in just a few lines of code
Project-URL: Homepage, https://github.com/strands-agents/harness-sdk
Project-URL: Bug Tracker, https://github.com/strands-agents/harness-sdk/issues
Project-URL: Documentation, https://strandsagents.com
Author-email: AWS <opensource@amazon.com>
License: Apache-2.0
License-File: LICENSE
License-File: NOTICE
Classifier: Development Status :: 5 - Production/Stable
Classifier: Intended Audience :: Developers
Classifier: License :: OSI Approved :: Apache Software License
Classifier: Operating System :: OS Independent
Classifier: Programming Language :: Python :: 3
Classifier: Programming Language :: Python :: 3.10
Classifier: Programming Language :: Python :: 3.11
Classifier: Programming Language :: Python :: 3.12
Classifier: Programming Language :: Python :: 3.13
Classifier: Programming Language :: Python :: 3.14
Classifier: Topic :: Scientific/Engineering :: Artificial Intelligence
Classifier: Topic :: Software Development :: Libraries :: Python Modules
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<div align="center">
<div>
<a href="https://strandsagents.com">
<picture>
<source media="(prefers-color-scheme: dark)" srcset="https://strandsagents.com/latest/assets/wordmark-github-dark.svg">
<img src="https://strandsagents.com/latest/assets/wordmark-github-light.svg" alt="Strands" width="320">
</picture>
</a>
</div>
<h1>
Strands Agents - Python SDK
</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>
<a href="https://github.com/strands-agents/harness-sdk/issues"><img alt="GitHub open issues" src="https://img.shields.io/github/issues/strands-agents/harness-sdk"/></a>
<a href="https://github.com/strands-agents/harness-sdk/pulls"><img alt="GitHub open pull requests" src="https://img.shields.io/github/issues-pr/strands-agents/harness-sdk"/></a>
<a href="https://github.com/strands-agents/harness-sdk/blob/main/LICENSE"><img alt="License" src="https://img.shields.io/github/license/strands-agents/harness-sdk"/></a>
<a href="https://pypi.org/project/strands-agents/"><img alt="PyPI version" src="https://img.shields.io/pypi/v/strands-agents"/></a>
<a href="https://python.org"><img alt="Python versions" src="https://img.shields.io/pypi/pyversions/strands-agents"/></a>
<a href="https://discord.gg/strands"><img alt="Strands Discord" src="https://img.shields.io/badge/Discord-Strands-5865F2?logo=discord&logoColor=white"/></a>
</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/harness-sdk/tree/main/strands-mcp">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.
## 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
Connect to 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")
```
The SDK works with both major versions of the `mcp` package through a built-in compatibility layer, so most code that uses `MCPClient` runs unchanged on either version. A fresh install resolves to mcp 2.x, and pinning `mcp<2` keeps you on 1.x. See [docs/MCP_VERSIONS.md](./docs/MCP_VERSIONS.md) for support status, the behavior differences on 2.x, and migration notes.
### 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 Bedrock Nova Sonic
- Google Gemini Live
- OpenAI Realtime API
**Installation:**
```bash
# Server-side only (no audio I/O dependencies)
pip install strands-agents[bidi]
# With all portable Bidi providers, terminal I/O, and audio processing (no local audio devices)
pip install strands-agents[bidi-all]
# For local microphone/speaker access, install PortAudio for your OS first, then:
pip install strands-agents[bidi,bidi-io,bidi-pyaudio]
```
> **Note**: Bedrock Nova Sonic requires Python 3.12+ due to its experimental AWS SDK dependency.
**Quick Example:**
```python
import asyncio
from strands.experimental.bidi.agent import BidiAgent
from strands.experimental.bidi.models import BedrockNovaSonicModel
from strands.experimental.bidi.io import AudioIO
from strands_tools import calculator, stop
async def main():
# Create bidirectional agent with Nova Sonic v2
model = BedrockNovaSonicModel(model_id="amazon.nova-2-sonic-v1:0")
agent = BidiAgent(model=model, tools=[calculator, stop])
# Setup audio I/O (local audio requires the bidi-pyaudio extra)
audio_io = AudioIO()
# Run with real-time audio streaming and terminal transcripts
# stop tool allows user to verbally stop agent execution
await agent.run(
inputs=[audio_io.input()],
outputs=[audio_io.output()]
)
if __name__ == "__main__":
asyncio.run(main())
```
> **Note**: `ConsoleIO` is included with the `bidi-io` extra. `AudioIO` requires
> the `bidi-io` and `bidi-pyaudio` extras plus the PortAudio system library. 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 streams using the `InputStream` and `OutputStream` protocols.
**Configuration Options:**
```python
from strands.experimental.bidi.models import BedrockNovaSonicModel
# Configure audio streams and Nova Sonic session parameters.
model = BedrockNovaSonicModel(
model_id="amazon.nova-2-sonic-v1:0",
audio={
"input": {"sample_rate": 16000},
"output": {"sample_rate": 16000},
},
voice="matthew",
params={
"turnDetectionConfiguration": {
"endpointingSensitivity": "MEDIUM"
},
"inferenceConfiguration": {
"maxTokens": 2048,
"temperature": 0.7
},
},
)
# Configure I/O devices
audio_io = AudioIO(
input_device_index=0, # Specific microphone
output_device_index=1, # Specific speaker
input_buffer_size=10,
output_buffer_size=10
)
```
## 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
```bash
pip install hatch
hatch test # run unit tests
hatch fmt # format & lint
```
## Contributing ❤️
We welcome contributions! See our [Contributing Guide](https://github.com/strands-agents/harness-sdk/blob/main/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](https://github.com/strands-agents/harness-sdk/blob/main/LICENSE.APACHE) file for details.
## Security
See [CONTRIBUTING](https://github.com/strands-agents/harness-sdk/blob/main/CONTRIBUTING.md#security-issue-notifications) for more information.
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