Hayhooks

# Hayhooks **Hayhooks** makes it easy to deploy and serve [Haystack](https://haystack.deepset.ai/) [Pipelines](https://docs.haystack.deepset.ai/docs/pipelines) and [Agents](https://docs.haystack.deepset.ai/docs/agents). With Hayhooks, you can: - 📦 **Deploy your Haystack pipelines and agents as REST APIs** with maximum flexibility and minimal boilerplate code. - 🛠️ **Expose your Haystack pipelines and agents over the MCP protocol**, making them available as tools in AI dev environments like [Cursor](https://cursor.com) or [Claude Desktop](https://claude.ai/download). Under the hood, Hayhooks runs as an [MCP Server](https://modelcontextprotocol.io/docs/concepts/architecture), exposing each pipeline and agent as an [MCP Tool](https://modelcontextprotocol.io/docs/concepts/tools). - 💬 **Integrate your Haystack pipelines and agents with [Open WebUI](https://openwebui.com)** as OpenAI-compatible chat completion backends with streaming support. - 🕹️ **Control Hayhooks core API endpoints through chat** - deploy, undeploy, list, or run Haystack pipelines and agents by chatting with [Claude Desktop](https://claude.ai/download), [Cursor](https://cursor.com), or any other MCP client. [](https://pypi.org/project/hayhooks) [](https://pypi.org/project/hayhooks) [](https://github.com/deepset-ai/hayhooks/actions/workflows/docker.yml) [](https://github.com/deepset-ai/hayhooks/actions/workflows/tests.yml) ## Documentation > 📚 **For detailed guides, examples, and API reference, check out our [comprehensive documentation](https://deepset-ai.github.io/hayhooks/).** ## Quick Start ### 1. Install Hayhooks ```bash # Install Hayhooks pip install hayhooks ``` ### 2. Start Hayhooks ```bash hayhooks run ``` ### 3. Create a simple agent Create a minimal agent wrapper with streaming chat support and a simple HTTP POST API: ```python from typing import AsyncGenerator from haystack.components.agents import Agent from haystack.dataclasses import ChatMessage from haystack.tools import Tool from haystack.components.generators.chat import OpenAIChatGenerator from hayhooks import BasePipelineWrapper, async_streaming_generator # Define a Haystack Tool that provides weather information for a given location. def weather_function(location): return f"The weather in {location} is sunny." weather_tool = Tool( name="weather_tool", description="Provides weather information for a given location.", parameters={ "type": "object", "properties": {"location": {"type": "string"}}, "required": ["location"], }, function=weather_function, ) class PipelineWrapper(BasePipelineWrapper): def setup(self) -> None: self.agent = Agent( chat_generator=OpenAIChatGenerator(model="gpt-4o-mini"), system_prompt="You're a helpful agent", tools=[weather_tool], ) # This will create a POST /my_agent/run endpoint # `question` will be the input argument and will be auto-validated by a Pydantic model async def run_api_async(self, question: str) -> str: result = await self.agent.run_async({"messages": [ChatMessage.from_user(question)]}) return result["replies"][0].text # This will create an OpenAI-compatible /chat/completions endpoint async def run_chat_completion_async( self, model: str, messages: list[dict], body: dict ) -> AsyncGenerator[str, None]: chat_messages = [ ChatMessage.from_openai_dict_format(message) for message in messages ] return async_streaming_generator( pipeline=self.agent, pipeline_run_args={ "messages": chat_messages, }, ) ``` Save as `my_agent_dir/pipeline_wrapper.py`. ### 4. Deploy it ```bash hayhooks pipeline deploy-files -n my_agent ./my_agent_dir ``` ### 5. Run it Call the HTTP POST API (`/my_agent/run`): ```bash curl -X POST http://localhost:1416/my_agent/run \ -H 'Content-Type: application/json' \ -d '{"question": "What can you do?"}' ``` Call the OpenAI-compatible chat completion API (streaming enabled): ```bash curl -X POST http://localhost:1416/chat/completions \ -H 'Content-Type: application/json' \ -d '{ "model": "my_agent", "messages": [{"role": "user", "content": "What can you do?"}] }' ``` Or [integrate it with Open WebUI](docs/features/openwebui-integration.md) and start chatting with it! ## Key Features ### 🚀 Easy Deployment - Deploy Haystack pipelines and agents as REST APIs with minimal setup - Support for both YAML-based and wrapper-based pipeline deployment - Automatic OpenAI-compatible endpoint generation ### 🌐 Multiple Integration Options - **MCP Protocol**: Expose pipelines as MCP tools for use in AI development environments - **Open WebUI Integration**: Use Hayhooks as a backend for Open WebUI with streaming support - **OpenAI Compatibility**: Seamless integration with OpenAI-compatible tools and frameworks ### 🔧 Developer Friendly - CLI for easy pipeline management - Flexible configuration options - Comprehensive logging and debugging support - Custom route and middleware support ### 📁 File Upload Support - Built-in support for handling file uploads in pipelines - Perfect for RAG systems and document processing ## Next Steps - [Quick Start Guide](docs/getting-started/quick-start.md) - Get started with Hayhooks - [Installation](docs/getting-started/installation.md) - Install Hayhooks and dependencies - [Configuration](docs/getting-started/configuration.md) - Configure Hayhooks for your needs - [Examples](docs/examples/overview.md) - Explore example implementations ## Community & Support - **GitHub**: [deepset-ai/hayhooks](https://github.com/deepset-ai/hayhooks) - **Issues**: [GitHub Issues](https://github.com/deepset-ai/hayhooks/issues) - **Documentation**: [Full Documentation](https://deepset-ai.github.io/hayhooks/) Hayhooks is actively maintained by the [deepset](https://deepset.ai/) team.