Which integrations are available for this server?

Supports containerized deployment of Hayhooks server for serving Haystack pipelines and agents. Hosts the Hayhooks repository, issue tracking, and continuous integration workflows. Provides [OpenAI](/mcp/servers/integrations/openai)-compatible chat completion endpoints with streaming support, allowing Haystack pipelines and agents to be used as drop-in replacements for OpenAI APIs. Distributes Hayhooks as a Python package for installation and dependency management.

How do I use Hayhooks?

1. Click on "Install Server". 2. Wait a few minutes for the server to deploy. Once ready, it will show a "Started" state. 3. In the chat, type `@` followed by the MCP server name and your instructions, e.g., "`@Hayhooks` deploy the RAG pipeline for our customer support docs" That's it! The server will respond to your query, and you can continue using it as needed. Here is a [step-by-step guide with screenshots](https://glama.ai/blog/2025-07-08-how-to-install-and-use-mcp-servers).

Hayhooks

Hayhooks makes it easy to deploy and serve Haystack Pipelines and 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 or Claude Desktop. Under the hood, Hayhooks runs as an MCP Server, exposing each pipeline and agent as an MCP Tool.
💬 Integrate your Haystack pipelines and agents with 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, Cursor, or any other MCP client.

PyPI - Version PyPI - Python Version Docker image release Tests

Documentation

📚 For detailed guides, examples, and API reference, check out our

Quick Start

1. Install Hayhooks

# Install Hayhooks pip install hayhooks

2. Start Hayhooks

hayhooks run

3. Create a simple agent

Create a minimal agent wrapper with streaming chat support and a simple HTTP POST API:

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

hayhooks pipeline deploy-files -n my_agent ./my_agent_dir

5. Run it

Call the HTTP POST API (/my_agent/run):

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):

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 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 - Get started with Hayhooks
Installation - Install Hayhooks and dependencies
Configuration - Configure Hayhooks for your needs
Examples - Explore example implementations

Community & Support

GitHub: deepset-ai/hayhooks
Issues: GitHub Issues
Documentation: Full Documentation

Hayhooks is actively maintained by the deepset team.

Hayhooks

Hayhooks

Documentation

Quick Start

1. Install Hayhooks

2. Start Hayhooks

3. Create a simple agent

4. Deploy it

5. Run it

Key Features

🚀 Easy Deployment

🌐 Multiple Integration Options

🔧 Developer Friendly

📁 File Upload Support

Next Steps

Community & Support

Resources

New MCP Servers

Latest Blog Posts

MCP directory API