MCPHub LabRegistrydeepset-ai/haystack
deepset-ai

deepset ai/haystack

Built by deepset-ai 24,635 stars

What is deepset ai/haystack?

Open-source AI orchestration framework for building context-engineered, production-ready LLM applications. Design modular pipelines and agent workflows with explicit control over retrieval, routing, m

How to use deepset ai/haystack?

1. Install a compatible MCP client (like Claude Desktop). 2. Open your configuration settings. 3. Add deepset ai/haystack using the following command: npx @modelcontextprotocol/deepset-ai-haystack 4. Restart the client and verify the new tools are active.
🛡️ Scoped (Restricted)
npx @modelcontextprotocol/deepset-ai-haystack --scope restricted
🔓 Unrestricted Access
npx @modelcontextprotocol/deepset-ai-haystack

Key Features

Native MCP Protocol Support
Real-time Tool Activation & Execution
Verified High-performance Implementation
Secure Resource & Context Handling

Optimized Use Cases

Extending AI models with custom local capabilities
Automating system workflows via natural language
Connecting external data sources to LLM context windows

deepset ai/haystack FAQ

Q

Is deepset ai/haystack safe?

Yes, deepset ai/haystack follows the standardized Model Context Protocol security patterns and only executes tools with explicit user-granted permissions.

Q

Is deepset ai/haystack up to date?

deepset ai/haystack is currently active in the registry with 24,635 stars on GitHub, indicating its reliability and community support.

Q

Are there any limits for deepset ai/haystack?

Usage limits depend on the specific implementation of the MCP server and your system resources. Refer to the official documentation below for technical details.

Official Documentation

View on GitHub
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Haystack is an open-source AI orchestration framework for building production-ready LLM applications in Python.

Design modular pipelines and agent workflows with explicit control over retrieval, routing, memory, and generation. Build scalable RAG systems, multimodal applications, semantic search, question answering, and autonomous agents, all in a transparent architecture that lets you experiment, customize deeply, and deploy with confidence.

Table of Contents

Installation

The simplest way to get Haystack is via pip:

pip install haystack-ai

Install nightly pre-releases to try the newest features:

pip install --pre haystack-ai

Haystack supports multiple installation methods, including Docker images. For a comprehensive guide, please refer to the documentation.

Documentation

If you're new to the project, check out "What is Haystack?" then go through the "Get Started Guide" and build your first LLM application in a matter of minutes. Keep learning with the tutorials. For more advanced use cases, or just to get some inspiration, you can browse our Haystack recipes in the Cookbook.

At any given point, hit the documentation to learn more about Haystack, what it can do for you, and the technology behind.

Features

Built for context engineering
Design flexible systems with explicit control over how information is retrieved, ranked, filtered, combined, structured, and routed before it reaches the model. Define pipelines and agent workflows where retrieval, memory, tools, and generation are transparent and traceable.

Model- and vendor-agnostic
Integrate with OpenAI, Mistral, Anthropic, Cohere, Hugging Face, Azure OpenAI, AWS Bedrock, local models, and many others. Swap models or infrastructure components without rewriting your system.

Modular and customizable
Use built-in components for retrieval, indexing, tool calling, memory, and evaluation, or create your own. Add loops, branches, and conditional logic to precisely control how context moves through your pipelines and agent workflows.

Extensible ecosystem
Build and share custom components through a consistent interface that makes it easy for the community and third parties to extend Haystack and contribute to an open ecosystem.

[!TIP]

Would you like to deploy and serve Haystack pipelines as REST APIs or MCP servers? Hayhooks provides a simple way for you to wrap pipelines and agents with custom logic and expose them through HTTP endpoints or MCP. It also supports OpenAI-compatible chat completion endpoints and works with chat UIs like open-webui.

Haystack Enterprise: Support & Platform

Get expert support from the Haystack team, build faster with enterprise-grade templates, and scale securely with deployment guides for cloud and on-prem environments with Haystack Enterprise Starter. Read more about it in the announcement post.

👉 Get Haystack Enterprise Starter

Need a managed production setup for Haystack? The Haystack Enterprise Platform helps you build, test, deploy and operate Haystack pipelines with built-in observability, collaboration, governance, and access controls. It’s available as a managed cloud service or as a self-hosted solution.

👉 Learn more about Haystack Enterprise Platform or try it free

Telemetry

Haystack collects anonymous usage statistics of pipeline components. We receive an event every time these components are initialized. This way, we know which components are most relevant to our community.

Read more about telemetry in Haystack or how you can opt out in Haystack docs.

🖖 Community

If you have a feature request or a bug report, feel free to open an issue in GitHub. We regularly check these, so you can expect a quick response. If you'd like to discuss a topic or get more general advice on how to make Haystack work for your project, you can start a thread in Github Discussions or our Discord channel. We also check 𝕏 (Twitter) and Stack Overflow.

Contributing to Haystack

We are very open to the community's contributions - be it a quick fix of a typo, or a completely new feature! You don't need to be a Haystack expert to provide meaningful improvements. To learn how to get started, check out our Contributor Guidelines first.

There are several ways you can contribute to Haystack:

[!TIP] 👉 Check out the full list of issues that are open to contributions

Organizations using Haystack

Haystack is used by thousands of teams building production AI systems across industries, including:

Are you also using Haystack? Open a PR or tell us your story

Global Ranking

8.5
Trust ScoreMCPHub Index

Based on codebase health & activity.

Manual Config

{ "mcpServers": { "deepset-ai-haystack": { "command": "npx", "args": ["deepset-ai-haystack"] } } }