Haystack
Haystack is an open-source AI orchestration framework for building production-ready LLM applications in Python. It allows users to design modular pipelines and agent workflows with explicit control over retrieval, routing, memory, and generation. Haystack is built for context engineering and provides native async support, making it suitable for scalable RAG systems, multimodal applications, semantic search, question answering, and autonomous agents. The framework is modular and customizable, allowing users to create their own components or use built-in ones for retrieval, indexing, tool calling, memory, and evaluation. Haystack is also model- and vendor-agnostic, making it easy to integrate with various models and infrastructure components. The framework has an extensible ecosystem, enabling the community and third parties to contribute to it and build custom components. Haystack provides a simple way to deploy and serve pipelines as REST APIs or MCP servers through Hayhooks. The framework is used by thousands of teams building production AI systems across industries, including technology, public sector, enterprise, and knowledge platforms. Haystack has a strong community and provides various resources, including documentation, tutorials, and a cookbook, to help users get started and learn more about the framework.
- Category
- AI Agents
- Pricing
- Open Source
Problem solved
Haystack solves the problem of building production-ready LLM applications by providing a flexible and customizable framework for designing modular pipelines and agent workflows. It addresses the needs of developers, ML engineers, and data scientists who want to create scalable and efficient AI systems. Haystack's explicit control over retrieval, routing, memory, and generation enables users to build context-engineered systems that can be easily customized and deployed. The framework's model- and vendor-agnostic nature also makes it easy to integrate with various models and infrastructure components, reducing the complexity and cost of building AI systems.
Use cases
- Building scalable RAG systems
- Creating multimodal applications
- Developing semantic search and question answering systems
- Designing autonomous agents
- Deploying AI systems in various industries, including technology, public sector, enterprise, and knowledge platforms
Audience
- Developers
- ML Engineers
- Data Scientists
Pros
- Flexible and customizable framework
- Native async support
- Modular and extensible ecosystem
- Model- and vendor-agnostic
- Strong community and resources
Cons
- Steep learning curve
- Requires expertise in AI and Python
- Limited support for certain models and infrastructure components
Official website: https://github.com/deepset-ai/haystack
Canonical LambdaBase page: https://www.lambdabase.com/tools/haystack