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ToolProduction Ready
haystack
by deepset-ai
Modular pipelines for production RAG, conversational agents, and retrieval-driven workflows
Python
Updated Aug 17, 2026
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What It Does
Orchestrates modular pipelines and agent workflows for production LLM applications. This aligns with the Agentic RAG Pattern for structuring retrieval and execution. Includes connectors to popular vector stores and model providers for end-to-end deployment, such as the Agent Service Mesh Pattern.
Key Benefits
As systems rely more on retrieval and multi-step delegation, having clear, testable pipelines is essential for understanding agent behavior. Haystack makes the flow of context, retrieval hits, and generator outputs explicit, which helps surface failure modes and isolate where trust breaks down. That visibility is vital for building reproducible agent track records and continuous evaluation in production, aligned with the Handoff Pattern and the Human-in-the-Loop Pattern.
Ideal For
Teams building production retrieval-augmented or conversational LLM applications that need explicit control over retrieval, routing, and scaling. See the Dynamic Task Routing Pattern for approaches to orchestrate task flow.
How It's Used
- Build production RAG pipelines with fine-grained control over retrievers, rankers, and generators
- Orchestrate agentic workflows that combine memory, routing, and generation for conversational systems
- Deploy scalable semantic search and multimodal retrieval using popular vector stores and model providers
Works With
openaihuggingfacelangchainelasticsearchmilvus
Topics
agent-frameworkagentic-aiagentic-ragagentsaiai-agentscontext-engineeringframeworkgenaigenerative-ai+10 more
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Keywords
multi-agent orchestrationretrieval-augmented-generationsemantic-searchcontext-engineering