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Haystack (deepset)

Open Source

About Haystack (deepset)

Haystack is an open-source AI orchestration framework for building production-grade agents. It is installed via pip. The framework orchestrates steps of an AI agent including retrieval, reasoning, memory, and tool use. It connects to various external AI tools and services. Haystack Pipelines can be serialized and deployed across environments.

Products

  • Haystack, AI Orchestration Framework: Open source AI framework for building production-ready, LLM-powered agents and applications.
  • Enterprise Support: Enterprise support package for the Haystack framework.
  • AI Orchestration Platform: Sovereign AI toolset built on Haystack to accelerate and scale AI use cases.

Used for

  • Processing images and transcribing audio as part of multimodal AI applications.
  • Building conversational bots using standardized generator interfaces.
  • Generating content using Jinja-2 prompt templates.
  • Building retrieval-augmented generation pipelines with hybrid retrieval and self-correction loops.
  • Designing AI agents with tool calling and branching or looping decision flows.

From Haystack (deepset)'s own website, read 2026-10-23. How we build listings.

How to evaluate LLM Infrastructure & APIs

This is how the CIOPages Research Team evaluates this category. It is not an assessment of Haystack (deepset). It comes from our Generative AI & LLM Platforms buyer guide.

25%
Model Capability & Task Fit
Reasoning and instruction-following on your tasks (not public leaderboards), multimodal coverage (text, image, audio, video) where you need it, context-window length for your documents, breadth of the model menu (frontier, mid, small), and measured quality on a held-out set of your real prompts
20%
Deployment, Data Residency & Governance
Where inference physically runs (multi-tenant API, your VPC, on-prem, air-gapped), explicit no-training-on-your-data and retention commitments, regional residency, SOC 2 / ISO 27001 / HIPAA / FedRAMP and EU AI Act alignment, and DLP, content filtering, and full prompt/response audit logging
15%
Build & Customization Tooling
First-class retrieval-augmented generation and grounding, managed fine-tuning and (where relevant) continued pre-training, an integrated evaluation and regression-testing harness, prompt/version management, and native or tightly integrated vector search over your corpora
15%
Agentic & Orchestration Readiness
Reliable tool/function calling, support for open agent protocols (MCP, A2A), a managed agent runtime with memory and state, multi-agent orchestration, and guardrails — permissions, human-in-the-loop checkpoints, and step-level tracing for autonomous workflows
15%
Portability & Lock-in Risk
How model-agnostic the API is, whether multiple model families are reachable through one interface, the real cost of swapping providers (prompt, tool-schema, and eval rework), reliance on open standards, and whether you can take fine-tuned weights or your data with you on exit
10%
Cost, Throughput & Operability
Token economics at your projected volume, batch and provisioned-throughput options, prompt-caching support, rate limits and quota headroom for production, p95 latency under load, regional availability and uptime SLAs, and built-in usage, cost, and quality observability

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Quick Facts

haystack.deepset.ai
CategoryAI & ML Platforms
SubcategoryLLM Infrastructure & APIs
DeploymentCloud, On-premises

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