# haystack

Open-source AI orchestration framework for building production-ready LLM applications with modular pipelines and RAG.

## Agent Decision Summary
- Risk level: low
- Source confidence: high
- Recommended workflows: Coding agent workflow, Memory or RAG workflow
- Permission surface: memory, messages
- Agent JSON: https://www.openagent.bot/memory-systems/haystack.agent.json

## Summary
Haystack by deepset is an open-source framework for building production-ready LLM applications. It provides modular pipeline architecture for retrieval-augmented generation, semantic search, question answering, and agent workflows — with built-in support for dozens of model providers, vector databases, and document stores.


## Guide
### What it is
Haystack is deepset's open-source framework for building production LLM applications. It uses a modular pipeline architecture for RAG, semantic search, QA, and agent workflows with extensive integration support.

### Why it matters
Haystack is one of the few LLM frameworks battle-tested in enterprise production environments, with comprehensive documentation and an active community.


### FAQ
- What is Haystack best used for?
  - Haystack excels at RAG pipelines, semantic search, document QA, and any LLM workflow that requires controlled retrieval and generation steps.
- Does Haystack support vector databases?
  - Yes, Haystack integrates with over a dozen vector databases including Pinecone, Weaviate, Qdrant, and Milvus.
- Is Haystack open source?
  - Yes, Haystack is open source under the Apache-2.0 license with 25K+ GitHub stars.
- Can I use Haystack with any LLM provider?
  - Yes, Haystack supports dozens of model providers through its generator and embedder components, including OpenAI, Cohere, and local models.
## What It Does
Haystack is deepset's open-source framework for building production LLM applications. It uses a modular pipeline architecture for RAG, semantic search, QA, and agent workflows with extensive integration support.

## How To Evaluate
Evaluate haystack by starting from the official sources, checking its repo interface surface, and running one narrow workflow before expanding scope. Recorded integrations include memory systems.

## Why It Matters
Haystack is one of the most mature open-source LLM frameworks, used in production by enterprises for RAG, document Q&A, and semantic search. Its pipeline architecture gives developers explicit control over retrieval, routing, memory, and generation — making complex workflows debuggable and maintainable.


## Best For
- Teams building production RAG systems with complex document processing pipelines
- Developers who need a modular, enterprise-grade LLM framework with explicit pipeline control
- Organizations deploying semantic search and QA systems across large document collections

## Not For
- Quick prototyping or single-model experiments (use a simpler library for those use cases)
- Pure chatbot applications that don't need retrieval or document processing

## What It Actually Does
- Rag: haystack surfaces rag as a core capability in its published project metadata and source links.
  - Why it matters: This gives readers a starting point for evaluating whether the project fits their workflow before visiting the source repository or docs.
- Memory: haystack surfaces memory as a core capability in its published project metadata and source links.
  - Why it matters: This gives readers a starting point for evaluating whether the project fits their workflow before visiting the source repository or docs.

## Typical Use Cases
- Personal memory: Use it as a candidate for personal memory when the project facts, license, and official links match your deployment requirements.

## How It Compares
- When to choose haystack: Compare it with nearby memory systems by looking at hosting model, integration surface, license, and whether the official docs show the workflow you need.

## Fit Matrix
- Coding agent workflow: strong. haystack has multiple signals for coding agent workflow, including matching tags, capabilities, category, or positioning. Required check: Run a small repository change and inspect the diff, tests, and rollback path.
- Memory or RAG workflow: strong. haystack has multiple signals for memory or rag workflow, including matching tags, capabilities, category, or positioning. Required check: Create, update, retrieve, correct, and delete memory or retrieval objects with real data.
- Evaluation and observability: partial. haystack has at least one signal for evaluation and observability, but should be checked against a real task before adoption. Required check: Add one repeatable test case and confirm results can run again in review or CI.
- Reusable skill workflow: partial. haystack has at least one signal for reusable skill workflow, but should be checked against a real task before adoption. Required check: Run one skill end to end and check whether it produces evidence or structured output.
- Browser automation: weak. haystack is not primarily positioned for browser automation in the current metadata. Required check: Run one non-sensitive website task and inspect clicks, waits, retries, and changed URLs.
- Connector or protocol layer: weak. haystack is not primarily positioned for connector or protocol layer in the current metadata. Required check: Connect one low-risk service, then inspect schemas, auth scope, errors, and logs.

## Evidence
- verified: haystack is listed as open source. Source: License metadata: Apache-2.0
- verified: haystack has a recorded GitHub repository: deepset-ai/haystack. Source: Resource facts and GitHub source link.
- inferred: haystack supports these recorded deployment modes: cloud. Source: OpenAgent decision signal metadata.
- inferred: haystack is tagged with rag, memory capabilities. Source: OpenAgent capability taxonomy.

## Missing Checks
- Dedicated docs link is missing.
- Repository freshness has not been recorded.

## Next Actions
- Inspect repository: https://github.com/deepset-ai/haystack
- Open Homepage: https://haystack.deepset.ai

## Facts
- Category: memory-systems
- Resource type: memory_system
- Open source: yes
- License: Apache-2.0
- Last verified: 2026-06-03
- GitHub repo: deepset-ai/haystack
- GitHub stars: 25447

## Capabilities
- rag
- memory

## Structured Use Case Tags
- personal-memory

## Getting Started
- Review the repository: https://github.com/deepset-ai/haystack
- Homepage: https://haystack.deepset.ai

## Links
- GitHub: https://github.com/deepset-ai/haystack
- Homepage: https://haystack.deepset.ai

## Structured Outputs
- JSON: https://www.openagent.bot/memory-systems/haystack.json
- Markdown: https://www.openagent.bot/memory-systems/haystack.md
- Agent JSON: https://www.openagent.bot/memory-systems/haystack.agent.json
- Canonical: https://www.openagent.bot/memory-systems/haystack