# Haystack — Open-Source RAG & Agent Framework

> Production-ready Python framework for building RAG pipelines, search systems, and AI agents with composable components. By deepset. Supports 30+ integrations. 20,000+ GitHub stars.

## Install

Save as a script file and run:

## Quick Use

```bash
pip install haystack-ai
```

```python
from haystack import Pipeline
from haystack.components.generators import OpenAIGenerator
from haystack.components.builders import PromptBuilder

pipeline = Pipeline()
pipeline.add_component("prompt", PromptBuilder(template="Answer: {{query}}"))
pipeline.add_component("llm", OpenAIGenerator(model="gpt-4o"))
pipeline.connect("prompt", "llm")

result = pipeline.run({"prompt": {"query": "What is RAG?"}})
print(result["llm"]["replies"][0])
```

---

## Intro

Haystack is a production-ready Python framework by deepset for building RAG pipelines, search systems, and AI agents with 20,000+ GitHub stars. Its composable pipeline architecture lets you connect retrievers, generators, rankers, and custom components like building blocks. Supports 30+ integrations including OpenAI, Anthropic, Pinecone, Weaviate, Elasticsearch, and more. Best for teams building production search and retrieval systems that need flexibility beyond simple API wrappers. Works with: Claude, GPT-4, Gemini, Cohere, local models via Ollama. Setup time: under 2 minutes.

---

## Core Concepts

### Composable Pipelines
Build RAG, search, and agent pipelines by connecting components:

```python
from haystack import Pipeline
from haystack.components.retrievers import InMemoryBM25Retriever
from haystack.components.generators import OpenAIGenerator
from haystack.components.builders import PromptBuilder
from haystack.document_stores.in_memory import InMemoryDocumentStore

# Create a RAG pipeline
store = InMemoryDocumentStore()
pipeline = Pipeline()
pipeline.add_component("retriever", InMemoryBM25Retriever(document_store=store))
pipeline.add_component("prompt", PromptBuilder(
    template="Context: {{documents}}\n\nQuestion: {{query}}\nAnswer:"
))
pipeline.add_component("llm", OpenAIGenerator())
pipeline.connect("retriever", "prompt.documents")
pipeline.connect("prompt", "llm")
```

### Document Processing
Ingest and process documents from any source:

```python
from haystack.components.converters import PyPDFToDocument
from haystack.components.preprocessors import DocumentCleaner, DocumentSplitter

converter = PyPDFToDocument()
cleaner = DocumentCleaner()
splitter = DocumentSplitter(split_by="sentence", split_length=3)
```

### Multiple Retrieval Strategies
- BM25 (keyword search)
- Dense retrieval (semantic search)
- Hybrid (keyword + semantic)
- Sparse retrieval

### 30+ Integrations

| Category | Integrations |
|----------|-------------|
| LLMs | OpenAI, Anthropic, Cohere, Ollama |
| Vector DBs | Pinecone, Weaviate, Qdrant, Chroma |
| Search | Elasticsearch, OpenSearch |
| Storage | S3, Azure Blob, Google Cloud |
| Evaluation | RAGAS, DeepEval |

### Agent Capabilities

```python
from haystack.components.agents import Agent

agent = Agent(
    generator=OpenAIGenerator(),
    tools=[search_tool, calculator_tool, web_tool]
)
result = agent.run("Research the latest AI trends and summarize")
```

### Key Stats
- 20,000+ GitHub stars
- 30+ integrations
- 500+ contributors
- Production-ready since 2019
- Used by Fortune 500 companies

### FAQ

**Q: What is Haystack?**
A: Haystack is an open-source Python framework by deepset for building production RAG pipelines, search systems, and AI agents with composable, pluggable components.

**Q: Is Haystack free?**
A: Yes, fully open-source under Apache 2.0 license. deepset offers managed cloud hosting.

**Q: How is Haystack different from LangChain?**
A: Haystack focuses on production-grade pipelines with strong typing and component validation. LangChain is more flexible but less opinionated. Haystack excels at search and retrieval use cases.

---

## Source & Thanks

> Created by [deepset](https://github.com/deepset-ai). Licensed under Apache 2.0.
>
> [haystack](https://github.com/deepset-ai/haystack) — ⭐ 20,000+

Thanks to the deepset team for making production RAG accessible to every developer.

---

<!-- ZH -->

## 快速使用

```bash
pip install haystack-ai
```

```python
from haystack import Pipeline
from haystack.components.generators import OpenAIGenerator
from haystack.components.builders import PromptBuilder

pipeline = Pipeline()
pipeline.add_component("prompt", PromptBuilder(template="回答: {{query}}"))
pipeline.add_component("llm", OpenAIGenerator())
pipeline.connect("prompt", "llm")
result = pipeline.run({"prompt": {"query": "什么是 RAG?"}})
```

---

## 简介

Haystack 是 deepset 团队开发的生产级 Python 框架，GitHub 20,000+ stars。用于构建 RAG 管道、搜索系统和 AI Agent，采用可组合的管道架构。支持 30+ 集成，包括 OpenAI、Anthropic、Pinecone、Elasticsearch 等。适合构建需要灵活性的生产搜索和检索系统。

---

## 来源与感谢

> Created by [deepset](https://github.com/deepset-ai). Licensed under Apache 2.0.
>
> [haystack](https://github.com/deepset-ai/haystack) — ⭐ 20,000+

---
Source: https://tokrepo.com/en/workflows/1d2ed652-f347-4452-93f0-0b9a696466c2
Author: Script Depot