deepset-ai/haystack
Open-source AI orchestration framework for building context-engineered, production-ready LLM applications. Design modular pipelines and agent workflows with explicit control over retrieval, routing, memory, and generation. Built for scalable agents, RAG, multimodal applications, semantic search, and conversational systems.
⚡ Executive Summary & Quick Facts
Authoritative Sourcehaystack is haystack is an open-source project written in Python: Open-source AI orchestration framework for building context-engineered, production-ready LLM applications. Design modular pipelines and agent workflows with explicit control over retrieval, routing, memory, and generation. Built for scalable agents, RAG, multimodal applications, semantic search, and conversational systems..
| Repository | deepset-ai/haystack ↗ |
|---|---|
| Primary Category | agent framework |
| Language & Stack | Python |
| Software License | Apache-2.0 |
| Community Traction | ★ 26.8K stars · ⑂ 3.3K forks · #569 globally |
| Maintenance | ● Active (Last commit: Oct 20, 2026) · 149 open issues |
| Target Audience | Developers and engineering teams building with Python, seeking reliable, tested, and actively maintained tooling for production workloads. |
Star History
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+670
STARS · LAST 30 DAYS
22
PER DAY
#109
MOST-STARRED Python
| Window | 7 days | 30 days | 90 days |
|---|---|---|---|
| Stars gained | +154 | +670 | +2K |
| Per day | 22 | 22 | 22 |
| Forks gained | +17 | +66 | +165 |
haystack gained 670 stars in the last 30 days, about 22 a day, and now has 26.8K. It is about 7 years old and has averaged roughly 3.8K stars a year. It ranks #109 among Python repositories and #569 across all languages on GitHubRepo.
Trending Record
1
DAYS ON TRENDING
#5041
BEST RANK
Oct 20, 2026
FIRST APPEARANCE
Active
STATUS TODAY
haystack has maintained a continuous presence across global trending indexes, peaking at #5041. Below is the 30-day activity profile:
💡 Overview
haystack is an open-source project written in Python: Open-source AI orchestration framework for building context-engineered, production-ready LLM applications. Design modular pipelines and agent workflows with explicit control over retrieval, routing, memory, and generation. Built for scalable agents, RAG, multimodal applications, semantic search, and conversational systems.
Engineered for speed, consistency, and developer ease, it solves common hurdles in agent-framework, agentic-ai, agentic-rag. It provides clear interfaces, comprehensive configuration options, and seamless integration with existing tools across the modern development stack.
⚡ Key Features
Optimized execution pipeline written in Python for predictable speed.
Zero-friction configuration with comprehensive sensible defaults out of the box.
Cross-platform runtime support across Linux, macOS, and Windows environments.
Strong typing and modular architecture designed for easy extension and maintainability.
Standardized CLI and API interfaces for smooth integration into CI/CD workflows.
Active community maintenance with regular dependency updates and security patches.
📥 Installation
$ pip install haystack-ai⚙ System Requirements
Platforms
- • macOS
- • Linux
- • Windows
Runtime & Dependencies
Python >= 3.9, pip, virtualenv
Architecture
x86_64, ARM64 (Apple Silicon & Graviton)
🧠 How It Works
haystack coordinates its core functionality through a modular Python pipeline. It parses configuration parameters, validates inputs, and resolves dependencies asynchronously. By minimizing runtime overhead and keeping allocations localized, it delivers predictable performance in both local development environments and automated production workloads.
🎯 Production Use Cases
Autonomous AI Agents
Orchestrate intelligent workflows and tool-calling routines with haystack.
Model Inference & Prompting
Integrate fast, local or cloud-hosted generative AI models directly into production code.
Context Memory & RAG
Augment language models with dynamic vector retrieval and structured project memory.
Developer Productivity
Automate repetitive engineering tasks, code generation, and test creation using AI agents.
🚀 Getting Started
Install haystack using your package manager: `pip install haystack-ai`
Initialize your project workspace or configuration file for haystack.
Import haystack into your codebase or invoke it directly from your terminal.
Execute your test suite or run `haystack --help` to verify successful setup.
👍 Strengths
⚠️ Considerations
⇄ Alternatives & Direct Competitors
👥 Who Should Use This
Developers and engineering teams building with Python, seeking reliable, tested, and actively maintained tooling for production workloads.
🏆 Nearby in the Rankings
deepset-ai/haystack is currently ranked #569 by stars across every repository tracked on GitHubRepo. These are adjacent projects:
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| #569 | deepset-ai/haystack This Project | Python | ★ 26.8K | |
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Frequently Asked Questions
What does haystack do? +
Open-source AI orchestration framework for building context-engineered, production-ready LLM applications. Design modular pipelines and agent workflows with explicit control over retrieval, routing, memory, and generation. Built for scalable agents, RAG, multimodal applications, semantic search, and conversational systems.
What language is haystack written in? +
The primary language is Python. Topics include: agent-framework, agentic-ai, agentic-rag, agents, ai.
Is haystack actively maintained? +
Yes, the last recorded push was on Oct 20, 2026 with 149 open issues being tracked.
How many stars does haystack have? +
haystack has 26,785 stars and 3,307 forks on GitHub.
How does haystack rank among GitHub repositories? +
With 26,785 stars, deepset-ai/haystack is ranked #569 globally across all repositories tracked on GitHubRepo and #109 among Python projects.
What license is haystack distributed under? +
The repository reports a Apache-2.0 license. Always verify the repository LICENSE file for legal terms.
