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View guide →Open-source AI orchestration framework for building context-engineered, production-ready LLM applications. Design modular pipelines and…
Haystack is an open-source ai agents project by deepset-ai (deepset-ai/haystack on GitHub). It has 26,646 GitHub stars and 3,227 forks, is released under the Apache-2.0 license, and was last updated on GitHub on 2 October 2026.
Application frameworks, agent runtimes, or voice stacks — choose by whether you are building apps, automations, or realtime voice.
Python or TypeScript packages install with one command; most include quickstart templates.
Give the agent functions it can call and a memory layer so it stays coherent across sessions.
Launch the agent, watch its tool calls and reasoning traces, and iterate on prompts and tools.
Agents act on the world: APIs, databases, browsers, and shell commands.
Crews and graphs of specialized agents collaborating on complex tasks.
Short and long-term memory so agents improve across sessions.
Traces of every decision and tool call — essential for debugging.
Low-latency stacks for conversational voice agents.
Swap underlying LLMs without rewriting your agent logic.
Download the source and move into the project folder.
git clone https://github.com/deepset-ai/haystack.git
cd haystackIsolate dependencies so nothing clashes with your other projects.
python -m venv .venv
source .venv/bin/activate # Windows: .venv\Scripts\activateInstall the requirements, then follow the README's launch command (it varies per project).
pip install -r requirements.txtFrameworks build your own; some repos here ship ready-made agents you can run directly.
Tool-calling quality matters more than raw size — strong mid-size models work well.
It is an open-source project at deepset-ai/haystack with 3,227 forks. Check the repo's contributors and recent commits to see how active maintenance is.
Sandbox tool access, confirm destructive actions, and review traces — treat agents like junior employees with supervision.
Self-hosted frameworks are free; you pay only for model inference (local or API).
A quickstart template in the framework's docs — most get a first agent running in minutes.
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