Agent Pack
Agent Pack is a collection of complex, pre-configured Haystack agents you can run as they are, customize, or copy as a blueprint for your own architecture.
| API reference | Agent Pack |
| GitHub link | https://github.com/deepset-ai/haystack-core-integrations/tree/main/integrations/agent_pack |
| Package name | agent-pack-haystack |
Overview
Language Models and Tools are the core building blocks of agents.
However, building a robust agent often requires more: an architecture that splits the work across sub-agents, techniques for keeping context small but focused, and ways to influence and interact with the agent loop.
Agent Pack combines these techniques into working agents. Each one is a complete architecture built from Haystack primitives (Agent, Tools, hooks, State, and Pipelines), exposed behind a single create_* entry point.
There are three ways to use an agent from the pack:
- Run it as is. Each agent has a factory that builds a ready-to-run agent with defaults chosen to work out of the box. For example,
create_deep_research_agent()returns an agent that takes a question and produces a report. - Customize it. Each entry point exposes parameters for changing models, adding tools, and configuring behavior specific to that type of agent. (This interface is still being refined to make customization more consistent and flexible.)
- Copy it. Read the implementation and take inspiration for your agents. These agents are built with this use case in mind, so individual parts should be easy to adapt.
In other words, you can use the agents in Agent Pack as either ready-made solutions or reference architectures.
Installation
Each agent has its own additional runtime dependencies and may require API keys. See its documentation page for these details.
Available agents
| Agent | Description |
|---|---|
| Deep Research Agent | Researches a question on the web and produces a structured Markdown report with citations. |
| Advanced RAG Agent | Inspects document-store metadata and builds Haystack filters to retrieve precisely, then answers with citations. |
Experimental
Agent Pack is experimental for the moment. Its APIs and agent architectures can change in any release, without following the usual deprecation policy.
Agent Pack is distributed separately from haystack-ai and its code lives in haystack-core-integrations.
Agentic architectures are evolving fast. Keeping it outside haystack-ai allows us to improve these agents rapidly, release updates independently, and avoid committing to stability before these patterns have settled.
Developing complex agents also helps us identify missing capabilities in Haystack. When a gap shows up, we can implement it in the pack first, and then, if it turns out to be generally useful, refine it and move it into Haystack.
Feedback
If you find a bug or have an idea for a complex agent that could belong in the pack, open an issue.