---
title: "Agent"
id: agent
slug: "/agent"
description: "The `Agent` component is a tool-using agent that interacts with chat-based LLMs and tools to solve complex queries iteratively. It can execute external tools, manage state across multiple LLM calls, and stop execution based on configurable `exit_conditions`."
---

# Agent

The `Agent` component is a tool-using agent that interacts with chat-based LLMs and tools to solve complex queries iteratively. It can execute external tools, manage state across multiple LLM calls, and stop execution based on configurable `exit_conditions`.

<div className="key-value-table">

|  |  |
| --- | --- |
| **Most common position in a pipeline** | After a [`ChatPromptBuilder`](../builders/chatpromptbuilder.mdx) or user input                     |
| **Mandatory init variables**           | `chat_generator`: An instance of a Chat Generator that supports tools                  |
| **Mandatory run variables**            | `messages`: A list of [`ChatMessage`](../../concepts/data-classes/chatmessage.mdx)s                              |
| **Output variables**                   | `messages`: Chat history with tool and model responses                                 |
| **API reference**                      | [Agents](/reference/agents-api)                                                               |
| **GitHub link**                        | https://github.com/deepset-ai/haystack/blob/main/haystack/components/agents/agent.py |

</div>

## Overview

The `Agent` component is a loop-based system that uses a chat-based large language model (LLM) and external tools to solve complex user queries. It works iteratively—calling tools, updating state, and generating prompts—until one of the configurable `exit_conditions` is met.

It can:

- Dynamically select tools based on user input,
- Maintain and validate runtime state using a schema,
- Stream token-level outputs from the LLM.

The `Agent` returns a dictionary containing:

- `messages`: the full conversation history,
- Additional dynamic keys based on `state_schema`.

### Parameters

To initialize the `Agent` component, you need to provide it with an instance of a Chat Generator that supports tools. You can pass a list of [tools](../../tools/tool.mdx) or [`ComponentTool`](../../tools/componenttool.mdx) instances, or wrap them in a [`Toolset`](../../tools/toolset.mdx) to manage them as a group.

You can additionally configure:

- A `system_prompt` for your Agent,
- A list of `exit_conditions` strings that will cause the agent to return. Can be either:
  - “text”, which means  that the Agent will exit as soon as the LLM replies only with a text response,
  - or specific tool names.
- A `state_schema` for one agent invocation run. It defines extra information – such as documents or context – that tools can read from or write to during execution. You can use this schema to pass parameters that tools can both produce and consume.
- `streaming_callback` to stream the tokens from the LLM directly in output.

:::info
For a complete list of available parameters, refer to the [Agents API Documentation](/reference/agents-api).
:::

### Agents as Tools

You can wrap an `Agent` using [`ComponentTool`](../../tools/componenttool.mdx) to create multi-agent systems where specialized agents act as tools for a coordinator agent.

When wrapping an `Agent` as a `ComponentTool`, use the `outputs_to_string` parameter with `{"source": "last_message"}` to extract only the agent's final response text, rather than the execution trace with tool calls to keep the coordinator agent's context clean and focused.

```python
## Wrap the agent as a ComponentTool with outputs_to_string
research_tool = ComponentTool(
    component=research_agent,  # another agent component
    name="research_specialist",
    description="A specialist that can research topics from the knowledge base",
    outputs_to_string={"source": "last_message"},  ## Extract only the final response
)

## Create a coordinator agent that uses the specialist
coordinator_agent = Agent(
    chat_generator=OpenAIChatGenerator(model="gpt-4o-mini"),
    tools=[research_tool],
    system_prompt="You are a coordinator that delegates research tasks to a specialist.",
    exit_conditions=["text"],
)

## Run
result = coordinator_agent.run(
    messages=[ChatMessage.from_user("Tell me about Haystack")],
)

print(result["last_message"].text)
```

### Streaming

You can stream output as it’s generated. Pass a callback to `streaming_callback`.
Use the built-in `print_streaming_chunk` to print text tokens and tool events (tool calls and tool results).

```python
from haystack.components.generators.utils import print_streaming_chunk

## Configure the Agent with a streaming callback
coordinator_agent = Agent(
    chat_generator=OpenAIChatGenerator(model="gpt-4o-mini"),
    tools=[research_tool],
    system_prompt="You are a coordinator that delegates research tasks to a specialist.",
    streaming_callback=print_streaming_chunk,
)
```

See our [Streaming Support](../generators/guides-to-generators/choosing-the-right-generator.mdx#streaming-support) docs to learn more how `StreamingChunk` works and how to write a custom callback.

Give preference to `print_streaming_chunk` by default. Write a custom callback only if you need a specific transport (for example, SSE/WebSocket) or custom UI formatting.

## Usage

### On its own

```python
from haystack.components.generators.chat import OpenAIChatGenerator
from haystack.dataclasses import ChatMessage
from haystack.tools.tool import Tool
from haystack.components.agents import Agent


## Tool Function
def calculate(expression: str) -> dict:
    try:
        result = eval(expression, {"__builtins__": {}})
        return {"result": result}
    except Exception as e:
        return {"error": str(e)}


## Tool Definition
calculator_tool = Tool(
    name="calculator",
    description="Evaluate basic math expressions.",
    parameters={
        "type": "object",
        "properties": {
            "expression": {
                "type": "string",
                "description": "Math expression to evaluate",
            },
        },
        "required": ["expression"],
    },
    function=calculate,
    outputs_to_state={"calc_result": {"source": "result"}},
)

## Agent Setup
agent = Agent(
    chat_generator=OpenAIChatGenerator(),
    tools=[calculator_tool],
    exit_conditions=["calculator"],
    state_schema={"calc_result": {"type": int}},
)

## Run the Agent
response = agent.run(messages=[ChatMessage.from_user("What is 7 * (4 + 2)?")])

## Output
print(response["messages"])
print("Calc Result:", response.get("calc_result"))
```

### In a pipeline

The example pipeline below creates a database assistant using `OpenAIChatGenerator`, `LinkContentFetcher`, and custom database tool. It reads the given URL and processes the page content, then builds a prompt for the AI. The assistant uses this information to write people's names and titles from the given page to the database.

```python
from haystack.components.agents import Agent
from haystack.components.generators.chat import OpenAIChatGenerator
from haystack.components.builders.chat_prompt_builder import ChatPromptBuilder
from haystack.components.converters.html import HTMLToDocument
from haystack.components.fetchers.link_content import LinkContentFetcher
from haystack.core.pipeline import Pipeline
from haystack.tools import tool
from haystack.document_stores.in_memory import InMemoryDocumentStore
from typing import Optional
from haystack.dataclasses import ChatMessage, Document

document_store = InMemoryDocumentStore()  # create a document store or an SQL database


@tool
def add_database_tool(
    name: str,
    surname: str,
    job_title: Optional[str],
    other: Optional[str],
):
    """Use this tool to add names to the database with information about them"""
    document_store.write_documents(
        [
            Document(
                content=name + " " + surname + " " + (job_title or ""),
                meta={"other": other},
            ),
        ],
    )
    return


database_assistant = Agent(
    chat_generator=OpenAIChatGenerator(model="gpt-4o-mini"),
    tools=[add_database_tool],
    system_prompt="""
    You are a database assistant.
    Your task is to extract the names of people mentioned in the given context and add them to a knowledge base,
    along with additional relevant information about them that can be extracted from the context.
    Do not use you own knowledge, stay grounded to the given context.
    Do not ask the user for confirmation.
    Instead, automatically update the knowledge base and return a brief summary of the people added,
    including the information stored for each.
    """,
    exit_conditions=["text"],
    max_agent_steps=100,
    raise_on_tool_invocation_failure=False,
)

extraction_agent = Pipeline()
extraction_agent.add_component("fetcher", LinkContentFetcher())
extraction_agent.add_component("converter", HTMLToDocument())
extraction_agent.add_component(
    "builder",
    ChatPromptBuilder(
        template=[
            ChatMessage.from_user("""
    {% for doc in docs %}
    {{ doc.content|default|truncate(25000) }}
    {% endfor %}
    """),
        ],
        required_variables=["docs"],
    ),
)

extraction_agent.add_component("database_agent", database_assistant)
extraction_agent.connect("fetcher.streams", "converter.sources")
extraction_agent.connect("converter.documents", "builder.docs")
extraction_agent.connect("builder", "database_agent")

agent_output = extraction_agent.run(
    {"fetcher": {"urls": ["https://haystack.deepset.ai/release-notes/v2.20.0"]}},
)

print(agent_output["database_agent"]["messages"][-1].text)
```

## Additional References

🧑‍🍳 Cookbook: [Build a GitHub Issue Resolver Agent](https://haystack.deepset.ai/cookbook/github_issue_resolver_agent)

📓 Tutorials:
- [Build a Tool-Calling Agent](https://haystack.deepset.ai/tutorials/43_building_a_tool_calling_agent)
- [Creating a Multi-Agent System](https://haystack.deepset.ai/tutorials/45_creating_a_multi_agent_system)
