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.

Most common position in a pipeline	After a ChatPromptBuilder or user input
Mandatory init variables	chat_generator: An instance of a Chat Generator that supports tools
Mandatory run variables	messages: A list of ChatMessages
Output variables	messages: Chat history with tool and model responses
API reference	Agents
GitHub link	https://github.com/deepset-ai/haystack/blob/main/haystack/components/agents/agent.py
Package name	haystack-ai

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,
• last_message: the final ChatMessage from the agent,
• Additional dynamic keys based on state_schema.

Parameters

chat_generator is the only mandatory parameter — an instance of a Chat Generator that supports tools. All other parameters are optional.

• tools: A list of tool or toolset instances the agent can call. Supported types: Tool, ComponentTool, PipelineTool, MCPTool, Toolset, MCPToolset, SearchableToolset.
• system_prompt: A plain string or Jinja2 template used as the system message for every run. If the template contains Jinja2 variables, those variables become additional inputs to run().
• user_prompt: A Jinja2 template appended to the user-provided messages on each run. Template variables become additional inputs to run(). Use required_variables to enforce which variables must be provided.
• exit_conditions: List of conditions that cause the agent to stop. Use ”text” to stop when the LLM replies without a tool call, or a tool name to stop once that tool has been executed. Defaults to [“text”].
• state_schema: Defines the agent's runtime state — a dict mapping key names to type configs (e.g. {“docs”: {“type”: list[Document]}}). Tools can read from and write to state keys via inputs_from_state and outputs_to_state. See State for full details.
• streaming_callback: A callback invoked for each streamed token. Use the built-in print_streaming_chunk for console output.
• max_agent_steps: Maximum number of LLM + tool call iterations before the agent stops. Defaults to 100.
• raise_on_tool_invocation_failure: If True, raises an exception when a tool call fails. If False (default), the error is passed back to the LLM as a message so it can recover.
• confirmation_strategies: A dict mapping tool names (or tuples of tool names) to a ConfirmationStrategy, enabling human review of tool calls before execution. See Human in the Loop.
• tool_invoker_kwargs: Additional keyword arguments forwarded to the internal ToolInvoker.

Runtime overrides

run() also accepts parameters that override the init-time configuration for a single call:

• tools: Pass a list of Tool/Toolset objects, or a list of tool name strings to select a subset of the agent's configured tools for this run.
• generation_kwargs: Additional keyword arguments forwarded to the LLM, overriding any set at init time (e.g. {“temperature”: 0.2}).

info

For the full parameter reference, see the Agents API Documentation.

Usage

On its own

python

from haystack.components.generators.chat import OpenAIChatGenerator
from haystack.dataclasses import ChatMessage
from haystack.tools import tool
from haystack.components.agents import Agent
from typing import Annotated


@tool(outputs_to_state={"calc_result": {"source": "result"}})
def calculator(
    expression: Annotated[str, "Math expression to evaluate, e.g. '7 * (4 + 2)'"],
) -> dict:
    """Evaluate basic math expressions."""
    try:
        result = eval(expression, {"__builtins__": {}})
        return {"result": result}
    except Exception as e:
        return {"error": str(e)}


agent = Agent(
    chat_generator=OpenAIChatGenerator(model="gpt-5.4-nano"),
    tools=[calculator],
    system_prompt="You are a helpful assistant. Always use the calculator tool to evaluate math expressions.",
    state_schema={"calc_result": {"type": int}},
)

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

print(response["last_message"].text)
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 import Document, Pipeline
from haystack.dataclasses import ChatMessage
from haystack.document_stores.in_memory import InMemoryDocumentStore
from haystack.tools import tool
from typing import Annotated, Optional

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


@tool
def add_database_tool(
    name: Annotated[str, "First name of the person"],
    surname: Annotated[str, "Last name of the person"],
    job_title: Annotated[Optional[str], "Job title or role of the person"] = None,
    other: Annotated[Optional[str], "Any other relevant information"] = None,
) -> str:
    """Add a person to the database with information about them."""
    document_store.write_documents(
        [
            Document(
                content=name + " " + surname + " " + (job_title or ""),
                meta={"other": other},
            ),
        ],
    )
    # Returning a confirmation lets the agent know the tool call succeeded
    return f"Successfully added {name} {surname} to the database."


database_assistant = Agent(
    chat_generator=OpenAIChatGenerator(model="gpt-5.4-nano"),
    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 your 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.
    """,
)

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://github.com/deepset-ai/haystack/releases/tag/v2.27.0"],
        },
    },
)

print(agent_output["database_agent"]["last_message"].text)

# Inspect what was written to the document store
written_docs = document_store.filter_documents()
print(f"\n{len(written_docs)} people added to the database:")
for doc in written_docs:
    print(f"  - {doc.content}")

In YAML

The example pipeline below fetches a webpage, converts its HTML to text, and builds a chat prompt combining the page content with a user query. The Agent then answers the question based on the provided content and can use its web search tool to find additional information if needed.

View YAML

yaml

components:
  agent:
    init_parameters:
      chat_generator:
        init_parameters:
          api_base_url: null
          api_key:
            env_vars:
            - OPENAI_API_KEY
            strict: true
            type: env_var
          generation_kwargs: {}
          http_client_kwargs: null
          max_retries: null
          model: gpt-5.4-nano
          organization: null
          streaming_callback: null
          timeout: null
          tools: null
          tools_strict: false
        type: haystack.components.generators.chat.openai.OpenAIChatGenerator
      confirmation_strategies: null
      exit_conditions:
      - text
      max_agent_steps: 5
      raise_on_tool_invocation_failure: false
      required_variables: null
      state_schema: {}
      streaming_callback: null
      system_prompt: You are a helpful assistant. Use the web search tool to find
        information when needed.
      tool_invoker_kwargs: null
      tools:
      - data:
          component:
            init_parameters:
              allowed_domains: null
              api_key:
                env_vars:
                - SERPERDEV_API_KEY
                strict: true
                type: env_var
              exclude_subdomains: false
              search_params: {}
              top_k: 3
            type: haystack.components.websearch.serper_dev.SerperDevWebSearch
          description: Search the web for current information on any topic
          inputs_from_state: null
          name: web_search
          outputs_to_state: null
          outputs_to_string: null
          parameters: null
        type: haystack.tools.component_tool.ComponentTool
      user_prompt: null
    type: haystack.components.agents.agent.Agent
  converter:
    init_parameters:
      extraction_kwargs: {}
      store_full_path: false
    type: haystack.components.converters.html.HTMLToDocument
  fetcher:
    init_parameters:
      client_kwargs:
        follow_redirects: true
        timeout: 3
      http2: false
      raise_on_failure: true
      request_headers: {}
      retry_attempts: 2
      timeout: 3
      user_agents:
      - haystack/LinkContentFetcher/2.27.0rc0
    type: haystack.components.fetchers.link_content.LinkContentFetcher
  prompt_builder:
    init_parameters:
      required_variables:
      - docs
      - query
      template:
      - content:
        - text: 'Based on the following content:

            {% for doc in docs %}

            {{ doc.content }}

            {% endfor %}

            Answer this question: {{ query }}'
        meta: {}
        name: null
        role: user
      variables: null
    type: haystack.components.builders.chat_prompt_builder.ChatPromptBuilder
connection_type_validation: true
connections:
- receiver: converter.sources
  sender: fetcher.streams
- receiver: prompt_builder.docs
  sender: converter.documents
- receiver: agent.messages
  sender: prompt_builder.prompt
max_runs_per_component: 100
metadata: {}

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

agent = Agent(
    chat_generator=OpenAIChatGenerator(model="gpt-5.4-nano"),
    tools=[...],
    system_prompt="...",
    streaming_callback=print_streaming_chunk,
)

See our 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.

Multimodal Inputs

Agents support multimodal inputs when paired with a vision-capable model such as gpt-5 (OpenAI) or gemini-2.5-flash (Google). Pass images alongside text by including ImageContent objects in the content_parts of a ChatMessage:

python

from haystack.dataclasses import ChatMessage, ImageContent

image = ImageContent.from_url("https://example.com/chart.png")
result = agent.run(
    messages=[
        ChatMessage.from_user(content_parts=["What does this chart show?", image]),
    ],
)

Tools can also return ImageContent directly, letting the agent fetch and reason about images dynamically during its loop. Two things are required: set outputs_to_string={"raw_result": True} so the ToolInvoker skips string conversion, and return a list[ImageContent] (the tool result type is str | Sequence[TextContent | ImageContent]).

The standard Chat Completions API doesn't support images in tool results — use OpenAIResponsesChatGenerator (OpenAI's Responses API) instead:

python

from typing import Annotated
from haystack.components.agents import Agent
from haystack.components.generators.chat import OpenAIResponsesChatGenerator
from haystack.dataclasses import ChatMessage, ImageContent
from haystack.tools import tool


@tool(outputs_to_string={"raw_result": True})
def fetch_image(
    url: Annotated[str, "URL of the image to fetch and analyze"],
) -> list[ImageContent]:
    """Fetch an image from a URL so the agent can analyze its contents."""
    return [ImageContent.from_url(url)]


agent = Agent(
    chat_generator=OpenAIResponsesChatGenerator(model="gpt-5"),
    tools=[fetch_image],
    system_prompt="You are a helpful assistant that can fetch and analyze images from URLs.",
)

result = agent.run(
    messages=[
        ChatMessage.from_user(
            "Fetch the image at https://picsum.photos/seed/haystack/640/480 and describe what you see.",
        ),
    ],
)
print(result["last_message"].text)

ImageContent can be created from a URL, a local file path, or a PDF page using the PDFToImageContent converter.

In a pipeline

When an Agent sits inside a pipeline, use ChatPromptBuilder with its string template format and the | templatize_part filter to pass images as structured content parts:

python

from haystack import Pipeline
from haystack.components.agents import Agent
from haystack.components.builders import ChatPromptBuilder
from haystack.components.generators.chat import OpenAIChatGenerator
from haystack.dataclasses import ImageContent

template = """
{% message role="user" %}
{{ question }}
{{ image | templatize_part }}
{% endmessage %}
"""

agent = Agent(
    chat_generator=OpenAIChatGenerator(model="gpt-5"),
    system_prompt="You are a helpful assistant that can analyze images.",
)
prompt_builder = ChatPromptBuilder(
    template=template,
    required_variables=["question", "image"],
)

pipeline = Pipeline()
pipeline.add_component("prompt_builder", prompt_builder)
pipeline.add_component("agent", agent)
pipeline.connect("prompt_builder.prompt", "agent.messages")

# Download or provide your own chart image as "chart.png"
image = ImageContent.from_file_path("chart.png")
result = pipeline.run(
    {
        "prompt_builder": {"question": "What does this chart show?", "image": image},
    },
)
print(result["agent"]["last_message"].text)

tip

See these cookbooks for complete multimodal agent examples:

• Multimodal Agents — image inputs and tool use with agents
• Gemma Chat RAG — vision model in a RAG pipeline

Multi-Agent Systems

You can wrap an Agent as a tool to build multi-agent systems where specialist agents handle focused subtasks and a coordinator agent plans and delegates.

See Multi-Agent Systems for a full guide, including the recommended @tool decorator approach for full interface control and ComponentTool for declarative configuration.

MCP Integration

Agents work with MCP in two directions:

• Consuming MCP tools: Pass MCPTool or MCPToolset instances in the tools list to call tools on any MCP-compatible server (filesystem, browser, databases, and more). See MCPTool and MCPToolset.
• Exposing as an MCP server: Use Hayhooks to deploy your agent and expose it as an MCP server, making it callable from any MCP-compatible client such as Claude Desktop or Cursor.

Additional References

📖 Related docs:

• State — managing shared data between tools
• Human in the Loop — intercepting tool calls for human review

📚 Tutorials:

• Build a Tool-Calling Agent
• Creating a Multi-Agent System
• Human-in-the-Loop with Haystack Agents

🧑‍🍳 Cookbook:

• Build a GitHub Issue Resolver Agent
• Multimodal Agents
• Gemma Chat RAG