---
title: "State"
id: state
slug: "/state"
description: "`State` is a container for storing shared information during Agent and Tool execution. It provides a structured way to maintain conversation history, share data between tools, and store intermediate results throughout an agent's workflow."
---

# State

`State` is a container for storing shared information during Agent and Tool execution. It provides a structured way to maintain conversation history, share data between tools, and store intermediate results throughout an agent's workflow.

## Overview

When building agents that use multiple tools, you often need tools to share information with each other. State solves this problem by providing centralized storage that all tools can read from and write to. For example, one tool might retrieve documents while another tool uses those documents to generate an answer.

State uses a schema-based approach where you define:

- What data can be stored,
- The type of each piece of data,
- How values are merged when updated.

### Supported Types

State supports standard Python types:

- Basic types: `str`, `int`, `float`, `bool`, `dict`,
- List types: `list`, `list[str]`, `list[int]`, `list[Document]`,
- Union types: `Union[str, int]`, `Optional[str]`,
- Custom classes and data classes.

### Automatic Message Handling

State automatically includes a `messages` field to store conversation history. You don't need to define this in your schema.

```python
## State automatically adds messages field
state = State(schema={"user_id": {"type": str}})

## The messages field is available
print("messages" in state.schema)  # True
print(state.schema["messages"]["type"])  # list[ChatMessage]

## Access conversation history
messages = state.get("messages", [])
```

The `messages` field uses `list[ChatMessage]` type and `merge_lists` handler by default, which means new messages are appended to the conversation history.

## Usage

### Creating State

Create State by defining a schema that specifies what data can be stored and their types:

```python
from haystack.components.agents.state import State

## Define the schema
schema = {
    "user_name": {"type": str},
    "documents": {"type": list},
    "count": {"type": int},
}

## Create State with initial data
state = State(schema=schema, data={"user_name": "Alice", "documents": [], "count": 0})
```

### Reading from State

Use the `get()` method to retrieve values:

```python
## Get a value
user_name = state.get("user_name")

## Get a value with a default if key doesn't exist
documents = state.get("documents", [])

## Check if a key exists
if state.has("user_name"):
    print(f"User: {state.get('user_name')}")
```

### Writing to State

Use the `set()` method to store or merge values:

```python
## Set a value
state.set("user_name", "Bob")

## Set list values (these are merged by default)
state.set("documents", [{"title": "Doc 1", "content": "Content 1"}])
```

## Schema Definition

The schema defines what data can be stored and how values are updated. Each schema entry consists of:

- `type` (required): The Python type that defines what kind of data can be stored (for example, `str`, `int`, `list`)
- `handler` (optional): A function that determines how new values are merged with existing values when you call `set()`

```python
{
    "parameter_name": {
        "type": SomeType,  # Required: Expected Python type for this field
        "handler": Optional[
            Callable[[Any, Any], Any]
        ],  # Optional: Function to merge values
    },
}
```

If you don't specify a handler, State automatically assigns a default handler based on the type.

### Default Handlers

Handlers control how values are merged when you call `set()` on an existing key. State provides two default handlers:

- `merge_lists`: Combines the lists together (default for list types)
- `replace_values`: Overwrites the existing value (default for non-list types)

```python
from haystack.components.agents.state.state_utils import merge_lists, replace_values

schema = {
    "documents": {"type": list},  # Uses merge_lists by default
    "user_name": {"type": str},  # Uses replace_values by default
    "count": {"type": int},  # Uses replace_values by default
}

state = State(schema=schema)

## Lists are merged by default
state.set("documents", [1, 2])
state.set("documents", [3, 4])
print(state.get("documents"))  # Output: [1, 2, 3, 4]

## Other values are replaced
state.set("user_name", "Alice")
state.set("user_name", "Bob")
print(state.get("user_name"))  # Output: "Bob"
```

### Custom Handlers

You can define custom handlers for specific merge behavior:

```python
def custom_merge(current_value, new_value):
    """Custom handler that merges and sorts lists."""
    current_list = current_value or []
    new_list = new_value if isinstance(new_value, list) else [new_value]
    return sorted(current_list + new_list)


schema = {"numbers": {"type": list, "handler": custom_merge}}

state = State(schema=schema)
state.set("numbers", [3, 1])
state.set("numbers", [2, 4])
print(state.get("numbers"))  # Output: [1, 2, 3, 4]
```

You can also override handlers for individual operations:

```python
def concatenate_strings(current, new):
    return f"{current}-{new}" if current else new


schema = {"user_name": {"type": str}}
state = State(schema=schema)

state.set("user_name", "Alice")
state.set("user_name", "Bob", handler_override=concatenate_strings)
print(state.get("user_name"))  # Output: "Alice-Bob"
```

## Using State with Agents

To use State with an Agent, define a state schema when creating the Agent. The Agent automatically manages State throughout its execution.

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


## Define a simple calculation tool
def calculate(expression: str) -> dict:
    """Evaluate a mathematical expression."""
    result = eval(expression, {"__builtins__": {}})
    return {"result": result}


## Create a tool that writes to state
calculator_tool = Tool(
    name="calculator",
    description="Evaluate basic math expressions",
    parameters={
        "type": "object",
        "properties": {"expression": {"type": "string"}},
        "required": ["expression"],
    },
    function=calculate,
    outputs_to_state={"calc_result": {"source": "result"}},
)

## Create agent with state schema
agent = Agent(
    chat_generator=OpenAIChatGenerator(),
    tools=[calculator_tool],
    state_schema={"calc_result": {"type": int}},
)

## Run the agent
result = agent.run(messages=[ChatMessage.from_user("Calculate 15 + 27")])

## Access the state from results
calc_result = result["calc_result"]
print(calc_result)  # Output: 42
```

## Tools and State

Tools interact with State through two mechanisms: `inputs_from_state` and `outputs_to_state`.

### Reading from State: `inputs_from_state`

Tools can automatically read values from State and use them as parameters. The `inputs_from_state` parameter maps state keys to tool parameter names.

```python
def search_documents(query: str, user_context: str) -> dict:
    """Search documents using query and user context."""
    return {"results": [f"Found results for '{query}' (user: {user_context})"]}


## Create tool that reads from state
search_tool = Tool(
    name="search",
    description="Search documents",
    parameters={
        "type": "object",
        "properties": {"query": {"type": "string"}, "user_context": {"type": "string"}},
        "required": ["query"],
    },
    function=search_documents,
    inputs_from_state={
        "user_name": "user_context",
    },  # Maps state's "user_name" to the tool’s input parameter “user_context”
)

## Define agent with state schema including user_name
agent = Agent(
    chat_generator=OpenAIChatGenerator(),
    tools=[search_tool],
    state_schema={"user_name": {"type": str}, "search_results": {"type": list}},
)

## Initialize agent with user context
result = agent.run(
    messages=[ChatMessage.from_user("Search for Python tutorials")],
    user_name="Alice",  # All additional kwargs passed to Agent at runtime are put into State
)
```

When the tool is invoked, the Agent automatically retrieves the value from State and passes it to the tool function.

### Writing to State: `outputs_to_state`

Tools can write their results back to State. The `outputs_to_state` parameter defines mappings from tool outputs to state keys.

The structure of the output is: `{”state_key”: {”source”: “tool_result_key”}}`.

```python
def retrieve_documents(query: str) -> dict:
    """Retrieve documents based on query."""
    return {
        "documents": [
            {"title": "Doc 1", "content": "Content about Python"},
            {"title": "Doc 2", "content": "More about Python"},
        ],
        "count": 2,
        "query": query,
    }


## Create tool that writes to state
retrieval_tool = Tool(
    name="retrieve",
    description="Retrieve relevant documents",
    parameters={
        "type": "object",
        "properties": {"query": {"type": "string"}},
        "required": ["query"],
    },
    function=retrieve_documents,
    outputs_to_state={
        "documents": {
            "source": "documents",
        },  # Maps tool's "documents" output to state's "documents"
        "result_count": {
            "source": "count",
        },  # Maps tool's "count" output to state's "result_count"
        "last_query": {
            "source": "query",
        },  # Maps tool's "query" output to state's "last_query"
    },
)

agent = Agent(
    chat_generator=OpenAIChatGenerator(),
    tools=[retrieval_tool],
    state_schema={
        "documents": {"type": list},
        "result_count": {"type": int},
        "last_query": {"type": str},
    },
)

result = agent.run(messages=[ChatMessage.from_user("Find information about Python")])

## Access state values from result
documents = result["documents"]
result_count = result["result_count"]
last_query = result["last_query"]
print(documents)  # List of retrieved documents
print(result_count)  # 2
print(last_query)  # "Find information about Python"
```

Each mapping can specify:

- `source`: Which field from the tool's output to use
- `handler`: Optional custom function for merging values

If you omit the `source`, the entire tool result is stored:

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


def get_user_info() -> dict:
    """Get user information."""
    return {"name": "Alice", "email": "alice@example.com", "role": "admin"}


## Tool that stores entire result
info_tool = Tool(
    name="get_info",
    description="Get user information",
    parameters={"type": "object", "properties": {}},
    function=get_user_info,
    outputs_to_state={
        "user_info": {},  # Stores entire result dict in state's "user_info"
    },
)

## Create agent with matching state schema
agent = Agent(
    chat_generator=OpenAIChatGenerator(),
    tools=[info_tool],
    state_schema={
        "user_info": {"type": dict},  # Schema must match the tool's output type
    },
)

## Run the agent
result = agent.run(messages=[ChatMessage.from_user("Get the user information")])

## Access the complete result from state
user_info = result["user_info"]
print(
    user_info,
)  # Output: {"name": "Alice", "email": "alice@example.com", "role": "admin"}
print(user_info["name"])  # Output: "Alice"
print(user_info["email"])  # Output: "alice@example.com"
```

### Combining Inputs and Outputs

Tools can both read from and write to State, enabling tool chaining:

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


def process_documents(documents: list, max_results: int) -> dict:
    """Process documents and return filtered results."""
    processed = documents[:max_results]
    return {"processed_docs": processed, "processed_count": len(processed)}


processing_tool = Tool(
    name="process",
    description="Process retrieved documents",
    parameters={
        "type": "object",
        "properties": {"max_results": {"type": "integer"}},
        "required": ["max_results"],
    },
    function=process_documents,
    inputs_from_state={"documents": "documents"},  # Reads documents from state
    outputs_to_state={
        "final_docs": {"source": "processed_docs"},
        "final_count": {"source": "processed_count"},
    },
)

agent = Agent(
    chat_generator=OpenAIChatGenerator(),
    tools=[retrieval_tool, processing_tool],  # Chain tools using state
    state_schema={
        "documents": {"type": list},
        "final_docs": {"type": list},
        "final_count": {"type": int},
    },
)

## Run the agent - tools will chain through state
result = agent.run(
    messages=[ChatMessage.from_user("Find and process 3 documents about Python")],
)

## Access the final processed results
final_docs = result["final_docs"]
final_count = result["final_count"]
print(f"Processed {final_count} documents")
print(final_docs)
```

## Complete Example

This example shows a multi-tool agent workflow where tools share data through State:

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


## Tool 1: Calculate factorial
def factorial(n: int) -> dict:
    """Calculate the factorial of a number."""
    result = math.factorial(n)
    return {"result": result}


factorial_tool = Tool(
    name="factorial",
    description="Calculate the factorial of a number",
    parameters={
        "type": "object",
        "properties": {"n": {"type": "integer"}},
        "required": ["n"],
    },
    function=factorial,
    outputs_to_state={"factorial_result": {"source": "result"}},
)


## Tool 2: Perform calculation
def calculate(expression: str) -> dict:
    """Evaluate a mathematical expression."""
    result = eval(expression, {"__builtins__": {}})
    return {"result": result}


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

## Create agent with both tools
agent = Agent(
    chat_generator=OpenAIChatGenerator(),
    tools=[calculator_tool, factorial_tool],
    state_schema={"calc_result": {"type": int}, "factorial_result": {"type": int}},
)

## Run the agent
result = agent.run(
    messages=[
        ChatMessage.from_user("Calculate the factorial of 5, then multiply it by 2"),
    ],
)

## Access state values from result
factorial_result = result["factorial_result"]
calc_result = result["calc_result"]

## Access conversation messages
for message in result["messages"]:
    print(f"{message.role}: {message.text}")
```
