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Version: 3.1-unstable

Hooks

compaction/hooks​

CompactionHook​

Compacts an Agent's conversation once it fills too much of the model's context window.

This before_llm Agent hook estimates the size of the conversation before each chat-generator call and, once it reaches compact_at of the window, hands it to a Compactor to bring back down to compact_to. Register it on an Agent under the before_llm hook point:

python
from haystack.components.agents import Agent
from haystack.components.generators.chat import OpenAIResponsesChatGenerator
from haystack.hooks.compaction import CompactionHook, SlidingWindowCompactor

hook = CompactionHook(
    compactor=SlidingWindowCompactor(),
    context_window=400_000,
    compact_at=0.7,
    compact_to=0.4,
)
agent = Agent(
    chat_generator=OpenAIResponsesChatGenerator(model="gpt-5.4-nano"),
    tools=[web_search],
    hooks={"before_llm": [hook]},
    max_agent_steps=50,
)

Size is measured by anchoring on the context_tokens state key - the chat generator's own count of the request it was sent plus its reply, which already covers the system prompt, the tool schemas, and the provider's chat-template overhead - and counting only the messages appended since that call. The estimate is therefore exact for the bulk of the conversation and approximate only for its most recent messages.

Compaction is lossy by nature, so the Agent works from a shorter record of the run afterwards. What survives is up to the compactor.

init​

python
__init__(
    compactor: Compactor,
    *,
    context_window: int,
    compact_at: float = 0.7,
    compact_to: float = 0.4,
    token_counter: TokenCounter | None = None
) -> None

Initialize the hook with a compactor and the window it has to fit in.

Parameters:

  • compactor (Compactor) – The Compactor that rewrites the conversation.
  • context_window (int) – The model's context window in tokens. Everything else is a fraction of this, so moving to a different model means changing only this number.
  • compact_at (float) – The fraction of the window at which compaction starts. Leave room above it for the reply and the tool results it triggers, which land on top of what was measured.
  • compact_to (float) – The fraction of the window compaction aims to bring the conversation down to. Lower means compacting less often but losing more each time.
  • token_counter (TokenCounter | None) – The TokenCounter used to size the messages the chat generator has not reported on yet. Defaults to ApproximateTokenCounter, which needs no extra dependency.

Raises:

  • ValueError – If context_window is not positive, or the fractions are not 0 < compact_to < compact_at <= 1.

run​

python
run(state: State) -> None

Compact state.data["messages"] if the conversation fills too much of the window.

Parameters:

  • state (State) – The Agent's live State. Read to decide whether to compact, and rewritten in place when the compactor returns a compacted conversation.

Returns:

  • None – None. The hook mutates state in place.

run_async​

python
run_async(state: State) -> None

Asynchronously compact state.data["messages"] if the conversation fills too much of the window.

Parameters:

  • state (State) – The Agent's live State. Read to decide whether to compact, and rewritten in place when the compactor returns a compacted conversation.

Returns:

  • None – None. The hook mutates state in place.

warm_up​

python
warm_up() -> None

Warm up the token counter and the compactor, which may hold resources such as a Chat Generator.

warm_up_async​

python
warm_up_async() -> None

Warm up the token counter and the compactor on the serving event loop.

close​

python
close() -> None

Release the compactor's resources.

close_async​

python
close_async() -> None

Release the compactor's async resources.

to_dict​

python
to_dict() -> dict[str, Any]

Serialize the hook, including its compactor and token counter.

Returns:

  • dict[str, Any] – A dictionary representation of the hook.

from_dict​

python
from_dict(data: dict[str, Any]) -> CompactionHook

Deserialize the hook, reconstructing its compactor and token counter.

Parameters:

  • data (dict[str, Any]) – A dictionary representation produced by to_dict.

Returns:

  • CompactionHook – The deserialized CompactionHook.

compaction/sliding_window​

SlidingWindowCompactor​

Bases: Compactor

Keeps the Agent's instructions, current task, and as much complete recent conversation as the target allows.

Leading system messages and the latest user message are protected. Historical turns are kept in full when they fit, and the current task's history is kept in complete Agent steps, where a step is an assistant message together with all immediately following tool results.

An omission_note is left where the removed messages used to sit: directly after the leading system messages when only historical turns were removed, and directly after the latest user message when the current task's own steps were removed. Only one note is ever present, since a later compaction folds an earlier note into its replacement.

python
from haystack.components.agents import Agent
from haystack.components.generators.chat import OpenAIResponsesChatGenerator
from haystack.hooks.compaction import CompactionHook, SlidingWindowCompactor

hook = CompactionHook(
    compactor=SlidingWindowCompactor(), context_window=400_000, compact_at=0.7, compact_to=0.4
)
agent = Agent(
    chat_generator=OpenAIResponsesChatGenerator(model="gpt-5.4-nano"),
    tools=[web_search],
    hooks={"before_llm": [hook]},
)

init​

python
__init__(
    *,
    min_keep_steps: int = 1,
    omission_note: str | None = _DEFAULT_OMISSION_NOTE
) -> None

Initialize the compactor.

Parameters:

  • min_keep_steps (int) – The fewest complete recent Agent steps to keep even when they exceed the target. A step is an assistant message and all immediately following tool results. 0 allows all completed steps to be removed when none fit.
  • omission_note (str | None) – The user message left in place of what was removed, or None to remove the messages silently. Include {num_removed} to have the number of removed messages substituted in.

Raises:

  • ValueError – If min_keep_steps is negative.

compact​

python
compact(
    messages: list[ChatMessage], target_tokens: int, token_counter: TokenCounter
) -> list[ChatMessage] | None

Drop older history while preserving the task anchor and a complete recent conversation window.

Parameters:

  • messages (list[ChatMessage]) – The conversation to compact, oldest to newest.
  • target_tokens (int) – The size the kept conversation should come in under.
  • token_counter (TokenCounter) – The TokenCounter to measure messages with.

Returns:

  • list[ChatMessage] | None – The conversation that survived, with an omission note if configured standing where the removed messages used to sit; or None when there is nothing to remove but an earlier note.

to_dict​

python
to_dict() -> dict[str, Any]

Serialize the compactor.

Returns:

  • dict[str, Any] – A dictionary representation of the compactor.

compaction/tool_result_pruning​

ToolResultPruningCompactor​

Bases: Compactor

Replaces the content of older tool results with a short placeholder, keeping the conversation's shape intact.

Tool output usually dominates a long Agent run, and most of it stops being useful once the model has acted on it. This compactor rewrites those results in place rather than removing messages, so every tool call keeps its matching result and the model can see what it ran and re-run it if needed.

python
from haystack.components.agents import Agent
from haystack.components.generators.chat import OpenAIResponsesChatGenerator
from haystack.hooks.compaction import CompactionHook, ToolResultPruningCompactor

hook = CompactionHook(
    compactor=ToolResultPruningCompactor(min_keep_steps=1),
    context_window=400_000,
    compact_at=0.7,
    compact_to=0.4,
)
agent = Agent(
    chat_generator=OpenAIResponsesChatGenerator(model="gpt-5.4-nano"),
    tools=[web_search],
    hooks={"before_llm": [hook]},
)

init​

python
__init__(
    *,
    min_keep_steps: int = 1,
    min_tokens: int = 200,
    placeholder: str = _DEFAULT_PLACEHOLDER,
    skip_meta_keys: tuple[str, ...] = ("tool_result_offloaded",)
) -> None

Initialize the compactor with the rules deciding which results it prunes.

Parameters:

  • min_keep_steps (int) – The minimum number of recent tool-calling Agent steps whose results remain untouched, even when they exceed the target. Must be at least 1, which ensures the current result batch remains intact until the model has acted on it.
  • min_tokens (int) – Only prune tool-result messages that use more than this many tokens. Small results cost little and are often the ones worth keeping.
  • placeholder (str) – The text left in place of a pruned result, replacing the built-in one. May contain {tool_name}, which is filled in with the name of the tool that produced the result.
  • skip_meta_keys (tuple[str, ...]) – Results whose meta contains any of these keys are left alone. The default covers results that a ToolResultOffloadHook already replaced with a reference to stored content: pruning one of those would destroy the reference the model needs to read it back.

Raises:

  • ValueError – If min_keep_steps is less than 1 or min_tokens is negative.

compact​

python
compact(
    messages: list[ChatMessage], target_tokens: int, token_counter: TokenCounter
) -> list[ChatMessage] | None

Replace the content of prunable tool results with a placeholder.

Results are considered oldest first and pruning stops as soon as the conversation reaches target_tokens. This keeps as much original output as possible. Results from the most recent min_keep_steps tool-calling Agent steps are never considered, even when the target cannot otherwise be reached. After measuring the initial conversation, the running total is updated with per-result token deltas to avoid repeatedly counting the full context.

Parameters:

  • messages (list[ChatMessage]) – The conversation to compact, oldest to newest.
  • target_tokens (int) – The size the compacted conversation should come in under.
  • token_counter (TokenCounter) – The TokenCounter used to measure the conversation before and after each replacement.

Returns:

  • list[ChatMessage] | None – The conversation with older tool results replaced, or None when no result was prunable.

to_dict​

python
to_dict() -> dict[str, Any]

Serialize the compactor.

Returns:

  • dict[str, Any] – A dictionary representation of the compactor.

compaction/types/protocol​

Compactor​

Bases: Protocol

Rewrites an Agent's conversation into a shorter one that carries the same working context.

A compactor is the how of context compaction; deciding when to compact is the caller's job, which CompactionHook does by comparing the context size against a fraction of the model's window. Strategies differ widely in cost and fidelity, from dropping the oldest messages outright to condensing them with an LLM.

Implementations must honor three rules:

  1. Return None unless the conversation actually gets smaller. Callers apply whatever else is returned, so judging whether compacting was worthwhile is the compactor's job.
  2. Return a new list; leave messages as it is. The caller owns that list and writes the returned one back.
  3. Keep tool calls and their results together. Do not retain a tool result after removing the assistant message that contains its originating call, or retain a tool call without all of its results. Chat-completion APIs reject these incomplete tool-call exchanges.

target_tokens is a goal, not a guarantee: a compactor that cannot reach it should get as close as it can rather than strip the conversation past what the Agent needs to keep working.

Implement to_dict so the compactor's settings survive serialization. The default from_dict passes them straight back to the constructor, which is enough for plain values; override it when to_dict emitted something that has to be rebuilt first, such as a Secret or a nested component.

compact​

python
compact(
    messages: list[ChatMessage], target_tokens: int, token_counter: TokenCounter
) -> list[ChatMessage] | None

Return a shorter replacement for messages, or None to leave it unchanged.

Parameters:

  • messages (list[ChatMessage]) – The conversation to compact, oldest to newest.
  • target_tokens (int) – The size the compacted conversation should come in under.
  • token_counter (TokenCounter) – The TokenCounter to measure messages with. The same one the caller sized the context with, so a compactor's measurements are consistent with the decision to compact.

Returns:

  • list[ChatMessage] | None – The replacement conversation, or None when this compactor has nothing to change.

compact_async​

python
compact_async(
    messages: list[ChatMessage], target_tokens: int, token_counter: TokenCounter
) -> list[ChatMessage] | None

Asynchronously return a shorter replacement for messages, or None to leave it unchanged.

The default implementation calls compact directly. Override it when compaction does I/O, so the event loop is not blocked.

Parameters:

  • messages (list[ChatMessage]) – The conversation to compact, oldest to newest.
  • target_tokens (int) – The size the compacted conversation should come in under.
  • token_counter (TokenCounter) – The TokenCounter to measure messages with. The same one the caller sized the context with, so a compactor's measurements are consistent with the decision to compact.

Returns:

  • list[ChatMessage] | None – The replacement conversation, or None when this compactor has nothing to change.

to_dict​

python
to_dict() -> dict[str, Any]

Serialize the compactor to a dictionary.

from_dict​

python
from_dict(data: dict[str, Any]) -> Compactor

Deserialize the compactor from a dictionary.

from_function​

FunctionHook​

Wraps a function (or a sync/async pair) into a serializable Hook.

Produced by the @hook decorator for the single-function case. To give a hook both an optimized sync and async path, construct it directly with both function and async_function set.

init​

python
__init__(
    function: Callable[[State], None] | None = None,
    async_function: Callable[[State], Awaitable[None]] | None = None,
) -> None

Initialize the hook with a synchronous function, an async function, or both.

Parameters:

  • function (Callable\[[State], None] | None) – The synchronous function invoked by run. Must be a regular function — coroutine functions should be passed to async_function instead. Either function or async_function (or both) must be set.
  • async_function (Callable\[[State], Awaitable[None]] | None) – Optional coroutine function awaited by run_async. When only async_function is set, run raises a RuntimeError. When only function is set, run_async calls function.

Raises:

  • ValueError – If neither is set, if function is a coroutine function, if async_function is not, or if a provided function does not declare a State-typed parameter.

run​

python
run(state: State) -> None

Run the synchronous function against the live State.

Parameters:

  • state (State) – The Agent's live State, mutated in place by the wrapped function.

Raises:

  • RuntimeError – If the hook only has an async_function; use the Agent's async run methods instead.

run_async​

python
run_async(state: State) -> None

Await the async function if set, otherwise call the synchronous function.

Parameters:

  • state (State) – The Agent's live State, mutated in place by the wrapped function.

to_dict​

python
to_dict() -> dict[str, Any]

Serialize the hook, storing each wrapped function as an importable reference.

Returns:

  • dict[str, Any] – A dictionary with the hook's type and the import paths of its sync/async functions.

from_dict​

python
from_dict(data: dict[str, Any]) -> FunctionHook

Deserialize the hook, resolving each function from its importable reference.

Parameters:

  • data (dict[str, Any]) – The serialized hook dictionary produced by to_dict.

Returns:

  • FunctionHook – The reconstructed FunctionHook.

hook​

python
hook(function: Callable[[State], None | Awaitable[None]]) -> FunctionHook

Wrap a function into a Hook the Agent can invoke during its run loop.

The decorated function receives the Agent's State and influences the run by mutating it in place. A coroutine function is wrapped as the hook's async path; a regular function as its sync path. To give a single hook both paths, construct a FunctionHook directly with both function and async_function.

Usage example​

python
from haystack.components.agents import Agent
from haystack.components.generators.chat import OpenAIChatGenerator
from haystack.hooks import hook
from haystack.components.agents.state import State
from haystack.dataclasses import ChatMessage
from haystack.tools import tool

@tool
def weather_tool(city: str) -> str:
    '''Get the current weather for a given city.'''
    return f"The weather in {city} is sunny."

@tool
def save(content: str) -> str:
    '''Save content to durable storage.'''
    return "Saved."

@hook
def require_save(state: State) -> None:
    if state.get("tool_call_counts", {}).get("save", 0) == 0:
        state.set("messages", [ChatMessage.from_system("You must call `save` before finishing.")])
        state.set("continue_run", True)

agent = Agent(chat_generator=OpenAIChatGenerator(), tools=[weather_tool, save], hooks={"on_exit": [require_save]})

Parameters:

  • function (Callable\[[State], None | Awaitable[None]]) – A callable taking the Agent's State and returning None (sync or async).

Returns:

  • FunctionHook – A FunctionHook wrapping the function.

human_in_the_loop/dataclasses​

ConfirmationUIResult​

Result of the confirmation UI interaction.

Parameters:

  • action (str) – The action taken by the user such as "confirm", "reject", or "modify". This action type is not enforced to allow for custom actions to be implemented.
  • feedback (str | None) – Optional feedback message from the user. For example, if the user rejects the tool execution, they might provide a reason for the rejection.
  • new_tool_params (dict[str, Any] | None) – Optional set of new parameters for the tool. For example, if the user chooses to modify the tool parameters, they can provide a new set of parameters here.

ToolExecutionDecision​

Decision made regarding tool execution.

Parameters:

  • tool_name (str) – The name of the tool to be executed.
  • execute (bool) – A boolean indicating whether to execute the tool with the provided parameters.
  • tool_call_id (str | None) – Optional unique identifier for the tool call. This can be used to track and correlate the decision with a specific tool invocation.
  • feedback (str | None) – Optional feedback message. For example, if the tool execution is rejected, this can contain the reason. Or if the tool parameters were modified, this can contain the modification details.
  • final_tool_params (dict[str, Any] | None) – Optional final parameters for the tool if execution is confirmed or modified.

to_dict​

python
to_dict() -> dict[str, Any]

Convert the ToolExecutionDecision to a dictionary representation.

Returns:

  • dict[str, Any] – A dictionary containing the tool execution decision details.

from_dict​

python
from_dict(data: dict[str, Any]) -> ToolExecutionDecision

Populate the ToolExecutionDecision from a dictionary representation.

Parameters:

  • data (dict[str, Any]) – A dictionary containing the tool execution decision details.

Returns:

  • ToolExecutionDecision – An instance of ToolExecutionDecision.

human_in_the_loop/hooks​

ConfirmationHook​

A before_tool Agent hook that applies Human-in-the-Loop confirmation strategies to pending tool calls.

Register it on an Agent to confirm, modify, or reject tool calls before they run:

python
from haystack.components.agents import Agent
from haystack.components.generators.chat import OpenAIChatGenerator
from haystack.tools import tool
from haystack.hooks.human_in_the_loop import (
    AlwaysAskPolicy,
    BlockingConfirmationStrategy,
    ConfirmationHook,
    NeverAskPolicy,
    RichConsoleUI,
    SimpleConsoleUI,
)

@tool
def delete_file(path: str) -> str:
    '''Delete the file at the given path.'''
    return f"Deleted {path}."

hook = ConfirmationHook(
    confirmation_strategies={
        "delete_file": BlockingConfirmationStrategy(
            confirmation_policy=NeverAskPolicy(), confirmation_ui=SimpleConsoleUI()
        )
    }
)
agent = Agent(chat_generator=OpenAIChatGenerator(), tools=[delete_file], hooks={"before_tool": [hook]})

A key may be a single tool name, a tuple of tool names sharing one strategy, or the wildcard "*" which applies to any tool without a more specific entry. More specific keys win, so you can set a default for all tools and override individual ones:

python
hook = ConfirmationHook(
    confirmation_strategies={
        "delete_file": BlockingConfirmationStrategy(
            confirmation_policy=AlwaysAskPolicy(), confirmation_ui=RichConsoleUI()
        ),
        "*": BlockingConfirmationStrategy(
            confirmation_policy=NeverAskPolicy(), confirmation_ui=SimpleConsoleUI()
        ),
    }
)

Request-scoped resources for the strategies (e.g. a WebSocket or queue) are passed per run via the Agent's hook_context argument (agent.run(messages=[...], hook_context={...})) and read by the hook with state.data.get("hook_context").

This hook only makes sense at the before_tool hook point, where the pending tool calls exist (between the model requesting tools and those tools running); the Agent enforces this and raises if it is registered elsewhere. Use a single ConfirmationHook with one entry per tool (or per tuple of tools) in confirmation_strategies rather than registering several hooks.

init​

python
__init__(
    confirmation_strategies: dict[str | tuple[str, ...], ConfirmationStrategy],
) -> None

Initialize the hook with its per-tool confirmation strategies.

Parameters:

  • confirmation_strategies (dict[str | tuple[str, ...], ConfirmationStrategy]) – Mapping of tool name (or a tuple of tool names) to its ConfirmationStrategy. The wildcard key "*" applies to any tool without a more specific entry.

run​

python
run(state: State) -> None

Confirm the pending tool calls, rewriting the messages in state to reflect modifications and rejections.

Parameters:

  • state (State) – The Agent's live State. Reads the available tools (state.data.get("tools")) and the per-run context (state.data.get("hook_context")), and the pending tool calls from the last message; writes the updated conversation back to messages. Reads go through state.data rather than state.get, which deep-copies and would break non-copyable resources (e.g. a WebSocket or client) in hook_context.

run_async​

python
run_async(state: State) -> None

Async version of run.

to_dict​

python
to_dict() -> dict[str, Any]

Serialize the hook, including its confirmation strategies (tuple keys become JSON-array strings).

from_dict​

python
from_dict(data: dict[str, Any]) -> ConfirmationHook

Deserialize the hook, reconstructing its confirmation strategies.

human_in_the_loop/policies​

AlwaysAskPolicy​

Bases: ConfirmationPolicy

Always ask for confirmation.

should_ask​

python
should_ask(
    tool_name: str, tool_description: str, tool_params: dict[str, Any]
) -> bool

Always ask for confirmation before executing the tool.

Parameters:

  • tool_name (str) – The name of the tool to be executed.
  • tool_description (str) – The description of the tool.
  • tool_params (dict[str, Any]) – The parameters to be passed to the tool.

Returns:

  • bool – Always returns True, indicating confirmation is needed.

NeverAskPolicy​

Bases: ConfirmationPolicy

Never ask for confirmation.

should_ask​

python
should_ask(
    tool_name: str, tool_description: str, tool_params: dict[str, Any]
) -> bool

Never ask for confirmation, always proceed with tool execution.

Parameters:

  • tool_name (str) – The name of the tool to be executed.
  • tool_description (str) – The description of the tool.
  • tool_params (dict[str, Any]) – The parameters to be passed to the tool.

Returns:

  • bool – Always returns False, indicating no confirmation is needed.

AskOncePolicy​

Bases: ConfirmationPolicy

Ask only once per tool with specific parameters.

init​

python
__init__() -> None

Creates an instance of AskOncePolicy.

should_ask​

python
should_ask(
    tool_name: str, tool_description: str, tool_params: dict[str, Any]
) -> bool

Ask for confirmation only once per tool with specific parameters.

Parameters:

  • tool_name (str) – The name of the tool to be executed.
  • tool_description (str) – The description of the tool.
  • tool_params (dict[str, Any]) – The parameters to be passed to the tool.

Returns:

  • bool – True if confirmation is needed, False if already asked with the same parameters.

update_after_confirmation​

python
update_after_confirmation(
    tool_name: str,
    tool_description: str,
    tool_params: dict[str, Any],
    confirmation_result: ConfirmationUIResult,
) -> None

Store the tool and parameters if the action was "confirm" to avoid asking again.

This method updates the internal state to remember that the user has already confirmed the execution of the tool with the given parameters.

Parameters:

  • tool_name (str) – The name of the tool that was executed.
  • tool_description (str) – The description of the tool.
  • tool_params (dict[str, Any]) – The parameters that were passed to the tool.
  • confirmation_result (ConfirmationUIResult) – The result from the confirmation UI.

human_in_the_loop/strategies​

BlockingConfirmationStrategy​

Confirmation strategy that blocks execution to gather user feedback.

init​

python
__init__(
    *,
    confirmation_policy: ConfirmationPolicy,
    confirmation_ui: ConfirmationUI,
    reject_template: str = REJECTION_FEEDBACK_TEMPLATE,
    modify_template: str = MODIFICATION_FEEDBACK_TEMPLATE,
    user_feedback_template: str = USER_FEEDBACK_TEMPLATE
) -> None

Initialize the BlockingConfirmationStrategy with a confirmation policy and UI.

Parameters:

  • confirmation_policy (ConfirmationPolicy) – The confirmation policy to determine when to ask for user confirmation.
  • confirmation_ui (ConfirmationUI) – The user interface to interact with the user for confirmation.
  • reject_template (str) – Template for rejection feedback messages. It should include a {tool_name} placeholder.
  • modify_template (str) – Template for modification feedback messages. It should include {tool_name} and {final_tool_params} placeholders.
  • user_feedback_template (str) – Template for user feedback messages. It should include a {feedback} placeholder.

run​

python
run(
    *,
    tool_name: str,
    tool_description: str,
    tool_params: dict[str, Any],
    tool_call_id: str | None = None,
    confirmation_strategy_context: dict[str, Any] | None = None
) -> ToolExecutionDecision

Run the human-in-the-loop strategy for a given tool and its parameters.

Parameters:

  • tool_name (str) – The name of the tool to be executed.
  • tool_description (str) – The description of the tool.
  • tool_params (dict[str, Any]) – The parameters to be passed to the tool.
  • tool_call_id (str | None) – Optional unique identifier for the tool call. This can be used to track and correlate the decision with a specific tool invocation.
  • confirmation_strategy_context (dict[str, Any] | None) – Optional dictionary for passing request-scoped resources. Useful in web/server environments to provide per-request objects (e.g., WebSocket connections, async queues, Redis pub/sub clients) that strategies can use for non-blocking user interaction.

Returns:

  • ToolExecutionDecision – A ToolExecutionDecision indicating whether to execute the tool with the given parameters, or a feedback message if rejected.

run_async​

python
run_async(
    *,
    tool_name: str,
    tool_description: str,
    tool_params: dict[str, Any],
    tool_call_id: str | None = None,
    confirmation_strategy_context: dict[str, Any] | None = None
) -> ToolExecutionDecision

Async version of run. Calls the sync run() method by default.

Parameters:

  • tool_name (str) – The name of the tool to be executed.
  • tool_description (str) – The description of the tool.
  • tool_params (dict[str, Any]) – The parameters to be passed to the tool.
  • tool_call_id (str | None) – Optional unique identifier for the tool call.
  • confirmation_strategy_context (dict[str, Any] | None) – Optional dictionary for passing request-scoped resources.

Returns:

  • ToolExecutionDecision – A ToolExecutionDecision indicating whether to execute the tool with the given parameters.

to_dict​

python
to_dict() -> dict[str, Any]

Serializes the BlockingConfirmationStrategy to a dictionary.

Returns:

  • dict[str, Any] – Dictionary with serialized data.

from_dict​

python
from_dict(data: dict[str, Any]) -> BlockingConfirmationStrategy

Deserializes the BlockingConfirmationStrategy from a dictionary.

Parameters:

  • data (dict[str, Any]) – Dictionary to deserialize from.

Returns:

  • BlockingConfirmationStrategy – Deserialized BlockingConfirmationStrategy.

human_in_the_loop/user_interfaces​

RichConsoleUI​

Bases: ConfirmationUI

Rich console interface for user interaction.

init​

python
__init__(console: Console | None = None) -> None

Creates an instance of RichConsoleUI.

get_user_confirmation​

python
get_user_confirmation(
    tool_name: str, tool_description: str, tool_params: dict[str, Any]
) -> ConfirmationUIResult

Get user confirmation for tool execution via rich console prompts.

Parameters:

  • tool_name (str) – The name of the tool to be executed.
  • tool_description (str) – The description of the tool.
  • tool_params (dict[str, Any]) – The parameters to be passed to the tool.

Returns:

  • ConfirmationUIResult – ConfirmationUIResult based on user input.

to_dict​

python
to_dict() -> dict[str, Any]

Serializes the RichConsoleConfirmationUI to a dictionary.

Returns:

  • dict[str, Any] – Dictionary with serialized data.

SimpleConsoleUI​

Bases: ConfirmationUI

Simple console interface using standard input/output.

get_user_confirmation​

python
get_user_confirmation(
    tool_name: str, tool_description: str, tool_params: dict[str, Any]
) -> ConfirmationUIResult

Get user confirmation for tool execution via simple console prompts.

Parameters:

  • tool_name (str) – The name of the tool to be executed.
  • tool_description (str) – The description of the tool.
  • tool_params (dict[str, Any]) – The parameters to be passed to the tool.

protocol​

Hook​

Bases: Protocol

A callable the Agent invokes at a point in its run loop, receiving the live State.

A hook influences the run only by mutating State in place. At least messages (the conversation), step_count, token_usage and tool_call_counts are available; any additional keys defined in the Agent's state_schema are available too. The same hook object can be registered under multiple hook points.

Implement this protocol directly for stateful hooks (e.g. one wrapping a component), or use the @hook decorator to wrap a plain (State) -> None function.

A hook may additionally define async def run_async(self, state: State) -> None for true async behavior; when absent, the Agent calls run during async runs. It is left off this protocol on purpose so sync-only hooks don't have to implement it.

A hook may also implement the optional lifecycle methods warm_up / warm_up_async and close / close_async. The Agent calls them from its own warm_up / warm_up_async and close / close_async, so a hook can defer opening clients or reading credentials until warm-up and release them on close. Because warm-up runs before every Agent run, hooks should avoid repeating expensive initialization, for example by returning early if a client has already been initialized.

run​

python
run(state: State) -> None

Run the hook against the live State, mutating it in place.

to_dict​

python
to_dict() -> dict[str, Any]

Serialize the hook to a dictionary.

from_dict​

python
from_dict(data: dict[str, Any]) -> Hook

Deserialize the hook from a dictionary.

tool_result_offloading/hooks​

ToolResultOffloadHook​

Offload tool results to a ToolResultStore, replacing them in the conversation with a compact pointer.

This after_tool Agent hook writes the full result to the store so the next LLM call sees a reference instead of the full result. Register it on an Agent under the after_tool hook point. Which tools offload, and under what condition, is controlled per tool by offload_strategies:

python
from haystack.components.agents import Agent
from haystack.components.generators.chat import OpenAIChatGenerator
from haystack.hooks.tool_result_offloading import (
    AlwaysOffload,
    FileSystemToolResultStore,
    NeverOffload,
    OffloadOverChars,
    ToolResultOffloadHook,
)

hook = ToolResultOffloadHook(
    store=FileSystemToolResultStore(root="tool_results"),
    offload_strategies={
        "web_search": AlwaysOffload(),          # force offload
        "get_time": NeverOffload(),             # opt out
        ("read_file", "list_dir"): OffloadOverChars(4000),  # tuple key: shared policy
        "*": OffloadOverChars(8000),            # wildcard default for any unlisted tool
    },
)
agent = Agent(
    chat_generator=OpenAIChatGenerator(model="gpt-5.4-nano"),
    tools=[web_search, get_time, read_file, list_dir],
    hooks={"after_tool": [hook]},
)

A key may be a single tool name, a tuple of tool names sharing one policy, or the wildcard "*" which applies to any tool without a more specific entry. More specific keys win. A tool with no matching key (and no "*") is not offloaded.

Only successful, text tool output is offloaded. Error results (including before_tool human-in-the-loop rejections) are always left in context. Non-text results (image or file content) are also left in context, and a warning is logged when such a result has a matching offload policy; supporting only text is a deliberate choice for now. Each result is offloaded at most once, even though the hook runs on every tool step.

The hook keeps no mutable state, so a single instance can be shared across concurrent runs. The constructor store, however, is shared by every run that does not override it — fine for single-user or local use, but in a multi-user server give each run its own isolated store (a per-session directory or sandbox) via hook_context under the key RESULT_STORE_CONTEXT_KEY (agent.run(messages=[...], hook_context={RESULT_STORE_CONTEXT_KEY: per_request_store})); it overrides the constructor store for that run. Isolating the store per run keeps concurrent users from colliding on store keys or reading each other's offloaded results — important especially when a bash/read tool is scoped to the store.

init​

python
__init__(
    store: ToolResultStore,
    offload_strategies: dict[str | tuple[str, ...], OffloadPolicy],
    *,
    preview_chars: int = 200
) -> None

Initialize the hook with a store and per-tool offload strategies.

Parameters:

  • store (ToolResultStore) – Where offloaded results are written. Can be overridden per run via hook_context.
  • offload_strategies (dict[str | tuple[str, ...], OffloadPolicy]) – Mapping of tool name (or a tuple of tool names, or the wildcard "*") to the OffloadPolicy that decides whether that tool's results are offloaded.
  • preview_chars (int) – Number of leading characters of the original result to include in the pointer left in the conversation, so the model knows roughly what was offloaded.

run​

python
run(state: State) -> None

Offload the freshly produced tool results in state.data["messages"] according to offload_strategies.

Considers only the trailing block of tool-result messages (the current step's results); earlier history is left untouched. Offloads each of those messages its policy opts in for, and writes the rewritten conversation back to messages only if at least one message changed.

Results are written to the store this run resolves to: a per-run store passed in state's hook_context under RESULT_STORE_CONTEXT_KEY if present, otherwise the store the hook was constructed with. Supply the per-run store when calling the Agent, e.g. agent.run(messages=[...], hook_context={RESULT_STORE_CONTEXT_KEY: per_request_store}). In a multi-user server, pass an isolated store per run this way so concurrent users write to separate locations and never read each other's results.

The hook keeps no mutable state, so a single instance is safe to share across concurrent runs; isolation comes entirely from giving each run its own store via hook_context.

Parameters:

  • state (State) – The Agent's live State. Reads the per-run store from hook_context and rewrites the offloaded tool-result messages back into messages.

Returns:

  • None – None. The hook mutates state in place.

to_dict​

python
to_dict() -> dict[str, Any]

Serialize the hook, including its store and per-tool offload strategies.

Returns:

  • dict[str, Any] – A dictionary representation of the hook.

from_dict​

python
from_dict(data: dict[str, Any]) -> ToolResultOffloadHook

Deserialize the hook, reconstructing its store and offload strategies.

Parameters:

  • data (dict[str, Any]) – A dictionary representation produced by to_dict.

Returns:

  • ToolResultOffloadHook – The deserialized ToolResultOffloadHook.

tool_result_offloading/policies​

AlwaysOffload​

Bases: OffloadPolicy

Offload every result of the tool it is assigned to.

should_offload​

python
should_offload(tool_name: str, result: str, state: State) -> bool

Decide whether to offload the given tool result.

Parameters:

  • tool_name (str) – The name of the tool that produced the result (unused; this policy always offloads).
  • result (str) – The tool result string (unused; this policy always offloads).
  • state (State) – The Agent's live State (unused; this policy always offloads).

Returns:

  • bool – Always True.

NeverOffload​

Bases: OffloadPolicy

Never offload; keep the tool's full result in context. Use to opt a tool out of a wildcard default.

should_offload​

python
should_offload(tool_name: str, result: str, state: State) -> bool

Decide whether to offload the given tool result.

Parameters:

  • tool_name (str) – The name of the tool that produced the result (unused; this policy never offloads).
  • result (str) – The tool result string (unused; this policy never offloads).
  • state (State) – The Agent's live State (unused; this policy never offloads).

Returns:

  • bool – Always False.

OffloadOverChars​

Bases: OffloadPolicy

Offload a result only when its string length exceeds threshold characters.

init​

python
__init__(threshold: int) -> None

Initialize the policy with its character threshold.

Parameters:

  • threshold (int) – Offload the result when its length in characters is strictly greater than this value.

should_offload​

python
should_offload(tool_name: str, result: str, state: State) -> bool

Decide whether to offload the given tool result based on its length.

Parameters:

  • tool_name (str) – The name of the tool that produced the result (unused; only length is considered).
  • result (str) – The tool result string whose length is compared against the threshold.
  • state (State) – The Agent's live State (unused; only length is considered).

Returns:

  • bool – True when result is longer than threshold characters, otherwise False.

to_dict​

python
to_dict() -> dict[str, Any]

Serialize the policy, including its threshold.

Returns:

  • dict[str, Any] – A dictionary representation of the policy.

tool_result_offloading/stores​

FileSystemToolResultStore​

Bases: ToolResultStore

A ToolResultStore that writes offloaded tool results to files under a root directory on the local file system.

python
from haystack.hooks.tool_result_offloading import FileSystemToolResultStore

store = FileSystemToolResultStore(root="tool_results")
reference = store.write(key="search_1.txt", content="...")
store.read(reference)

init​

python
__init__(root: str | Path) -> None

Initialize the store with the root directory results are written under.

Parameters:

  • root (str | Path) – Directory under which result files are written. Created on first write if it does not exist.

write​

python
write(*, key: str, content: str) -> str

Write content to <root>/<key>, creating parent directories, and return the file path.

The resolved target must stay within the root directory: a key that escapes it (e.g. containing ../ or an absolute path) is rejected, so a tool-provided key cannot write outside the store.

Parameters:

  • key (str) – Relative file name for the result within the store root.
  • content (str) – The tool result to persist.

Returns:

  • str – The absolute path the content was written to, as a string, for use with read.

Raises:

  • ValueError – If key resolves to a location outside the store root.

read​

python
read(reference: str) -> str

Read back the content previously written to reference.

The resolved reference must stay within the store root: callers must treat it as an opaque store-scoped reference, not as an arbitrary filesystem path.

Parameters:

  • reference (str) – A store reference returned by write.

Returns:

  • str – The stored content.

Raises:

  • ValueError – If reference resolves to a location outside the store root.

to_dict​

python
to_dict() -> dict[str, Any]

Serialize the store, storing its root directory as a string.

Returns:

  • dict[str, Any] – A dictionary representation of the store.

from_dict​

python
from_dict(data: dict[str, Any]) -> FileSystemToolResultStore

Deserialize the store from a dictionary.

Parameters:

  • data (dict[str, Any]) – A dictionary representation produced by to_dict.

Returns:

  • FileSystemToolResultStore – The deserialized FileSystemToolResultStore.

tool_result_offloading/types/protocol​

ToolResultStore​

Bases: Protocol

A place a ToolResultOffloadHook writes offloaded tool results to, and reads them back from.

Implementations decide where and how the content lives (local disk, an isolated sandbox filesystem, object storage, ...). write returns an opaque reference string that the Agent puts in the conversation in place of the full result; read resolves that reference back to the original content.

Implement both to_dict and from_dict to make a custom store serializable; the default implementations below cover stores whose constructor takes no arguments.

write​

python
write(*, key: str, content: str) -> str

Persist content under key and return an opaque reference to it.

Parameters:

  • key (str) – A stable, per-result identifier the hook derives from the tool call (e.g. a file name).
  • content (str) – The tool result to persist.

Returns:

  • str – A reference string (e.g. a path or URI) that read can later resolve.

read​

python
read(reference: str) -> str

Return the content previously stored under reference.

to_dict​

python
to_dict() -> dict[str, Any]

Serialize the store to a dictionary.

from_dict​

python
from_dict(data: dict[str, Any]) -> ToolResultStore

Deserialize the store from a dictionary.

OffloadPolicy​

Bases: Protocol

Decides, per tool result, whether the ToolResultOffloadHook offloads it to the store or leaves it in context.

A ToolResultOffloadHook maps tool names to policies, so different tools can offload under different conditions (always, never, or a custom rule such as a size threshold).

Implement both to_dict and from_dict to make a custom policy serializable; the default implementations below cover policies whose constructor takes no arguments.

should_offload​

python
should_offload(tool_name: str, result: str, state: State) -> bool

Return whether the given tool result should be offloaded.

Parameters:

  • tool_name (str) – The name of the tool that produced the result.
  • result (str) – The tool result as a string (the content that would otherwise stay in the conversation).
  • state (State) – The Agent's live State, for policies that decide based on run context.

Returns:

  • bool – True to offload the result to the store, False to leave it in context.

to_dict​

python
to_dict() -> dict[str, Any]

Serialize the policy to a dictionary.

from_dict​

python
from_dict(data: dict[str, Any]) -> OffloadPolicy

Deserialize the policy from a dictionary.