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Processors API

History processors for managing conversation context. These are re-exported from summarization-pydantic-ai.

create_summarization_processor

Factory function for creating a summarization processor with sensible defaults.

Signature

Python
def create_summarization_processor(
    model: str = "openai:gpt-4.1",
    trigger: ContextSize | list[ContextSize] | None = ("tokens", 170000),
    keep: ContextSize = ("messages", 20),
    max_input_tokens: int | None = None,
    token_counter: TokenCounter | None = None,
    summary_prompt: str | None = None,
) -> SummarizationProcessor

Parameters

Parameter Type Default Description
model str "openai:gpt-4.1" Model for generating summaries
trigger ContextSize \| list[ContextSize] \| None ("tokens", 170000) When to trigger summarization
keep ContextSize ("messages", 20) How much context to keep
max_input_tokens int \| None None Max tokens (required for fraction triggers)
token_counter TokenCounter \| None None Custom token counting function
summary_prompt str \| None None Custom summarization prompt

Returns

SummarizationProcessor - Configured processor instance.

Example

Python
from pydantic_deep import create_deep_agent, create_summarization_processor

processor = create_summarization_processor(
    trigger=("tokens", 100000),
    keep=("messages", 20),
)

agent = create_deep_agent(history_processors=[processor])

SummarizationProcessor

Dataclass for LLM-based conversation summarization.

Definition

Python
@dataclass
class SummarizationProcessor:
    model: str
    trigger: ContextSize | list[ContextSize] | None = None
    keep: ContextSize = ("messages", 20)
    token_counter: TokenCounter = count_tokens_approximately
    summary_prompt: str = DEFAULT_SUMMARY_PROMPT
    max_input_tokens: int | None = None
    trim_tokens_to_summarize: int | None = 4000

Attributes

Attribute Type Description
model str Model to use for generating summaries
trigger ContextSize \| list[ContextSize] \| None Threshold(s) that trigger summarization
keep ContextSize How much context to keep after summarization
token_counter TokenCounter Function to count tokens in messages
summary_prompt str Prompt template for generating summaries
max_input_tokens int \| None Maximum input tokens (required for fraction triggers)
trim_tokens_to_summarize int \| None Maximum tokens to include when generating summary

Methods

__call__

Python
async def __call__(self, messages: list[ModelMessage]) -> list[ModelMessage]

Process messages and summarize if needed. This is called automatically by pydantic-ai's history processor mechanism.

Example

Python
from pydantic_deep import SummarizationProcessor

processor = SummarizationProcessor(
    model="openai:gpt-4.1",
    trigger=[
        ("messages", 50),
        ("tokens", 100000),
    ],
    keep=("messages", 10),
    trim_tokens_to_summarize=4000,
)

create_sliding_window_processor

Factory function for creating a sliding window processor with sensible defaults.

Signature

Python
def create_sliding_window_processor(
    trigger: ContextSize | list[ContextSize] | None = ("messages", 100),
    keep: ContextSize = ("messages", 50),
    max_input_tokens: int | None = None,
    token_counter: TokenCounter | None = None,
) -> SlidingWindowProcessor

Parameters

Parameter Type Default Description
trigger ContextSize \| list[ContextSize] \| None ("messages", 100) When to trigger trimming
keep ContextSize ("messages", 50) How much context to keep
max_input_tokens int \| None None Max tokens (required for fraction triggers)
token_counter TokenCounter \| None None Custom token counting function

Returns

SlidingWindowProcessor - Configured processor instance.

Example

Python
from pydantic_deep import create_deep_agent, create_sliding_window_processor

processor = create_sliding_window_processor(
    trigger=("messages", 100),
    keep=("messages", 50),
)

agent = create_deep_agent(history_processors=[processor])

SlidingWindowProcessor

Dataclass for zero-cost message trimming without LLM calls.

Definition

Python
@dataclass
class SlidingWindowProcessor:
    trigger: ContextSize | list[ContextSize] | None = None
    keep: ContextSize = ("messages", 50)
    token_counter: TokenCounter = count_tokens_approximately
    max_input_tokens: int | None = None

Attributes

Attribute Type Description
trigger ContextSize \| list[ContextSize] \| None Threshold(s) that trigger trimming
keep ContextSize How much context to keep after trimming
token_counter TokenCounter Function to count tokens in messages
max_input_tokens int \| None Maximum input tokens (required for fraction triggers)

Methods

__call__

Python
def __call__(self, messages: list[ModelMessage]) -> list[ModelMessage]

Process messages and trim if needed. Note: This is a synchronous method (no LLM calls).

Example

Python
from pydantic_deep import SlidingWindowProcessor

processor = SlidingWindowProcessor(
    trigger=("tokens", 100000),
    keep=("messages", 50),
)

Type Aliases

ContextSize

Python
ContextFraction = tuple[Literal["fraction"], float]
ContextTokens = tuple[Literal["tokens"], int]
ContextMessages = tuple[Literal["messages"], int]

ContextSize = ContextFraction | ContextTokens | ContextMessages

Specifies context size thresholds:

  • ("messages", N) - Number of messages
  • ("tokens", N) - Number of tokens
  • ("fraction", F) - Fraction of max_input_tokens (0 < F <= 1)

TokenCounter

Python
TokenCounter = Callable[[Sequence[ModelMessage]], int]

Function type for custom token counting.

Next Steps