2514 Processors 1f5dbcf0

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

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

Returns

SummarizationProcessor - Configured processor instance.

Example

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

@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

Methods

__call__

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

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

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

Returns

SlidingWindowProcessor - Configured processor instance.

Example

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

@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

Methods

__call__

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

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

Example

from pydantic_deep import SlidingWindowProcessor

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

Type Aliases

ContextSize

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

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

Function type for custom token counting.

EvictionProcessor

History processor that evicts large tool outputs to files. See Eviction.

Definition

@dataclass
class EvictionProcessor:
    backend: BackendProtocol
    token_limit: int = 20_000
    eviction_path: str = "/large_tool_results"
    head_lines: int = 5
    tail_lines: int = 5

Factory

from pydantic_deep import create_eviction_processor

processor = create_eviction_processor(
    backend=StateBackend(),
    token_limit=20000,
)

patch_tool_calls_processor

History processor that fixes orphaned tool calls in message history.

from pydantic_deep.processors.patch import patch_tool_calls_processor

# Use as history processor
agent = Agent("openai:gpt-4.1", history_processors=[patch_tool_calls_processor])

# Or via create_deep_agent
agent = create_deep_agent(patch_tool_calls=True)

ContextManagerMiddleware

Dual-protocol component from summarization-pydantic-ai. Acts as both history processor and AgentMiddleware.

Factory

from pydantic_ai_summarization import create_context_manager_middleware

middleware = create_context_manager_middleware(
    max_tokens=200_000,
    compress_threshold=0.9,
    pct, cur, mx: print(f"{pct:.0%}"),
)

See History Processors for details.

Next Steps

• Agent API - Agent factory and configuration
• Types API - Type definitions