Summarization for Pydantic AI

Automatic conversation summarization for unlimited context

Three strategies for managing agent context: intelligent LLM-based summarization, zero-cost sliding window trimming, and real-time context manager middleware with token tracking.

Installation

pip install summarization-pydantic-ai

Two strategies for keeping agent conversations within context limits. LLM-based summarization intelligently compresses older messages while preserving key information — triggered by message count, token count, or context fraction. Zero-cost sliding window trimming simply drops the oldest messages with a safe cutoff that never breaks tool call/response pairs. A real-time context manager middleware tracks token usage live, truncates long tool outputs, and auto-detects model context windows.

Features

Quick Start

from pydantic_ai import Agent
from pydantic_ai_summarization import create_summarization_processor

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

agent = Agent(
    "openai:gpt-4o",
    history_processors=[processor],
)

result = await agent.run("Hello!")

Use Cases