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Changelog

All notable changes to this project will be documented in this file.

The format is based on Keep a Changelog, and this project adheres to Semantic Versioning.

[0.0.1] - 2025-01-20

Added

  • SummarizationProcessor - History processor that uses LLM to intelligently summarize older messages when context limits are reached
  • Configurable triggers: message count, token count, or fraction of context window
  • Configurable retention: keep last N messages, tokens, or fraction
  • Custom token counter support
  • Custom summary prompt support

  • Safe cutoff detection - never splits tool call/response pairs

  • SlidingWindowProcessor - Zero-cost history processor that simply discards old messages

  • Same trigger and retention options as SummarizationProcessor
  • No LLM calls - instant, deterministic processing

  • Ideal for high-throughput scenarios

  • Factory functions for convenient processor creation:

  • create_summarization_processor() - with sensible defaults

  • create_sliding_window_processor() - with sensible defaults

  • Utility functions:

  • count_tokens_approximately() - heuristic token counter (~4 chars per token)

  • format_messages_for_summary() - formats messages for LLM summarization

  • Type definitions:

  • ContextSize - union type for trigger/keep configuration
  • ContextFraction, ContextTokens, ContextMessages - specific context size types

  • TokenCounter - callable type for custom token counters

  • Documentation:

  • Full MkDocs documentation with Material theme
  • Concepts, examples, and API reference
  • Integration examples with pydantic-ai