Compaction

As a conversation grows, its message history can approach the model's context window. Compaction keeps it in check by shrinking older messages — trimming, clearing, or summarizing them — while preserving recent context and tool-call integrity. Pydantic AI supports this at several levels, from provider-native APIs to model-agnostic history editing.

Provider-native compaction

Some providers expose a built-in compaction API that runs on their side. Pydantic AI wraps these as capabilities:

Provider	Capability	Details
OpenAI Responses API	[OpenAICompaction][pydantic_ai.models.openai.OpenAICompaction]	OpenAI compaction
Anthropic	[AnthropicCompaction][pydantic_ai.models.anthropic.AnthropicCompaction]	Anthropic compaction

Each uses the corresponding provider API, so it's only available on that provider.

Model-agnostic compaction

To compact on any model, edit the message history yourself with a history processor wrapped as a [ProcessHistory][pydantic_ai.capabilities.ProcessHistory] capability — this works with every provider. Common patterns:

• Keep only recent messages — a zero-cost sliding window over the most recent turns.
• Summarize old messages — use a (cheaper) model to condense older messages into a summary.

Pydantic AI Harness

Pydantic AI Harness packages a menu of ready-made, model-agnostic compaction strategies: mostly zero-LLM history editing — sliding-window trimming, clearing old tool results, deduplicating repeated file reads, clamping oversized message parts — plus LLM summarization for when that's not enough, and a TieredCompaction orchestrator (the recommended default) that escalates from cheap to expensive strategies only as far as needed to fit the target.