---
title: "Context management"
description: "Keep long-running tool loops within model context limits while preserving the full work trail."
---

> Documentation Index
> Fetch the complete documentation index at: https://docs.cortavyn.org/llms.txt
> Use this file to discover all available pages before exploring further.

# Context management

`ContextPolicy` controls how much work stays inline in a deep-agent conversation. Its defaults limit iterations, inline tool-result characters and estimated tokens, and retained history characters and estimated tokens.

When a tool result exceeds either inline limit, the harness writes the complete result under `context/tool-results/` in the current workspace, returns a short preview to the model, and emits `ContextOffloaded`. The model can then use `read_file` to inspect the complete result only when it needs it.

```java
var policy = new ContextPolicy(
    30, 16_000, 100_000,
    4_000, 25_000,
    128_000, 1_000);

var agent = DeepAgent.builder(model).contextPolicy(policy).build();
```

Set `contextWindowTokens` when the deployment's actual context window is known. The effective history budget reserves room for a subsequent completion. Keep the configured workspace writable when offloading may occur.

Before every model turn, the harness compacts history. If a provider nevertheless reports a context-window overflow, it asks the model for a conversation summary and retries the original turn once with that compacted context. Other provider errors are propagated normally.

`PromptCachePolicy` is enabled by default and adds Cortavyn's cache marker to static system, memory, and skill context. Provider adapters decide whether and how to use that marker. Call `promptCachePolicy(PromptCachePolicy.disabled())` for a deployment where static-prompt caching is undesirable.

Source: https://docs.cortavyn.org/deep-agent/context-management/index.mdx