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Conversation context is the Agent’s short-term memory, containing all messages in the current session (user input, Agent replies, tool calls and results). Proper context management is critical for the Agent’s reasoning quality and cost control.

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Context Structure

Each conversation turn consists of:
A single turn may include multiple tool calls (controlled by agent_max_steps). All tool calls and results are retained in context until compressed or trimmed.

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Key Configuration

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Compression Strategy

When context exceeds limits, the system automatically compresses to free space. The process has multiple stages:

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1. Tool Result Truncation

Before each decision loop, the system checks tool call results in historical turns. Results exceeding 20,000 characters are truncated, keeping only the beginning and end with a truncation notice. Current turn results are not affected.

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2. Turn Trimming

When conversation turns exceed agent_max_context_turns:
  • The oldest half of complete turns is trimmed (preserving tool call chain integrity)
  • Trimmed messages are summarized by LLM and written to the daily memory file
  • Once the LLM summary is ready, it is also injected into the first user message of the retained context, helping the model maintain conversational continuity
  • Summary injection runs asynchronously in the background and takes effect from the next turn onward

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3. Token Budget Trimming

After turn trimming, if tokens still exceed the budget:
  • Fewer than 5 turns: All turns undergo text compression — each turn keeps only the first user text and last Agent reply, removing intermediate tool call chains
  • 5 or more turns: The first half of turns is trimmed again, with discarded content written to memory and a context summary injected

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4. Overflow Emergency Handling

When the model API returns a context overflow error:
  1. All current messages are summarized and written to memory
  2. Aggressive trimming is applied (tool results limited to 10K chars, user text to 10K, max 5 turns)
  3. If still overflowing, the entire conversation context is cleared

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5. Manual Compaction

Besides the automatic strategies above, you can compact context on demand with the /compact command. It reuses the same logic as automatic trimming — older turns are summarized by the LLM and injected into the retained context, while the most recent turns are kept intact. Unlike auto-trim, it runs immediately regardless of current token usage, which is handy for freeing context before a long task.

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Session Persistence

Conversation messages are persisted to a local database, automatically restored after service restart. Restore strategy:
  • Restores the most recent max(3, max_context_turns / 6) turns
  • Only retains each turn’s user text and Agent final reply, not intermediate tool call chains
  • Sessions older than 30 days are automatically cleaned up

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Commands

Use these commands in chat to manage context:
After clearing context, the Agent “forgets” previous conversation content. Content that was already written to long-term memory can still be retrieved via memory search.