# Context Repair & Rolling Summary
`active-call` provides advanced context management features to handle complex conversational scenarios.
## Context Repair
This feature addresses the "fragmentation" issue caused by pauses in speech.
For example:
1. User: "My car broke down..."
2. (Pause)
3. Bot: "Where are you?"
4. User: "...at the station."
Without context repair, the bot would likely repeat the question "Where are you?".
With context repair, the system detects that the user's second statement is a continuation of the first, removes the bot's interruption, and merges the user's messages.
The LLM sees: User: "My car broke down... at the station."
### Configuration
Enable this feature by adding `"context_repair"` to the `features` list in your playbook `llm` config.
```markdown
---
llm:
features:
- "context_repair"
repair_window_ms: 3000 # Window to detect continuation (ms), default: 3000
---
```
## Rolling Summary
For long conversations, this feature automatically summarizes the history to prevent token overflow while maintaining context.
### Configuration
Enable this feature by adding `"rolling_summary"` to the `features` list.
```markdown
---
llm:
features:
- "rolling_summary"
summary_limit: 20 # Number of messages before triggering summary, default: 20
---
```