Engines

Engines are the LLMs that power your agents. Each agent uses two engines—a main engine and a secondary engine—selected during agent creation. You can modify engine assignments anytime as your needs change.

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Main Engine

The main engine handles direct task execution on your systems: navigating filesystems, reading and modifying files, interacting with services via CLI or MCP, executing commands, and managing configurations. This engine does the computational heavy-lifting and requires large context handling, high accuracy, and precise function calling. It typically consumes the most tokens due to the substantial context needed for reliable execution.

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Secondary Engine

The secondary engine handles orchestration and oversight to improve main engine accuracy.

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Context Length

Different LLMs handle varying context sizes with different reliability levels:

Context Length	Suitable For	Limitations
1M+ tokens	Large files (10,000+ lines), extensive codebases, complex multi-step operations	Higher cost per operation
600K tokens	Most standard operations, medium-sized files	Occasional memory management needed
264K tokens	Simple tasks, smaller files	Requires more frequent task archiving

Larger context windows reduce hallucinations when processing extensive data but cost more tokens. Check your inference provider’s docs for specific model context limits.

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Task Archiving

Archiving removes task history from an agent’s context window, preventing overflow without reinitializing the agent. Archive all tasks: Select your agent → Clear Memory Archive individual tasks: Open the task → Archive Task View history: Default view shows current tasks. Toggle Archived Tasks to review cleared history. When approaching context limits but needing to preserve specific context, archive only unrelated tasks while keeping relevant history intact.

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Modifying Engines

You can change engines without completely reinitializing your agent:

1. Right-click the agent → Edit Agent
2. Choose new engines for main and secondary roles
3. Optionally clear task history, update the agent’s specialty, or modify credentials

Full resets: If you notice performance issues, consider changing engines and clearing context simultaneously for a fresh start. Some inference providers or LLM families excel at specific task types.

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Best Practices

Match engines to your workload—use higher-context models for complex, file-heavy operations. Monitor token usage to optimize cost versus performance, and test different main/secondary pairings for specific use cases. Archive proactively rather than waiting for context limits. Clear irrelevant history regularly. Document which engine combinations work best for different task types, and balance model power against operational expenses based on task criticality.

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Choosing Engines

Consider these factors: Task complexity: More complex operations benefit from higher-capability main engines. File sizes: Large codebases or config files need substantial context windows. Accuracy requirements: Critical operations warrant premium models despite higher costs. Provider ecosystem: Some tasks benefit from specific provider capabilities like function calling implementations.