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Theo is not a model proxy. Every API call passes through a multi-stage pipeline that handles classification, skill injection, model selection, tool execution, and response formatting.

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The Five Stages

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1. Intent Classification

Determines the task type (fast, think, code, image, research, roast, etc.) using a multi-tier classification system. Skipped if you set mode explicitly.

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2. Skill Loading

Loads domain expertise from two sources: the user’s installed skills (persistent) and per-request skills (ephemeral skills[] array). Each skill injects system prompt extensions and tool definitions.

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3. Engine Routing

Selects the optimal Theo engine based on the resolved mode. If the primary engine is unavailable, failover routes to the backup automatically.

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4. Agent Loop

For requests with tools, Theo enters an iterative loop: the engine reasons, calls a tool, observes the result, and continues until done. For simple prompts with no tools, this collapses to a single call.

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5. Response

The final output is formatted with content, engine metadata, tool execution log, artifacts, follow-up suggestions, and usage data. The request is billed, cached, and audited.

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Performance

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Pipeline Diagram

See How Theo Thinks for the complete stage-by-stage breakdown with examples.