Theo vs. Alternatives

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The Developer’s Choice

1. Direct model APIs — Call a single provider directly
2. Model proxy — Use a unified gateway for multi-model access
3. Framework — Build your own pipeline with LangChain / LangGraph
4. Orchestration API — Use Theo for classification, routing, skills, tools, and billing in one call

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Comparison

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Code Comparison

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DIY Approach — You handle everything

const model = pickModelForTask(prompt);          // you build this
const skills = loadDomainKnowledge(userContext);  // you build this
const tools = resolveToolDefinitions(skills);     // you build this
const systemPrompt = buildPrompt(persona, skills, tools); // you build this
const response = await llmProvider.complete({ model, messages, tools });
const toolResults = await executeTools(response.tool_calls); // you build this
const finalResponse = await llmProvider.complete({ model, messages: [..., toolResults] });
await debitCredits(user, response.usage);        // you build this
await logAudit(user, response);                  // you build this

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Theo — One call

import { Theo } from "@hitheo/sdk";

const theo = new Theo({ apiKey: "theo_sk_..." });
const response = await theo.complete({
  prompt: "Check inventory across all warehouses",
  skills: ["inventory-check"],
  persona: { system_prompt: "You are Nova..." },
});

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When to Use What

• You want AI features without building orchestration infrastructure
• You need multi-model routing with automatic failover
• You want domain expertise via installable skills
• You’re building an E.V.I. (embedded AI with custom persona)
• You need built-in billing, caching, and audit

• You just need model access with unified billing
• You’re building your own orchestration layer

• You need total control over every pipeline decision
• Your use case requires custom agent architectures
• You have the engineering team to build and maintain the infrastructure

• You only need one model from one provider
• You have specific provider requirements (SLA, data residency)