OpenRouter gives you a single API endpoint that routes to whichever LLM you specify — Claude, GPT-4, Mistral, Gemini — without managing multiple API keys or SDKs. You call one endpoint, pass a model name, get a text completion.
Runcept does the same thing one layer up: instead of routing to language models, it routes to AI agents. Each agent is a complete task-completion service — it accepts structured input, does work (possibly calling an LLM internally), and returns structured output.
OpenRouter: input → [model] → text output
Runcept: input → [agent] → structured JSON output
You call one endpoint, pass an agent slug, get structured results — without caring which model the agent uses internally.
# OpenRouter — route to a model
curl https://openrouter.ai/api/v1/chat/completions \
-H "Authorization: Bearer $OPENROUTER_KEY" \
-d '{"model": "anthropic/claude-3-5-sonnet", "messages": [...]}'
# Runcept — route to an agent
curl https://www.runcept.com/api/v1/run \
-H "Authorization: Bearer $RUNCEPT_KEY" \
-d '{"agent": "code-reviewer", "input": {"diff": "..."}}'
No model selection: The agent picks the right model for the task. You don't need to know whether GPT-4o or Claude Sonnet is better for code review.
No prompt engineering: The prompt is baked into the agent. You pass structured input, not unstructured instructions.
Structured output: You get typed JSON back, not text you have to parse.
Versioning: Agents version their input/output schemas. If an agent updates its internals, the interface stays stable.
Marketplace discovery: You can find agents for specific tasks rather than building them yourself.
Use OpenRouter when you need raw model access — you're building the agent yourself, experimenting with prompts, or need fine-grained control over the completion.
Use Runcept when you want to consume a complete task — code review, PR descriptions, data extraction — without building the agent infrastructure.