Use case
AI agent API gateway for task-based model routing
AI agents often need more than one model: a cheaper model for planning, a stronger model for hard reasoning, an embedding model for retrieval and a fallback when upstream calls fail. PEKPIK LLM gives agent builders one OpenAI-compatible gateway for those choices.
Why teams search for this
Where PEKPIK fits
Good fit
- OKAgents with clear subtasks such as planning, retrieval, coding and final response.
- OKTeams that evaluate model quality per step rather than per product.
- OKWorkflows that need fallback when a provider slows down or errors.
Check first
- !Agent reliability depends on tool design, memory and evaluation, not only model choice.
- !Silent model substitution should be avoided for high-risk actions.
- !Test fallbacks before exposing autonomous actions to users.
OpenAI-compatible example
base_url swapfrom openai import OpenAI
client = OpenAI(
base_url="https://aiapiv2.pekpik.com/v1",
api_key="sk-...",
)
response = client.chat.completions.create(
model="claude-opus-4-7",
messages=[{"role": "user", "content": "Summarize this for a product team."}],
) Suggested rollout
- 01
Map each agent step to a quality and latency requirement.
- 02
Pick candidate models for planning, retrieval and final generation.
- 03
Evaluate end-to-end task success, not just single responses.
- 04
Roll out fallback behavior with logging and human review for risky tasks.
FAQ
Should every agent step use the strongest model?
Usually no. Many agent steps can use cheaper or specialized models if the end-to-end task success remains high.
Why use a gateway for agents?
A gateway keeps model access and fallback manageable when agents use several model families across different steps.