Use case

LLM API for AI agents that use multiple model roles

Agent systems often split work across model roles: planning, tool selection, code generation, retrieval and final response. A single-provider integration can make those choices rigid. PEKPIK LLM gives agent teams one OpenAI-compatible gateway for comparing and routing supported models by step.

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Primary query

LLM API for AI agents

Why teams search for this

Use different model families for planning, tool use and final answers.

Compare task success across model choices without rewriting provider code.

Keep fallback behavior explicit for failed calls or weak intermediate outputs.

Control cost by routing simpler agent steps to cheaper candidate models.

Where PEKPIK fits

Good fit

OKAgents with multiple steps, tools or retrieval calls.
OKTeams evaluating model quality by end-to-end task success.
OKProducts that need fallback and cost control before scaling agent usage.

Check first

!Tool behavior and JSON formatting should be tested model by model.
!Autonomous workflows need guardrails beyond model routing.
!Fallback should be visible in logs for debugging and quality review.

OpenAI-compatible example

base_url swap

from 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

List each agent step and its quality, latency and cost requirements.
02

Pick candidate models for planning, tool selection and final response.
03

Score end-to-end task success instead of isolated chat quality only.
04

Roll out with logging for tool calls, fallback events and user-visible failures.

FAQ

Should an AI agent use one model for every step?

Not always. Many agents benefit from smaller models for routine steps and stronger models for reasoning-heavy or user-facing steps.

Can PEKPIK guarantee agent reliability?

No. PEKPIK provides model access and routing options. Agent reliability still depends on tools, prompts, evaluations and guardrails.