Route Agent Tool Calls to Fireworks When You Need Speed Without Frontier Cost

Fireworks AI's OpenAI-compatible API serves open models at 4x GPT-4 speed -- swapping it in for non-critical agent tool calls takes two lines: change the base URL and the model name.

Multi-agent systems often fan out the same tool call to dozens of tasks: extract entities from documents, classify a category, parse a structured payload. These don't need frontier intelligence -- they need speed and low cost. Fireworks is built for exactly this routing slot.

The two-line swap

If you already use the OpenAI Python SDK (or any OpenAI-compatible client), pointing it at Fireworks is a drop-in change:

from openai import OpenAI

client = OpenAI(
    api_key="fw_...",                         # your Fireworks API key
    base_url="https://api.fireworks.ai/inference/v1",
)

response = client.chat.completions.create(
    model="accounts/fireworks/models/firefunction-v2",
    messages=[{"role": "user", "content": "Extract the JSON fields from this receipt: ..."}],
    response_format={"type": "json_object"},
)

That's it. The same tools= list, tool_choice=, and response_format= parameters work identically.

What Fireworks is fast at

• FireFunction-v2: structured outputs and function calling at ~4x GPT-4 speed on the Artificial Analysis benchmark -- the go-to model for tool-calling subagents
• Open weights at low latency: Llama 3.3, Qwen 2.5, DeepSeek V3.2, Kimi K2.5 -- all served on dedicated kernels optimized for throughput
• Serverless pricing: pay per token, no reservation; Priority tier for lowest latency, Standard for cost

When to route here vs Claude

The pattern is simple: if the task needs reasoning or synthesis, use Claude. If it's extraction, classification, or structured parsing, route to Fireworks FireFunction-v2. The difference in cost and speed justifies the split even for modest workloads.

def call_agent(task_type: str, prompt: str) -> str:
    if task_type in ("extract", "classify", "parse"):
        return fireworks_client.chat.completions.create(
            model="accounts/fireworks/models/firefunction-v2",
            messages=[{"role": "user", "content": prompt}],
        ).choices[0].message.content
    else:
        return anthropic_client.messages.create(
            model="claude-sonnet-5-20251022",
            max_tokens=1024,
            messages=[{"role": "user", "content": prompt}],
        ).content[0].text

Sources: Fireworks AI inference platform · FireFunction on HuggingFace · Fireworks AI docs · Blazing-fast inference on open models