What makes a good tool-calling LLM API?

It must reliably emit function calls AND consume tool results back into an answer across many turns, without returning empty. The OpenAI tools format is standard; the hard part is multi-turn reliability and surviving provider rate limits, which is where failover and tool-call repair matter.

Why does function calling return empty sometimes?

Some reasoning models return empty content on the turn where they should synthesize a tool result into a reply. CodeBurst detects this empty tool-synthesis response and retries with a corrected format so you get a usable answer.

Is CodeBurst's tool-calling API OpenAI-compatible?

Yes. It uses the standard /v1/chat/completions endpoint with the tools parameter, so existing function-calling code works by changing the base URL and key.

Guide · Updated June 2026

The best tool-calling LLM API in 2026

Emitting one function call is easy — every model demos it. The hard part is the second half of tool calling: taking the tool's result and folding it back into a coherent answer, turn after turn, without dropping the ball. That's where most APIs quietly fail. CodeBurst is an OpenAI-compatible tool-calling API built for the whole loop — with automatic failover and tool-call repair.

Tool calling is two halves — and the second one breaks

A tool-calling round trip has two model turns: (1) decide which function to call and with what arguments, then (2) read the function's result and synthesize a reply. Most "does it support tools?" checks only test half one. Half two is where things go wrong in production:

Empty synthesis turns. A number of strong reasoning models return empty content on turn two. Your code gets a blank message and the conversation dies — even though the model is capable.
Rate limits between turns. Multi-tool workflows fire many calls quickly and trip a provider's per-minute cap mid-sequence.

What CodeBurst does differently

Failure	CodeBurst
Empty content on the tool-synthesis turn	Detects it and retries with a corrected format, returning a real answer instead of a blank.
Provider rate-limits mid-sequence	Reroutes to a healthy provider in the same request — the call still succeeds.
Hard step needs more reliability	Escalate to `codeburst-swarm` for a multi-model vote.

Standard OpenAI tools format

from openai import OpenAI

client = OpenAI(base_url="https://codeburst.ai/api/v1", api_key="YOUR_CODEBURST_KEY")

resp = client.chat.completions.create(
    model="codeburst-agent",
    messages=[{"role":"user","content":"What's the weather in Bali?"}],
    tools=[{
      "type":"function",
      "function":{
        "name":"get_weather",
        "description":"Get current weather",
        "parameters":{"type":"object","properties":{"city":{"type":"string"}},"required":["city"]}
      }
    }],
)

Your tool schema is unchanged — it's the same format you already use. CodeBurst just makes the full round trip reliable.

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FAQ

What's the best tool-calling LLM API?
One that handles the full round trip reliably — emit and synthesize — across many turns. CodeBurst adds failover and tool-call repair on top of the standard format.

Why does function calling return empty?
Some reasoning models go blank on the synthesis turn; CodeBurst retries with a corrected format.

Is it OpenAI-compatible?
Yes — standard tools parameter on /v1/chat/completions.