LLM failover for AI agents
The single biggest reason agents fail in production isn't a dumb model — it's a missing model. A provider rate-limits mid-loop, or returns an empty tool turn, and the whole task aborts. LLM failover is the fix: reroute to a healthy provider within the same request, and recover from empty tool turns, so the loop finishes. Here's why agents need it more than anything else, and how to add it without writing retry code.
Why agents are uniquely exposed
Failure probability compounds with call count. If one call has a 1% chance of hitting a rate limit, a 50-call agent task has a ~40% chance of hitting at least one. A chatbot rarely notices; an agent aborts the task. Two failure modes dominate:
- Provider rate limits. Burst traffic from a single run trips a per-minute cap mid-task.
- Empty tool-synthesis turns. Some reasoning models return blank content on the turn where they should fold a tool result into a reply — the loop stalls with no error to retry.
What real failover looks like
| Level | Behavior |
|---|---|
| None | Provider error is returned to your agent; the task aborts. |
| Retry same model | Waits out the rate limit — slow, and useless if the provider is down. |
| Multi-provider failover | Reroutes to a healthy provider in the same request; the call still succeeds. |
| + Tool-call repair | Detects empty tool turns and retries with a corrected format — recovers the stall, not just the error. |
Add it without writing retry code
You can build failover yourself — health checks, provider rotation, backoff — or point your agent at a router that does it. With CodeBurst, every model name is already a multi-provider chain with tool-call repair:
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", # multi-provider chain + tool-call repair
messages=[...],
tools=[...],
)No retry loop, no provider list, no backoff logic in your agent — the failover lives behind the model name. Health probes keep the chain fresh, so a degraded provider is dropped before it reaches you.
Get started
Get an API key Best LLM for AI agents →FAQ
What is LLM failover?
Automatically rerouting to another model/provider when the first fails — ideally within the same request so the agent never sees the error.
Why do agents need it more than chatbots?
Many calls per task compound failure probability; one failure mid-loop can abort the whole task.
How do I add it?
Point your OpenAI-compatible client at CodeBurst and use codeburst-agent — failover and tool-call repair are built in.