The best LLM for LangChain
LangChain is where most people build their first real LLM app — chains, agents, RAG, tools, all wired together. The more of that you compose, the more model calls a single request fans out into, and the more a provider rate-limit or empty tool turn anywhere in the graph can take the whole thing down. CodeBurst gives LangChain one model endpoint that fails over across providers and repairs empty tool turns, so every chain and agent in your app holds up.
Composition multiplies calls — and failure points
A simple chain makes one call. An agent executor with tools, a retrieval step, and a few sub-chains makes many — and each is a chance to hit a provider's rate limit or an empty tool-synthesis turn. As your LangChain app grows from a demo into something real, the backend's reliability becomes the ceiling on yours.
What CodeBurst adds
| Failure | CodeBurst |
|---|---|
| A chain/agent call rate-limits | Reroutes to a healthy provider in the same request; the call returns. |
| Empty tool-synthesis turn in an agent | codeburst-agent retries with a corrected format. |
| A critical step needs more rigor | Point that step's model at codeburst-swarm for a multi-model vote. |
Configure the model once
Point ChatOpenAI at CodeBurst and reuse it everywhere:
from langchain_openai import ChatOpenAI
llm = ChatOpenAI(
model="codeburst-agent",
base_url="https://codeburst.ai/api/v1",
api_key="YOUR_CODEBURST_KEY",
)
# use `llm` in chains, agent executors, and tools — the same instance everywhere.Your chains, prompts, tools and retrievers are untouched. Building a stateful graph? The same model works in LangGraph nodes.
Get started
Get an API key How agent failover works →FAQ
How do I use CodeBurst with LangChain?ChatOpenAI(model="codeburst-agent", base_url="https://codeburst.ai/api/v1", api_key=...), reused across chains and agents.
Why route through a router?
Composed apps make many calls; CodeBurst fails over and repairs tool turns so they don't stall.
Works with LangGraph?
Yes — same ChatOpenAI config.