langchain-failover 0.1.0

Primary/secondary failover wrapper for LangChain chat models, with tool-calling preserved across failover.

langchain-failover

A tiny, dependency-light primary/secondary failover wrapper for LangChain chat models. Point it at two chat models; it serves from the primary, transparently falls back to the secondary on connection errors, and switches back the moment the primary recovers — and tool-calling keeps working across the failover.

from langchain_openai import ChatOpenAI
from langchain_failover import FailoverChatModel

primary = ChatOpenAI(base_url="http://gpu-box:8001/v1", api_key="x", model="local")
backup  = ChatOpenAI(base_url="http://cpu-box:8002/v1", api_key="x", model="local")

llm = FailoverChatModel(primary=primary, secondary=backup)

llm.invoke("Summarise this incident…")   # served by primary
# …primary host dies…
llm.invoke("And the next one?")           # transparently served by backup
# …primary comes back…
llm.invoke("One more")                     # back on primary, logged as recovered

Install

pip install langchain-failover            # core
pip install "langchain-failover[openai]"  # + langchain-openai for create_failover_llm

Why not RunnableWithFallbacks / .with_fallbacks()?

LangChain ships per-invocation fallbacks, and they're great for what they do. This package exists for the cases they don't cover well:

• Stateful recovery. FailoverChatModel remembers which leg it's on and logs the transition both ways (active property tells you). .with_fallbacks() is stateless — every call re-tries the (possibly still-dead) primary first.
• Tool-calling survives failover. bind_tools is overridden to bind on both legs and return another FailoverChatModel. With strict langchain-core (>=1.4, where BaseChatModel.bind_tools raises by default) naïve wrappers break at bind time; agents using this one keep working.
• Connection-aware, not blanket. It only fails over on connection/network errors (walking the exception's __cause__/__context__ chain, so a socket error wrapped three layers deep still counts). A ValueError from a bad prompt propagates instead of being silently retried on a second endpoint.
• Mid-stream safety. During stream(), it only fails over if the primary dies before the first token — so you never get duplicated, half-streamed output.

Local-model convenience

If you run local OpenAI-compatible servers (vLLM, mlx-lm, Ollama, LM Studio) and don't want to hardcode model names, create_failover_llm auto-discovers the served model id from each endpoint's /models:

from langchain_failover import create_failover_llm

llm = create_failover_llm(
    primary_url="http://localhost:8001/v1",
    secondary_url="http://localhost:8002/v1",
)

Bonus helper

extract_token_metrics(response.response_metadata) normalises token counts and timings across OpenAI-compatible and Ollama metadata shapes into a single {input_tokens, output_tokens, prompt_time, generation_time} dict.

License

MIT