LangChain

PulseNeko5/20/26Less than 1 minute

Wiring LangChain to Bridge is one line: change the LLM instance's base_url.

langchain-openai

from langchain_openai import ChatOpenAI

llm = ChatOpenAI(
    model="gpt-5.1",
    base_url="https://bridge.pulseneko.com/v1",
    api_key="sk-your-key",
)

print(llm.invoke("Hello").content)

Env var route:

export OPENAI_API_KEY=sk-your-key
export OPENAI_BASE_URL=https://bridge.pulseneko.com/v1

then drop api_key / base_url from constructors.

langchain-anthropic

from langchain_anthropic import ChatAnthropic

llm = ChatAnthropic(
    model="claude-opus-4-7",
    base_url="https://bridge.pulseneko.com",
    api_key="sk-your-key",
    max_tokens=1024,
)

print(llm.invoke("Hello").content)

LangGraph

Use any LLM instance above. Multiple agent steps all consume the same Bridge key.

Embeddings

from langchain_openai import OpenAIEmbeddings

emb = OpenAIEmbeddings(
    model="text-embedding-3-large",
    base_url="https://bridge.pulseneko.com/v1",
    api_key="sk-your-key",
)

LlamaIndex

from llama_index.llms.openai import OpenAI

llm = OpenAI(
    model="gpt-5.1",
    api_base="https://bridge.pulseneko.com/v1",
    api_key="sk-your-key",
)

LlamaIndex uses api_base (not base_url) — easy to mis-type.

Vercel AI SDK

import { createOpenAI } from "@ai-sdk/openai";

const bridge = createOpenAI({
  baseURL: "https://bridge.pulseneko.com/v1",
  apiKey: process.env.PULSENEKO_KEY,
});

const result = await generateText({
  model: bridge("gpt-5.1"),
  prompt: "Hello",
});