LangChain

LangChain connects to RedPill through its OpenAI integration. Use the Chat Completions base URL and an exact model ID from the live catalog.

export REDPILL_AI_API_KEY="YOUR_API_KEY"
pip install -U langchain langchain-openai
npm install @langchain/openai @langchain/core

Send a message

import os

from langchain_openai import ChatOpenAI

model = ChatOpenAI(
    model="z-ai/glm-5.2",
    base_url="https://api.redpill.ai/v1",
    api_key=os.environ["REDPILL_AI_API_KEY"],
)
response = model.invoke("Explain attestation in one sentence.")
print(response.content)

import { ChatOpenAI } from "@langchain/openai";

const apiKey = process.env.REDPILL_AI_API_KEY;
if (!apiKey) throw new Error("REDPILL_AI_API_KEY is required");

const model = new ChatOpenAI({
  model: "z-ai/glm-5.2",
  apiKey,
  configuration: { baseURL: "https://api.redpill.ai/v1" },
});
const response = await model.invoke("Explain attestation in one sentence.");
console.log(response.content);

Use model.stream(...) instead of model.invoke(...) to render tokens as they arrive.

Build an agent

LangChain’s create_agent runtime is built on LangGraph. Bound each run and validate tool input:

from langchain.agents import create_agent
from langchain.tools import tool


@tool
def lookup_service(name: str) -> str:
    """Return the owner of an internal service."""
    owners = {"gateway": "inference", "dashboard": "product"}
    return owners.get(name, "unknown")


agent = create_agent(
    model=model,
    tools=[lookup_service],
    system_prompt="Use tools when a service owner is requested.",
)
result = agent.invoke(
    {"messages": [{"role": "user", "content": "Who owns the gateway service?"}]},
    config={"recursion_limit": 4},
)
print(result["messages"][-1].content)

Choose a model whose supported_features includes tools.

Add retrieval

Query the embedding catalog, then configure OpenAIEmbeddings with a current model:

from langchain_core.vectorstores import InMemoryVectorStore
from langchain_openai import OpenAIEmbeddings

embeddings = OpenAIEmbeddings(
    model="qwen/qwen3-embedding-8b",
    base_url="https://api.redpill.ai/v1",
    api_key=os.environ["REDPILL_AI_API_KEY"],
)
store = InMemoryVectorStore.from_texts(
    ["Receipts bind a request and response to the serving path."],
    embedding=embeddings,
)
print(store.similarity_search("What does a receipt prove?", k=1)[0].page_content)

Verification boundary

This base-URL integration does not verify ACI locally. TypeScript applications that require an attested, TLS-bound connection can inject connectAci().fetch through LangChain’s configuration hook and audit the retained receipt, using the same pattern as the OpenAI SDK.

Get started

Use in agents

Build with the API

Features

How it works

Verify and control

Providers and trust

RedPill Chat

Send a message

Build an agent

Add retrieval

Verification boundary

​Send a message

​Build an agent

​Add retrieval

​Verification boundary

​Related

Send a message

Build an agent

Add retrieval

Verification boundary

Related