How to Build an Integration Between Gate.AI and LlamaIndex

Gate.AI LlamaIndex Integration Guide

Gate.AI provides an OpenAI-compatible API that lets LlamaIndex applications call Gate.AI models by using a custom API base URL and API key. This allows developers to route LLM requests into index and query workflows. Per the Gate.AI documentation, as of June 2026, the OpenAI-compatible base URL is /openai/v1. LlamaIndex’s OpenAILike wrapper is designed specifically for third-party OpenAI-compatible APIs. This guide covers a Python-based gate.ai + LlamaIndex integration. It does not cover selecting embedding models for production, deploying a vector database, or designing enterprise access control.

Prerequisites

• You have a Gate.AI API key, and your account has sufficient balance.
• Your local development environment has Python 3.10 or later installed.

For more background on API setup, refer to Gate.AI API Integration Guide.

What you’ll be able to do after finishing this guide

After completing the gate.ai + LlamaIndex integration, you can use LlamaIndex to call a Gate.AI model. You’ll verify the connection with a direct chat request, and run a simple query engine smoke test.

This guide mainly covers the OpenAI-compatible LlamaIndex LLM setup. It does not cover production embedding models, the document import process, or configuration for hosted vector storage.

Step 1: Create a Gate.AI API key

This step prepares the credentials needed to authenticate Gate.AI requests from LlamaIndex.

• Open Gate.AI, go to Dashboard → Settings → API Keys, create an API key, and copy it into a secure local secret store. Gate.AI’s documentation shows API keys starting with sk-or-v1- in the settings examples (as of June 2026).
• Do not hardcode the API key in application code. Using environment variables is recommended.

Step 2: Install the LlamaIndex-related packages

This step installs LlamaIndex and the OpenAI-compatible LLM wrapper package needed for the gate.ai + LlamaIndex integration.

python -m venv .venv
source .venv/bin/activate
pip install llama-index llama-index-llms-openai-like

The llama-index-llms-openai-like package is released specifically for OpenAI-compatible APIs. It provides the OpenAILike integration used below.

Step 3: Export the Gate.AI environment variables

This step stores the API key and model identifier outside of your Python files.

export GATEAI_API_KEY="YOUR_API_KEY"
export GATEAI_MODEL_ID="YOUR_MODEL_ID"

Replace YOUR_API_KEY with your Gate.AI API key, and replace YOUR_MODEL_ID with a model ID copied from the Gate.AI model list or marketplace. For first testing, it’s best to use a fixed model ID to make errors easier to diagnose.

Step 4: Configure the OpenAILike LLM

This step tells LlamaIndex to send LLM calls to Gate.AI’s OpenAI-compatible endpoint instead of the default OpenAI API.

import os
from llama_index.core.llms import ChatMessage
from llama_index.llms.openai_like import OpenAILike
gateai_api_key = os.environ["GATEAI_API_KEY"]
gateai_model_id = os.environ["GATEAI_MODEL_ID"]
llm = OpenAILike(
    model=gateai_model_id,
    api_base="https://api.gate.ai/openai/v1",
    api_key=gateai_api_key,
    context_window=3900,
    max_tokens=512,
    is_chat_model=True,
    is_function_calling_model=False,
)
response = llm.chat([
    ChatMessage(
        role="user",
        content="Reply with one sentence: Gate.AI is connected to LlamaIndex."
    )
])
print(response.message.content)

You should see a short response from the model, confirming that Gate.AI is connected to LlamaIndex. If the request returns 401, check your API key. If it returns 404, make sure the base URL is exactly https://api.gate.ai/openai/v1, not https://api.gate.ai/v1.

Step 5: Connect Gate.AI to the LlamaIndex query engine

This step verifies that LlamaIndex can use the configured Gate.AI LLM in a basic query workflow.

import os
from llama_index.core import Document, Settings, VectorStoreIndex
from llama_index.core import MockEmbedding
from llama_index.llms.openai_like import OpenAILike
llm = OpenAILike(
    model=os.environ["GATEAI_MODEL_ID"],
    api_base="https://api.gate.ai/openai/v1",
    api_key=os.environ["GATEAI_API_KEY"],
    context_window=3900,
    max_tokens=512,
    is_chat_model=True,
    is_function_calling_model=False,
)
Settings.llm = llm
# Only for smoke testing. Replace MockEmbedding with a production embedding model for real RAG.
Settings.embed_model = MockEmbedding(embed_dim=384)
documents = [
    Document(
        text="Gate.AI is configured as the LLM provider for this LlamaIndex test."
    )
]
index = VectorStoreIndex.from_documents(documents)
query_engine = index.as_query_engine()
answer = query_engine.query("Which LLM provider is configured?")
print(answer)

LlamaIndex documentation shows that MockEmbedding is a configurable embedding object. This guide uses MockEmbedding only to avoid adding production embedding dependencies during smoke tests. For real Retrieval-Augmented Generation (RAG) scenarios, use a production embedding model instead, and ensure the embedding model fits your data, cost, and security requirements.

What do these configuration options control?

The most important configuration option is api_base. Gate.AI documentation states that the OpenAI-compatible API path must use /openai/v1. A common mistake is using only /v1 and omitting the /openai prefix.

Is your Gate.AI + LlamaIndex integration not working? Troubleshooting checklist

• Symptom: The request returns 401 or authentication fails.
  • Cause: Missing, expired, incorrectly copied API key, or you didn’t export it in the current shell.
  • Fix: Re-export GATEAI_API_KEY, restart your terminal session if needed, and confirm the key in Gate.AI Dashboard → Settings → API Keys.
• Symptom: The request returns 404.
  • Cause: Wrong base URL. Common cases are https://api.gate.ai/v1 or using a full endpoint path instead of the base URL.
  • Fix: Set api_base to https://api.gate.ai/openai/v1.
• Symptom: LlamaIndex throws an unknown model error.
  • Cause: Your app may use the standard OpenAI wrapper, so it validates only OpenAI model names instead of third-party model IDs.
  • Fix: Import OpenAILike from llama_index.llms.openai_like, and pass the Gate.AI model ID via model.
• Symptom: The direct LLM test works, but the query engine test fails during the indexing phase.
  • Cause: The query engine needs an embedding model to build a vector index.
  • Fix: Use MockEmbedding for smoke tests. For real RAG flows, configure a production embedding model.
• Symptom: The response is empty, or the model isn’t available.
  • Cause: The selected model ID isn’t enabled for your Gate.AI account, or routing isn’t configured.
  • Fix: Copy the current model ID from Gate.AI, confirm your account balance, and retry with a short prompt.

What can you configure or build next?

Once your local gate.ai + LlamaIndex integration passes testing, you can reuse the same Gate.AI API setup to connect development tools and related frameworks:

• If you want editor AI coding assistance, use Gate.AI in Cursor.
• If you need terminal coding workflows with Anthropic-compatible settings, use Gate.AI with Claude Code.
• If you’re comparing agent or graph-based workflow orchestration frameworks, use Gate.AI with LangChain and LangGraph.

The next step for a production-grade LlamaIndex app is usually to replace MockEmbedding, add document loading, and choose a vector store that matches your retrieval needs.

FAQ

Can I use the standard LlamaIndex OpenAI wrapper to connect Gate.AI?
For the first gate.ai + LlamaIndex integration, it’s recommended to use OpenAILike, which is built for third-party OpenAI-compatible APIs. The standard OpenAI wrapper may work in some cases, but model-name validation can cause unnecessary configuration errors.

Can I use auto for the model parameters?
For first testing, use a fixed Gate.AI model ID to help diagnose connection issues. Automatic routing should only be used after you confirm that your Gate.AI account and workflow support it.

Does LlamaIndex require an embedding model?
Direct LLM calls don’t require an embedding model. Vector-based RAG workflows typically need an embedding model. In this example, MockEmbedding is used only to focus on smoke testing the Gate.AI LLM connection.

Which base URL should I use for Gate.AI in my LlamaIndex app?
Use https://api.gate.ai/openai/v1 for api_base. Do not use https://api.gate.ai/v1, and do not append /chat/completions to api_base.