Integration guideOpenAI-compatible

Use a model with LangChain through IteraCompute.

Connect models to chains and agents.

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Connect in three steps

1. Create an API key

Create or sign in to your IteraCompute account, then create a key in the Console.

2. Configure the base URL

Configure ChatOpenAI with base_url. Use the Chat Completions path rather than Responses API.

3. Select the model

Use the exact API Model ID for your chosen model. Check its context and capabilities before enabling agent or vision features.

Choose your model

Model used in the examples

Example configuration

Keep API keys in your server environment or tool credential store. Replace the example key field with your own key where applicable.

Python · pip install langchain-openai

import os
from langchain_openai import ChatOpenAI

llm = ChatOpenAI(
    model="qwen/qwen3.8-27b",
    base_url="https://api.iteracompute.com/v1",
    api_key=os.environ["ITERACOMPUTE_API_KEY"],
    use_responses_api=False,
    stream_usage=False,
    max_tokens=512,
    extra_body={"chat_template_kwargs": {"enable_thinking": False}},
)
print(llm.invoke("Hello!").content)

Version and verification

Configuration reference · reviewed 2026-10-15. A reproducible end-to-end tool test has not been recorded here. Record your installed version and test date when running the steps below.

Success criterion

Run llm.invoke and check non-empty AIMessage.content. Test tool execution separately from this text example.

Record the installed version

python -m pip show langchain-openai

Test the configured tool itself after the API check below. An HTTP 200 or a cURL result alone does not verify that tool.

Test the connection

cURL · Chat Completions

# Bash / zsh / WSL; set ITERACOMPUTE_API_KEY in your environment first.
curl --fail-with-body https://api.iteracompute.com/v1/chat/completions \
  -H "Authorization: Bearer $ITERACOMPUTE_API_KEY" \
  -H "Content-Type: application/json" \
  -d '{"model":"qwen/qwen3.8-27b","messages":[{"role":"user","content":"Reply with exactly: Hello!"}],"max_tokens":512}'

Check that choices[0].message.content contains visible text; HTTP 200 alone is insufficient. Reasoning may consume the output budget. For free Qwen, use the first free call example to disable thinking, then send a prompt inside your tool.

Selected model capabilities

These are API capabilities of the selected model. Feature availability in your workflow also depends on the tool and its settings; reasoning controls vary by model.

Chat CompletionsSupported
Tool callingSupported
ReasoningSupported
Image inputSupported
StreamingSupported

Troubleshooting

Wrong package or endpoint

Import ChatOpenAI from langchain_openai. Keep base_url ending in /v1 and use_responses_api=False for this Chat Completions recipe.

Streaming usage errors

This compatible-endpoint example sets stream_usage=False. Do not assume every OpenAI-specific streaming option is accepted by the selected model.

Agent does not run tools

A text invoke only tests text generation. Bind tools separately, inspect the returned tool calls and execute a bounded test tool before claiming agent support.

429 / Request limits

Check account-wide monthly usage and TPM. Honor Retry-After and avoid parallel retries. Free Qwen has no automatic paid overage; other models use their published prices.

401 / 403: check the key and model permission. Free Qwen requires no balance or card. For paid models, also check available balance.

Keep exploring

LlamaIndex

Power retrieval and agent workflows.

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