LangChain integration

Connect LangChain through an OpenAI-compatible TackleKey endpoint

LangChain applications should verify the model name, base URL, key handling, and request logs before moving agents or retrieval chains to larger workloads.

Create account Read docs

Minimal configuration example

from langchain_openai import ChatOpenAI

llm = ChatOpenAI(
    api_key="tk-project-key",
    base_url="https://api.tacklekey.com/v1",
    model="openai/gpt-4o-mini",
    max_tokens=64,
)

print(llm.invoke("Return one health-check sentence.").content)

Configuration checklist

Item	How to verify
Package version	Use a LangChain OpenAI package version that supports a custom base URL.
Model availability	Confirm the model on TackleKey live pricing before running a chain.
Agent limits	Start with one direct chat request before enabling loops or tools.
Observability	Use TackleKey logs to separate prompt errors, key errors, and provider errors.

Next steps

Live pricing

Check input, output, and capability pricing before larger usage.

Examples hub

Copy cURL, Node.js, Python, or client configuration examples.

Troubleshooting

Debug 401, 429, invalid model, base URL, and billing issues.

Try TackleKey with one OpenAI-compatible change

Create an account, generate a project API key, then replace your client base URL with the TackleKey endpoint. Keep keys server-side and verify live pricing before scaling traffic.

Create account Run first request