Test your n4n.ai LangChain setup with a simple prompt chain

You want to test your LangChain n4n.ai setup before building production workflows. The fastest way to confirm everything works — authentication, routing, model availability, and streaming — is a minimal prompt chain that exercises the full request path. This walkthrough takes you from a fresh environment to a verified chain in about ten minutes.

Step 1: Confirm prerequisites

You need Python 3.10 or newer and a valid n4n.ai API key. If you don’t have a key yet, generate one in the dashboard and keep it handy. You’ll also want pip and venv available.

python3 --version
# Python 3.10.12 (or newer)

Create an isolated environment so this test doesn’t pollute your global packages:

python3 -m venv .venv
source .venv/bin/activate
pip install --upgrade pip

Step 2: Install the required packages

LangChain’s OpenAI-compatible client works with n4n.ai because the gateway speaks the OpenAI API specification. Install the LangChain OpenAI integration and the core package:

pip install langchain-openai langchain-core

If you prefer the community package that bundles common integrations, you can use langchain-community instead, but the two packages above are sufficient for this test.

Step 3: Configure environment variables

Store your API key and base URL in the environment rather than hardcoding them. The base URL for n4n.ai is https://api.n4n.ai/v1.

export N4N_API_KEY="sk-your-key-here"
export N4N_BASE_URL="https://api.n4n.ai/v1"

Verify the variables are set:

echo $N4N_API_KEY
echo $N4N_BASE_URL

Step 4: Create a minimal test script

Write a file named test_chain.py that builds a two-step prompt chain: the first step summarizes a short text, and the second step rewrites that summary as a tweet. This exercises chat completion, prompt templating, and chaining — the core primitives you’ll use in production.

import os
from langchain_openai import ChatOpenAI
from langchain_core.prompts import ChatPromptTemplate
from langchain_core.output_parsers import StrOutputParser

# Initialize the client pointed at n4n.ai
llm = ChatOpenAI(
    model="gpt-4o-mini",          # any model available on the gateway
    api_key=os.getenv("N4N_API_KEY"),
    base_url=os.getenv("N4N_BASE_URL"),
    temperature=0.2,
    max_tokens=256,
)

# Step 1: Summarize the input text in one sentence
summarize_prompt = ChatPromptTemplate.from_template(
    "Summarize the following text in a single sentence:\n\n{text}"
)
summarize_chain = summarize_prompt | llm | StrOutputParser()

# Step 2: Rewrite the summary as a tweet (under 280 chars, with hashtags)
tweet_prompt = ChatPromptTemplate.from_template(
    "Rewrite this summary as an engaging tweet under 280 characters. "
    "Include 2-3 relevant hashtags:\n\n{summary}"
)
tweet_chain = tweet_prompt | llm | StrOutputParser()

# Compose the full chain: text -> summary -> tweet
full_chain = {"summary": summarize_chain} | tweet_chain

# Test input
sample_text = (
    "LangChain is a framework for developing applications powered by language models. "
    "It provides modular components for prompt management, memory, agents, and "
    "integration with external data sources. Developers use it to build chatbots, "
    "document analysis tools, and autonomous agents."
)

if __name__ == "__main__":
    print("Running prompt chain test...\n")
    result = full_chain.invoke({"text": sample_text})
    print("=== FINAL OUTPUT ===")
    print(result)
    print("\n=== SUCCESS ===")
    print("Chain executed without errors.")

Step 5: Run the test and verify output

Execute the script:

python test_chain.py

Expected output shape (your exact wording will differ):

Running prompt chain test...

=== FINAL OUTPUT ===
LangChain lets you build LLM apps with modular components for prompts, memory, agents & data integrations. 🔗🤖 #LangChain #LLM #AI

=== SUCCESS ===
Chain executed without errors.

What success looks like

• The script prints a tweet-length string with hashtags.
• No AuthenticationError, RateLimitError, or connection errors appear.
• Latency is reasonable (typically 1–3 seconds per hop on gpt-4o-mini).

If you see the success banner, your test langchain n4n.ai setup is working. The gateway accepted your credentials, routed the request to an available provider, and returned completions for both chain steps.

Step 6: Add streaming verification (optional but recommended)

Streaming confirms that the gateway forwards server-sent events correctly — critical for UX in production. Replace the invoke call with stream and iterate chunks:

if __name__ == "__main__":
    print("Running streaming prompt chain test...\n")
    print("=== STREAMING OUTPUT ===")
    for chunk in full_chain.stream({"text": sample_text}):
        print(chunk, end="", flush=True)
    print("\n\n=== SUCCESS ===")
    print("Streaming chain executed without errors.")

Run it again. You should see the tweet appear token-by-token rather than all at once. If streaming stalls or returns a single block after a long pause, check your network or proxy configuration — some corporate proxies buffer SSE responses.

Step 7: Test model routing and fallback

n4n.ai supports multiple providers per model. To verify routing works, request a model that maps to several upstreams (for example, gpt-4o or claude-3.5-sonnet) and inspect the response headers. The gateway returns provider metadata in the x-provider header when you enable verbose logging.

import httpx
from langchain_openai import ChatOpenAI

# Enable HTTP logging to see routed provider
httpx_logger = httpx.Client(event_hooks={"response": [lambda r: print(f"Provider: {r.headers.get('x-provider', 'unknown')}")]})

llm = ChatOpenAI(
    model="gpt-4o",
    api_key=os.getenv("N4N_API_KEY"),
    base_url=os.getenv("N4N_BASE_URL"),
    http_client=httpx_logger,
    temperature=0.2,
)

chain = ChatPromptTemplate.from_template("Say 'ok' in one word.") | llm | StrOutputParser()
chain.invoke({})

Run this snippet. You should see a line like Provider: openai or Provider: azure in the console, confirming the gateway selected an upstream. If you hit a rate limit on one provider, the gateway automatically fails over — you can simulate this by exhausting a test quota on a specific provider and observing that the chain still completes via a different upstream.

Step 8: Validate usage metering

Per-token metering is built into the gateway. After a successful run, check your dashboard’s usage page. You should see two request entries (one per chain step) with prompt_tokens, completion_tokens, and total_tokens populated. If the dashboard shows zero usage but the script succeeded, verify you’re hitting the correct base URL and that your API key has metering enabled.

Common failure modes and fixes

Extending the test for your stack

Once the minimal chain passes, swap components to match your production design:

• Different models: Change model="gpt-4o-mini" to any model ID from the gateway’s model list (GET /models).
• Structured output: Replace StrOutputParser with JsonOutputParser and a Pydantic schema.
• Memory: Insert RunnableWithMessageHistory between steps to test conversation context.
• Tools/agents: Wrap the LLM with create_tool_calling_agent and verify tool calls route through the gateway.

Each substitution should keep the test passing. If a change breaks the chain, you’ve isolated the integration point that needs debugging — exactly what a smoke test is for.

Clean up

Deactivate the virtual environment when you’re done:

deactivate

You can delete the .venv directory and test_chain.py unless you want to commit the test to your repo as a CI step.

A passing prompt chain tells you the authentication path, model routing, streaming, and metering all work end-to-end. Keep this script in your repository and run it in CI on every deploy — it catches configuration drift before it reaches production.