Learning how to use the Kimi AI API with Python can feel confusing when you are new to APIs, environment variables, and AI models. This complete beginner guide explains how to create an API key, prepare Python, install the required package, send your first prompt, customize the response, and stream output. Kimi uses an OpenAI-compatible API format, so you can make a working request with the familiar OpenAI Python SDK and a small amount of code.
Who This Guide Is For
This guide is designed for beginners, Python learners, automation specialists, and developers who want to add Kimi AI to a script. You should know how to open a terminal and save a Python file, but you do not need previous API experience.
Learn how to use the Kimi AI API with Python, create a secure API key, send your first request, stream responses, and fix common errors. Start coding.
What Is the Kimi AI API?
The Kimi AI API is a developer interface from Moonshot AI that lets software send prompts to Kimi models and receive generated responses. According to the official Kimi API overview, the platform supports text generation, multi-turn conversations, file parsing, web search, and other core capabilities.
The API primarily exposes a Chat Completions interface. You send a model name and a list of messages; the service returns an assistant message. Kimi does not browse the internet or access external databases automatically. Those actions require supported tools or your own integrations.
Kimi is compatible with the OpenAI API format. Python developers can therefore use the OpenAI SDK while changing the API key, base URL, and model name to Kimi values.
The official quickstart currently recommends kimi-k3 for beginners. Kimi K3 has a 1-million-token context window and is intended for coding, knowledge work, and complex reasoning (Kimi API Quickstart).
What You Need Before Starting
Prepare the following:
- Python 3.9 or newer
- A Kimi Open Platform account
- A Kimi API key
- OpenAI Python package version 1.0 or newer
- A terminal, code editor, and available API balance
Kimi Membership, Kimi Code, and the Kimi API Open Platform are separate products, so their keys and balances are not interchangeable. The Kimi K3 documentation says access is unlocked after a successful top-up of at least $1.
How to Use the Kimi AI API With Python Step by Step
Step 1: Create a Kimi API Key
Open the Kimi API Platform, sign in, and visit the API Keys area. Create a new key and copy it immediately to a secure location.
Treat your API key like a password. Do not publish it, upload it to GitHub, or hard-code it inside a Python file. Revoke and replace the key immediately if it becomes exposed.
Your key must come from the same regional platform as the endpoint you use. Kimi’s troubleshooting guide says that a mismatched key and endpoint can return a 401 authentication error.
Step 2: Create a Python Project and Virtual Environment
Create a project folder, move into it, and create a virtual environment:
mkdir kimi-python-guide
cd kimi-python-guide
python3 -m venv .venv
Activate the environment on macOS or Linux:
source .venv/bin/activate
On Windows PowerShell, use:
.venv\Scripts\Activate.ps1
A virtual environment keeps this project’s packages separate from packages installed for other Python projects.
Step 3: Install the OpenAI Python SDK
Install or upgrade the SDK inside your active virtual environment:
python3 -m pip install –upgrade “openai>=1.0”
Kimi does not require a separate beginner-only Python package for this request. Its OpenAI API compatibility guide confirms that developers can use the OpenAI Python SDK by changing the base_url and api_key configuration.
Step 4: Save Your API Key as an Environment Variable
On macOS or Linux, run:
export MOONSHOT_API_KEY=”YOUR_KIMI_API_KEY”
On Windows PowerShell, run:
$env:MOONSHOT_API_KEY=”YOUR_KIMI_API_KEY”
Replace the placeholder with your real Kimi API key. Keep the quotation marks, and never publish the completed command.
This setting applies to your current terminal session. You may need to set the variable again after closing and reopening the terminal.
Want to conduct faster and more reliable research? Read our complete guide on how to use Kimi AI for deep research to find credible sources, verify citations, and create structured research reports.
Step 5: Write Your First Kimi API Python Script
Create a file named app.py and add the following code:
import os
from openai import OpenAI
client = OpenAI(
api_key=os.environ[“MOONSHOT_API_KEY”],
base_url=”https://api.moonshot.ai/v1″,
)
completion = client.chat.completions.create(
model=”kimi-k3″,
messages=[
{
“role”: “system”,
“content”: “You are a helpful assistant. Explain ideas clearly for beginners.”,
},
{
“role”: “user”,
“content”: “Explain what an API is in three short bullet points.”,
},
],
)
print(completion.choices[0].message.content)
The important parts are:
- api_key reads your secret from the environment.
- base_url directs the SDK to Kimi.
- model selects kimi-k3.
- messages contains your instructions and prompt.
- completion.choices[0].message.content contains the final answer.
Kimi K3 always uses thinking mode. Its official documentation supports reasoning_effort values of low, high, and max, with max used by default. Leaving this setting out keeps your first example simple.
Step 6: Run the Python Script
Save the file and run:
python3 app.py
If your configuration is correct, the terminal will display a generated explanation. The exact wording may change between requests because AI model responses are not guaranteed to be identical.
You can now edit the user message and run the file again. State the intended audience, output format, and desired length clearly in each prompt.
For example, you could ask Kimi to:
- Explain a piece of Python code
- Summarize customer feedback
- Create an article outline
- Classify support requests
- Draft structured product information
Step 7: Stream Kimi AI Responses in Python
For longer answers, streaming displays content as it arrives instead of making you wait for the complete response.
Replace the request section with:
stream = client.chat.completions.create(
model=”kimi-k3″,
messages=[
{
“role”: “user”,
“content”: “Explain Python functions to a beginner.”,
}
],
stream=True,
)
for chunk in stream:
delta = chunk.choices[0].delta
if delta.content:
print(delta.content, end=””, flush=True)
print()
Kimi’s streaming documentation explains that streaming responses arrive in separate chunks.
Kimi K3 can also return reasoning content separately. However, a basic application can print only delta.content when it needs the final answer.
How to Customize Your Kimi API Request
The system message defines the assistant’s role and response rules, while the user message provides the current task.
For complex reasoning, add one of the following values to your request:
reasoning_effort=”high”
Or:
reasoning_effort=”max”
Use reasoning_effort=”low” when you want a lighter response. Do not add a thinking parameter because Kimi K3’s thinking mode cannot be disabled.
You can also replace kimi-k3 with another model available to your API key. Kimi recommends kimi-k2.7-code-highspeed for high-speed coding scenarios and kimi-k2.6 as a general-purpose option.
Check the current Kimi model list before changing your code because model availability and supported parameters can differ.
Common Kimi API Errors and Fixes
KeyError: ‘MOONSHOT_API_KEY’
This error means Python cannot find the environment variable. Set your API key in the same terminal session and run the script again.
Also confirm that you are using the exact environment variable name:
MOONSHOT_API_KEY
401 Authentication Error
Confirm that your key came from the Kimi API Open Platform rather than Kimi Code, Kimi Membership, or another Kimi product.
You should also confirm that the key’s regional platform matches your API endpoint.
404 or model_not_found
Check the model name for spelling mistakes and confirm that your account can access it.
Kimi recommends calling GET /v1/models with the same key when diagnosing model-access problems. Older tutorials may contain discontinued model names, so compare your code with the current model list.
429 Rate Limit or Insufficient Quota
Check your available account balance and rate limits.
A request may return a 429 error when the available balance is exhausted or the account exceeds its request or token limits.
Incomplete or Truncated Output
Inspect the request’s finish reason:
print(completion.choices[0].finish_reason)
If the result is length, the response reached the max_completion_tokens limit. Kimi recommends increasing this value appropriately or using its continuation workflow.
Remember that max_completion_tokens limits the maximum number of generated tokens. It does not force Kimi to produce an answer of that exact length.
Important Limitations and Security Notes
The Kimi API is separate from Kimi’s consumer membership. Billing is based on token usage, and prices can vary between models. Review Kimi’s current pricing before estimating your production costs.
Never expose your API key inside browser-side JavaScript or a public code repository. For a web application, keep the key on a secure server and let the frontend communicate with your own backend.
AI-generated responses can contain mistakes. Validate important facts, calculations, code, and business information before using them.
Standard chat requests also do not browse the web automatically. Kimi’s help center states that Deep Research and presentation generation are consumer-product features rather than API endpoints.
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Final Thoughts
The simplest way to use the Kimi AI API with Python is to create an Open Platform key, store it in MOONSHOT_API_KEY, install the OpenAI SDK, set Kimi’s base URL, and call client.chat.completions.create() with kimi-k3.
Start with the basic script before adding streaming, multi-turn conversation history, files, structured output, or tools. This makes authentication and model errors easier to identify.
Once your first response works, you have a strong foundation for creating chatbots, content workflows, internal assistants, and Python automations.
Disclaimer
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Frequently Asked Questions
Can I use the Kimi AI API with Python?
Yes, you can use the Kimi AI API with Python through the OpenAI Python SDK. Configure the client with your Kimi API key and https://api.moonshot.ai/v1, then send requests through the Chat Completions interface.
Which Python package does the Kimi API use?
The official quickstart uses the openai Python package, version 1.0 or newer. Kimi’s API is compatible with the OpenAI format, although some model parameters and behaviors differ.
Which Kimi model should a beginner choose?
A beginner should start with kimi-k3, according to Kimi’s current quickstart. Before switching models, check the official model list and confirm that the selected model is accessible through your API key.
Is the Kimi AI API free?
The Kimi API Open Platform is a pay-as-you-go product rather than part of Kimi Membership. The Kimi K3 documentation says access requires a successful top-up of at least $1, after which usage is billed according to token consumption and current model pricing.
Why does my Kimi API key return a 401 error?
A Kimi API key can return a 401 error when it belongs to a different Kimi product or regional platform, is invalid, or does not match the selected endpoint. Verify the key source and endpoint before creating another key.
How do I stream Kimi API responses in Python?
To stream Kimi API responses in Python, add stream=True to the chat-completion request and loop over the returned chunks. Print chunk.choices[0].delta.content whenever content is present.
