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You can integrate DeepSeek by sending authenticated HTTP requests to its OpenAI-compatible API, or by using a compatible SDK pointed at DeepSeek’s base URL. Create a DeepSeek API key, keep it on your server, set https://api.deepseek.com as the OpenAI-format base URL, and call POST /chat/completions with a model and messages array. For incremental output, add stream: true and consume the server-sent events until the data: [DONE] terminator arrives. This guide shows the complete setup, request formats, Python and JavaScript examples, streaming and usage handling, JSON responses, security controls, and the failure modes most likely to affect a production integration.
What do I need before calling the DeepSeek API?
You need a DeepSeek account with an API key, a server-side runtime, and a current model identifier. The key uses HTTP Bearer authentication. Your application should make the request from a trusted backend or another protected execution environment, not from browser JavaScript or a mobile app bundle where anyone can extract and reuse the credential.
- API key: Create one in the DeepSeek account console before making requests.
- Secret storage: Put the key in an environment variable, cloud secret manager, or other server-side vault.
- Runtime: Use Python, Node.js, or any HTTP client that can send HTTPS requests and read streamed responses.
- Model: Select a model currently listed in DeepSeek’s model documentation. The current documentation lists
deepseek-flashanddeepseek-v4-pro; names and availability can change.
DeepSeek’s API documentation was checked on October 25, 2026. Model capabilities, context limits, parameters, and prices are time-sensitive, so verify them in the live documentation before deploying or publishing a fixed configuration.
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Authentication is standard HTTP Bearer authentication: send your key in the Authorization header as Bearer YOUR_KEY. The same credential can be supplied to a compatible SDK through its api_key option.
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Store the key outside your source code
For local development, define an environment variable rather than putting the key in a committed file:
export DEEPSEEK_API_KEY="your-secret-key"In production, inject the value through your platform’s secret manager. Restrict access to the process that needs it, rotate the key if it is exposed, and redact authorization headers from logs. Never place a reusable key in HTML, a public JavaScript bundle, a desktop application distributed to users, or a mobile package.
Make a raw authenticated request
The following request demonstrates the wire format. The OpenAI-compatible base URL is https://api.deepseek.com, and chat completions use POST /chat/completions.
curl https://api.deepseek.com/chat/completions
-H "Content-Type: application/json"
-H "Authorization: Bearer $DEEPSEEK_API_KEY"
-d '{
"model": "deepseek-flash",
"messages": [
{"role": "system", "content": "You are a helpful assistant."},
{"role": "user", "content": "Give me three concise onboarding tips."}
]
}'Keep the key in the process environment when using this command. Do not replace the variable with a literal key in shell history, documentation, or a ticket.
Which base URL and endpoint should I use?
Use the base URL that matches the compatibility format and append the documented route. For the OpenAI format, use https://api.deepseek.com and call POST /chat/completions. DeepSeek also documents an Anthropic-compatible base URL at https://api.deepseek.com/anthropic; use that only with an Anthropic-format client and request schema.
| Integration format | Base URL | Typical route in this guide | Request shape |
|---|---|---|---|
| OpenAI-compatible | https://api.deepseek.com |
POST /chat/completions |
model plus a messages array |
| Anthropic-compatible | https://api.deepseek.com/anthropic |
Use the route required by the Anthropic-compatible SDK | Use the schema expected by that SDK |
Do not combine the Anthropic base URL with an OpenAI client’s route assumptions. A client library may append paths automatically, so inspect its configuration rules before manually adding /chat/completions.
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How do I configure the OpenAI-compatible SDK in Python?
Install a current version of the Python package that provides the OpenAI-compatible client, then override its base URL and API key. The SDK pattern below follows DeepSeek’s documented quickstart configuration.
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import os
from openai import OpenAI
client = OpenAI(
api_key=os.environ.get("DEEPSEEK_API_KEY"),
base_url="https://api.deepseek.com",
)
response = client.chat.completions.create(
model="deepseek-flash",
messages=[
{"role": "system", "content": "You are a helpful assistant."},
{"role": "user", "content": "Hello!"},
],
)
print(response.choices[0].message.content)Before starting the process, verify that DEEPSEEK_API_KEY is present. A missing value can produce an authentication failure or an SDK-specific configuration error. The exact model identifier and supported options should be checked against the current DeepSeek model page rather than assumed from an older code sample.
Configure a JavaScript or TypeScript server
The JavaScript client follows the same idea: supply DeepSeek’s base URL and read the secret from the server environment. Keep this code in a backend route, worker, or other private runtime.
import OpenAI from "openai";
const client = new OpenAI({
apiKey: process.env.DEEPSEEK_API_KEY,
baseURL: "https://api.deepseek.com"
});
const response = await client.chat.completions.create({
model: "deepseek-flash",
messages: [
{ role: "system", content: "You are a helpful assistant." },
{ role: "user", content: "Summarize this customer note in two sentences." }
]
});
console.log(response.choices[0].message.content);Pin and update your SDK deliberately. Compatibility means the client can use the documented format; it does not mean every provider-specific parameter or response detail is identical across library versions.
What fields does a chat-completions request require?
A request needs a model and a messages array. Each message has a role such as system or user, plus its content. The endpoint reference also documents options including stream, thinking, reasoning_effort, and max_tokens.
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systemmessage: Set durable behavior, output constraints, and safety rules.usermessage: Supply the current task and data, ideally with a clear delimiter between instructions and untrusted text.- Conversation history: Send only the turns needed for the task so the request fits the selected model’s context limit.
max_tokens: Reserve enough output space for the answer. A low limit can truncate text or leave structured output incomplete.reasoning_effortandthinking: Use only when the currently selected model and API documentation support them; availability can vary by model.
Handle the response as untrusted generated text. Validate lengths, escape it for the destination (HTML, SQL, shell, or another format), and apply application-level authorization before taking an action based on it.
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How do I stream a response as it is generated?
Set stream to true. Instead of waiting for one final JSON body, the server sends a sequence of data-only server-sent events containing partial message deltas. Append each non-empty content delta to your response buffer or render it in the user interface, then stop when the event payload is data: [DONE].
Python streaming pattern
stream = client.chat.completions.create(
model="deepseek-flash",
messages=[
{"role": "user", "content": "Explain compound interest briefly."}
],
stream=True,
)
for chunk in stream:
if not chunk.choices:
continue
delta = chunk.choices[0].delta.content
if delta:
print(delta, end="", flush=True)This is the SDK-shaped handling pattern documented for the endpoint. Client versions differ in how they expose the terminal event, usage metadata, and exceptions, so test the behavior of the version you install. Do not assume every chunk contains text: some chunks can carry role, finish, or usage information instead.
Raw HTTP and server-sent events
When using a low-level HTTP client, read the response body incrementally. SSE records are separated by blank lines and commonly contain a line beginning with data:. Remove that prefix, parse JSON data events, and treat [DONE] as a control marker rather than JSON.
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- Open an HTTPS request with the Bearer header and
stream: truein the JSON body. - Read bytes until an SSE event boundary is reached; do not assume one network read equals one event.
- Ignore keep-alive or empty records and parse each data payload that is not
[DONE]. - Extract the delta content when present and append it in order.
- Stop on
[DONE], a documented finish indication, or a fatal transport error. - Close the connection and retain the partial text only if your product can safely label it incomplete.
Include usage in a stream
The endpoint reference documents stream_options.include_usage. Use it only together with stream: true. When enabled, usage information is supplied in the last chunk as described by the reference. Code defensively because the final usage-bearing chunk may not contain ordinary text content, and an interrupted connection may prevent usage metadata from arriving.
stream = client.chat.completions.create(
model="deepseek-flash",
messages=[{"role": "user", "content": "List five file-naming rules."}],
stream=True,
stream_options={"include_usage": True},
)
parts = []
usage = None
for chunk in stream:
if chunk.choices:
text = chunk.choices[0].delta.content
if text:
parts.append(text)
print(text, end="", flush=True)
if getattr(chunk, "usage", None):
usage = chunk.usage
complete_text = "".join(parts)How can I request JSON output safely?
Set response_format to {"type": "json_object"} when the selected model and endpoint support it, and explicitly instruct the model in the prompt to produce JSON. The format option encourages JSON syntax; it does not guarantee that the result matches your application’s schema or business rules.
response = client.chat.completions.create(
model="deepseek-flash",
messages=[
{
"role": "system",
"content": "Return only a JSON object with keys: title and priority."
},
{"role": "user", "content": "Classify: payment page shows an error."}
],
response_format={"type": "json_object"},
)
raw = response.choices[0].message.contentParse the returned text with a strict JSON parser, validate required keys and types, and reject or repair invalid data through a controlled fallback. The reference warns that output can be cut off when the token limit is reached; a syntactically incomplete object must not be treated as a successful record.
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How should a production integration handle errors and disconnects?
Separate authentication, request validation, provider errors, and transport failures so your application can respond appropriately.
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Authentication and configuration failures
- 401 or equivalent authentication error: Confirm the environment variable is loaded, the key has not been revoked, and the Bearer header is being sent to the intended host.
- 404 or route error: Check that the client’s base URL and automatically appended path match the selected compatibility format.
- Unsupported model or parameter: Compare the model name and option set with the current model and endpoint documentation.
Rate limits, timeouts, and provider errors
- Use bounded connect and read timeouts; streaming requests need a read policy that allows normal pauses without hanging forever.
- Retry only transient failures, with exponential backoff and jitter. Never blindly retry a request that may have triggered an irreversible downstream action.
- Cap concurrent requests and queue work so a traffic spike does not exhaust your provider allowance or server resources.
- Log a request identifier, latency, status, model, and token usage when available, but redact prompts containing personal data and never log the API key.
Interrupted streams
A dropped connection can leave you with a partial answer and no final usage chunk. Mark the result incomplete, allow the user to retry, and design idempotency or deduplication around any action taken after generation. Do not concatenate a retried stream with the first one unless your application explicitly knows how to reconcile duplicate text.
What security controls should surround the API call?
Use a backend proxy when a browser or mobile client needs AI functionality. The proxy authenticates your user, applies quotas, filters inputs, attaches the DeepSeek key, and returns only the result your client is allowed to see.
- Restrict outbound requests to the documented HTTPS host and verify TLS normally; do not disable certificate validation.
- Apply per-user and per-IP rate limits before forwarding requests.
- Set maximum prompt size and maximum output limits to control cost and denial-of-service risk.
- Remove secrets, session tokens, and unnecessary personal information from prompts.
- Escape streamed text before inserting it into the DOM; treat model output as data, not trusted HTML.
- Rotate keys, review access logs, and revoke any key that appears in a repository, build artifact, issue, or client download.
How should I choose a model and account for changing prices?
Choose from the models currently published by DeepSeek for your account and workload. The documentation checked for this guide lists deepseek-flash and deepseek-v4-pro, along with context limits and feature information, but those identifiers and capabilities are not permanent.
Pricing is a commercial value rather than a stable technical constant. DeepSeek’s pricing page states that prices may vary, so check the live page immediately before launch and whenever you change models. Record the model, input/output token accounting, billing currency, and retrieval date in your internal cost notes; do not hard-code an old price into a user-facing estimate.
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Both formats are documented compatibility layers, but they use different base URLs and client conventions. The OpenAI-compatible path in this guide uses https://api.deepseek.com, the /chat/completions route, and a messages-based request. The Anthropic-compatible path uses https://api.deepseek.com/anthropic and should be called through an Anthropic-format client with that client’s expected route and fields.
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| Decision point | OpenAI-compatible choice | Anthropic-compatible choice |
|---|---|---|
| Base URL | https://api.deepseek.com |
https://api.deepseek.com/anthropic |
| Client ecosystem | OpenAI-format SDKs and HTTP clients | Anthropic-format SDKs and HTTP clients |
| Schema used here | model, messages, stream |
Use the schema required by the Anthropic-compatible SDK |
| Performance comparison | Not established by the API compatibility documentation | Not established by the API compatibility documentation |
Select the format that minimizes changes in your existing application. Compatibility reduces integration work, but it does not establish equal support for every optional parameter, event shape, or model feature.
What should I test before shipping?
- Run a non-streaming request with a deliberately small prompt and confirm the expected text path in the SDK response.
- Run a streaming request and verify that multiple deltas render in order and that the terminal condition closes the connection.
- Test an empty or malformed key and confirm the application returns a safe configuration error without exposing the secret.
- Test a timeout and an interrupted stream; verify that partial output is labeled and retry behavior is bounded.
- Test JSON output at the maximum configured length and reject truncated or schema-invalid data.
- Confirm that browser and mobile clients never receive the reusable provider key.
- Check the current model page for model names, context limits, supported parameters, and price before release.
The smallest reliable architecture is a private server function that validates input, calls the documented endpoint, streams or returns sanitized output, and records minimal operational telemetry. Add queues, caching, moderation, and persistence only where your product requirements justify them.
Frequently Asked Questions
Can I call the DeepSeek API directly from a browser?
A browser can make a request technically, but embedding a reusable provider key exposes it to every user and browser extension. Put the call behind your own authenticated server endpoint and issue short-lived, application-specific permissions to the frontend instead.
Why does my streaming loop receive chunks with no text?
Streaming events can carry role changes, finish information, usage metadata, or control markers rather than visible content. Check that the choices collection and delta content exist before appending, and handle the final usage event separately.
How do I know whether a model name is still valid?
Treat model identifiers as configuration that must be checked against DeepSeek’s current model documentation. A name that worked in an earlier deployment can become unavailable, gain different limits, or be replaced without changing your client library.
What happens if a streamed JSON response ends halfway through an object?
The token limit or a broken connection can leave incomplete JSON. Keep the accumulated text marked as partial, run a strict parser and schema validator, and ask the user or a controlled retry path to regenerate rather than silently storing malformed data.
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