SenseNova LLM API Integration Guide

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This document explains how to integrate the SenseNova LLM API on SenseTime's SenseCore platform.

Important

SenseNova 6.8 Flash Lite Preview is an early preview release. Model behavior, availability, and interfaces may evolve before the official release.

1. Sign up and obtain an API Key
2. Using with Agent Frameworks
3. Models
4. Basic invocation
5. Recommended sampling parameters
6. Multi-turn dialogue
7. Image (multimodal) input
8. Streaming output
9. Calling via the OpenAI SDK
10. Error codes

1. Sign up and obtain an API Key

Visit the SenseNova platform and complete registration plus identity verification:

https://platform.sensenova.cn/console

In the console sidebar, go to Management Center → API Key Management → Create API Key.

After creation, copy and store the key immediately — the full value is shown only once. If a key leaks, delete or disable it on the same page and create a new one.

In the examples below, replace every <YOUR_API_KEY> with the key you created.

2. Using with Agent Frameworks

SenseNova 6.8 Flash Lite Preview needs an agent runtime, appropriate tools, and the official skill library to deliver an end-to-end delegated workflow. A direct API call alone does not provide browser or application control.

Recommended runtime: OpenClaw or hermes-agent.
Recommended LLM: pair it with the SenseNova platform API — use the API Key from Section 1 (free token plan available).
Install & setup: see SenseNova-Skills INSTALL.md.

During the preview period, the shared installation guide may still show the 6.7 model name. Configure the provider with sensenova-6.8-flash-lite instead.

2.1 Installing SenseNova-Skills

Recommended: just ask the agent to install them for you. Hand it the repo URL and let it clone and copy the contents into the right directory, e.g.:

"Please install https://github.com/OpenSenseNova/SenseNova-Skills into your skills directory."

After installation you may need to restart the agent service manually before the new skills are picked up.

Agent	Target directory
OpenClaw	`~/.openclaw/skills/`
hermes-agent	`~/.hermes/skills/`

Prefer to install manually?

Clone the repo, then copy (or symlink) the subdirectories under skills/ into the target directory:

git clone https://github.com/OpenSenseNova/SenseNova-Skills.git --depth=1
mkdir -p ~/.openclaw/skills
cp -r SenseNova-Skills/skills/* ~/.openclaw/skills/

For Hermes, just swap the directory to ~/.hermes/skills/.

3. Models

SenseNova 6.8 Flash Lite Preview — a lightweight preview model taking a major step toward Delegated Intelligence.

Stable long-horizon execution, maintaining goals and constraints across hundreds of steps and multiple phases
Dynamic sub-agent collaboration, organizing more than ten specialist agents for parallel work
Native multimodal fusion, connecting text, images, charts, documents, video, web pages, and application interfaces
Self-correction, with rollback, replanning, tool use, and result verification along one task trajectory

API model identifier: sensenova-6.8-flash-lite

4. Basic invocation

4.1 curl

curl --location 'https://token.sensenova.cn/v1/chat/completions' \
  --header 'Content-Type: application/json' \
  --header 'Authorization: Bearer <YOUR_API_KEY>' \
  --data '{
    "model": "sensenova-6.8-flash-lite",
    "max_tokens": 2000,
    "messages": [
      {"role": "user", "content": "Hi, please briefly introduce yourself."}
    ]
  }'

4.2 Python

import os
import requests

API_KEY = os.environ["SENSENOVA_API_KEY"]
URL = "https://token.sensenova.cn/v1/chat/completions"

resp = requests.post(
    URL,
    headers={
        "Authorization": f"Bearer {API_KEY}",   # Bearer token auth
        "Content-Type": "application/json",
    },
    json={
        "model": "sensenova-6.8-flash-lite",
        "max_tokens": 2000,                     # Max output tokens (incl. reasoning)
        "messages": [
            {"role": "user", "content": "Hi, please briefly introduce yourself."},
        ],
    },
    timeout=60,
)
resp.raise_for_status()
data = resp.json()
print(data["choices"][0]["message"])

4.3 Typical response shape

{
  "id": "da48c12a-...",
  "request_id": "da48c12a-...",
  "model": "sensenova-6.8-flash-lite",
  "object": "chat.completion",
  "created": 1776952631,
  "choices": [{
    "index": 0,
    "message": {
      "role": "assistant",
      "content": "Hi! I'm SenseNova...",
      "reasoning": "Thinking Process: ..."
    },
    "finish_reason": "stop"
  }],
  "usage": {
    "prompt_tokens": 37,
    "completion_tokens": 762,
    "total_tokens": 799,
    "prompt_tokens_details": {"cached_tokens": 0, "audio_tokens": 0}
  }
}

finish_reason: stop for normal completion, length if max_tokens is hit.
message.content: the final answer.
message.reasoning: the chain of thought from a reasoning model.
total_tokens = prompt_tokens + completion_tokens. The upper bound is the model's context window — there is no request parameter to cap it directly.

5. Recommended sampling parameters

We suggest the following parameter combinations by mode and task type:

Mode	Task type	`temperature`	`top_p`	`top_k`	`presence_penalty`	`repetition_penalty`
Thinking mode	General	1.0	0.95	20	1.5	1.0
Thinking mode	Precise coding (e.g. WebDev)	0.6	0.95	20	0.0	1.0
Instruct (non-thinking) mode	General	0.7	0.8	20	1.5	1.0
Instruct (non-thinking) mode	Reasoning	1.0	1.0	40	2.0	1.0

Example — "general task + thinking mode":

curl 'https://token.sensenova.cn/v1/chat/completions' \
  -H 'Content-Type: application/json' \
  -H 'Authorization: Bearer <YOUR_API_KEY>' \
  -d '{
    "model": "sensenova-6.8-flash-lite",
    "max_tokens": 2000,
    "temperature": 1.0,
    "top_p": 0.95,
    "top_k": 20,
    "min_p": 0.0,
    "presence_penalty": 1.5,
    "repetition_penalty": 1.0,
    "messages": [
      {"role": "user", "content": "Write a poem about spring."}
    ]
  }'

resp = requests.post(URL, headers=headers, timeout=60, json={
    "model": "sensenova-6.8-flash-lite",
    "max_tokens": 2000,
    "temperature": 1.0,
    "top_p": 0.95,
    "top_k": 20,
    "min_p": 0.0,
    "presence_penalty": 1.5,
    "repetition_penalty": 1.0,
    "messages": [
        {"role": "user", "content": "Write a poem about spring."},
    ],
})
print(resp.json()["choices"][0]["message"]["content"])

6. Multi-turn dialogue

6.1 curl

curl 'https://token.sensenova.cn/v1/chat/completions' \
  -H 'Content-Type: application/json' \
  -H 'Authorization: Bearer <YOUR_API_KEY>' \
  -d '{
    "model": "sensenova-6.8-flash-lite",
    "max_tokens": 2000,
    "messages": [
      {"role": "system",    "content": "You are a concise assistant. Reply in 20 words or fewer."},
      {"role": "user",      "content": "What is the capital of France?"},
      {"role": "assistant", "content": "Paris."},
      {"role": "user",      "content": "And Germany?"}
    ]
  }'

6.2 Python

# Maintain dialogue history: append in chronological order.
history = [
    {"role": "system", "content": "You are a concise assistant. Reply in 20 words or fewer."},
]

def chat(user_msg: str) -> str:
    history.append({"role": "user", "content": user_msg})
    resp = requests.post(URL, headers=headers, timeout=60, json={
        "model": "sensenova-6.8-flash-lite",
        "max_tokens": 2000,
        "messages": history,
    })
    reply = resp.json()["choices"][0]["message"].get("content", "")
    # When echoing history back, only keep `content` — never include `reasoning`.
    history.append({"role": "assistant", "content": reply})
    return reply

print(chat("What is the capital of France?"))   # -> Paris.
print(chat("And Germany?"))                     # -> Berlin.

Notes:

role may be system, user, or assistant.
When echoing the previous reply, include only content — do not echo back reasoning.
Multi-turn significantly increases prompt_tokens. For long histories, summarize or truncate.

7. Image (multimodal) input

SenseNova 6.8 Flash Lite Preview accepts images in the OpenAI Vision-compatible format, supporting both URL and Base64.

7.1 Via URL (curl)

curl 'https://token.sensenova.cn/v1/chat/completions' \
  -H 'Content-Type: application/json' \
  -H 'Authorization: Bearer <YOUR_API_KEY>' \
  -d '{
    "model": "sensenova-6.8-flash-lite",
    "max_tokens": 2000,
    "messages": [{
      "role": "user",
      "content": [
        {"type": "text",  "text": "What is in the image?"},
        {"type": "image_url", "image_url": {
          "url": "https://example.com/photo.jpg"
        }}
      ]
    }]
  }'

The server downloads the URL — the image must be anonymously accessible. If unreachable, you'll get an image down failed error; switch to Base64 or upload to an accessible object store.

7.2 Via URL (Python)

resp = requests.post(URL, headers=headers, timeout=120, json={
    "model": "sensenova-6.8-flash-lite",
    "max_tokens": 2000,
    "messages": [{
        "role": "user",
        "content": [
            {"type": "text",  "text": "What is in the image?"},
            {"type": "image_url", "image_url": {
                "url": "https://example.com/photo.jpg",
            }},
        ],
    }],
})
print(resp.json()["choices"][0]["message"]["content"])

7.3 Via Base64 (curl)

# Build a Data URL (Linux/macOS).
B64=$(base64 -w 0 photo.png 2>/dev/null || base64 -i photo.png)

curl 'https://token.sensenova.cn/v1/chat/completions' \
  -H 'Content-Type: application/json' \
  -H 'Authorization: Bearer <YOUR_API_KEY>' \
  -d '{
    "model": "sensenova-6.8-flash-lite",
    "max_tokens": 2000,
    "messages": [{
      "role": "user",
      "content": [
        {"type": "text", "text": "Describe this image."},
        {"type": "image_url", "image_url": {
          "url": "data:image/png;base64,'"${B64}"'"
        }}
      ]
    }]
  }'

7.4 Via Base64 (Python)

import base64
import mimetypes
import requests

def to_data_url(path: str) -> str:
    mime = mimetypes.guess_type(path)[0] or "image/png"
    with open(path, "rb") as f:
        b64 = base64.b64encode(f.read()).decode()
    return f"data:{mime};base64,{b64}"

resp = requests.post(URL, headers=headers, timeout=120, json={
    "model": "sensenova-6.8-flash-lite",
    "max_tokens": 2000,
    "messages": [{
        "role": "user",
        "content": [
            {"type": "text",  "text": "Describe this image."},
            {"type": "image_url", "image_url": {
                "url": to_data_url("./photo.png"),
            }},
        ],
    }],
})
print(resp.json()["choices"][0]["message"]["content"])

7.5 Multi-image input (Python)

Append additional image_url objects within the same message's content array:

resp = requests.post(URL, headers=headers, timeout=120, json={
    "model": "sensenova-6.8-flash-lite",
    "max_tokens": 2000,
    "messages": [{
        "role": "user",
        "content": [
            {"type": "text",      "text": "Compare the differences between the two images."},
            {"type": "image_url", "image_url": {"url": "https://example.com/a.jpg"}},
            {"type": "image_url", "image_url": {"url": "https://example.com/b.jpg"}},
        ],
    }],
})

Image guidelines:

Common formats are supported (PNG, JPEG, …).
Compress to a long edge of at most 2048 px before upload to save tokens and latency.
Prefer URL for large images; Base64 inflates the request body considerably.

8. Streaming output

Set stream to true and the server pushes incremental results via Server-Sent Events (SSE).

8.1 curl

curl -N 'https://token.sensenova.cn/v1/chat/completions' \
  -H 'Content-Type: application/json' \
  -H 'Authorization: Bearer <YOUR_API_KEY>' \
  -d '{
    "model": "sensenova-6.8-flash-lite",
    "stream": true,
    "max_tokens": 2000,
    "messages": [{"role": "user", "content": "Write a poem about spring."}]
  }'

Event-stream excerpt:

data: {"choices":[{"delta":{"reasoning":"Thinking"},"finish_reason":""}], ...}
data: {"choices":[{"delta":{"content":"Spring"},"finish_reason":""}], ...}
data: {"choices":[{"delta":{"content":" wind"},"finish_reason":""}], ...}
...
data: {"choices":[{"delta":{},"finish_reason":"stop"}], ...}
data: {"choices":[], "usage":{"prompt_tokens":38,"completion_tokens":441,"total_tokens":479}}
data: [DONE]

8.2 Python

import json
import requests

with requests.post(URL, headers=headers, stream=True, timeout=120, json={
    "model": "sensenova-6.8-flash-lite",
    "stream": True,
    "max_tokens": 2000,
    "messages": [{"role": "user", "content": "Write a poem about spring."}],
}) as r:
    for line in r.iter_lines(decode_unicode=True):
        if not line or not line.startswith("data:"):
            continue
        payload = line[5:].strip()
        if payload == "[DONE]":          # End-of-stream marker
            break

        chunk = json.loads(payload)

        # Before finishing, the server pushes a usage-only event (choices is empty).
        if not chunk.get("choices"):
            print("\n[usage]", chunk.get("usage"))
            continue

        delta = chunk["choices"][0].get("delta", {})

        # `delta` may carry `reasoning` (thinking) or `content` (final text).
        # Most front-ends only display `content` and ignore `reasoning`.
        if "content" in delta:
            print(delta["content"], end="", flush=True)

Key points:

Each event begins with data: and is separated by a blank line.
delta may contain reasoning (incremental thinking) or content (incremental output). Reasoning models emit a large amount of reasoning before producing content.
Just before completion, the server pushes a single usage-only event (choices: []).
Receiving data: [DONE] indicates the stream is finished — clients should stop reading.

9. Calling via the OpenAI SDK

The endpoint is compatible with OpenAI's Chat Completions protocol, so you can use openai-python directly:

import os
from openai import OpenAI

client = OpenAI(
    api_key=os.environ["SENSENOVA_API_KEY"],
    base_url="https://token.sensenova.cn/v1",
)

completion = client.chat.completions.create(
    model="sensenova-6.8-flash-lite",
    max_tokens=2000,
    temperature=0.7,
    messages=[{"role": "user", "content": "Hello"}],
)
print(completion.choices[0].message.content)

Streaming:

stream = client.chat.completions.create(
    model="sensenova-6.8-flash-lite",
    max_tokens=2000,
    stream=True,
    messages=[{"role": "user", "content": "Write a poem"}],
)
for chunk in stream:
    if chunk.choices and chunk.choices[0].delta.content:
        print(chunk.choices[0].delta.content, end="", flush=True)

When using the SDK, the reasoning field may not be exposed on the canonical objects. To retrieve it, call the raw HTTP endpoint directly.

10. Error codes

Error response shape:

{
  "error": {
    "message": "model is not found",
    "type": "not_found_error",
    "code": "5"
  }
}

HTTP	error.type	Meaning	Suggested action
400	`invalid_request_error`	Malformed parameters, e.g. image download failure	Check parameter shape and image URL accessibility
401	`authentication_error`	API key invalid or expired	Recreate the key in the console
403	—	Lacking permission or risk-blocked	Check account permissions and content compliance
404	`not_found_error`	Model or endpoint does not exist	Double-check the spelling of `model`
429	—	Rate limited	Retry with exponential backoff
5xx	—	Server-side issue	Retry later; if persistent, file a ticket

For further support, sign in to the SenseCore console to file a ticket or browse the latest official docs.

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SenseNova LLM API Integration Guide

Table of Contents

1. Sign up and obtain an API Key

2. Using with Agent Frameworks

2.1 Installing SenseNova-Skills

3. Models

4. Basic invocation

4.1 curl

4.2 Python

4.3 Typical response shape

5. Recommended sampling parameters

6. Multi-turn dialogue

6.1 curl

6.2 Python

7. Image (multimodal) input

7.1 Via URL (curl)

7.2 Via URL (Python)

7.3 Via Base64 (curl)

7.4 Via Base64 (Python)

7.5 Multi-image input (Python)

8. Streaming output

8.1 curl

8.2 Python

9. Calling via the OpenAI SDK

10. Error codes

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SenseNova LLM API Integration Guide

Table of Contents

1. Sign up and obtain an API Key

2. Using with Agent Frameworks

2.1 Installing SenseNova-Skills

3. Models

4. Basic invocation

4.1 curl

4.2 Python

4.3 Typical response shape

5. Recommended sampling parameters

6. Multi-turn dialogue

6.1 curl

6.2 Python

7. Image (multimodal) input

7.1 Via URL (curl)

7.2 Via URL (Python)

7.3 Via Base64 (curl)

7.4 Via Base64 (Python)

7.5 Multi-image input (Python)

8. Streaming output

8.1 curl

8.2 Python

9. Calling via the OpenAI SDK

10. Error codes