JieKou AI

https://docs.jiekou.ai/
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JieKou AI is a unified large model API platform providing enterprise-grade access to diverse AI...
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### Run DeepSearcher Example

Source: https://docs.jiekou.ai/docs/integration/deepsearcher

Execute the basic example script from the project's root directory to start the DeepSearcher process.

```bash
python examples/basic_example.py
```

--------------------------------

### Install OpenCode

Source: https://docs.jiekou.ai/docs/integration/opencode

Use this command to install OpenCode. Refer to the official website for alternative installation methods.

```bash
curl -fsSL https://opencode.ai/install | bash
```

--------------------------------

### Quick Start: Call Gemini via OpenAI and Native Protocols

Source: https://docs.jiekou.ai/docs/providers/gemini

Use these examples to make your first call to Gemini models. The OpenAI protocol uses `chat/completions`, while the native protocol uses `gemini/v1/models/{model}:generateContent`. Both examples assume non-streaming mode; for streaming, change the path to `streamGenerateContent`.

```bash
curl https://api.jiekou.ai/openai/chat/completions \
  -H "Content-Type: application/json" \
  -H "Authorization: Bearer <YOUR-API-KEY>" \
  -d '{
      "model": "gemini-2.5-flash",
      "messages": [{"role": "user", "content": "What is the capital of France?"}],
      "reasoning_effort": "low"
    }'
```

```bash
curl https://api.jiekou.ai/gemini/v1/models/gemini-2.5-flash:generateContent \
  -H "Content-Type: application/json" \
  -H "Authorization: Bearer <YOUR-API-KEY>" \
  -d '{
      "contents": [{"role": "user", "parts": [{"text": "What is the capital of France?"}]}],
      "generationConfig": {
          "thinkingConfig": {
              "thinkingBudget": 1024
          }
      }
    }'
```

--------------------------------

### Quick Start: OpenAI Protocol

Source: https://docs.jiekou.ai/docs/providers/gemini

Example of how to make a request to Gemini models using the OpenAI chat/completions protocol.

```APIDOC
## Quick Start: OpenAI Protocol

This example demonstrates how to interact with Gemini models using the familiar OpenAI chat/completions API.

### Method
POST

### Endpoint
https://api.jiekou.ai/openai/chat/completions

### Request Body
```json
{
  "model": "gemini-2.5-flash",
  "messages": [
    {
      "role": "user",
      "content": "What is the capital of France?"
    }
  ],
  "reasoning_effort": "low"
}
```

### Request Example
```bash
curl https://api.jiekou.ai/openai/chat/completions \
  -H "Content-Type: application/json" \
  -H "Authorization: Bearer <YOUR-API-KEY>" \
  -d '{
      "model": "gemini-2.5-flash",
      "messages": [{"role": "user", "content": "What is the capital of France?"}],
      "reasoning_effort": "low"
    }'
```
```

--------------------------------

### Quick Start: Gemini Native Protocol

Source: https://docs.jiekou.ai/docs/providers/gemini

Example of how to make a request to Gemini models using the native Gemini protocol.

```APIDOC
## Quick Start: Gemini Native Protocol

This example shows how to use the native Gemini API for model interactions.

### Method
POST

### Endpoint
https://api.jiekou.ai/gemini/v1/models/{model}:generateContent

### Request Body
```json
{
  "contents": [
    {
      "role": "user",
      "parts": [{"text": "What is the capital of France?"}]
    }
  ],
  "generationConfig": {
    "thinkingConfig": {
      "thinkingBudget": 1024
    }
  }
}
```

### Request Example
```bash
curl https://api.jiekou.ai/gemini/v1/models/gemini-2.5-flash:generateContent \
  -H "Content-Type: application/json" \
  -H "Authorization: Bearer <YOUR-API-KEY>" \
  -d '{
      "contents": [{"role": "user", "parts": [{"text": "What is the capital of France?"}]}],
      "generationConfig": {"thinkingConfig": {"thinkingBudget": 1024}}
    }'
```
```

--------------------------------

### Start OpenCode

Source: https://docs.jiekou.ai/docs/integration/opencode

Navigate to your project directory and run the 'opencode' command to start the AI coding agent.

```bash
cd /path/to/project  # Enter project path

opencode  # Start OpenCode
```

--------------------------------

### Start LangBot Docker Container

Source: https://docs.jiekou.ai/docs/integration/langbot

Use Docker Compose to start the LangBot service. This command assumes Docker and Docker Compose are installed. For users in mainland China, alternative repository and image sources are provided.

```bash
docker compose up
```

--------------------------------

### Example OpenClaw Configuration

Source: https://docs.jiekou.ai/docs/integration/openclaw

A complete example demonstrating the 'models' and 'agents' configurations for integrating with the Jiekou API.

```json
"models": {
    "mode": "merge",
    "providers": {
      "Jiekou": {
        "baseUrl": "https://api.jiekou.ai/openai/v1",
        "apiKey": "sk_xxxxxxxxxxxxxxxxxx",
        "api": "openai-completions",
        "models": [
          {
            "id": "gpt-5.4",
            "name": "gpt-5.4 (Custom Provider)",
            "reasoning": false,
            "input": [
              "text"
            ],
            "cost": {
              "input": 0,
              "output": 0,
              "cacheRead": 0,
              "cacheWrite": 0
            },
            "contextWindow": 16000,
            "maxTokens": 4096
          }
        ]
      }
    }
  },
  "agents": {
    "defaults": {
      "model": {
        "primary": "jiekou/gpt-5.4"
      },
      "models": {
        "Jiekou/gpt-5.4": {
          "alias": "gpt-5.4"
        }
      },
      "workspace": "/Users/0000/.openclaw/workspace",
      "compaction": {
        "mode": "safeguard"
      },
      "maxConcurrent": 4,
      "subagents": {
        "maxConcurrent": 8
      }
    }
  },

```

--------------------------------

### Copy example configuration file

Source: https://docs.jiekou.ai/docs/integration/openmanus

Copies the example configuration file to create a new configuration file for OpenManus. This file will be edited to set API keys and model details.

```bash
cp config/config.example.toml config/config.toml
```

--------------------------------

### Install Anthropic SDK

Source: https://docs.jiekou.ai/docs/model/llm-anthropic-compatibility

Install the necessary Anthropic SDK for your project using pip for Python or npm for Node.js.

```bash
pip install anthropic
```

```bash
npm install @anthropic-ai/sdk
```

--------------------------------

### Quick Install OpenClaw

Source: https://docs.jiekou.ai/docs/integration/openclaw

Use this command to quickly install OpenClaw. Ensure you have Node.js version 22 or higher.

```bash
curl -fsSL https://openclaw.ai/install.sh | bash
```

--------------------------------

### Install Jiekou AI Agent Skills

Source: https://docs.jiekou.ai/docs/support/quickstart

Use this command to install the Jiekou AI Agent Skills, allowing direct calls to Jiekou AI capabilities within your Agent. This command is executed via npx.

```bash
npx skills add jiekouai/jiekou-skills --skill jiekou-docs
```

--------------------------------

### Install OpenManus dependencies

Source: https://docs.jiekou.ai/docs/integration/openmanus

Installs all project dependencies listed in the requirements.txt file using the uv package manager within the activated virtual environment.

```bash
uv pip install -r requirements.txt
```

--------------------------------

### Local MCP Configuration Example

Source: https://docs.jiekou.ai/docs/integration/opencode

This Python code snippet demonstrates setting up a local MCP server for calculations using the MCP SDK. It includes necessary imports and the basic structure for a stdio server.

```python
import asyncio
import math
from mcp.server import Server
from mcp.server.stdio import stdio_server
from mcp.types import Tool, TextContent
```

--------------------------------

### Install Codex CLI

Source: https://docs.jiekou.ai/docs/integration/codex

Install the Codex CLI globally using npm. For MacOS users, Homebrew is an alternative installation method.

```bash
npm install -g @openai/codex

```

```bash
brew install codex

```

--------------------------------

### Install Claude Code

Source: https://docs.jiekou.ai/docs/integration/claudecode

Install Claude Code globally using npm. Ensure Node.js version 18 or higher is installed before proceeding.

```bash
npm install -g @anthropic-ai/claude-code
```

--------------------------------

### Set Up Python Virtual Environment

Source: https://docs.jiekou.ai/docs/integration/deepsearcher

Create and activate a Python virtual environment. Ensure your Python version is 3.10 or higher.

```bash
#MAKE SURE the python version is greater than or equal to 3.10
python3 -m venv .venv
source .venv/bin/activate
```

--------------------------------

### Install uv package manager

Source: https://docs.jiekou.ai/docs/integration/openmanus

Installs uv, a fast Python package manager, using a curl script. This is a prerequisite for installing OpenManus dependencies.

```bash
curl -LsSf https://astral.sh/uv/install.sh | sh
```

--------------------------------

### Install DeepSearcher Dependencies

Source: https://docs.jiekou.ai/docs/integration/deepsearcher

Install the necessary Python dependencies for DeepSearcher using pip.

```bash
pip install -e .
```

--------------------------------

### Full Code Example for Structured Output

Source: https://docs.jiekou.ai/docs/model/llm-structured-outputs

Provides a complete Python script demonstrating how to set up the OpenAI client, define a system prompt and a JSON schema for expense tracking, make the API call, and parse the structured JSON response.

```python
from openai import OpenAI
import json

client = OpenAI(
    base_url="https://api.jiekou.ai/openai",
    api_key="<Your API Key>",
)

model = "qwen/qwen-2.5-72b-instruct"

# 使用 JSON Schema 进行结构化输出的示例
# 此示例创建了一个用于提取费用信息的 schema

# 定义用于费用跟踪的系统提示
system_prompt = """You are an expense tracking assistant. 
Extract expense information from the user's input and format it according to the provided schema."""

# 定义用于结构化响应的 JSON schema
response_format = {
    "type": "json_schema",
    "json_schema": {
        "name": "expense_tracking_schema",
        "schema": {
            "type": "object",
            "properties": {
                "expenses": {
                    "type": "array",
                    "items": {
                        "type": "object",
                        "properties": {
                            "description": {
                                "type": "string",
                                "description": "Description of the expense"
                            },
                            "amount": {
                                "type": "number",
                                "description": "Amount spent in dollars"
                            },
                            "date": {
                                "type": "string",
                                "description": "When the expense occurred"
                            },
                            "category": {
                                "type": "string",
                                "description": "Category of expense (e.g., food, office, travel)"
                            }
                        },
                        "required": [
                            "description",
                            "amount"
                        ]
                    }
                },
                "total": {
                    "type": "number",
                    "description": "Total amount of all expenses"
                }
            },
            "required": [
                "expenses",
                "total"
            ],
        },
    },
}

chat_completion = client.chat.completions.create(
    model=model,
    messages=[
        {
            "role": "system",
            "content": system_prompt,
        },
        {
            "role": "user",
            "content": """I spent $120 on dinner at an Italian restaurant last Friday with my colleagues.
Also bought office supplies for $45 on Monday.""",
        },
    ],
    max_tokens=1024,
    temperature=0.8,
    stream=False,
    response_format=response_format,
)

response_content = chat_completion.choices[0].message.content

# 解析并美化 JSON
try:
    json_response = json.loads(response_content)
    prettified_json = json.dumps(json_response, indent=2)
    print(prettified_json)
except json.JSONDecodeError:
    print("Could not parse response as JSON. Raw response:")
    print(response_content)
```

--------------------------------

### Initialize OpenAI Client

Source: https://docs.jiekou.ai/docs/model/llm-function-calling

Initialize the OpenAI client with your API key and base URL. This is the first step to interact with the API.

```python
from openai import OpenAI
import json

client = OpenAI(
    base_url="https://api.jiekou.ai/openai",
    api_key="<Your API Key>",
)

model = "deepseek/deepseek-v3"
```

--------------------------------

### Run Codex CLI

Source: https://docs.jiekou.ai/docs/integration/codex

Run the Codex CLI after setting your JIEKOU API key as an environment variable. Replace `<API_KEY>` with your actual key.

```bash
OPENAI_API_KEY=<API_KEY> codex

```

--------------------------------

### Initialize OpenAI Client

Source: https://docs.jiekou.ai/docs/model/llm-structured-outputs

Initialize the OpenAI client with your API base URL and key. This is a prerequisite for making API calls.

```python
from openai import OpenAI

client = OpenAI(
    base_url="https://api.jiekou.ai/openai",
    api_key="<Your API Key>",
)

model = "qwen/qwen-2.5-72b-instruct"
```

--------------------------------

### Generate Video from Start and End Frames

Source: https://docs.jiekou.ai/docs/models/reference-vidu-q1-startend2video

This API generates a video using a starting and ending frame. It's an asynchronous operation, returning a task ID for result retrieval.

```APIDOC
## Generate Video from Start and End Frames

### Description
Generates a dynamic video from a starting and ending frame, incorporating creative storytelling and animation effects. This is an asynchronous API that returns a task ID. You should use this task ID to query the [Query Task Result API](/docs/models/reference-get-async-task-result) to retrieve the video generation results.

### Method
POST

### Endpoint
`/api/v1/vidu/q1/startend2video`

### Headers
- **Content-Type** (string) - Required - Enum value: `application/json`
- **Authorization** (string) - Required - Bearer authentication format: Bearer {{API Key}}

### Request Body
- **images** (string[]) - Required - Two images: the first is the start frame, the second is the end frame. Supports public URLs or Base64 format. Aspect ratio must be close (between 0.8-1.25). Supported formats: png, jpeg, jpg, webp. Max size: 50MB. Base64 decoded length must be less than 10MB and include a content type string (e.g., `data:image/png;base64,{base64_encode}`).
- **prompt** (string) - Optional - Prompt description, max 1500 characters.
- **duration** (integer) - Optional - Video duration in seconds. Defaults to 5 seconds; currently only supports the `5` second option.
- **seed** (integer) - Optional - Random seed for video generation. Defaults to a random numerical seed. Manually set values will override the default random seed.
- **resolution** (string) - Optional - Output video resolution. Defaults to 1080p; currently only supports the `1080p` option.
- **movement_amplitude** (string) - Optional - Movement amplitude of objects in the video. Default value: `auto`. Optional values: `auto`, `small`, `medium`, `large`.
- **bgm** (boolean) - Optional - Whether to add background music to the generated video. Default value: `false`. Optional values: `true`, `false`. If set to true, suitable BGM will be automatically added.

### Response
#### Success Response (200)
- **task_id** (string) - Required - The task ID of the asynchronous task. You should use this task ID to request the [Query Task Result API](/docs/models/reference-get-async-task-result) to get the generation result.
```
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