> ## Documentation Index
> Fetch the complete documentation index at: https://docs.venice.ai/llms.txt
> Use this file to discover all available pages before exploring further.
# Venice API
> Venice API 文档 —— 通过一个 API 密钥私密、不受限制地访问 OpenAI 兼容的聊天、图像、音频和视频模型。
通往私密、无限制访问智能的 API。
单个 API 密钥背后的 OpenAI 兼容 chat、image、audio 和 video。
```bash curl theme={"system"}
curl https://api.venice.ai/api/v1/chat/completions \
-H "Authorization: Bearer $VENICE_API_KEY" \
-H "Content-Type: application/json" \
-d '{
"model": "zai-org-glm-5-1",
"messages": [{"role": "user", "content": "Build without permission."}]
}'
```
```ts TypeScript theme={"system"}
import OpenAI from "openai";
const client = new OpenAI({
apiKey: process.env.VENICE_API_KEY,
baseURL: "https://api.venice.ai/api/v1",
});
const res = await client.chat.completions.create({
model: "zai-org-glm-5-1",
messages: [{ role: "user", content: "Build without permission." }],
});
```
```python Python theme={"system"}
import os
from openai import OpenAI
client = OpenAI(
api_key=os.environ["VENICE_API_KEY"],
base_url="https://api.venice.ai/api/v1",
)
res = client.chat.completions.create(
model="zai-org-glm-5-1",
messages=[{"role": "user", "content": "Build without permission."}],
)
```
端点
一个 API，涵盖所有模态
单个 API 密钥背后的 Chat、image、audio、video 和 embeddings。
另外还有 embeddings、文件输入、MCP 工具 和 钱包支付。查看所有端点 →
Agent
为 AI agent 而构建
为消息、编码和链上 agent 提供的私密推理、MCP 工具和钱包资助的工作流。
探索 AI Agents 中心 →
模型
热门模型
Venice 上一些使用最多的模型。将 ID 用作您的 `model` 参数。
250+ 个模型
文本、图像、音频和视频
浏览目录 →
工具
聊天模型的内置工具
使用 `venice_parameters` 或 Venice 原生端点开启网页搜索、附加文件或查询区块链。
通过 `enable_web_search` 为任何文本模型添加带引用的实时网页搜索。
```bash Curl theme={"system"}
curl https://api.venice.ai/api/v1/chat/completions \
-H "Authorization: Bearer $VENICE_API_KEY" \
-H "Content-Type: application/json" \
-d '{
"model": "zai-org-glm-5-1",
"messages": [{"role": "user", "content": "What are the latest developments in AI?"}],
"venice_parameters": {
"enable_web_search": "auto"
}
}'
```
```ts TypeScript theme={"system"}
import OpenAI from "openai";
const client = new OpenAI({
apiKey: process.env.VENICE_API_KEY!,
baseURL: "https://api.venice.ai/api/v1",
});
const completion = await client.chat.completions.create({
model: "zai-org-glm-5-1",
messages: [{ role: "user", content: "What are the latest developments in AI?" }],
// @ts-expect-error - Venice-specific parameter
venice_parameters: {
enable_web_search: "auto",
},
});
console.log(completion.choices[0].message.content);
```
```python Python theme={"system"}
import os
from openai import OpenAI
client = OpenAI(
api_key=os.environ["VENICE_API_KEY"],
base_url="https://api.venice.ai/api/v1",
)
response = client.chat.completions.create(
model="zai-org-glm-5-1",
messages=[{"role": "user", "content": "What are the latest developments in AI?"}],
extra_body={
"venice_parameters": {
"enable_web_search": "auto",
}
},
)
print(response.choices[0].message.content)
```
```bash Model Suffix theme={"system"}
# Alternative: append parameters directly to the model ID
curl https://api.venice.ai/api/v1/chat/completions \
-H "Authorization: Bearer $VENICE_API_KEY" \
-H "Content-Type: application/json" \
-d '{
"model": "zai-org-glm-5-1:enable_web_search=on&enable_web_citations=true",
"messages": [{"role": "user", "content": "What are the latest developments in AI?"}]
}'
```
设置 `enable_web_scraping: true`，模型在回答之前会获取并读取用户消息中的任何 URL。
```bash Curl theme={"system"}
curl https://api.venice.ai/api/v1/chat/completions \
-H "Authorization: Bearer $VENICE_API_KEY" \
-H "Content-Type: application/json" \
-d '{
"model": "openai-gpt-55",
"messages": [
{"role": "user", "content": "Summarize this post in five bullets: https://venice.ai/blog/how-to-use-venice-api"}
],
"venice_parameters": {
"enable_web_scraping": true
}
}'
```
```ts TypeScript theme={"system"}
import OpenAI from "openai";
const client = new OpenAI({
apiKey: process.env.VENICE_API_KEY!,
baseURL: "https://api.venice.ai/api/v1",
});
const response = await client.chat.completions.create({
model: "openai-gpt-55",
messages: [
{
role: "user",
content:
"Summarize this post in five bullets: https://venice.ai/blog/how-to-use-venice-api",
},
],
// @ts-expect-error - Venice-specific parameter
venice_parameters: {
enable_web_scraping: true,
},
});
console.log(response.choices[0].message.content);
```
```python Python theme={"system"}
import os
from openai import OpenAI
client = OpenAI(
api_key=os.environ["VENICE_API_KEY"],
base_url="https://api.venice.ai/api/v1",
)
response = client.chat.completions.create(
model="openai-gpt-55",
messages=[
{
"role": "user",
"content": "Summarize this post in five bullets: https://venice.ai/blog/how-to-use-venice-api",
}
],
extra_body={
"venice_parameters": {
"enable_web_scraping": True,
}
},
)
print(response.choices[0].message.content)
```
将 PDF、Office 文档、代码和文本文件（最大 25MB）直接附加到聊天请求。完整的格式列表请参阅 [文件输入指南](/guides/features/file-inputs)。
```bash Curl theme={"system"}
# Encode a local file as a base64 data URL, then send it inline
FILE_B64=$(base64 q3-report.pdf | tr -d '\n')
curl https://api.venice.ai/api/v1/chat/completions \
-H "Authorization: Bearer $VENICE_API_KEY" \
-H "Content-Type: application/json" \
-d "{
\"model\": \"openai-gpt-55\",
\"messages\": [
{
\"role\": \"user\",
\"content\": [
{\"type\": \"text\", \"text\": \"Summarize this report in five bullets and list the main risks.\"},
{\"type\": \"file\", \"file\": {\"filename\": \"q3-report.pdf\", \"file_data\": \"data:application/pdf;base64,${FILE_B64}\"}}
]
}
]
}"
```
```ts TypeScript theme={"system"}
import OpenAI from "openai";
import { readFile } from "node:fs/promises";
const client = new OpenAI({
apiKey: process.env.VENICE_API_KEY!,
baseURL: "https://api.venice.ai/api/v1",
});
const pdf = await readFile("q3-report.pdf");
const fileData = `data:application/pdf;base64,${pdf.toString("base64")}`;
const response = await client.chat.completions.create({
model: "openai-gpt-55",
messages: [
{
role: "user",
content: [
{ type: "text", text: "Summarize this report in five bullets and list the main risks." },
// @ts-expect-error - Venice file input block
{ type: "file", file: { filename: "q3-report.pdf", file_data: fileData } },
],
},
],
});
console.log(response.choices[0].message.content);
```
```python Python theme={"system"}
import base64
import os
from pathlib import Path
from openai import OpenAI
client = OpenAI(
api_key=os.environ["VENICE_API_KEY"],
base_url="https://api.venice.ai/api/v1",
)
path = Path("q3-report.pdf")
file_data = "data:application/pdf;base64," + base64.b64encode(path.read_bytes()).decode("utf-8")
response = client.chat.completions.create(
model="openai-gpt-55",
messages=[
{
"role": "user",
"content": [
{"type": "text", "text": "Summarize this report in five bullets and list the main risks."},
{"type": "file", "file": {"filename": "q3-report.pdf", "file_data": file_data}},
],
}
],
)
print(response.choices[0].message.content)
```
使用您的 Venice 密钥或 x402 钱包跨 11 条支持的链代理 JSON-RPC 2.0 调用。链、方法和 credit 层级请参阅 [Crypto RPC 参考](/api-reference/endpoint/crypto/rpc)。
```bash Curl theme={"system"}
curl https://api.venice.ai/api/v1/crypto/rpc/ethereum-mainnet \
-H "Authorization: Bearer $VENICE_API_KEY" \
-H "Content-Type: application/json" \
-d '{
"jsonrpc": "2.0",
"method": "eth_blockNumber",
"params": [],
"id": 1
}'
```
```ts TypeScript theme={"system"}
const response = await fetch(
"https://api.venice.ai/api/v1/crypto/rpc/base-mainnet",
{
method: "POST",
headers: {
Authorization: `Bearer ${process.env.VENICE_API_KEY}`,
"Content-Type": "application/json",
},
body: JSON.stringify([
{ jsonrpc: "2.0", method: "eth_chainId", params: [], id: 1 },
{ jsonrpc: "2.0", method: "eth_blockNumber", params: [], id: 2 },
]),
}
);
const results = await response.json();
console.log(results);
```
```python Python theme={"system"}
import os
import requests
response = requests.post(
"https://api.venice.ai/api/v1/crypto/rpc/ethereum-mainnet",
headers={
"Authorization": f"Bearer {os.environ['VENICE_API_KEY']}",
"Content-Type": "application/json",
},
json={
"jsonrpc": "2.0",
"method": "eth_getBalance",
"params": ["0xd8dA6BF26964aF9D7eEd9e03E53415D37aA96045", "latest"],
"id": 1,
},
)
print(response.json())
```
定价
充值、质押或按请求付费
使用 credit 为账户充值，质押 DIEM 以获得每日津贴，或在 Base 上使用 USDC 完全跳过账户。
Credits
USD 或加密货币
以美元或加密货币按用量付费。Credit 永不过期，适用于所有端点。
购买 Credit
DIEM
每日津贴
一次性质押 DIEM 或 VVV，每天赚取固定的推理津贴，无每次调用费用。
了解 DIEM
x402
Base 上的 USDC
从任何 Base 钱包使用 USDC 按请求付费。无需账户或 API 密钥，为 agent 而构建。
阅读 x402 指南
有问题或反馈？加入我们的 [Discord](https://discord.gg/askvenice)。