Manus
透過 LiteLLM 相容於 OpenAI 的 Responses API 使用 Manus AI 代理程式。
| 屬性 | 詳細資訊 |
|---|---|
| 說明 | Manus 是一個 AI 代理程式平台,適用於複雜推理任務、文件分析,以及具備非同步任務執行的多步驟工作流程。 |
| LiteLLM 上的提供者路由 | manus/{agent_profile} |
| 支援的操作 | /responses(Responses API)、/files(Files API) |
| 提供者文件 | Manus API ↗ |
模型格式
manus/{agent_profile}
範例:
manus/manus-1.6- 通用代理程式manus/manus-1.6-lite- 適用於簡單任務的輕量型代理程式manus/manus-1.6-max- 適用於複雜分析的進階代理程式
LiteLLM Python SDK
Basic Usage
import litellm
import os
import time
# Set API key
os.environ["MANUS_API_KEY"] = "your-manus-api-key"
# Create task
response = litellm.responses(
model="manus/manus-1.6",
input="What's the capital of France?",
)
print(f"Task ID: {response.id}")
print(f"Status: {response.status}") # "running"
# Poll until complete
task_id = response.id
while response.status == "running":
time.sleep(5)
response = litellm.get_response(
response_id=task_id,
custom_llm_provider="manus",
)
print(f"Status: {response.status}")
# Get results
if response.status == "completed":
for message in response.output:
if message.role == "assistant":
print(message.content[0].text)
LiteLLM AI Gateway
設定
config.yaml
model_list:
- model_name: manus-agent
litellm_params:
model: manus/manus-1.6
api_key: os.environ/MANUS_API_KEY
Start Proxy
litellm --config config.yaml
使用方式
- cURL
- OpenAI SDK
Create Task
# Create task
curl -X POST http://localhost:4000/responses \
-H "Authorization: Bearer your-proxy-key" \
-H "Content-Type: application/json" \
-d '{
"model": "manus-agent",
"input": "What is the capital of France?"
}'
# Response
{
"id": "task_abc123",
"status": "running",
"metadata": {
"task_url": "https://manus.im/app/task_abc123"
}
}
Poll for Completion
# Check status (repeat until status is "completed")
curl http://localhost:4000/responses/task_abc123 \
-H "Authorization: Bearer your-proxy-key"
# When completed
{
"id": "task_abc123",
"status": "completed",
"output": [
{
"role": "user",
"content": [{"text": "What is the capital of France?"}]
},
{
"role": "assistant",
"content": [{"text": "The capital of France is Paris."}]
}
]
}
Create Task and Poll
import openai
import time
client = openai.OpenAI(
base_url="http://localhost:4000",
api_key="your-proxy-key"
)
# Create task
response = client.responses.create(
model="manus-agent",
input="What is the capital of France?"
)
print(f"Task ID: {response.id}")
print(f"Status: {response.status}") # "running"
# Poll until complete
task_id = response.id
while response.status == "running":
time.sleep(5)
response = client.responses.retrieve(response_id=task_id)
print(f"Status: {response.status}")
# Get results
if response.status == "completed":
for message in response.output:
if message.role == "assistant":
print(message.content[0].text)
運作方式
Manus 以非同步代理程式 API運作:
- 建立任務:當您呼叫
litellm.responses()時,Manus 會建立一個任務並立即回傳status: "running" - 任務執行:代理程式在背景中處理您的請求
- 輪詢完成狀態:您必須持續呼叫
litellm.get_response()或client.responses.retrieve(),直到狀態變更為"completed" - 取得結果:完成後,
output欄位會包含完整對話
任務狀態:
running- 代理程式正在積極處理pending- 代理程式正在等待輸入completed- 任務已成功完成error- 任務失敗
Production Usage
對於正式環境應用程式,請改用 webhooks 來接收任務完成通知,而非輪詢。
支援的參數
| 參數 | 支援 | 備註 |
|---|---|---|
input | ✅ | 文字、圖片或結構化內容 |
stream | ✅ | 假串流(任務以非同步方式執行) |
max_output_tokens | ✅ | 限制回應長度 |
previous_response_id | ✅ | 用於多輪對話 |
Files API
Manus 支援用於文件分析與處理的檔案上傳。檔案可以先上傳,再於 Responses API 呼叫中參照。
LiteLLM Python SDK
Upload, Use, Retrieve, and Delete Files
import litellm
import os
# Set API key
os.environ["MANUS_API_KEY"] = "your-manus-api-key"
# Upload file
file_content = b"This is a document for analysis."
created_file = await litellm.acreate_file(
file=("document.txt", file_content),
purpose="assistants",
custom_llm_provider="manus",
)
print(f"Uploaded file: {created_file.id}")
# Use file with Responses API
response = await litellm.aresponses(
model="manus/manus-1.6",
input=[
{
"role": "user",
"content": [
{"type": "input_text", "text": "Summarize this document."},
{"type": "input_file", "file_id": created_file.id},
],
},
],
extra_body={"task_mode": "agent", "agent_profile": "manus-1.6-agent"},
)
print(f"Response: {response.id}")
# Retrieve file
retrieved_file = await litellm.afile_retrieve(
file_id=created_file.id,
custom_llm_provider="manus",
)
print(f"File details: {retrieved_file.filename}, {retrieved_file.bytes} bytes")
# Delete file
deleted_file = await litellm.afile_delete(
file_id=created_file.id,
custom_llm_provider="manus",
)
print(f"Deleted: {deleted_file.deleted}")
LiteLLM AI Gateway
- cURL
- OpenAI SDK
Upload File
# Upload file
curl -X POST http://localhost:4000/v1/files \
-H "Authorization: Bearer your-proxy-key" \
-F "file=@document.txt" \
-F "purpose=assistants" \
-F "custom_llm_provider=manus"
# Response
{
"id": "file_abc123",
"object": "file",
"bytes": 1024,
"created_at": 1234567890,
"filename": "document.txt",
"purpose": "assistants",
"status": "uploaded"
}
Use File with Responses API
# Create response with file
curl -X POST http://localhost:4000/responses \
-H "Authorization: Bearer your-proxy-key" \
-H "Content-Type: application/json" \
-d '{
"model": "manus-agent",
"input": [
{
"role": "user",
"content": [
{"type": "input_text", "text": "Summarize this document."},
{"type": "input_file", "file_id": "file_abc123"}
]
}
]
}'
Retrieve File
# Get file details
curl http://localhost:4000/v1/files/file_abc123 \
-H "Authorization: Bearer your-proxy-key"
# Response
{
"id": "file_abc123",
"object": "file",
"bytes": 1024,
"created_at": 1234567890,
"filename": "document.txt",
"purpose": "assistants",
"status": "uploaded"
}
Delete File
# Delete file
curl -X DELETE http://localhost:4000/v1/files/file_abc123 \
-H "Authorization: Bearer your-proxy-key"
# Response
{
"id": "file_abc123",
"object": "file",
"deleted": true
}
Upload, Use, Retrieve, and Delete Files
import openai
client = openai.OpenAI(
base_url="http://localhost:4000",
api_key="your-proxy-key"
)
# Upload file
with open("document.txt", "rb") as f:
created_file = client.files.create(
file=f,
purpose="assistants",
extra_body={"custom_llm_provider": "manus"}
)
print(f"Uploaded file: {created_file.id}")
# Use file with Responses API
response = client.responses.create(
model="manus-agent",
input=[
{
"role": "user",
"content": [
{"type": "input_text", "text": "Summarize this document."},
{"type": "input_file", "file_id": created_file.id}
]
}
]
)
print(f"Response: {response.id}")
# Retrieve file
retrieved_file = client.files.retrieve(created_file.id)
print(f"File: {retrieved_file.filename}, {retrieved_file.bytes} bytes")
# Delete file
deleted_file = client.files.delete(created_file.id)
print(f"Deleted: {deleted_file.deleted}")