/assistants

淘汰公告

OpenAI 已淘汰 Assistants API。它將於 2026 年 8 月 26 日 關閉。

請考慮改為遷移到 Responses API。詳情請參閱 OpenAI 的遷移指南。

涵蓋 Threads、Messages、Assistants。

LiteLLM 目前涵蓋：

建立 Assistants
刪除 Assistants
取得 Assistants
建立 Thread
取得 Thread
新增 Messages
取得 Messages
執行 Thread

支援的提供者:

OpenAI
Azure OpenAI
OpenAI 相容 API

快速開始

呼叫既有的 Assistant。

取得 Assistant
當使用者開始對話時建立 Thread。
當使用者提出問題時，將 Messages 新增至 Thread。
在 Thread 上執行 Assistant，透過呼叫模型與工具來產生回應。

SDK + PROXY

SDK
PROXY

建立 Assistant

import litellm
import os 

# setup env
os.environ["OPENAI_API_KEY"] = "sk-.."

assistant = litellm.create_assistants(
            custom_llm_provider="openai",
            model="gpt-4-turbo",
            instructions="You are a personal math tutor. When asked a question, write and run Python code to answer the question.",
            name="Math Tutor",
            tools=[{"type": "code_interpreter"}],
)

### ASYNC USAGE ### 
# assistant = await litellm.acreate_assistants(
#             custom_llm_provider="openai",
#             model="gpt-4-turbo",
#             instructions="You are a personal math tutor. When asked a question, write and run Python code to answer the question.",
#             name="Math Tutor",
#             tools=[{"type": "code_interpreter"}],
# )

取得 Assistant

from litellm import get_assistants, aget_assistants
import os 

# setup env
os.environ["OPENAI_API_KEY"] = "sk-.."

assistants = get_assistants(custom_llm_provider="openai")

### ASYNC USAGE ### 
# assistants = await aget_assistants(custom_llm_provider="openai")

建立 Thread

from litellm import create_thread, acreate_thread
import os 

os.environ["OPENAI_API_KEY"] = "sk-.."

new_thread = create_thread(
            custom_llm_provider="openai",
            messages=[{"role": "user", "content": "Hey, how's it going?"}],  # type: ignore
        )

### ASYNC USAGE ### 
# new_thread = await acreate_thread(custom_llm_provider="openai",messages=[{"role": "user", "content": "Hey, how's it going?"}])

將 Messages 新增至 Thread

from litellm import create_thread, get_thread, aget_thread, add_message, a_add_message
import os 

os.environ["OPENAI_API_KEY"] = "sk-.."

## CREATE A THREAD
_new_thread = create_thread(
            custom_llm_provider="openai",
            messages=[{"role": "user", "content": "Hey, how's it going?"}],  # type: ignore
        )

## OR retrieve existing thread
received_thread = get_thread(
            custom_llm_provider="openai",
            thread_id=_new_thread.id,
        )

### ASYNC USAGE ### 
# received_thread = await aget_thread(custom_llm_provider="openai", thread_id=_new_thread.id,)

## ADD MESSAGE TO THREAD
message = {"role": "user", "content": "Hey, how's it going?"}
added_message = add_message(
            thread_id=_new_thread.id, custom_llm_provider="openai", **message
        )

### ASYNC USAGE ### 
# added_message = await a_add_message(thread_id=_new_thread.id, custom_llm_provider="openai", **message)

在 Thread 上執行 Assistant

from litellm import get_assistants, create_thread, add_message, run_thread, arun_thread
import os 

os.environ["OPENAI_API_KEY"] = "sk-.."
assistants = get_assistants(custom_llm_provider="openai")

## get the first assistant ###
assistant_id = assistants.data[0].id

## GET A THREAD
_new_thread = create_thread(
            custom_llm_provider="openai",
            messages=[{"role": "user", "content": "Hey, how's it going?"}],  # type: ignore
        )

## ADD MESSAGE
message = {"role": "user", "content": "Hey, how's it going?"}
added_message = add_message(
            thread_id=_new_thread.id, custom_llm_provider="openai", **message
        )

## 🚨 RUN THREAD
response = run_thread(
            custom_llm_provider="openai", thread_id=thread_id, assistant_id=assistant_id
        )

### ASYNC USAGE ### 
# response = await arun_thread(custom_llm_provider="openai", thread_id=thread_id, assistant_id=assistant_id)

print(f"run_thread: {run_thread}")

assistant_settings:
  custom_llm_provider: azure
  litellm_params: 
    api_key: os.environ/AZURE_API_KEY
    api_base: os.environ/AZURE_API_BASE
    api_version: os.environ/AZURE_API_VERSION

$ litellm --config /path/to/config.yaml

# RUNNING on http://0.0.0.0:4000

建立 Assistant

curl "http://localhost:4000/v1/assistants" \
  -H "Content-Type: application/json" \
  -H "Authorization: Bearer sk-1234" \
  -d '{
    "instructions": "You are a personal math tutor. When asked a question, write and run Python code to answer the question.",
    "name": "Math Tutor",
    "tools": [{"type": "code_interpreter"}],
    "model": "gpt-4-turbo"
  }'

取得 Assistant

curl "http://0.0.0.0:4000/v1/assistants?order=desc&limit=20" \
  -H "Content-Type: application/json" \
  -H "Authorization: Bearer sk-1234"

建立 Thread

curl http://0.0.0.0:4000/v1/threads \
  -H "Content-Type: application/json" \
  -H "Authorization: Bearer sk-1234" \
  -d ''

取得 Thread

curl http://0.0.0.0:4000/v1/threads/{thread_id} \
  -H "Content-Type: application/json" \
  -H "Authorization: Bearer sk-1234"

將 Messages 新增至 Thread

curl http://0.0.0.0:4000/v1/threads/{thread_id}/messages \
  -H "Content-Type: application/json" \
  -H "Authorization: Bearer sk-1234" \
  -d '{
      "role": "user",
      "content": "How does AI work? Explain it in simple terms."
    }'

在 Thread 上執行 Assistant

curl http://0.0.0.0:4000/v1/threads/thread_abc123/runs \
  -H "Authorization: Bearer sk-1234" \
  -H "Content-Type: application/json" \
  -d '{
    "assistant_id": "asst_abc123"
  }'

串流

SDK
PROXY

from litellm import run_thread_stream 
import os

os.environ["OPENAI_API_KEY"] = "sk-.."

message = {"role": "user", "content": "Hey, how's it going?"}  

data = {"custom_llm_provider": "openai", "thread_id": _new_thread.id, "assistant_id": assistant_id, **message}

run = run_thread_stream(**data)
with run as run:
    assert isinstance(run, AssistantEventHandler)
    for chunk in run: 
      print(f"chunk: {chunk}")
    run.until_done()

curl -X POST 'http://0.0.0.0:4000/threads/{thread_id}/runs' \
-H 'Authorization: Bearer sk-1234' \
-H 'Content-Type: application/json' \
-d '{
      "assistant_id": "asst_6xVZQFFy1Kw87NbnYeNebxTf",
      "stream": true
}'

👉 Proxy API 參考

Azure OpenAI

設定

assistant_settings:
  custom_llm_provider: azure
  litellm_params: 
    api_key: os.environ/AZURE_API_KEY
    api_base: os.environ/AZURE_API_BASE

curl

curl -X POST "http://localhost:4000/v1/assistants" \
  -H "Content-Type: application/json" \
  -H "Authorization: Bearer sk-1234" \
  -d '{
    "instructions": "You are a personal math tutor. When asked a question, write and run Python code to answer the question.",
    "name": "Math Tutor",
    "tools": [{"type": "code_interpreter"}],
    "model": "<my-azure-deployment-name>"
  }'

OpenAI 相容 API

若要呼叫相容於 openai 的 Assistants API（例如 Astra Assistants API），只要將 openai/ 加到模型名稱即可：

設定

assistant_settings:
  custom_llm_provider: openai
  litellm_params: 
    api_key: os.environ/ASTRA_API_KEY
    api_base: os.environ/ASTRA_API_BASE

curl

curl -X POST "http://localhost:4000/v1/assistants" \
  -H "Content-Type: application/json" \
  -H "Authorization: Bearer sk-1234" \
  -d '{
    "instructions": "You are a personal math tutor. When asked a question, write and run Python code to answer the question.",
    "name": "Math Tutor",
    "tools": [{"type": "code_interpreter"}],
    "model": "openai/<my-astra-model-name>"
  }'

支援的提供者:​

快速開始​

SDK + PROXY​

串流​

👉 Proxy API 參考​

Azure OpenAI​

OpenAI 相容 API​