litellm-mirror/docs/my-website/docs/response_api.md

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/responses [Beta]

LiteLLM provides a BETA endpoint in the spec of OpenAI's /responses API

Feature	Supported	Notes
Cost Tracking	✅	Works with all supported models
Logging	✅	Works across all integrations
End-user Tracking	✅
Streaming	✅
Fallbacks	✅	Works between supported models
Loadbalancing	✅	Works between supported models
Supported LiteLLM Versions	1.63.8+
Supported LLM providers	All LiteLLM supported providers

Usage

LiteLLM Python SDK

Non-streaming

import litellm

# Non-streaming response
response = litellm.responses(
    model="openai/o1-pro",
    input="Tell me a three sentence bedtime story about a unicorn.",
    max_output_tokens=100
)

print(response)

Streaming

import litellm

# Streaming response
response = litellm.responses(
    model="openai/o1-pro",
    input="Tell me a three sentence bedtime story about a unicorn.",
    stream=True
)

for event in response:
    print(event)

Non-streaming

import litellm
import os

# Set API key
os.environ["ANTHROPIC_API_KEY"] = "your-anthropic-api-key"

# Non-streaming response
response = litellm.responses(
    model="anthropic/claude-3-5-sonnet-20240620",
    input="Tell me a three sentence bedtime story about a unicorn.",
    max_output_tokens=100
)

print(response)

Streaming

import litellm
import os

# Set API key
os.environ["ANTHROPIC_API_KEY"] = "your-anthropic-api-key"

# Streaming response
response = litellm.responses(
    model="anthropic/claude-3-5-sonnet-20240620",
    input="Tell me a three sentence bedtime story about a unicorn.",
    stream=True
)

for event in response:
    print(event)

Non-streaming

import litellm
import os

# Set credentials - Vertex AI uses application default credentials
# Run 'gcloud auth application-default login' to authenticate
os.environ["VERTEXAI_PROJECT"] = "your-gcp-project-id"
os.environ["VERTEXAI_LOCATION"] = "us-central1"

# Non-streaming response
response = litellm.responses(
    model="vertex_ai/gemini-1.5-pro",
    input="Tell me a three sentence bedtime story about a unicorn.",
    max_output_tokens=100
)

print(response)

Streaming

import litellm
import os

# Set credentials - Vertex AI uses application default credentials
# Run 'gcloud auth application-default login' to authenticate
os.environ["VERTEXAI_PROJECT"] = "your-gcp-project-id"
os.environ["VERTEXAI_LOCATION"] = "us-central1"

# Streaming response
response = litellm.responses(
    model="vertex_ai/gemini-1.5-pro",
    input="Tell me a three sentence bedtime story about a unicorn.",
    stream=True
)

for event in response:
    print(event)

Non-streaming

import litellm
import os

# Set AWS credentials
os.environ["AWS_ACCESS_KEY_ID"] = "your-access-key-id"
os.environ["AWS_SECRET_ACCESS_KEY"] = "your-secret-access-key"
os.environ["AWS_REGION_NAME"] = "us-west-2"  # or your AWS region

# Non-streaming response
response = litellm.responses(
    model="bedrock/anthropic.claude-3-sonnet-20240229-v1:0",
    input="Tell me a three sentence bedtime story about a unicorn.",
    max_output_tokens=100
)

print(response)

Streaming

import litellm
import os

# Set AWS credentials
os.environ["AWS_ACCESS_KEY_ID"] = "your-access-key-id"
os.environ["AWS_SECRET_ACCESS_KEY"] = "your-secret-access-key"
os.environ["AWS_REGION_NAME"] = "us-west-2"  # or your AWS region

# Streaming response
response = litellm.responses(
    model="bedrock/anthropic.claude-3-sonnet-20240229-v1:0",
    input="Tell me a three sentence bedtime story about a unicorn.",
    stream=True
)

for event in response:
    print(event)

Non-streaming

import litellm
import os

# Set API key for Google AI Studio
os.environ["GEMINI_API_KEY"] = "your-gemini-api-key"

# Non-streaming response
response = litellm.responses(
    model="gemini/gemini-1.5-flash",
    input="Tell me a three sentence bedtime story about a unicorn.",
    max_output_tokens=100
)

print(response)

Streaming

import litellm
import os

# Set API key for Google AI Studio
os.environ["GEMINI_API_KEY"] = "your-gemini-api-key"

# Streaming response
response = litellm.responses(
    model="gemini/gemini-1.5-flash",
    input="Tell me a three sentence bedtime story about a unicorn.",
    stream=True
)

for event in response:
    print(event)

LiteLLM Proxy with OpenAI SDK

First, set up and start your LiteLLM proxy server.

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

# RUNNING on http://0.0.0.0:4000

First, add this to your litellm proxy config.yaml:

model_list:
  - model_name: openai/o1-pro
    litellm_params:
      model: openai/o1-pro
      api_key: os.environ/OPENAI_API_KEY

Non-streaming

from openai import OpenAI

# Initialize client with your proxy URL
client = OpenAI(
    base_url="http://localhost:4000",  # Your proxy URL
    api_key="your-api-key"             # Your proxy API key
)

# Non-streaming response
response = client.responses.create(
    model="openai/o1-pro",
    input="Tell me a three sentence bedtime story about a unicorn."
)

print(response)

Streaming

from openai import OpenAI

# Initialize client with your proxy URL
client = OpenAI(
    base_url="http://localhost:4000",  # Your proxy URL
    api_key="your-api-key"             # Your proxy API key
)

# Streaming response
response = client.responses.create(
    model="openai/o1-pro",
    input="Tell me a three sentence bedtime story about a unicorn.",
    stream=True
)

for event in response:
    print(event)

First, add this to your litellm proxy config.yaml:

model_list:
  - model_name: anthropic/claude-3-5-sonnet-20240620
    litellm_params:
      model: anthropic/claude-3-5-sonnet-20240620
      api_key: os.environ/ANTHROPIC_API_KEY

Non-streaming

from openai import OpenAI

# Initialize client with your proxy URL
client = OpenAI(
    base_url="http://localhost:4000",  # Your proxy URL
    api_key="your-api-key"             # Your proxy API key
)

# Non-streaming response
response = client.responses.create(
    model="anthropic/claude-3-5-sonnet-20240620",
    input="Tell me a three sentence bedtime story about a unicorn."
)

print(response)

Streaming

from openai import OpenAI

# Initialize client with your proxy URL
client = OpenAI(
    base_url="http://localhost:4000",  # Your proxy URL
    api_key="your-api-key"             # Your proxy API key
)

# Streaming response
response = client.responses.create(
    model="anthropic/claude-3-5-sonnet-20240620",
    input="Tell me a three sentence bedtime story about a unicorn.",
    stream=True
)

for event in response:
    print(event)

First, add this to your litellm proxy config.yaml:

model_list:
  - model_name: vertex_ai/gemini-1.5-pro
    litellm_params:
      model: vertex_ai/gemini-1.5-pro
      vertex_project: your-gcp-project-id
      vertex_location: us-central1

Non-streaming

from openai import OpenAI

# Initialize client with your proxy URL
client = OpenAI(
    base_url="http://localhost:4000",  # Your proxy URL
    api_key="your-api-key"             # Your proxy API key
)

# Non-streaming response
response = client.responses.create(
    model="vertex_ai/gemini-1.5-pro",
    input="Tell me a three sentence bedtime story about a unicorn."
)

print(response)

Streaming

from openai import OpenAI

# Initialize client with your proxy URL
client = OpenAI(
    base_url="http://localhost:4000",  # Your proxy URL
    api_key="your-api-key"             # Your proxy API key
)

# Streaming response
response = client.responses.create(
    model="vertex_ai/gemini-1.5-pro",
    input="Tell me a three sentence bedtime story about a unicorn.",
    stream=True
)

for event in response:
    print(event)

First, add this to your litellm proxy config.yaml:

model_list:
  - model_name: bedrock/anthropic.claude-3-sonnet-20240229-v1:0
    litellm_params:
      model: bedrock/anthropic.claude-3-sonnet-20240229-v1:0
      aws_access_key_id: os.environ/AWS_ACCESS_KEY_ID
      aws_secret_access_key: os.environ/AWS_SECRET_ACCESS_KEY
      aws_region_name: us-west-2

Non-streaming

from openai import OpenAI

# Initialize client with your proxy URL
client = OpenAI(
    base_url="http://localhost:4000",  # Your proxy URL
    api_key="your-api-key"             # Your proxy API key
)

# Non-streaming response
response = client.responses.create(
    model="bedrock/anthropic.claude-3-sonnet-20240229-v1:0",
    input="Tell me a three sentence bedtime story about a unicorn."
)

print(response)

Streaming

from openai import OpenAI

# Initialize client with your proxy URL
client = OpenAI(
    base_url="http://localhost:4000",  # Your proxy URL
    api_key="your-api-key"             # Your proxy API key
)

# Streaming response
response = client.responses.create(
    model="bedrock/anthropic.claude-3-sonnet-20240229-v1:0",
    input="Tell me a three sentence bedtime story about a unicorn.",
    stream=True
)

for event in response:
    print(event)

First, add this to your litellm proxy config.yaml:

model_list:
  - model_name: gemini/gemini-1.5-flash
    litellm_params:
      model: gemini/gemini-1.5-flash
      api_key: os.environ/GEMINI_API_KEY

Non-streaming

from openai import OpenAI

# Initialize client with your proxy URL
client = OpenAI(
    base_url="http://localhost:4000",  # Your proxy URL
    api_key="your-api-key"             # Your proxy API key
)

# Non-streaming response
response = client.responses.create(
    model="gemini/gemini-1.5-flash",
    input="Tell me a three sentence bedtime story about a unicorn."
)

print(response)

Streaming

from openai import OpenAI

# Initialize client with your proxy URL
client = OpenAI(
    base_url="http://localhost:4000",  # Your proxy URL
    api_key="your-api-key"             # Your proxy API key
)

# Streaming response
response = client.responses.create(
    model="gemini/gemini-1.5-flash",
    input="Tell me a three sentence bedtime story about a unicorn.",
    stream=True
)

for event in response:
    print(event)