API Key
# env variable
os.environ['PERPLEXITYAI_API_KEY']
Sample Usage
from litellm import completion
import os
os.environ['PERPLEXITYAI_API_KEY']=""
response = completion(
model="perplexity/sonar-pro",
messages=messages
)
print(response)
Sample Usage - Streaming
from litellm import completion
import os
os.environ['PERPLEXITYAI_API_KEY']=""
response = completion(
model="perplexity/sonar-pro",
messages=messages,
stream=True
)
for chunk in response:
print(chunk)
Reasoning Effort
Requires v1.72.6+
info
See full guide on Reasoning with LiteLLM here
You can set the reasoning effort by setting the reasoning_effort parameter.
- SDK
- Proxy
from litellm import completion
import os
os.environ['PERPLEXITYAI_API_KEY']=""
response = completion(
model="perplexity/sonar-reasoning",
messages=messages,
reasoning_effort="high"
)
print(response)
- Setup config.yaml
model_list:
-model_name: perplexity-sonar-reasoning-model
litellm_params:
model: perplexity/sonar-reasoning
api_key: os.environ/PERPLEXITYAI_API_KEY
- Start proxy
litellm --config /path/to/config.yaml
- Test it!
Replace anything with your LiteLLM Proxy Virtual Key, if setup.
curl http://0.0.0.0:4000/v1/chat/completions \
-H "Content-Type: application/json" \
-H "Authorization: Bearer anything" \
-d '{
"model": "perplexity-sonar-reasoning-model",
"messages": [{"role": "user", "content": "Who won the World Cup in 2022?"}],
"reasoning_effort": "high"
}'
Supported Models
All models listed here https://docs.perplexity.ai/docs/model-cards are supported. Just do model=perplexity/<model-name>.
| Model Name | Function Call |
|---|---|
| sonar-deep-research | completion(model="perplexity/sonar-deep-research", messages) |
| sonar-reasoning-pro | completion(model="perplexity/sonar-reasoning-pro", messages) |
| sonar-reasoning | completion(model="perplexity/sonar-reasoning", messages) |
| sonar-pro | completion(model="perplexity/sonar-pro", messages) |
| sonar | completion(model="perplexity/sonar", messages) |
| r1-1776 | completion(model="perplexity/r1-1776", messages) |
Agent API (Responses API)
Requires v1.72.6+
Using Presets
Presets provide optimized defaults for specific use cases. Start with a preset for quick setup:
- SDK
- Proxy
from litellm import responses
import os
os.environ['PERPLEXITY_API_KEY']=""
# Using the pro-search preset
response = responses(
model="perplexity/preset/pro-search",
input="What are the latest developments in AI?",
custom_llm_provider="perplexity",
)
print(response.output)
- Setup config.yaml
model_list:
-model_name: perplexity-pro-search
litellm_params:
model: perplexity/preset/pro-search
api_key: os.environ/PERPLEXITY_API_KEY
- Start proxy
litellm --config /path/to/config.yaml
- Test it!
curl http://0.0.0.0:4000/v1/responses \
-H "Content-Type: application/json" \
-H "Authorization: Bearer anything" \
-d '{
"model": "perplexity-pro-search",
"input": "What are the latest developments in AI?"
}'
Using Third-Party Models
Access models from OpenAI, Anthropic, Google, xAI, and other providers through Perplexity's unified API:
- OpenAI
- Anthropic
- xAI
from litellm import responses
import os
os.environ['PERPLEXITY_API_KEY']=""
response = responses(
model="perplexity/openai/gpt-5.2",
input="Explain quantum computing in simple terms",
custom_llm_provider="perplexity",
max_output_tokens=500,
)
print(response.output)
from litellm import responses
import os
os.environ['PERPLEXITY_API_KEY']=""
response = responses(
model="perplexity/anthropic/claude-sonnet-4-5",
input="Write a short story about a robot learning to paint",
custom_llm_provider="perplexity",
max_output_tokens=500,
)
print(response.output)
from litellm import responses
import os
os.environ['PERPLEXITY_API_KEY']=""
response = responses(
model="perplexity/google/gemini-2.5-flash",
input="Explain the concept of neural networks",
custom_llm_provider="perplexity",
max_output_tokens=500,
)
print(response.output)
from litellm import responses
import os
os.environ['PERPLEXITY_API_KEY']=""
response = responses(
model="perplexity/xai/grok-4-1-fast-non-reasoning",
input="What makes a good AI assistant?",
custom_llm_provider="perplexity",
max_output_tokens=500,
)
print(response.output)
Web Search Tool
Enable web search capabilities to access real-time information:
from litellm import responses
import os
os.environ['PERPLEXITY_API_KEY']=""
response = responses(
model="perplexity/openai/gpt-5.2",
input="What's the weather in San Francisco today?",
custom_llm_provider="perplexity",
tools=[{"type":"web_search"}],
instructions="You have access to a web_search tool. Use it for questions about current events.",
)
print(response.output)
Function Calling
The Agent API supports custom function tools. Pass function tools through unchanged:
from litellm import responses
import os
os.environ['PERPLEXITY_API_KEY']=""
response = responses(
model="perplexity/openai/gpt-5.2",
input="What's the weather in San Francisco?",
custom_llm_provider="perplexity",
tools=[
{"type":"web_search"},
{
"type":"function",
"function":{
"name":"get_weather",
"description":"Get the current weather for a location",
"parameters":{
"type":"object",
"properties":{
"location":{"type":"string"},
"unit":{"type":"string","enum":["celsius","fahrenheit"]},
},
},
},
},
],
instructions="Use tools when appropriate.",
)
print(response.output)
Structured Outputs
Request JSON schema structured outputs via the text parameter:
from litellm import responses
import os
os.environ['PERPLEXITY_API_KEY']=""
response = responses(
model="perplexity/preset/pro-search",
input="Extract key facts about the Eiffel Tower",
custom_llm_provider="perplexity",
text={
"format":{
"type":"json_schema",
"name":"facts",
"schema":{
"type":"object",
"properties":{
"name":{"type":"string"},
"height_meters":{"type":"number"},
"year_built":{"type":"integer"},
},
"required":["name","height_meters","year_built"],
},
"strict":True,
}
},
)
print(response.output)
Reasoning Effort (Responses API)
Control the reasoning effort level for reasoning-capable models:
from litellm import responses
import os
os.environ['PERPLEXITY_API_KEY']=""
response = responses(
model="perplexity/openai/gpt-5.2",
input="Solve this complex problem step by step",
custom_llm_provider="perplexity",
reasoning={"effort":"high"},# Options: low, medium, high
max_output_tokens=1000,
)
print(response.output)
Multi-Turn Conversations
Use message arrays for multi-turn conversations with context:
from litellm import responses
import os
os.environ['PERPLEXITY_API_KEY']=""
response = responses(
model="perplexity/anthropic/claude-sonnet-4-5",
input=[
{"type":"message","role":"system","content":"You are a helpful assistant."},
{"type":"message","role":"user","content":"What are the latest AI developments?"},
],
custom_llm_provider="perplexity",
instructions="Provide detailed, well-researched answers.",
max_output_tokens=800,
)
print(response.output)
Streaming Responses
Stream responses for real-time output:
from litellm import responses
import os
os.environ['PERPLEXITY_API_KEY']=""
response = responses(
model="perplexity/openai/gpt-5.2",
input="Tell me a story about space exploration",
custom_llm_provider="perplexity",
stream=True,
max_output_tokens=500,
)
for chunk in response:
ifhasattr(chunk,'type'):
if chunk.type=="response.output_text.delta":
print(chunk.delta, end="", flush=True)
Supported Third-Party Models
| Provider | Model Name | Function Call |
|---|---|---|
| OpenAI | gpt-5.2 | responses(model="perplexity/openai/gpt-5.2", ...) |
| OpenAI | gpt-5.1 | responses(model="perplexity/openai/gpt-5.1", ...) |
| OpenAI | gpt-5-mini | responses(model="perplexity/openai/gpt-5-mini", ...) |
| Anthropic | claude-opus-4-6 | responses(model="perplexity/anthropic/claude-opus-4-6", ...) |
| Anthropic | claude-opus-4-5 | responses(model="perplexity/anthropic/claude-opus-4-5", ...) |
| Anthropic | claude-sonnet-4-5 | responses(model="perplexity/anthropic/claude-sonnet-4-5", ...) |
| Anthropic | claude-haiku-4-5 | responses(model="perplexity/anthropic/claude-haiku-4-5", ...) |
| gemini-3-pro-preview | responses(model="perplexity/google/gemini-3-pro-preview", ...) | |
| gemini-3-flash-preview | responses(model="perplexity/google/gemini-3-flash-preview", ...) | |
| gemini-2.5-pro | responses(model="perplexity/google/gemini-2.5-pro", ...) | |
| gemini-2.5-flash | responses(model="perplexity/google/gemini-2.5-flash", ...) | |
| xAI | grok-4-1-fast-non-reasoning | responses(model="perplexity/xai/grok-4-1-fast-non-reasoning", ...) |
| Perplexity | sonar | responses(model="perplexity/perplexity/sonar", ...) |
Available Presets
| Preset Name | Function Call |
|---|---|
| fast-search | responses(model="perplexity/preset/fast-search", ...) |
| pro-search | responses(model="perplexity/preset/pro-search", ...) |
| deep-research | responses(model="perplexity/preset/deep-research", ...) |
| advanced-deep-research | responses(model="perplexity/preset/advanced-deep-research", ...) |
Complete Example
from litellm import responses
import os
os.environ['PERPLEXITY_API_KEY']=""
# Comprehensive example with multiple features
response = responses(
model="perplexity/openai/gpt-5.2",
input="Research the latest developments in quantum computing and provide sources",
custom_llm_provider="perplexity",
tools=[
{"type":"web_search"},
{"type":"fetch_url"}
],
instructions="Use web_search to find relevant information and fetch_url to retrieve detailed content from sources. Provide citations for all claims.",
max_output_tokens=1000,
temperature=0.7,
)
print(f"Response ID: {response.id}")
print(f"Model: {response.model}")
print(f"Status: {response.status}")
print(f"Output: {response.output}")
print(f"Usage: {response.usage}")
info
For more information about passing provider-specific parameters, go here