Perplexity AI (pplx-api) | liteLLM (original) (raw)

https://www.perplexity.ai

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)

Supported Models​

All models listed here https://docs.perplexity.ai/docs/model-cards are supported. Just do model=perplexity/<model-name>.

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)

Using Third-Party Models​

Access models from OpenAI, Anthropic, Google, xAI, and other providers through Perplexity's unified API:

• OpenAI
• Anthropic
• Google
• 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)

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:
    if hasattr(chunk, 'type'):
        if chunk.type == "response.output_text.delta":
            print(chunk.delta, end="", flush=True)

Supported Third-Party Models​

Available Presets​

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