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Perplexity AI (pplx-api)

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.

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>.

Model NameFunction Call
sonar-deep-researchcompletion(model="perplexity/sonar-deep-research", messages)
sonar-reasoning-procompletion(model="perplexity/sonar-reasoning-pro", messages)
sonar-reasoningcompletion(model="perplexity/sonar-reasoning", messages)
sonar-procompletion(model="perplexity/sonar-pro", messages)
sonarcompletion(model="perplexity/sonar", messages)
r1-1776completion(model="perplexity/r1-1776", messages)

Agentic Research API (Responses API)​

Requires v1.72.6+

Using Presets​

Presets provide optimized defaults for specific use cases. Start with a preset for quick setup:

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:

from litellm import responses
import os

os.environ['PERPLEXITY_API_KEY'] = ""

response = responses(
    model="perplexity/openai/gpt-4o",
    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-4o",
    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)

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-3-5-sonnet-20241022",
    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-4o",
    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​

ProviderModel NameFunction Call
OpenAIgpt-4oresponses(model="perplexity/openai/gpt-4o", ...)
OpenAIgpt-4o-miniresponses(model="perplexity/openai/gpt-4o-mini", ...)
OpenAIgpt-5.2responses(model="perplexity/openai/gpt-5.2", ...)
Anthropicclaude-3-5-sonnet-20241022responses(model="perplexity/anthropic/claude-3-5-sonnet-20241022", ...)
Anthropicclaude-3-5-haiku-20241022responses(model="perplexity/anthropic/claude-3-5-haiku-20241022", ...)
Googlegemini-2.0-flash-expresponses(model="perplexity/google/gemini-2.0-flash-exp", ...)
Googlegemini-2.0-flash-thinking-expresponses(model="perplexity/google/gemini-2.0-flash-thinking-exp", ...)
xAIgrok-2-1212responses(model="perplexity/xai/grok-2-1212", ...)
xAIgrok-2-vision-1212responses(model="perplexity/xai/grok-2-vision-1212", ...)

Available Presets​

Preset NameFunction Call
fast-searchresponses(model="perplexity/preset/fast-search", ...)
pro-searchresponses(model="perplexity/preset/pro-search", ...)
deep-researchresponses(model="perplexity/preset/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-4o",
    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