--- title: "Perplexity AI (pplx-api)" description: "import Tabs from '@theme/Tabs'; import TabItem from '@theme/TabItem';" type: skill canonical_url: https://claudary.paisolsolutions.com/skills/perplexity source: "Claudary" difficulty: intermediate author: "Claude Code Knowledge Pack" date: 2026-07-10T11:31:55.131Z license: CC-BY-4.0 attribution: "Perplexity AI (pplx-api) — Claudary (https://claudary.paisolsolutions.com/skills/perplexity)" --- # Perplexity AI (pplx-api) import Tabs from '@theme/Tabs'; import TabItem from '@theme/TabItem'; ## Overview import Tabs from '@theme/Tabs'; import TabItem from '@theme/TabItem'; # Perplexity AI (pplx-api) https://www.perplexity.ai ## API Key ```python # env variable os.environ['PERPLEXITYAI_API_KEY'] ``` ## Sample Usage ```python from litellm import completion import os os.environ['PERPLEXITYAI_API_KEY'] = "" response = completion( model="perplexity/sonar-pro", messages=messages ) print(response) ``` ## Sample Usage - Streaming ```python 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](../reasoning_content) ::: You can set the reasoning effort by setting the `reasoning_effort` parameter. ```python from litellm import completion import os os.environ['PERPLEXITYAI_API_KEY'] = "" response = completion( model="perplexity/sonar-reasoning", messages=messages, reasoning_effort="high" ) print(response) ``` 1. Setup config.yaml ```yaml model_list: - model_name: perplexity-sonar-reasoning-model litellm_params: model: perplexity/sonar-reasoning api_key: os.environ/PERPLEXITYAI_API_KEY ``` 2. Start proxy ```bash litellm --config /path/to/config.yaml ``` 3. Test it! Replace `anything` with your LiteLLM Proxy Virtual Key, if [setup](../proxy/virtual_keys). ```bash 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 | 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: ```python 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) ``` 1. Setup config.yaml ```yaml model_list: - model_name: perplexity-pro-search litellm_params: model: perplexity/preset/pro-search api_key: os.environ/PERPLEXITY_API_KEY ``` 2. Start proxy ```bash litellm --config /path/to/config.yaml ``` 3. Test it! ```bash 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: ```python 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) ``` ```python 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) ``` ```python 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) ``` ```python 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: ```python 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: ```python 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: ```python 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: ```python 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: ```python 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: ```python 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 | 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", ...)` | | Google | gemini-3-pro-preview | `responses(model="perplexity/google/gemini-3-pro-preview", ...)` | | Google | gemini-3-flash-preview | `responses(model="perplexity/google/gemini-3-flash-preview", ...)` | | Google | gemini-2.5-pro | `responses(model="perplexity/google/gemini-2.5-pro", ...)` | | Google | 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 ```python 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](../completion/provider_specific_params.md) ::: --- Source: [Claudary](https://claudary.paisolsolutions.com/skills/perplexity) · https://claudary.paisolsolutions.com