Perplexity Research

Conduct deep research using Perplexity Agent API with web search, reasoning, and multi-model analysis. Use when the user needs current information, market research, trend analysis, investment insights, or comprehensive research on any topic requiring web search and reasoning capabilities.

Research assistant powered by Perplexity Agent API with web search and reasoning capabilities.

Quick Start

The Perplexity client is available at scripts/perplexity_client.py in this skill folder.

Default model: openai/gpt-5.2 (GPT latest)

Key capabilities:

• Web search for current information
• High reasoning effort for deep analysis
• Multi-model comparison
• Streaming responses
• Cost tracking

Common Research Patterns

1. Deep Research Query

Use for comprehensive analysis requiring web search and reasoning:

python

# Import from skill scripts folder
import sys
from pathlib import Path
sys.path.insert(0, str(Path(__file__).parent / "scripts"))
from perplexity_client import PerplexityClient

client = PerplexityClient()
result = client.research_query(
    query="Your research question here",
    model="openai/gpt-5.2",
    reasoning_effort="high",
    max_tokens=2000
)

if "error" not in result:
    print(result["answer"])
    print(f"Tokens: {result['tokens']}, Cost: ${result['cost']}")

2. Quick Web Search

Use for time-sensitive or current information:

python

result = client.search_query(
    query="Your question about current events",
    model="openai/gpt-5.2",
    max_tokens=1000
)

3. Model Comparison

Use when output quality is critical:

python

results = client.compare_models(
    query="Your question",
    models=["openai/gpt-5.2", "anthropic/claude-3-5-sonnet", "google/gemini-2.0-flash"],
    max_tokens=300
)

for result in results:
    if "error" not in result:
        print(f"\n{result['model']}: {result['answer']}")

4. Streaming for Long Responses

Use for better UX with lengthy analysis:

python

client.stream_query(
    query="Your question",
    model="openai/gpt-5.2",
    use_search=True,
    max_tokens=2000
)

Research Workflow

When conducting research:

1. Initial exploration: Use research_query() with web search enabled
2. Validate findings: Compare key insights across models with compare_models()
3. Deep dive: Use streaming for detailed analysis on specific aspects
4. Cost-aware: Monitor token usage and costs in results

Model Selection

Default: openai/gpt-5.2 (Latest GPT model)

Alternative models:

• anthropic/claude-3-5-sonnet - Strong reasoning, balanced performance
• google/gemini-2.0-flash - Fast, cost-effective
• meta/llama-3.3-70b - Open source alternative

Switch models based on:

• Quality needs (GPT-5.2 for best results)
• Speed requirements (Gemini Flash for quick answers)
• Cost constraints (compare costs in results)

Reasoning Effort Levels

Control analysis depth with reasoning_effort:

• "low" - Quick answers, minimal reasoning
• "medium" - Balanced reasoning (default for most queries)
• "high" - Deep analysis, comprehensive research (recommended for research)

Environment Setup

Ensure PERPLEXITY_API_KEY is set:

bash

export PERPLEXITY_API_KEY='your_api_key_here'

Or create .env file in the skill's scripts/ directory:

text

PERPLEXITY_API_KEY=your_api_key_here

Error Handling

All methods return error information:

python

result = client.research_query("Your question")

if "error" in result:
    print(f"Error: {result['error']}")
    # Handle error appropriately
else:
    # Process successful result
    print(result["answer"])

Cost Optimization

• Use max_tokens to limit response length
• Start with lower reasoning effort, increase if needed
• Use search_query() instead of research_query() for simpler questions
• Monitor costs via result["cost"] field

Integration Examples

Investment Research

python

client = PerplexityClient()

# Market analysis
result = client.research_query(
    query="Analyze recent developments in AI chip market and key competitors",
    reasoning_effort="high"
)

# Company deep dive
result = client.search_query(
    query="Latest earnings report for NVIDIA Q4 2025"
)

# Multi-model validation
results = client.compare_models(
    query="What are the biggest risks in the semiconductor industry?",
    models=["openai/gpt-5.2", "anthropic/claude-3-5-sonnet"]
)

Trend Analysis

python

# Current trends with web search
result = client.research_query(
    query="Emerging trends in sustainable investing and ESG adoption rates",
    reasoning_effort="high",
    max_tokens=2000
)

# Stream for real-time updates
client.stream_query(
    query="Latest developments in quantum computing commercialization",
    use_search=True
)

Multi-Turn Research

python

# Build context across multiple queries
messages = [
    {"role": "user", "content": "What is the current state of fusion energy?"},
    {"role": "assistant", "content": "...previous response..."},
    {"role": "user", "content": "Which companies are leading in this space?"}
]

result = client.conversation(
    messages=messages,
    use_search=True
)

Best Practices

1. Default to research_query() for most research tasks - it combines web search with high reasoning
2. Use streaming for user-facing applications to show progress
3. Compare models for critical decisions or when quality is paramount
4. Set reasonable max_tokens - 1000 for summaries, 2000+ for deep analysis
5. Track costs - access via result["cost"] and result["tokens"]
6. Handle errors gracefully - always check for "error" key in results

API Reference

See reference.md for complete API documentation, or scripts/perplexity_client.py for:

• Full method signatures
• Additional parameters
• CLI usage examples
• Implementation details

Command Line Usage

Run from the skill directory:

bash

# Research mode
python scripts/perplexity_client.py research "Your question"

# Web search
python scripts/perplexity_client.py search "Your question"

# Streaming
python scripts/perplexity_client.py stream "Your question"

# Compare models
python scripts/perplexity_client.py compare "Your question"