Bright Data Web Research Agent

A minimal OpenAI Agents SDK example for a deep-research agent that uses Bright Data APIs for deterministic web search and page fetching.

The demo asks a company/product/market question, searches the web, reads source pages, and returns schema-validated JSON with citations.

python3 -m venv .venv
source .venv/bin/activate
pip install -e .
cp .env.example .env

Fill in .env:

OPENAI_API_KEY=...
BRIGHT_DATA_API_TOKEN=...
BRIGHT_DATA_SERP_ZONE=serp_api1
BRIGHT_DATA_UNLOCKER_ZONE=web_unlocker1

python -m bright_research_agent.agent \
  "What is the market positioning of Perplexity's enterprise search product?"

Progress and tool-call logs are written to stderr so stdout remains valid JSON:

python -m bright_research_agent.agent \
  "What is the market positioning of Perplexity's enterprise search product?" \
  --log-level INFO

Set --log-level WARNING or LOG_LEVEL=WARNING for quieter output.

If the OpenAI request times out, give the model call more room and reduce turns:

python -m bright_research_agent.agent \
  "What is the current landscape of GTM engineering?" \
  --openai-timeout 300 \
  --max-turns 6

The final output is JSON matching the Pydantic schema in src/bright_research_agent/schemas.py.

• SERP discovery through Bright Data SERP API.
• Page retrieval through Bright Data Unlocker API.
• OpenAI Agents SDK tool orchestration.
• Pydantic output validation for citation-backed research JSON.

• Treat scraped content as untrusted input. The agent instructions explicitly tell the model not to follow instructions found inside retrieved pages.
• Keep max_sources low during demos so the workflow stays fast and inexpensive.
• This API-first version is the clearest starting point for retries, concurrency, metrics, and cost controls.