---
name: autonomous-web-research-agent
version: 1.0.0
description: Autonomously research a given query on the web using multiple search
  tools and generate a structured report with summary, detailed sections, source tracking,
  and bias analysis.
inputs:
- 'query (string): the research question or topic to investigate'
- 'tools (list, optional): selected web search/tools to use (e.g., Tavily, Google,
  NewsAPI, DuckDuckGo)'
- 'api_keys (dict, optional): credentials for LLM and external search APIs'
- 'model_config (dict, optional): LLM provider and parameters'
steps:
- 1. Accept user query and optional tool selections.
- '2. Initialize agent framework (e.g., LangGraph) with integrated tools: web search
  (Tavily, Google, DuckDuckGo), news API, web scraping.'
- 3. Decompose query into sub-questions if needed and iteratively call tools to gather
  relevant information.
- 4. Extract and deduplicate content from retrieved sources, tracking source metadata
  (URL, tool used).
- 5. Use a large language model to synthesize findings into an executive summary and
  detailed sections.
- 6. Analyze potential biases or limitations of gathered sources.
- 7. Compile a structured report object (ResearchReport) containing query, summary,
  sections, sources, biases.
- 8. Optionally present report via a UI (e.g., Streamlit) or return as JSON.
outputs:
- 'ResearchReport (JSON/dict) with fields: query (string), summary (string), sections
  (list of {heading, content}), sources (list of {url, tool_used, title}), potential_biases
  (string)'
- Optional UI rendering of report with source badges and expandable sections
tags: []
metadata:
  source_repo: https://github.com/DennisDRX/Faraday-Web-Researcher-Agent.git
  extracted_at: ''
  confidence: 0.85
---

# autonomous-web-research-agent

Autonomously research a given query on the web using multiple search tools and generate a structured report with summary, detailed sections, source tracking, and bias analysis.

## Steps

1. 1. Accept user query and optional tool selections.
2. 2. Initialize agent framework (e.g., LangGraph) with integrated tools: web search (Tavily, Google, DuckDuckGo), news API, web scraping.
3. 3. Decompose query into sub-questions if needed and iteratively call tools to gather relevant information.
4. 4. Extract and deduplicate content from retrieved sources, tracking source metadata (URL, tool used).
5. 5. Use a large language model to synthesize findings into an executive summary and detailed sections.
6. 6. Analyze potential biases or limitations of gathered sources.
7. 7. Compile a structured report object (ResearchReport) containing query, summary, sections, sources, biases.
8. 8. Optionally present report via a UI (e.g., Streamlit) or return as JSON.

## Inputs

- query (string): the research question or topic to investigate
- tools (list, optional): selected web search/tools to use (e.g., Tavily, Google, NewsAPI, DuckDuckGo)
- api_keys (dict, optional): credentials for LLM and external search APIs
- model_config (dict, optional): LLM provider and parameters

## Outputs

- ResearchReport (JSON/dict) with fields: query (string), summary (string), sections (list of {heading, content}), sources (list of {url, tool_used, title}), potential_biases (string)
- Optional UI rendering of report with source badges and expandable sections

## Failure Modes

- Missing or invalid API keys causing tool authentication failures
- Rate limits or network errors from search APIs
- Insufficient or low-quality search results leading to incomplete report
- LLM hallucination or mis-summarization despite source tracking
- Parsing errors in HTML/scraped content

## Source

Extracted from: [https://github.com/DennisDRX/Faraday-Web-Researcher-Agent.git](https://github.com/DennisDRX/Faraday-Web-Researcher-Agent.git)
Confidence: 0.85