Hermes Pipeline
28b0c483b8
Extracted from: https://github.com/DennisDRX/Faraday-Web-Researcher-Agent.git Score: 1.0
78 lines
3.7 KiB
Markdown
78 lines
3.7 KiB
Markdown
---
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name: autonomous-web-research-agent
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version: 1.0.0
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description: Autonomously research a given query on the web using multiple search
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tools and generate a structured report with summary, detailed sections, source tracking,
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and bias analysis.
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inputs:
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- 'query (string): the research question or topic to investigate'
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- 'tools (list, optional): selected web search/tools to use (e.g., Tavily, Google,
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NewsAPI, DuckDuckGo)'
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- 'api_keys (dict, optional): credentials for LLM and external search APIs'
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- 'model_config (dict, optional): LLM provider and parameters'
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steps:
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- 1. Accept user query and optional tool selections.
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- '2. Initialize agent framework (e.g., LangGraph) with integrated tools: web search
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(Tavily, Google, DuckDuckGo), news API, web scraping.'
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- 3. Decompose query into sub-questions if needed and iteratively call tools to gather
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relevant information.
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- 4. Extract and deduplicate content from retrieved sources, tracking source metadata
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(URL, tool used).
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- 5. Use a large language model to synthesize findings into an executive summary and
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detailed sections.
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- 6. Analyze potential biases or limitations of gathered sources.
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- 7. Compile a structured report object (ResearchReport) containing query, summary,
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sections, sources, biases.
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- 8. Optionally present report via a UI (e.g., Streamlit) or return as JSON.
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outputs:
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- 'ResearchReport (JSON/dict) with fields: query (string), summary (string), sections
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(list of {heading, content}), sources (list of {url, tool_used, title}), potential_biases
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(string)'
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- Optional UI rendering of report with source badges and expandable sections
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tags: []
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metadata:
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source_repo: https://github.com/DennisDRX/Faraday-Web-Researcher-Agent.git
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extracted_at: ''
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confidence: 0.85
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---
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# autonomous-web-research-agent
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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.
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## Steps
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1. 1. Accept user query and optional tool selections.
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2. 2. Initialize agent framework (e.g., LangGraph) with integrated tools: web search (Tavily, Google, DuckDuckGo), news API, web scraping.
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3. 3. Decompose query into sub-questions if needed and iteratively call tools to gather relevant information.
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4. 4. Extract and deduplicate content from retrieved sources, tracking source metadata (URL, tool used).
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5. 5. Use a large language model to synthesize findings into an executive summary and detailed sections.
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6. 6. Analyze potential biases or limitations of gathered sources.
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7. 7. Compile a structured report object (ResearchReport) containing query, summary, sections, sources, biases.
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8. 8. Optionally present report via a UI (e.g., Streamlit) or return as JSON.
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## Inputs
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- query (string): the research question or topic to investigate
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- tools (list, optional): selected web search/tools to use (e.g., Tavily, Google, NewsAPI, DuckDuckGo)
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- api_keys (dict, optional): credentials for LLM and external search APIs
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- model_config (dict, optional): LLM provider and parameters
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## Outputs
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- ResearchReport (JSON/dict) with fields: query (string), summary (string), sections (list of {heading, content}), sources (list of {url, tool_used, title}), potential_biases (string)
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- Optional UI rendering of report with source badges and expandable sections
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## Failure Modes
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- Missing or invalid API keys causing tool authentication failures
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- Rate limits or network errors from search APIs
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- Insufficient or low-quality search results leading to incomplete report
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- LLM hallucination or mis-summarization despite source tracking
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- Parsing errors in HTML/scraped content
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## Source
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Extracted from: [https://github.com/DennisDRX/Faraday-Web-Researcher-Agent.git](https://github.com/DennisDRX/Faraday-Web-Researcher-Agent.git)
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Confidence: 0.85
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