> ## Documentation Index > Fetch the complete documentation index at: https://mintlify.com/RightNow-AI/openfang/llms.txt > Use this file to discover all available pages before exploring further. # Researcher Hand > Autonomous deep researcher that conducts exhaustive investigations with cross-referencing and fact-checking ## Overview Researcher Hand is an AI-powered deep research agent that conducts thorough investigations, cross-references sources, fact-checks claims, and produces comprehensive structured reports. **Category:** Productivity\ **Icon:** πŸ§ͺ ## What It Does Decompose complex questions into sub-questions and identify source types Execute searches across web, academic papers, news, and specialized databases Verify claims across multiple independent sources Check primary sources, known debunkings, and authoritative databases Generate structured reports with citations and confidence levels ## Configuration ### Research Depth | Setting | Sources | Passes | Use Case | | -------------- | ------- | ------------------------ | ------------------------------------------- | | **Quick** | 5-10 | 1 pass | Fast answers, straightforward questions | | **Thorough** | 20-30 | Cross-referenced | Balanced depth (default) | | **Exhaustive** | 50+ | Multi-pass, fact-checked | Critical decisions, academic-level research | ### Output Style | Style | Format | Length | Use Case | | ------------- | ------------------------------ | ---------- | --------------------------------- | | **Brief** | Executive summary | 1-2 pages | Quick answers, busy executives | | **Detailed** | Structured report | 5-10 pages | Standard research (default) | | **Academic** | Formal paper | Variable | Research papers, formal citations | | **Executive** | Key findings + recommendations | 2-3 pages | Decision-makers | ### Quality Controls | Setting | Description | | ----------------------- | ------------------------------------------------------------- | | **Source Verification** | Cross-check claims across multiple sources | | **Max Sources** | 10, 30, 50, unlimited | | **Auto Follow-Up** | Research tangential questions discovered during investigation | | **Save Research Log** | Detailed log of queries and source evaluations | | **Citation Style** | `inline_url`, `footnotes`, `academic_apa`, `numbered` | ## Activation ### Basic Setup ```bash theme={null} openfang hand activate researcher ``` Configure research settings: ```bash theme={null} openfang hand config researcher \ --set research_depth="thorough" \ --set output_style="detailed" \ --set source_verification="true" \ --set max_sources="30" \ --set citation_style="inline_url" ``` ### Example Workflow ``` Depth: Thorough (20-30 sources, cross-referenced) Style: Detailed report Verification: Enabled > Research: What are the most effective AI agent architectures as of 2026? ``` Researcher Hand will: 1. Decompose into sub-questions: * What agent architectures exist? * How are they evaluated? * Which perform best on benchmarks? * What are real-world use cases? 2. Execute 15-20 targeted searches 3. Fetch and evaluate 25-30 sources 4. Cross-reference key claims 5. Fact-check critical assertions 6. Generate 8-page report with citations 7. Save as `research_ai_agent_architectures_2026-03-06.md` ## How It Works ### 1. Question Analysis Identifies question type and decomposes: | Type | Strategy | Example | | --------------- | -------------------------- | ----------------------------------- | | **Factual** | Authoritative sources | "What is the capital of France?" | | **Comparative** | Multi-perspective analysis | "React vs Vue in 2026?" | | **Causal** | Evidence chains | "Why did Silicon Valley Bank fail?" | | **Predictive** | Trend analysis | "Will AGI arrive by 2030?" | | **How-to** | Step-by-step with examples | "How to build an AI agent?" | | **Survey** | Comprehensive landscape | "What are all the LLM providers?" | **Example decomposition:** ``` Question: "What are the most effective AI agent architectures as of 2026?" Type: Survey + Comparative Sub-questions: 1. What agent architectures currently exist? 2. How is "effectiveness" measured in agent benchmarks? 3. Which architectures perform best on standard benchmarks? 4. What are the trade-offs (speed, cost, reliability)? 5. What are real-world deployment examples? ``` ### 2. Search Strategy Construction For each sub-question, build 3-5 queries: **Direct queries:** ``` "AI agent architectures" "LLM agent frameworks explained" "agent architecture guide" ``` **Expert queries:** ``` "AI agent architecture research paper" "site:arxiv.org agent architecture" "agent framework comparison analysis" ``` **Comparison queries:** ``` "ReAct vs Plan-and-Execute agents" "agent architecture pros cons" "best AI agent framework 2026" ``` **Temporal queries:** ``` "AI agent architecture 2026" "latest agent framework" "agent architecture update" ``` **Deep queries:** ``` "agent architecture case study" "agent benchmark data" "agent architecture statistics" ``` ### 3. Information Gathering For each search query: `web_search(query)` to find results Check URL domain and snippet relevance before fetching `web_fetch(url)` for promising sources Key claims, data points, expert quotes, methodology, publication date, author credentials **Source quality evaluation (CRAAP test):** | Criteria | Questions | Score | | ------------- | -------------------------------------- | ----- | | **Currency** | When published? Still relevant? | A-F | | **Relevance** | Directly addresses question? | A-F | | **Authority** | Who wrote it? What credentials? | A-F | | **Accuracy** | Claims verifiable? Sources cited? | A-F | | **Purpose** | Informational, persuasive, commercial? | A-F | **Example evaluation:** ``` Source: "Agent Architectures in 2026" - arxiv.org/abs/2601.12345 Currency: A (published Jan 2026) Relevance: A (directly compares architectures) Authority: A (researchers from Stanford, cited 45 times) Accuracy: A (methodology described, datasets linked) Purpose: A (academic research, no commercial bias) Overall: A (authoritative source) ``` If `save_research_log = true`: ```markdown theme={null} # Research Log: AI Agent Architectures ## Query 1: "AI agent architectures 2026" Results: 10 Fetched: 3 - arxiv.org/abs/2601.12345 (A) - Comprehensive comparison - medium.com/@author/agents (C) - Overview, lacks depth - vendor.com/agents (D) - Commercial content, biased ``` ### 4. Cross-Reference & Synthesis If `source_verification = true`: **Verify key claims:** ``` Claim: "ReAct agents outperform Plan-and-Execute on HotPotQA by 15%" Sources: 1. arxiv.org/abs/2601.12345 - "ReAct: 68.2%, Plan-and-Execute: 59.1%" 2. paperswithcode.com/sota/hotpotqa - Confirms ReAct leads 3. github.com/react-paper - Official benchmark code Verification: βœ“ Verified (3 independent sources) Confidence: High ``` **Flag contradictions:** ``` Claim: "LangGraph is the most popular agent framework" Source A (blog): "LangGraph dominates with 50k+ GitHub stars" Source B (GitHub): LangGraph has 12k stars, AutoGPT has 160k Contradiction: ⚠️ Sources disagree Resolution: Check primary source (GitHub actual stats) Result: Source A is incorrect/outdated ``` **Synthesis:** ```markdown theme={null} ## Finding 1: Agent Architecture Landscape Consensus view (5 sources agree): - ReAct: Reasoning + acting in interleaved steps - Plan-and-Execute: Separate planning and execution phases - Reflection: Iterative self-critique and improvement - LLM Compiler: Parallel tool execution with DAG planning Minority view (1 source): - "Hybrid architectures outperform pure approaches" (needs more evidence) Gaps in knowledge: - Limited data on production deployment costs - No standardized benchmark for long-running agents ``` ### 5. Fact-Check Pass For critical claims: 1. **Find primary source** β€” Original research, official data, not secondary reporting 2. **Check for debunkings** β€” Search "\[claim] debunked" or "\[claim] false" 3. **Verify statistics** β€” Cross-check against authoritative databases 4. **Flag weak evidence** β€” Single-source claims, contested assertions **Confidence levels:** | Level | Criteria | | -------------- | ---------------------------------------- | | **Verified** | 3+ authoritative sources confirm | | **Likely** | 2 sources or 1 authoritative source | | **Unverified** | Single source, plausible but unconfirmed | | **Disputed** | Sources disagree | ### 6. Report Generation #### Detailed Report (Default) ```markdown theme={null} # Research Report: AI Agent Architectures in 2026 **Date**: 2026-03-06 | **Sources Consulted**: 28 | **Confidence**: High ## Executive Summary As of March 2026, the AI agent architecture landscape has consolidated around four primary approaches: ReAct (reasoning-acting), Plan-and-Execute (decomposition), Reflection (self-critique), and LLM Compiler (parallelization). Empirical benchmarks show ReAct leading on question-answering tasks (HotPotQA: 68%), while Plan-and-Execute excels on multi-step workflows (WebShop: 71%). Production deployments favor hybrid approaches combining strengths of multiple architectures. ## Detailed Findings ### 1. Agent Architecture Taxonomy **ReAct (Reasoning + Acting)** Source: [ReAct: Synergizing Reasoning and Acting in Language Models](https://arxiv.org/abs/2210.03629) Confidence: Verified ReAct interleaves reasoning steps ("Thought:") with actions ("Action:") in a loop. Each cycle produces an observation that feeds the next reasoning step. **Performance:** - HotPotQA: 68.2% (verified, 3 sources) - FEVER: 71.4% (verified, 2 sources) **Strengths:** Interpretable, handles dynamic tasks **Weaknesses:** Sequential bottleneck, token-inefficient --- ### 2. Comparative Performance | Architecture | HotPotQA | WebShop | GAIA | Avg | |--------------|----------|---------|------|-----| | ReAct | 68.2% | 59.1% | 34.2% | 53.8% | | Plan-and-Execute | 59.1% | 71.3% | 41.0% | 57.1% | | Reflection | 64.7% | 65.2% | 38.9% | 56.3% | | LLM Compiler | 66.1% | 68.4% | 43.7% | 59.4% | Source: [Agent Architecture Benchmark 2026](https://arxiv.org/abs/2601.12345) Confidence: High (primary research, peer-reviewed) --- ## Key Data Points | Metric | Value | Source | Confidence | |--------|-------|--------|------------| | GitHub stars (LangGraph) | 12,034 | GitHub API | Verified | | Median agent latency | 3.2s | Anthropic blog | Likely | | Production cost (1M runs) | $850 | Estimate from sources | Unverified | ## Contradictions & Open Questions **Contradiction:** Cost estimates vary widely Source A claims $500/1M runs, Source B claims $1200/1M. Likely explanation: Depends heavily on model choice and caching. **Open question:** Long-running agent stability No standardized benchmark exists for agents running 100+ steps. Gap in research literature. ## Sources ### Primary Sources (A-tier) 1. [ReAct: Synergizing Reasoning and Acting](https://arxiv.org/abs/2210.03629) - Original ReAct paper, 1200+ citations 2. [Agent Architecture Benchmark 2026](https://arxiv.org/abs/2601.12345) - Comprehensive comparison, Stanford 3. [LLM Compiler: Parallel Function Calling](https://arxiv.org/abs/2312.04511) - UC Berkeley research ### Secondary Sources (B-tier) 4. [LangGraph Documentation](https://langchain.com/langgraph) - Official framework docs 5. [Anthropic: Building Reliable Agents](https://anthropic.com/blog/agents) - Engineering best practices ### Supporting Sources (C-tier) 6. [Medium: Agent Architectures Overview](https://medium.com/@author/agents) - Good overview, lacks rigor [... 22 more sources listed with ratings ...] ``` #### Brief Report ```markdown theme={null} # Research: AI Agent Architectures (2026) ## Key Findings - **Four main architectures**: ReAct, Plan-and-Execute, Reflection, LLM Compiler - **Best for QA**: ReAct (68% on HotPotQA) - **Best for workflows**: Plan-and-Execute (71% on WebShop) - **Best overall**: LLM Compiler (59% average across benchmarks) - **Production trend**: Hybrid approaches combining multiple strategies ## Sources 1. [Agent Benchmark 2026](https://arxiv.org/abs/2601.12345) - Stanford research 2. [ReAct Paper](https://arxiv.org/abs/2210.03629) - Original framework 3. [Anthropic Agents Guide](https://anthropic.com/blog/agents) - Best practices 4. [LangGraph Docs](https://langchain.com/langgraph) - Implementation 5. [LLM Compiler](https://arxiv.org/abs/2312.04511) - Parallel execution ``` #### Academic Report ```markdown theme={null} # A Survey of AI Agent Architectures in 2026 ## Abstract This survey examines the current landscape of AI agent architectures... ## Introduction Autonomous AI agents have emerged as a critical application of large language models (LLMs). This paper surveys the architectural approaches... ## Methodology We conducted a systematic review of 28 sources including peer-reviewed papers, official documentation, and benchmark repositories... ## Findings ### 3.1 ReAct Architecture Yao et al. (2022) introduced ReAct, which synergizes reasoning and acting... ## Discussion ## Conclusion ## References Anthropic. (2026). Building Reliable Agents. Retrieved from https://anthropic.com/blog/agents Yao, S., Zhao, J., Yu, D., Du, N., Shafran, I., Narasimhan, K., & Cao, Y. (2022). ReAct: Synergizing Reasoning and Acting in Language Models. arXiv:2210.03629. [... APA-formatted references ...] ``` ## Output | File | Description | | -------------------------------- | ------------------------------------------ | | `research_[topic]_YYYY-MM-DD.md` | Main research report | | `research_log_YYYY-MM-DD.md` | Detailed query and source log (if enabled) | ## Dashboard Metrics * **Queries Solved** β€” Research questions answered * **Sources Cited** β€” Total unique sources used * **Reports Generated** β€” Number of reports delivered * **Active Investigations** β€” In-progress research ## Tips & Best Practices **For best results:** * Use Thorough depth for most questions (Quick often misses nuances) * Enable source verification for important decisions * Review sources section β€” check if Hand found authoritative sources * Ask follow-up questions if findings are unclear * Export to PDF for sharing with stakeholders **Researcher Hand never fabricates sources or data.** Every claim is traceable to an actual source. If you see "No reliable sources found," that's accurate β€” not a failure. ### Common Issues **"No reliable sources found"**\ Question may be too niche, recent, or speculative. Try broadening the question. **"Sources disagree"**\ This is valuable information! The Hand will report both perspectives. **"Report is too long"**\ Switch to `output_style="brief"` for executive summaries. **"Missing key source"**\ If you know of a critical source, mention it: "Research X, and be sure to check Y source." ## Advanced Usage ### Multi-Language Research ```bash theme={null} openfang hand config researcher --set language="spanish" ``` ``` Investigar: ΒΏCuΓ‘les son las mejores arquitecturas de agentes IA en 2026? ``` ### Follow-Up Research ``` Based on your previous research on agent architectures, now research: Which architecture is best for production chatbots? ``` ### Comparative Deep-Dive ``` Research: Compare ReAct vs Plan-and-Execute agent architectures. Depth: Exhaustive Style: Academic ``` ## Next Steps Monitor research topics continuously Make predictions based on research findings