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The TA can provide a lab-owned API key through the `OPENAI_API_KEY` environment variable and run the system with `--mode live`.\n\nThis repository corresponds to the CrewAI system for the homework. It is independent from the LangGraph-based Re:mind system submitted separately.\n\n## Quick Start\n\nRecommended Python version: 3.10 or 3.11.\n\nDownload the repository ZIP, extract it, open a terminal in the extracted folder, then run:\n\n```bash\npip install -r requirements.txt\nexport OPENAI_API_KEY=your_api_key_here\nexport USE_STUB=0\npython main.py --mode live --input data/sample_inputs/wsdm_idea.txt\n```\n\nFor Windows PowerShell:\n\n```powershell\n$env:OPENAI_API_KEY=\"your_api_key_here\"\n$env:USE_STUB=\"0\"\npython main.py --mode live --input data/sample_inputs/wsdm_idea.txt\n```\n\nThis command runs the full 8-agent CrewAI workflow and generates:\n\n- `outputs/research_brief.md`\n- `outputs/advisor_message.md`\n- `outputs/action_plan.json`\n- `outputs/agent_outputs.json`\n- `outputs/run_log.json`\n\nIf the TA wants to inspect an example without running the program first, see `sample_outputs/`.\n\n## Why This Is an Agentic System\n\nResearchPilot Crew uses 8 specialized agents that collaborate as a research team. The workflow routes the input, chains agent outputs, uses local tools, applies guardrails, reflects through a harsh reviewer, writes outputs to files, and records evaluation logs.\n\nLive mode contains a CrewAI sequential crew implemented with `Agent`, `Task`, `Crew`, and `Process.sequential`.\n\n## Agents\n\n| # | Agent | Role | Main Output |\n|---|---|---|---|\n| 1 | Intake & Triage Agent | Classifies the idea and chooses the route | task type, urgency, constraints, missing info |\n| 2 | Research Question Architect | Turns the idea into a research frame | problem, gap, research question, contributions |\n| 3 | Literature Scout | Finds related work directions using local paper cards | search queries, seed papers, reading priority |\n| 4 | Experiment Designer | Designs datasets, baselines, metrics, and ablations | experiment plan |\n| 5 | Startup Validator | Connects the idea to Re:mind or MVP validation | target users, pain points, MVP feature |\n| 6 | Execution Planner | Creates a 48-hour plan | today, tomorrow, this week, do-not-do list |\n| 7 | Harsh Reviewer | Critiques novelty, feasibility, clarity, and scope | reviewer scores and required revision |\n| 8 | Report Writer | Synthesizes final Markdown outputs | research brief and advisor message |\n\n## Agentic Design Patterns\n\n| Pattern | Implementation |\n|---|---|\n| Multi-agent collaboration | 8 agents divide triage, research framing, literature, experiment design, startup validation, execution, review, and reporting. |\n| Prompt chaining | Each stage uses previous structured outputs as context for the next stage. |\n| Routing | Intake & Triage Agent routes to `wsdm_paper`, `remind_mvp`, `lab_meeting`, `professor_outreach`, or `general`. |\n| Tool use | Local tools include paper search, JSON/Markdown saving, memory load/save, priority scoring, validation, and scope detection. |\n| Reflection / critic-reviewer | Harsh Reviewer critiques the plan, and Report Writer incorporates the critique. |\n| Memory management | `data/project_memory.json` stores previous run summaries. |\n| Guardrails / safety | The runner handles empty input, scope creep, missing dataset, impossible deadlines, and too many TODOs. |\n| Exception handling and recovery | Missing API key, CrewAI import failure, saving errors, and invalid JSON-like outputs are handled without crashing. |\n| Evaluation and monitoring | `outputs/run_log.json` records mode, timestamp, agents, patterns, warnings, completeness score, TODO count, and fallback status. |\n\n## Setup\n\nFrom this folder:\n\n```bash\npip install -r requirements.txt\n```\n\nIf `python` is not on PATH on Windows, use the Python executable installed by your environment or IDE. The source code itself has no hardcoded absolute paths.\n\nLive mode environment setup:\n\n```bash\ncopy .env.example .env\n```\n\nThen add `OPENAI_API_KEY` and keep `USE_STUB=0`.\n\n## CLI Run Instructions\n\nRun the WSDM/Re:mind sample:\n\n```bash\npython main.py --mode live --input data/sample_inputs/wsdm_idea.txt\n```\n\nRun with raw text:\n\n```bash\npython main.py --mode live --text \"I want to build a WSDM paper from Re:mind long-term memory retrieval.\"\n```\n\nAuto mode uses live mode when `OPENAI_API_KEY` exists and `USE_STUB` is not `1`:\n\n```bash\npython main.py --mode auto --input data/sample_inputs/wsdm_idea.txt\n```\n\n## Streamlit Run Instructions\n\n```bash\nstreamlit run app.py\n```\n\nThe Streamlit UI calls the same backend function as the CLI:\n\n```python\nrun_research_pilot()\n```\n\nThere is no duplicated agent logic in the frontend.\n\n## Reliability Note\n\nIf live CrewAI execution fails because of environment, dependency, or API-key issues, the runner records the issue in `outputs/run_log.json` and uses its built-in deterministic fallback so that output files remain inspectable.\n\nFor the WSDM sample, the deterministic fallback preserves the same output contract:\n\n- a selective long-term memory retrieval research question,\n- related work directions,\n- datasets such as real counselor records, PsychEval, ESConv, and synthetic multi-session data,\n- baselines such as no memory, full history, BM25, vector retrieval, and selective retrieval,\n- metrics such as evidence precision, faithfulness, completeness, review burden, and safety-boundary violation,\n- a 48-hour plan and advisor update.\n\n## Output Files\n\nEach run saves:\n\n| File | Purpose |\n|---|---|\n| `outputs/research_brief.md` | Polished research-to-action brief |\n| `outputs/advisor_message.md` | Short sendable advisor update |\n| `outputs/action_plan.json` | Structured today/tomorrow/this-week plan |\n| `outputs/agent_outputs.json` | Raw structured output from each agent |\n| `outputs/run_log.json` | Monitoring and evaluation log |\n\n## Troubleshooting\n\n- If live CrewAI execution fails, inspect `outputs/run_log.json` for the recorded warning and generated output file paths.\n- If `python` is not recognized, use the Python launcher or interpreter path available in your environment.\n- If output files are not visible, check the `Saved files` paths printed by the CLI.\n- If input is empty, the system returns a helpful result and writes output files instead of crashing.\n","readmeExcerpt":"ResearchPilot Crew ResearchPilot Crew is a CrewAI-based multi-agent system that turns an unstructured research or startup idea dump into a structured research brief, experiment plan, MVP validation plan, 48-hour action plan, advisor update message, and execution logs. 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