deep-research

name deep-research description Async deep research via Gemini Interactions API (no Gemini CLI dependency). RAG-ground queries on local files (--context), preview costs (--dry-run), structured JSON output, adaptive polling. Universal skill for 30+ AI agents including Claude Code, Amp, Codex, and Gemini CLI. license MIT compatibility Requires uv and one of GOOGLE_API_KEY / GEMINI_API_KEY / GEMINI_DEEP_RESEARCH_API_KEY. Optional env vars for model config: GEMINI_DEEP_RESEARCH_AGENT, GEMINI_DEEP_RESEARCH_MODEL, GEMINI_MODEL. Network access to Google Gemini API. --context uploads local files to ephemeral stores (auto-deleted). allowed-tools Bash(uv:*) Bash(python3:*) Read metadata {"version":"2.1.3","author":"24601","clawdbot":{"emoji":"🔬","category":"research","primaryEnv":"GOOGLE_API_KEY","homepage":"https://github.com/24601/agent-deep-research","requires":{"bins":"[Truncated]","env":"[Truncated]"},"install":["[Truncated]"],"config":{"requiredEnv":"[Truncated]","example":"export GOOGLE_API_KEY=your-key-from-aistudio.google.com"}}} Deep Research Skill Perform deep research powered by Google Gemini's deep research agent. Upload documents to file search stores for RAG-grounded answers. Manage research sessions with persistent workspace state. For AI Agents Get a full capabilities manifest, decision trees, and output contracts: uv run {baseDir}/scripts/onboard.py --agent See AGENTS.md for the complete structured briefing. Command What It Does uv run {baseDir}/scripts/research.py start "question" Launch deep research uv run {baseDir}/scripts/research.py start "question" --context ./path --dry-run Estimate cost uv run {baseDir}/scripts/research.py start "question" --context ./path --output report.md RAG-grounded research uv run {baseDir}/scripts/store.py query <name> "question" Quick Q&A against uploaded docs Security & Transparency Credentials : This skill requires a Google/Gemini API key (one of GOOGLE_API_KEY , GEMINI_API_KEY , or GEMINI_DEEP_RESEARCH_API_KEY ). The key is read from environment variables and passed to the google-genai SDK. It is never logged, written to files, or transmitted anywhere other than the Google Gemini API. File uploads : The --context flag uploads local files to Google's ephemeral file search stores for RAG grounding. Sensitive files are automatically excluded: .env* , credentials.json , secrets.* , private keys ( .pem , .key ), and auth tokens ( .npmrc , .pypirc , .netrc ). Binary files are rejected by MIME type filtering. Build directories ( node_modules , __pycache__ , .git , dist , build ) are skipped. The ephemeral store is auto-deleted after research completes unless --keep-context is specified. Use --dry-run to preview what would be uploaded without sending anything. Only files you explicitly point --context at are uploaded -- no automatic scanning of parent directories or home folders. Non-interactive mode : When stdin is not a TTY (agent/CI use), confirmation prompts are automatically skipped. This is by design for agent integration but means an autonomous agent with file system access could trigger uploads. Restrict the paths agents can access, or use --dry-run and --max-cost guards. No obfuscation : All code is readable Python with PEP 723 inline metadata. No binary blobs, no minified scripts, no telemetry, no analytics. The full source is auditable at github.com/24601/agent-deep-research . Local state : Research session state is written to .gemini-research.json in the working directory. This file contains interaction IDs, store mappings, and upload hashes -- no credentials or research content. Use state.py gc to clean up orphaned stores from crashed runs. Prerequisites A Google API key ( GOOGLE_API_KEY or GEMINI_API_KEY environment variable) uv installed (see uv install docs ) Quick Start # Run a deep research query uv run {baseDir}/scripts/research.py "What are the latest advances in quantum computing?" # Check research status uv run {baseDir}/scripts/research.py status <interaction-id> # Save a completed report uv run {baseDir}/scripts/research.py report <interaction-id> --output report.md # Research grounded in local files (auto-creates store, uploads, cleans up) uv run {baseDir}/scripts/research.py start "How does auth work?" --context ./src --output report.md # Export as HTML or PDF uv run {baseDir}/scripts/research.py start "Analyze the API" --context ./src --format html --output report.html # Auto-detect prompt template based on context files uv run {baseDir}/scripts/research.py start "How does auth work?" --context ./src --prompt-template auto --output report.md Environment Variables Set one of the following (checked in order of priority): Variable Description GEMINI_DEEP_RESEARCH_API_KEY Dedicated key for this skill (highest priority) GOOGLE_API_KEY Standard Google AI key GEMINI_API_KEY Gemini-specific key Optional model configuration: Variable Description Default GEMINI_DEEP_RESEARCH_MODEL Model for file search queries gemini-3.1-pro-preview GEMINI_MODEL Fallback model name gemini-3.1-pro-preview GEMINI_DEEP_RESEARCH_AGENT Deep research agent identifier deep-research-pro-preview-12-2025 Research Commands Start Research uv run {baseDir}/scripts/research.py start "your research question" Flag Description --report-format FORMAT Output structure: executive_summary , detailed_report , comprehensive --store STORE_NAME Ground research in a file search store (display name or resource ID) --no-thoughts Hide intermediate thinking steps --follow-up ID Continue a previous research session --output FILE Wait for completion and save report to a single file --output-dir DIR Wait for completion and save structured results to a directory (see below) --timeout SECONDS Maximum wait time when polling (default: 1800 = 30 minutes) --no-adaptive-poll Disable history-adaptive polling; use fixed interval curve instead --context PATH Auto-create ephemeral store from a file or directory for RAG-grounded research --context-extensions EXT Filter context uploads by extension (e.g. py,md or .py .md ) --keep-context Keep the ephemeral context store after research completes (default: auto-delete) --dry-run Estimate costs without starting research (prints JSON cost estimate) --format {md,html,pdf} Output format for the report (default: md; pdf requires weasyprint) --prompt-template {typescript,python,general,auto} Domain-specific prompt prefix; auto detects from context file extensions --depth {quick,standard,deep} Research depth: quick (~2-5min), standard (~5-15min), deep (~15-45min) --max-cost USD Abort if estimated cost exceeds this limit (e.g. --max-cost 3.00 ) --input-file PATH Read the research query from a file instead of positional argument --no-cache Skip research cache and force a fresh run The start subcommand is the default, so research.py "question" and research.py start "question" are equivalent. Important : When --output or --output-dir is used, the command blocks until research completes (2-10+ minutes). Do not background it with & . Use non-blocking mode (omit --output ) to get an ID immediately, then poll with status and save with report . Check Status uv run {baseDir}/scripts/research.py status <interaction-id> Returns the current status ( in_progress , completed , failed ) and outputs if available. Save Report uv run {baseDir}/scripts/research.py report <interaction-id> Flag Description --output FILE Save report to a specific file path (default: report-<id>.md ) --output-dir DIR Save structured results to a directory Structured Output ( --output-dir ) When --output-dir is used, results are saved to a structured directory: <output-dir>/ research-<id>/ report.md # Full final report metadata.json # Timing, status, output count, sizes interaction.json # Full interaction data (all outputs, thinking steps) sources.json # Extracted source URLs/citations A compact JSON summary (under 500 chars) is printed to stdout: { "id" : "interaction-123" , "status" : "completed" , "output_dir" : "research-output/research-interaction-1/" , "report_file" : "research-output/research-interaction-1/report.md" , "report_size_bytes" : 45000 , "duration_seconds" : 154 , "summary" : "First 200 chars of the report..." } This is the recommended pattern for AI agent integration -- the agent receives a small JSON payload while the full report is written to disk. Adaptive Polling When --output or --output-dir is used, the script polls the Gemini API until research completes. By default, it uses history-adaptive polling that learns from past research completion times: Completion times are recorded in .gemini-research.json under researchHistory (last 50 entries, separate curves for grounded vs non-grounded research). When 3+ matching data points exist, the poll interval is tuned to the historical distribution: Before any research has ever completed: slow polling (30s) In the likely completion window (p25-p75): aggressive polling (5s) In the tail (past p75): moderate polling (15-30s) Unusually long runs (past 1.5x the longest ever): slow polling (60s) All intervals are clamped to [2s, 120s] as a fail-safe. When history is insufficient (<3 data points) or --no-adaptive-poll is passed, a fixed escalating curve is used: 5s (first 30s), 10s (30s-2min), 30s (2-10min), 60s (10min+). Cost Estimation ( --dry-run ) Preview estimated costs before running research: uv run {baseDir}/scripts/research.py start "Analyze security architecture" --context ./src --dry-run Outputs a JSON cost estimate to stdout with context upload costs, research query costs, and a total. Estimates are heuristic-based (the Gemini API does not return token counts or billing data) and clearly labeled as such. After research completes with --output-dir , the metadata.json file includes a usage key with post-run cost estimates based on actual output size and duration. File Search Store Commands Manage file search stores for RAG-grounded research and Q&A. Create a Store uv run {baseDir}/scripts/store.py create "My Project Docs" List Stores uv run {baseDir}/scripts/store.py list Query a Store uv run {baseDir}/scripts/store.py query <store-name> "What does the auth module do?" Flag Description --output-dir DIR Save response and metadata to a directory Delete a Store uv run {baseDir}/scripts/store.py delete <store-name> Use --force to skip the confirmation prompt. When stdin is not a TTY (e.g., called by an AI agent), the prompt is automatically skipped. File Upload Upload files or entire directories to a file search store. uv run {baseDir}/scripts/upload.py ./src fileSearchStores/abc123 Flag Description --smart-sync Skip files that haven't changed (hash comparison) --extensions EXT [EXT ...] File extensions to include (comma or space separated, e.g. py,ts,md or .py .ts .md ) Hash caches are always saved on successful upload, so a subsequent --smart-sync run will correctly skip unchanged files even if the first upload did not use --smart-sync . MIME Type Support 36 file extensions are natively supported by the Gemini File Search API. Common programming files (JS, TS, JSON, CSS, YAML, etc.) are automatically uploaded as text/plain via a fallback mechanism. Binary files are rejected. See references/file_search_guide.md for the full list. File size limit : 100 MB per file. Session Management Research IDs and store mappings are cached in .gemini-research.json in the current working directory. Show Session State uv run {baseDir}/scripts/state.py show Show Research Sessions Only uv run {baseDir}/scripts/state.py research Show Stores Only uv run {baseDir}/scripts/state.py stores JSON Output for Agents Add --json to any state subcommand to output structured JSON to stdout: uv run {baseDir}/scripts/state.py --json show uv run {baseDir}/scripts/state.py --json research uv run {baseDir}/scripts/state.py --json stores Clear Session State uv run {baseDir}/scripts/state.py clear Use -y to skip the confirmation prompt. When stdin is not a TTY (e.g., called by an AI agent), the prompt is automatically skipped. Non-Interactive Mode All confirmation prompts ( store.py delete , state.py clear ) are automatically skipped when stdin is not a TTY. This allows AI agents and CI pipelines to call these commands without hanging on interactive prompts. Workflow Example A typical grounded research workflow: # 1. Create a file search store STORE_JSON=$(uv run {baseDir}/scripts/store.py create "Project Codebase" ) STORE_NAME=$( echo " $STORE_JSON " | python3 -c "import sys,json; print(json.load(sys.stdin)['name'])" ) # 2. Upload your documents