name: deep-research
description: |
  Deep web research workflow. Plans an investigation, decomposes it
  into sub-questions researched in parallel, grounds the work in a
  local knowledge corpus, vets the credibility of cited sources, runs
  a reflexion self-critique loop to revise weak or incomplete findings,
  delegates the final write-up to a focused sub-agent, checks that the
  cited sources are reachable, and gates the result behind human
  approval. A reviewer's free-form feedback at the approval step feeds
  back into another research pass.

  This is the canonical Coyote graph-agent reference: it exercises every
  node type (script, llm, rag, map, agent, input, approval, end) and
  both static fan-out and dynamic map fan-out.

version: "1.0"

global_tools:
  - web_search_coyote.sh
  - fetch_url_via_curl.sh
  - search_arxiv.sh

mcp_servers:
  - ddg-search

conversation_starters:
  - "How does HTTP/3 differ from HTTP/2?"
  - "Summarize recent advances in solid-state battery chemistry"

settings:
  max_loop_iterations: 40
  log_state_snapshots: false
  validate_before_run: true
  max_concurrency: 4

initial_state:
  research_feedback: ""
  research_attempts: 0
  local_context: ""
  local_sources: ""

start: parse_request

nodes:

  parse_request:
    id: parse_request
    type: script
    script: scripts/parse_request.py
    next: bootstrap_research

  ask_topic:
    id: ask_topic
    type: input
    question: "What would you like me to research?"
    validation: "len(input) > 0"
    state_updates:
      topic: "{{input}}"
    next: bootstrap_research

  bootstrap_research:
    id: bootstrap_research
    type: script
    script: scripts/bootstrap_research.py
    next: [plan, knowledge_lookup]

  plan:
    id: plan
    type: llm
    instructions: |
      You are a research planner. Given a topic, produce a focused
      research plan and decompose it into 3-5 specific sub-questions
      that can each be researched independently in parallel.

      The plan is a short narrative naming the key questions and the
      kinds of sources that would be authoritative. The sub-questions
      are precise, self-contained queries (each one is sent on its own
      to a separate research worker, so they must be answerable
      without each other's context).
    prompt: "Research topic: {{topic}}"
    tools: []
    output_schema:
      type: object
      properties:
        research_plan:
          type: string
          description: A short plan narrative.
        questions:
          type: array
          items: { type: string }
          minItems: 1
          maxItems: 6
          description: 3-5 specific, self-contained sub-questions.
      required: [research_plan, questions]
    next: research_each_question

  knowledge_lookup:
    id: knowledge_lookup
    type: rag
    documents:
      - ./knowledge/
    query: "{{topic}}"
    top_k: 6
    chunk_size: 1000
    chunk_overlap: 100
    state_updates:
      local_context: "{{output.context}}"
      local_sources: "{{output.sources}}"
    next: research_each_question

  research_each_question:
    id: research_each_question
    type: map
    over: "{{questions}}"
    as: question
    branch: research_one_question
    collect_into: question_findings
    max_concurrency: 3
    next: combine_findings

  research_one_question:
    id: research_one_question
    type: llm
    instructions: |
      You are a web research assistant. Investigate the SINGLE question
      given to you using your tools: search the web, fetch and read
      pages, and search arXiv for academic sources.

      Rules:
        - Every factual claim must be backed by a real source you
          actually retrieved. Never fabricate URLs, page titles,
          authors, or DOIs.
        - Prefer primary and authoritative sources over aggregators.
        - Where sources disagree, report the disagreement rather than
          papering over it.
        - Put the URL (or DOI) inline next to each claim it supports.

      Return organized findings in plain text. Do not include
      meta-commentary about the process.
    prompt: |
      Research question: {{question}}

      Local context that may help:
      {{local_context}}

      {{research_feedback}}
    tools:
      - web_search_coyote
      - fetch_url_via_curl
      - search_arxiv
      - mcp:ddg-search
    max_iterations: 10
    max_attempts: 2
    temperature: 0.1

  combine_findings:
    id: combine_findings
    type: script
    script: scripts/combine_findings.py
    next: vet_sources

  vet_sources:
    id: vet_sources
    type: llm
    instructions: |
      You assess the credibility of the sources cited in a set of
      research findings. For every distinct source URL in the findings,
      call the `classify_source` tool to get its credibility tier. Then
      summarize: which claims rest on HIGH-credibility sources, and
      which rest on PREPRINT or UNVERIFIED sources and so need
      corroboration. Do NOT do any new research -- assess only what is
      already cited.
    prompt: |
      Findings to assess:
      {{findings}}
    tools:
      - classify_source
    max_iterations: 15
    state_updates:
      source_assessment: "{{output}}"
    next: critique

  critique:
    id: critique
    type: llm
    instructions: |
      You are a meticulous research reviewer. Judge whether the
      findings below are good enough to synthesize a complete,
      well-supported report that answers the research plan.

      Mark the findings REVISE if ANY of these hold:
        - A research-plan question is unanswered or only weakly
          addressed.
        - A factual claim has no source, or cites a source that looks
          fabricated.
        - The findings lean on a single source where corroboration is
          needed.
        - A key claim rests only on a PREPRINT or UNVERIFIED source,
          per the source credibility assessment below.
        - An obvious counter-perspective or recent development is
          missing.
      Otherwise mark them PASS.

      Respond in EXACTLY this format, nothing else:

      VERDICT: <PASS or REVISE>
      FEEDBACK: <if REVISE, be specific and actionable -- name the gaps
      and what kind of source would close them; if PASS, write "none">
    prompt: |
      Research plan:
      {{research_plan}}

      Findings under review:
      {{findings}}

      Source credibility assessment:
      {{source_assessment}}
    tools: []
    state_updates:
      critique: "{{output}}"
    next: reflexion_gate

  reflexion_gate:
    id: reflexion_gate
    type: script
    script: scripts/reflexion_gate.py
    next: synthesize

  synthesize:
    id: synthesize
    type: agent
    agent: report-writer
    prompt: |
      Research topic: {{topic}}

      Findings (organized by sub-question, with inline citations):
      {{findings}}

      Source credibility assessment:
      {{source_assessment}}

      Produce the final report following your instructions.
    timeout: 300
    state_updates:
      report: "{{output}}"
    next: verify_sources

  verify_sources:
    id: verify_sources
    type: script
    script: scripts/verify_sources.py
    next: approve

  approve:
    id: approve
    type: approval
    question: |
      Research report on: {{topic}}

      {{report}}

      ----
      {{source_check}}
      ----

      Accept this report? Pick "accept" or "reject", or type specific
      feedback to send the research back for another pass.
    options:
      - "accept"
      - "reject"
    routes:
      "accept": end_accepted
      "reject": end_rejected
    on_other: incorporate_feedback
    state_updates:
      decision: "{{choice}}"

  incorporate_feedback:
    id: incorporate_feedback
    type: script
    script: scripts/incorporate_feedback.py

  end_accepted:
    id: end_accepted
    type: end
    output: "{{report}}"

  end_rejected:
    id: end_rejected
    type: end
    output: "Research on '{{topic}}' was rejected and discarded."
