coyote/assets/agents/deep-research/graph.yaml

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: ''
  # Fault-path defaults. `questions: []` also keeps the
  # knowledge_lookup lane alive for one super-step when `plan` dies inside the
  # static fan-out: research_each_question maps over zero items while the
  # plan_fault → end_fault route renders the failure sentinel.
  pipeline_faults: []
  pipeline_notes: ''
  fault_text: ''
  questions: []

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]
    # On success plan_failure holds the node's structured JSON output; on
    # failure the engine writes "LLM node ...failed: <chain>" into it BEFORE
    # routing to the fallback (schema properties are only merged on success).
    state_updates:
      plan_failure: '{{output}}'
    fallback: plan_fault
    next: research_each_question

  plan_fault:
    id: plan_fault
    type: script
    description: Fail-closed marker for a dead planning stage (reached only via plan's fallback) — normalizes the failure text captured in plan_failure into a PIPELINE-FAULT and continues to end_fault; no plan means nothing to research, so the DEEP_RESEARCH FAILED sentinel always renders. The sibling knowledge_lookup lane survives one more super-step by mapping over the empty initial_state `questions` list.
    script: scripts/plan_fault.py
    fallback: end_fault
    next: end_fault

  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
    output_key: finding
    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
    state_updates:
      finding: '{{output}}'
    # On failure the engine writes "LLM node ...failed: <chain>" into `finding`
    # via the state_updates above BEFORE routing to the fallback. The marker
    # is branch-local: a map branch's fallback must stay inside the branch.
    fallback: question_fault

  question_fault:
    id: question_fault
    type: script
    description: Fail-closed marker for a dead research lane (reached only via research_one_question's fallback route inside the map branch). Normalizes the engine's failure text in `finding` into a PIPELINE-FAULT finding the map collects — one dead lane never sinks the whole map. combine_findings later lifts PIPELINE-FAULT findings into pipeline_faults for the report's Pipeline notes.
    script: scripts/question_fault.py

  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}}'
    fallback: vet_fault
    next: critique

  vet_fault:
    id: vet_fault
    type: script
    description: Degrade-visibly marker for a dead vetting stage (reached only via vet_sources' fallback) — vetting is a quality lane, not the product. Records a PIPELINE-FAULT, rewrites source_assessment to a neutral "unvetted" note, and continues to critique, the stage vet_sources' own next pointed to.
    script: scripts/vet_fault.py
    fallback: critique
    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}}'
    fallback: critique_fault
    next: reflexion_gate

  critique_fault:
    id: critique_fault
    type: script
    description: Degrade-visibly marker for a dead critique stage (reached only via critique's fallback) — critique is a quality lane, not the product. Records a PIPELINE-FAULT and rewrites `critique` to a neutral note with deliberately NO "VERDICT:" line, so reflexion_gate's malformed-critique PASS default proceeds to synthesis; then continues to reflexion_gate, the stage critique's own next pointed to.
    script: scripts/critique_fault.py
    fallback: reflexion_gate
    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
    max_attempts: 2
    state_updates:
      report: '{{output}}'
    # On failure the engine writes "Agent node failed: <chain>" into `report`
    # via the state_updates above BEFORE routing to the fallback.
    fallback: synth_fault
    next: verify_sources

  synth_fault:
    id: synth_fault
    type: script
    description: Fail-closed marker for a dead synthesis stage (reached only via synthesize's fallback) — no report means no product. Normalizes the "Agent node failed:" text captured in `report` into a PIPELINE-FAULT and continues to end_fault, which renders the DEEP_RESEARCH FAILED sentinel.
    script: scripts/synth_fault.py
    fallback: end_fault
    next: end_fault

  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}}{{pipeline_notes}}
      ----

      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}}{{pipeline_notes}}'

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

  end_fault:
    id: end_fault
    type: end
    output: 'DEEP_RESEARCH FAILED — PIPELINE-FAULT: {{fault_text}}'