294 lines
7.8 KiB
YAML
294 lines
7.8 KiB
YAML
name: deep-research
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description: |
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Deep web research workflow. Plans an investigation, decomposes it
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into sub-questions researched in parallel, grounds the work in a
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local knowledge corpus, vets the credibility of cited sources, runs
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a reflexion self-critique loop to revise weak or incomplete findings,
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delegates the final write-up to a focused sub-agent, checks that the
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cited sources are reachable, and gates the result behind human
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approval. A reviewer's free-form feedback at the approval step feeds
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back into another research pass.
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This is the canonical Coyote graph-agent reference: it exercises every
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node type (script, llm, rag, map, agent, input, approval, end) and
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both static fan-out and dynamic map fan-out.
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version: "1.0"
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temperature: 0.0
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global_tools:
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- web_search_coyote.sh
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- fetch_url_via_curl.sh
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- search_arxiv.sh
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mcp_servers:
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- ddg-search
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conversation_starters:
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- "How does HTTP/3 differ from HTTP/2?"
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- "Summarize recent advances in solid-state battery chemistry"
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settings:
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max_loop_iterations: 40
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log_state_snapshots: false
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validate_before_run: true
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max_concurrency: 4
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initial_state:
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research_feedback: ""
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research_attempts: 0
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local_context: ""
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local_sources: ""
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start: parse_request
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nodes:
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parse_request:
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id: parse_request
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type: script
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script: scripts/parse_request.py
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next: bootstrap_research
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ask_topic:
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id: ask_topic
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type: input
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question: "What would you like me to research?"
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validation: "len(input) > 0"
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state_updates:
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topic: "{{input}}"
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next: bootstrap_research
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bootstrap_research:
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id: bootstrap_research
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type: script
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script: scripts/bootstrap_research.py
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next: [plan, knowledge_lookup]
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plan:
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id: plan
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type: llm
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instructions: |
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You are a research planner. Given a topic, produce a focused
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research plan and decompose it into 3-5 specific sub-questions
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that can each be researched independently in parallel.
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The plan is a short narrative naming the key questions and the
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kinds of sources that would be authoritative. The sub-questions
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are precise, self-contained queries (each one is sent on its own
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to a separate research worker, so they must be answerable
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without each other's context).
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prompt: "Research topic: {{topic}}"
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tools: []
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output_schema:
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type: object
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properties:
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research_plan:
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type: string
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description: A short plan narrative.
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questions:
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type: array
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items: { type: string }
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minItems: 1
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maxItems: 6
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description: 3-5 specific, self-contained sub-questions.
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required: [research_plan, questions]
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next: research_each_question
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knowledge_lookup:
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id: knowledge_lookup
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type: rag
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documents:
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- ./knowledge/
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query: "{{topic}}"
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top_k: 6
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chunk_size: 1000
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chunk_overlap: 100
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state_updates:
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local_context: "{{output.context}}"
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local_sources: "{{output.sources}}"
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next: research_each_question
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research_each_question:
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id: research_each_question
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type: map
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over: "{{questions}}"
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as: question
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branch: research_one_question
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collect_into: question_findings
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max_concurrency: 3
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next: combine_findings
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research_one_question:
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id: research_one_question
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type: llm
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instructions: |
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You are a web research assistant. Investigate the SINGLE question
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given to you using your tools: search the web, fetch and read
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pages, and search arXiv for academic sources.
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Rules:
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- Every factual claim must be backed by a real source you
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actually retrieved. Never fabricate URLs, page titles,
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authors, or DOIs.
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- Prefer primary and authoritative sources over aggregators.
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- Where sources disagree, report the disagreement rather than
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papering over it.
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- Put the URL (or DOI) inline next to each claim it supports.
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Return organized findings in plain text. Do not include
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meta-commentary about the process.
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prompt: |
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Research question: {{question}}
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Local context that may help:
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{{local_context}}
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{{research_feedback}}
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tools:
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- web_search_coyote
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- fetch_url_via_curl
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- search_arxiv
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- mcp:ddg-search
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max_iterations: 10
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max_attempts: 2
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temperature: 0.1
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combine_findings:
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id: combine_findings
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type: script
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script: scripts/combine_findings.py
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next: vet_sources
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vet_sources:
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id: vet_sources
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type: llm
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instructions: |
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You assess the credibility of the sources cited in a set of
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research findings. For every distinct source URL in the findings,
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call the `classify_source` tool to get its credibility tier. Then
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summarize: which claims rest on HIGH-credibility sources, and
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which rest on PREPRINT or UNVERIFIED sources and so need
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corroboration. Do NOT do any new research -- assess only what is
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already cited.
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prompt: |
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Findings to assess:
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{{findings}}
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tools:
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- classify_source
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max_iterations: 15
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state_updates:
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source_assessment: "{{output}}"
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next: critique
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critique:
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id: critique
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type: llm
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instructions: |
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You are a meticulous research reviewer. Judge whether the
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findings below are good enough to synthesize a complete,
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well-supported report that answers the research plan.
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Mark the findings REVISE if ANY of these hold:
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- A research-plan question is unanswered or only weakly
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addressed.
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- A factual claim has no source, or cites a source that looks
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fabricated.
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- The findings lean on a single source where corroboration is
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needed.
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- A key claim rests only on a PREPRINT or UNVERIFIED source,
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per the source credibility assessment below.
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- An obvious counter-perspective or recent development is
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missing.
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Otherwise mark them PASS.
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Respond in EXACTLY this format, nothing else:
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VERDICT: <PASS or REVISE>
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FEEDBACK: <if REVISE, be specific and actionable -- name the gaps
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and what kind of source would close them; if PASS, write "none">
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prompt: |
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Research plan:
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{{research_plan}}
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Findings under review:
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{{findings}}
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Source credibility assessment:
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{{source_assessment}}
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tools: []
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state_updates:
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critique: "{{output}}"
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next: reflexion_gate
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reflexion_gate:
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id: reflexion_gate
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type: script
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script: scripts/reflexion_gate.py
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next: synthesize
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synthesize:
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id: synthesize
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type: agent
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agent: report-writer
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prompt: |
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Research topic: {{topic}}
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Findings (organized by sub-question, with inline citations):
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{{findings}}
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Source credibility assessment:
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{{source_assessment}}
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Produce the final report following your instructions.
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timeout: 300
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state_updates:
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report: "{{output}}"
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next: verify_sources
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verify_sources:
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id: verify_sources
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type: script
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script: scripts/verify_sources.py
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next: approve
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approve:
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id: approve
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type: approval
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question: |
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Research report on: {{topic}}
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{{report}}
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----
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{{source_check}}
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----
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Accept this report? Pick "accept" or "reject", or type specific
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feedback to send the research back for another pass.
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options:
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- "accept"
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- "reject"
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routes:
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"accept": end_accepted
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"reject": end_rejected
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on_other: incorporate_feedback
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state_updates:
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decision: "{{choice}}"
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incorporate_feedback:
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id: incorporate_feedback
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type: script
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script: scripts/incorporate_feedback.py
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end_accepted:
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id: end_accepted
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type: end
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output: "{{report}}"
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end_rejected:
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id: end_rejected
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type: end
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output: "Research on '{{topic}}' was rejected and discarded."
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