coyote/assets/agents/deep-research

deep-research

A deep web research agent, built as a Coyote graph agent. It 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 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.

Unlike a regular agent (which takes a goal and improvises the steps), this agent runs a fixed graph: every request goes through the same plan -> parallel research -> vet -> critique -> synthesize -> verify -> approve pipeline.

This agent is also the canonical reference for the Coyote graph system: it exercises every node type (script, llm, rag, map, agent, input, approval, end) and both static fan-out and dynamic map fan-out. If you are learning how to build a graph agent, this is the file to read alongside the Graph-Agents wiki.

Workflow

17 nodes. -> is the static route; a script node can also route dynamically via _next. The ▶▶ line is a parallel super-step — those branches run concurrently:

parse_request (script)              -> bootstrap_research   (or -> ask_topic if no topic)
ask_topic (input)                   -> bootstrap_research
bootstrap_research (script)         -> [plan, knowledge_lookup]   ▶▶ parallel
plan (llm + output_schema)          -> research_each_question
knowledge_lookup (rag)              -> research_each_question
research_each_question (map)        -> combine_findings    (spawns one branch per question)
  └─ research_one_question (llm)    (atomic; runs N×, joins at map)
combine_findings (script)           -> vet_sources
vet_sources (llm + custom tool)     -> critique
critique (llm)                      -> reflexion_gate
reflexion_gate (script)             -> synthesize  (or -> research_each_question: reflexion loop)
synthesize (agent: report-writer)   -> verify_sources
verify_sources (script)             -> approve
approve (approval)                  -> end_accepted          ("accept")
                                    -> end_rejected          ("reject")
                                    -> incorporate_feedback   (any free-form answer)
incorporate_feedback (script)       -> research_each_question (the human-feedback loop)

Node-type breakdown

Type	Nodes
script (Python)	parse_request, bootstrap_research, combine_findings, reflexion_gate, verify_sources, incorporate_feedback
llm (tools: [])	plan, critique
llm (with tool whitelist)	research_one_question, vet_sources
rag	knowledge_lookup — local corpus retrieval
map	research_each_question — dynamic fan-out per sub-question
agent	synthesize — spawns the report-writer sub-agent
input	ask_topic
approval	approve
end	end_accepted, end_rejected

Parallel execution

The graph has two parallel super-steps where Coyote's BSP scheduler runs branches concurrently.

1. Context loading (plan ‖ knowledge_lookup) — after bootstrap_research, the LLM planner (which decomposes the topic into sub-questions) and the RAG retrieval over the local knowledge/ corpus run side by side. They write disjoint state keys (plan writes research_plan and questions; knowledge_lookup writes local_context and local_sources) so no reducer is needed.

2. Per-question research (research_each_question map) — the plan emits a questions array (3-5 entries, enforced by its output_schema). The map node spawns one parallel branch per question (max_concurrency: 3). Each branch is an isolated research_one_question LLM invocation with web tools, instructed to investigate exactly its assigned question. Outputs collect into question_findings in input order, then combine_findings joins them into a single findings Markdown document for downstream nodes.

settings.max_concurrency: 4 is the graph-wide cap; the per-map override (max_concurrency: 3 on research_each_question) is deliberately lower to leave headroom for the planner's tool calls running alongside RAG.

Local knowledge corpus

knowledge_lookup is a rag node — it runs hybrid (vector + keyword) retrieval over every file in knowledge/. The directory ships with a small research-style-notes.md so the RAG node has something to retrieve against on a clean install; drop your own Markdown notes, PDFs, or text files into knowledge/ to bias the research toward your local context.

The knowledge base is built once, at agent-load time, into ~/.config/coyote/agents/deep-research/knowledge_lookup.yaml. Because the node fully specifies its build config (embedding_model, chunk_size, chunk_overlap), the build is non-interactive. Delete that cached file after adding or changing knowledge to force a rebuild.

Sub-agent: report-writer

The synthesize node is an agent node that spawns the report-writer sub-agent (assets/agents/report-writer/). This is the agent-as-tool pattern: the orchestrating graph delegates the writing phase to a focused sub-agent dedicated to coherent prose, while the research phase uses different (typically cheaper) LLM nodes for fast-and-many-question investigation.

The report-writer sub-agent has no tools — it cannot access the web, cannot search, and cannot invent facts. It reads only the findings it is given and produces a final Markdown report preserving every inline citation. See assets/agents/report-writer/README.md for details.

Tools and tool scoping

This agent demonstrates Coyote's three tool sources and how an llm node's tools: whitelist scopes them per node.

The agent's full tool universe, declared in graph.yaml:

• Global tools (global_tools): web_search_coyote, fetch_url_via_curl, search_arxiv - Coyote's built-in tool scripts.
• MCP server (mcp_servers): ddg-search - a DuckDuckGo web search MCP server. Referenced in a whitelist as mcp:ddg-search.
• Custom agent tool (tools.sh): classify_source - a deterministic source-credibility classifier shipped with this agent.

No node receives all of these. Each llm node's tools: whitelist narrows the universe to exactly what that step needs:

Node	tools: whitelist	Draws from
plan, critique	[]	nothing - pure reasoning
research_one_question	web_search_coyote, fetch_url_via_curl, search_arxiv, mcp:ddg-search	global tools + MCP
vet_sources	classify_source	the custom tool only

research_one_question (each parallel branch of the map) can search and fetch but cannot classify sources; vet_sources can classify sources but cannot touch the web. That separation is the point of the tools: whitelist: a node gets only the tools its job calls for, never the agent's full set.

The classify_source custom tool (tools.sh) takes a URL and returns a credibility tier (government, academic, preprint, organization, unverified) derived from the host and top-level domain. It is deterministic - exactly the kind of logic a tool should own rather than the LLM guessing.

Web search may require API-key configuration; see the Tools docs. fetch_url_via_curl, search_arxiv, and classify_source work without a key.

Setup

research_one_question (each parallel branch of the map) uses the ddg-search MCP server via mcp:ddg-search. It is one of Coyote's default MCP servers; make sure it is registered in ~/.config/coyote/mcp.json (run coyote --install mcp_config to restore the default template if it is missing). If ddg-search is unavailable, the branches still have their global web-search tools to fall back on.

The synthesize node spawns the report-writer sub-agent. Both agents ship with coyote agents install; if you install one manually, install both so the agent reference resolves.

Reflexion

The agent has two loops, both built with script nodes that route via _next. The engine allows back-edges at runtime; the validator only rejects cycles built from static next / routes edges, so script _next loops are always allowed.

Automated reflexion loop. After the parallel research map and vet_sources, the critique node reviews the merged findings against the research plan and the source credibility assessment, and emits VERDICT: PASS or VERDICT: REVISE with specific feedback. reflexion_gate.py then:

• PASS -> continue to synthesize.
• REVISE, budget remaining -> loop back to research_each_question, with the critique injected as research_feedback so every parallel branch sees it on the retry.
• REVISE, budget spent -> continue to synthesize anyway (the human approval step is the final backstop).

The budget is MAX_REFLEXION_REVISIONS in reflexion_gate.py (default 2, so the research map runs at most 3 times per pass).

Human-feedback loop. At approve the user answers accept, reject, or types their own feedback. A free-form answer routes via the approval node's on_other to incorporate_feedback.py, which folds that text into research_feedback and loops back to research_each_question for another parallel pass.

settings.max_loop_iterations (40) is the engine's infinite-loop backstop: it caps the total visits to any single node.

Running

coyote agents install                  # ships deep-research
coyote -a deep-research "How does HTTP/3 differ from HTTP/2?"
coyote -a deep-research "Recent advances in solid-state batteries"
coyote -a deep-research                # no prompt -> triggers ask_topic

Anti-hallucination

• research_one_question (each map branch) is instructed to back every claim with a real retrieved source and never to fabricate URLs, titles, or DOIs.
• vet_sources classifies every cited source so weak sources are visible to the critique step.
• critique independently reviews the merged findings and sends weak or uncited work back for another parallel research pass.
• synthesize (the report-writer sub-agent) is grounded: it may use only the gathered findings and must keep each claim's inline source. It has no tools and cannot browse the web.
• verify_sources probes every cited URL / DOI with an HTTP HEAD request and reports which are unreachable, so the human reviewer sees broken citations before approving.

Customizing

• Loop budget. MAX_REFLEXION_REVISIONS in reflexion_gate.py.
• Map concurrency. The research_each_question node's max_concurrency: 3 caps simultaneous web-research branches. Raise to investigate more questions in parallel; lower to be gentle on rate-limited providers.
• Per-node model. Add model: anthropic:... to any llm node. Cheap models work well for plan / critique / vet_sources; the heavy intelligence is needed in research_one_question and the report-writer sub-agent.
• Tool scope. Narrow the research_one_question node's tools: list to constrain where each branch looks (for example, drop web_search_coyote and mcp:ddg-search to force arXiv-only research).
• Local knowledge. Drop files into knowledge/ to bias every research branch toward your local context (see the Local knowledge corpus section above).
• Different writer. Replace agent: report-writer on the synthesize node with the name of any other agent. The orchestrator does not care what kind of agent the writer is.
• Skip approval. Point both approve routes at end_accepted, or wire verify_sources straight to an end node.

Files

assets/agents/deep-research/
  graph.yaml                    - agent config + 17-node workflow
  tools.sh                      - classify_source custom tool
  README.md                     - this file
  knowledge/
    README.md                   - corpus-format notes
    research-style-notes.md     - starter knowledge file (replace with your notes)
  scripts/
    parse_request.py            - _next: bootstrap_research, or ask_topic if no topic
    bootstrap_research.py       - fan-out source: next [plan, knowledge_lookup]
    combine_findings.py         - joins map output (question_findings) into findings
    reflexion_gate.py           - _next: research_each_question (revise) or synthesize
    verify_sources.py           - HTTP HEAD on cited URLs / DOIs
    incorporate_feedback.py     - _next: research_each_question, with user feedback

See also assets/agents/report-writer/ — the sub-agent the synthesize node spawns.