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Deep research agent

Five-agent research pipeline. The coordinator decomposes the question and dispatches researchers in parallel. A fact-checker verifies claims at zero temperature. The writer produces the synthesis on Sonnet. The editor polishes the prose. Routes the cheap turns to DeepSeek and the premium turns to Claude.

lines 92cost ~$0.40 per session, 70% on cheap modelsinstall hub://digitorn/deep-research
Install in one command
digitorn install hub://digitorn/deep-research
What you get
  • 5-agent pipeline with citations on every claim
  • Fact-checker at temperature 0 to catch hallucinations
  • Mixed-provider routing (Anthropic + DeepSeek)
  • Working memory for cross-turn dedup
Credentials this template uses

Click each one for the credential setup command, scope guidance, and provider details.

The full YAML, ready to fork

app.yaml
1app:2  app_id: deep-research3  name: "Deep research"4  version: "1.0.0"5  description: "5-agent research pipeline with citations."6  category: "research"78modules:9  web:10    config:11      search_backend: duckduckgo12  memory:13    config:14      working_memory: true15      todo_list: true16  agent_spawn: {}1718execution:19  mode: one_shot20  entry_agent: coordinator2122agents:23  - id: coordinator24    role: coordinator25    modules: [{agent_spawn: [Agent]}, {memory: [set_goal, remember]}]26    brain:27      provider: anthropic28      model: claude-sonnet-4-629      credential:30        ref: anthropic_main31        scope: per_user32        provider: anthropic33      temperature: 0.234    system_prompt: |35      Decompose the question into 2-4 angles. Spawn researchers in parallel36      via Agent(specialist='researcher', prompt='...') plus a fact_checker.37      Wait on all of them with Agent(agent_ids=[...]). Then call writer,38      then editor, sequentially.3940  - id: researcher41    role: specialist42    modules:43      - {web: [search, fetch]}44      - {memory: [remember]}45    brain:46      provider: deepseek47      model: deepseek-chat48      credential:49        ref: deepseek_main50        scope: per_user51        provider: deepseek52      temperature: 0.25354  - id: fact_checker55    role: specialist56    modules:57      - {web: [search]}58    brain:59      provider: deepseek60      model: deepseek-chat61      credential: deepseek_main62      temperature: 0.06364  - id: writer65    role: specialist66    brain:67      provider: anthropic68      model: claude-sonnet-4-669      credential: anthropic_main70      temperature: 0.47172  - id: editor73    role: specialist74    brain:75      provider: anthropic76      model: claude-sonnet-4-677      credential: anthropic_main78      temperature: 0.0

How it works

researcher pool with web tools only
Two researcher specialists run in parallel via the Agent tool. Each calls web.search and web.fetch on its assigned angle, then writes findings into shared memory.
fact-checker at temperature 0
The fact-checker has a narrower toolset (web.search only) and runs at zero temperature so it answers verification queries deterministically. Outputs a yes/no/citation triple per claim.
writer + editor as final stage
The writer takes the merged findings and produces the synthesis on Sonnet. The editor passes over it for terseness and flow at zero temperature. Both run sequentially, only after the parallel research stage finishes.
Read the deep dive

How we cut our coding agent's bill by 60% with model routing

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