Research

Research Session Orchestrator

Run a full multi-source research session: classify the domain, dispatch parallel agents, synthesize findings, and output a formatted report.

Workflow

Step 1: Classify Domain

Run the domain classifier on the topic:

from tome.scripts.domain_classifier import classify
result = classify(topic)
# result.domain, result.triz_depth, result.channel_weights

If confidence < 0.6, ask the user to confirm or override the domain classification before proceeding.

Step 2: Plan Research

from tome.scripts.research_planner import plan
research_plan = plan(result)
# research_plan.channels, research_plan.weights, research_plan.triz_depth

Step 3: Create Session

from tome.session import SessionManager
mgr = SessionManager(Path.cwd())
session = mgr.create(topic, result.domain, result.triz_depth, research_plan.channels)

Step 4: Dispatch Agents

Launch research agents in parallel using the Agent tool. Use this mapping:

Rules:

• Always dispatch code and discourse agents
• Dispatch academic agent only if "academic" is in

research_plan.channels

• Dispatch triz agent only if "triz" is in

research_plan.channels AND triz_depth != "light"

• Dispatch all eligible agents in a SINGLE message

(parallel, not sequential)

Each agent prompt must include: 1. The topic string 2. The domain classification 3. Any channel-specific context (subreddits for discourse, triz_depth for triz) 4. Instruction to return findings as JSON

Step 5: Collect and Synthesize

After all agents return:

1. Parse each agent's findings into Finding objects 2. Merge using tome.synthesis.merger.merge_findings() 3. Rank using tome.synthesis.ranker.rank_findings()

Step 6: Generate Output

from tome.output.report import format_report, format_brief, format_transcript

# Default to report format
output = format_report(session)

# Save to docs/research/
output_path = f"docs/research/{session.id}-{slug}.md"

Save the session state:

mgr.save(session)

Step 7: Present Results

Display a brief summary to the user:

• Number of findings per channel
• Top 3 findings by relevance
• Path to saved report

Then offer interactive refinement: "Use /tome:dig \"subtopic\" to explore specific areas."

Error Handling

• If an agent fails, continue with remaining agents
• If all agents fail, report the error and suggest

manual research approaches

• If synthesis produces 0 findings, state this clearly

rather than generating an empty report

• Save session state even on partial failure

Output Format Selection

Exit Criteria

• [ ] Domain classified before agents are dispatched; if confidence

< 0.6, user confirmation is requested before proceeding

• [ ] Code and discourse agents always dispatched; academic and triz

agents dispatched only when their channels are in the plan; all eligible agents sent in a single parallel message

• [ ] Session saved to docs/research/{session.id}-{slug}.md after

synthesis regardless of whether all agents succeeded

• [ ] Top 3 findings by relevance score displayed to the user with

the path to the saved report

• [ ] If all agents fail, error reported and manual alternatives

suggested; an empty report is never generated