config.yaml

# 12_streaming — Streaming Agent Output
#
# A delegate orchestration with three agents: researcher, analyst, coordinator.
# The example shows how to stream all agent lifecycle events in real time
# using wire_event_queue() from strands_compose.
#
# Run:
#   uv run python examples/12_streaming/main.py

models:
  default:
    provider: bedrock
    model_id: openai.gpt-oss-20b-1:0

agents:
  researcher:
    model: default
    hooks:
      - type: strands_compose.hooks:ToolNameSanitizer
    system_prompt: |
      You are a research analyst. Given a topic:
        1. Identify the 5-7 most relevant facts.
        2. Present them as concise, numbered bullet points.
      Return only your factual briefing — nothing else.

  analyst:
    model: default
    hooks:
      - type: strands_compose.hooks:ToolNameSanitizer
    system_prompt: |
      You are an insights analyst. Given a research briefing:
        1. Surface 2-3 actionable insights or emerging trends.
        2. Explain the "so what" for a practitioner in plain language.
      Return only your analysis — nothing else.

  coordinator:
    model: default
    hooks:
      - type: strands_compose.hooks:ToolNameSanitizer
    system_prompt: |
      You orchestrate a two-step research -> analysis.
      For every request:
        1. Call researcher to gather the relevant facts.
        2. Pass those facts to analyst for interpretation.
        3. Study the analyst's output and prepare final response.
      Delegate all work you can, your job is to coordinate and synthesize,
      not to research or analyze yourself.

orchestrations:
  main:
    mode: delegate
    entry_name: coordinator
    connections:
      - agent: researcher
        description: "Research the topic and return a factual briefing."
      - agent: analyst
        description: "Analyse the briefing and return key practitioner insights."

entry: main