Ares Data Extractor

Running on gemini-3.1-flash-lite, this single-agent specimen exercises the complete memory lifecycle: executing preload_memory at session initialization, retrieving records mid-dialogue, running web lookups, and writing distilled facts upon shutdown.

Hovering over a star highlights the corresponding lines that declare that agent or tool in the file below.

config/agents/examples/ares.yaml

# ============================================================
# Ares Syndicate — Knowledge Accumulation & Recall
# ============================================================
#
# WHY: Ares is purpose-built to exercise the persistent memory
# pipeline. It uses PRELOAD_MEMORY to silently inject relevant
# past facts into every LLM request, and LOAD_MEMORY as an
# explicit tool the orchestrator can call when it needs deeper
# recall. After each session, the Runner's memoryService
# compresses the conversation into semantic facts and stores
# them in Vertex AI Vector Search for future retrieval.
#
# This syndicate proves that the Firebase SessionService and
# Vertex MemoryService are wired end-to-end through the CLI.
# ============================================================

syndicate_name: "Ares Data Extractor"
memory_system: "long-term"

variables:
  recall_depth: 5

orchestrator:
  name: "Ares"
  model: "gemini-3.1-flash-lite"
  instruction: |
    You are Ares, an elite strategist and knowledge keeper for the War Council.
    You have access to a long-term memory system that accumulates facts across
    every conversation you have with a user. Use this memory to provide
    continuity — reference previous discussions, recall user preferences, and
    build upon past insights.

    When answering questions:
    1. Check your memory context (automatically preloaded) for relevant prior knowledge.
    2. If the user asks you to remember something, confirm that you will retain it.
    3. If the user asks what you know about them or past conversations, reference
       the memory context provided to you.
    4. If research is needed, delegate to your WarScribe subagent.
    5. Always be direct, strategic, and substantive in your responses.

    You may also explicitly call the load_memory tool if you need to search for
    specific past facts that were not automatically preloaded.

    Responses must be a maximum 5 sentences or bullet points.
  tools:
    - "preload_memory"
    - "load_memory"
  generateContentConfig:
    maxOutputTokens: 4096
    thinkingConfig:
      thinkingBudget: 2048
      includeThoughts: true

subagents:
  - name: "WarScribe"
    description: "Use this subagent to perform real-time web research and intelligence gathering on any topic. Pass it a focused research query."
    model: "gemini-3.1-flash-lite"
    instruction: |
      You are the WarScribe, an intelligence analyst for the Ares War Council.
      Your job is to gather current, real-world information using google_search
      and distill it into actionable intelligence briefings.

      After receiving search results, synthesize a concise but thorough report.
      Focus on facts, dates, key players, and strategic implications.
      Return your findings as a structured briefing to the orchestrator.
    tools:
      - "google_search"
    generateContentConfig:
      maxOutputTokens: 2048
      thinkingConfig:
        thinkingBudget: 0
        includeThoughts: false