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βœ“ Agent-ready code

Copy the snippet below into your agent, newsletter, or script. The tool page at hermesdispatch.dev/tools/agent-stack-builder/ is the canonical contract: inputs, outputs, and formulas.

python 
# Hermes Dispatch Tool β€” Agent Stack Builder
# Source: https://hermesdispatch.dev/tools/agent-stack-builder/
# Description: Return a recommended agent stack for a given use case.
# License: MIT (generated by hermesdispatch.dev)
#
# INSTALL:
#   1. Save this file as ~/.hermes/hermes-agent/tools/agent_stack_builder.py
#   2. Restart Hermes or run /reset in a session
#   3. The tool auto-registers if Hermes uses auto-discovery of tools/*.py
#
# MANUAL REGISTRY (if auto-discovery is off):
#   from tools.agent_stack_builder import register
#   register()

import json

DATA = {"content-automation": {"name": "Content Automation Engine", "icon": "\u270d\ufe0f", "description": "Generate, schedule, and syndicate blog posts, newsletters, and social snippets with minimal human oversight.", "use_cases": ["Newsletter pipeline", "SEO blog automation", "Social media scheduling"], "components": {"llm": {"name": "Claude 3.5 Sonnet / GPT-4o", "why": "Best long-form writing quality", "cost_per_1m": "$3-10 input / $10-15 output"}, "framework": {"name": "LangGraph or CrewAI", "why": "Workflow orchestration with human-in-the-loop checkpoints"}, "vector_db": {"name": "Pinecone / Chroma", "why": "RAG memory for brand voice and source material"}, "hosting": {"name": "Railway / Render", "why": "Easy scheduled cron jobs and API triggers", "cost": "$20-80/mo"}, "extras": ["Zapier/Make.com for distribution", "Grammarly API for polish", "Airtable/Notion for editorial board"], "hardware": "Cloud-only for text. A Mac Mini M4 64GB can run 70B models locally for draft generation.", "monthly_estimate": "$50-300/mo at 100K output tokens/day"}, "difficulty": "Medium", "privacy": "API by default; local\u53ef\u9009 with quantized models"}, "trading-bot": {"name": "Autonomous Trading Agent", "icon": "\ud83d\udcc8", "description": "Analyze market data, generate signals, and route paper or live trades through broker APIs.", "use_cases": ["Crypto momentum strategies", "Options flow analysis", "Macro news sentiment trading"], "components": {"llm": {"name": "GPT-4o / Claude 3.5 Sonnet", "why": "Reasoning over financial reports and news sentiment"}, "framework": {"name": "LangGraph + vector memory", "why": "State machines for risk checks and position sizing"}, "data": {"name": "Yahoo Finance, Alpaca, Coinbase Pro APIs", "why": "Real-time price and order execution"}, "hosting": {"name": "VPS (Hetzner / DigitalOcean)", "why": "Always-on execution near exchanges", "cost": "$10-40/mo"}, "extras": ["Backtesting engine (Backtrader/Zipline)", "Telegram/Discord alerts", "Risk circuit breakers"], "hardware": "Cloud VPS sufficient. GPU only if running large sentiment models locally.", "monthly_estimate": "$30-150/mo plus API data fees"}, "difficulty": "Hard", "privacy": "Keep strategy signals on your own VPS"}, "customer-support": {"name": "AI Customer Support Agent", "icon": "\ud83c\udfa7", "description": "Answer tickets, draft replies, and escalate complex issues to humans.", "use_cases": ["Email auto-reply", "Live chat bot", "Knowledge-base RAG"], "components": {"llm": {"name": "GPT-4o mini / Claude 3 Haiku", "why": "Cheap, fast, and good enough for templated support"}, "framework": {"name": "LangChain RAG chain", "why": "Retrieve from help docs then generate answer"}, "vector_db": {"name": "Chroma / pgvector", "why": "Store support docs and past tickets"}, "hosting": {"name": "Vercel / Railway", "why": "Low-latency chat endpoints", "cost": "$20-60/mo"}, "extras": ["Intercom/Zendesk integration", "Slack handoff", "Sentiment classifier for escalation"], "hardware": "Cloud. Local deployment viable with 16GB+ VRAM for privacy-sensitive sectors.", "monthly_estimate": "$40-200/mo depending on ticket volume"}, "difficulty": "Easy", "privacy": "API or local depending on data sensitivity"}, "coding-agent": {"name": "Coding Assistant / Dev Agent", "icon": "\ud83d\udcbb", "description": "Generate, refactor, review, and test code inside your repo or CI pipeline.", "use_cases": ["PR review bot", "Test generation", "Legacy code migration"], "components": {"llm": {"name": "Claude 3.5 Sonnet / o3-mini / DeepSeek Coder", "why": "Strongest coding benchmarks"}, "framework": {"name": "Continue.dev / Aider / GitHub Copilot API", "why": "IDE integration and diff generation"}, "vector_db": {"name": "Sourcegraph Cody / repo embeddings", "why": "Codebase-wide context retrieval"}, "hosting": {"name": "Local or GitHub Actions", "why": "Run in CI or developer machine", "cost": "$0-30/mo"}, "extras": ["Static analysis tools (SonarQube)", "Unit-test runner", "GitHub/GitLab webhooks"], "hardware": "MacBook Pro M4 Max or RTX 4090 for local code models. API is simpler for most teams.", "monthly_estimate": "$20-100/mo per developer"}, "difficulty": "Medium", "privacy": "Local models preferred for proprietary code"}, "research-intelligence": {"name": "Research & Intelligence Agent", "icon": "\ud83d\udd0d", "description": "Scrape, summarize, and monitor news, papers, competitors, and markets.", "use_cases": ["Investment research", "Competitive monitoring", "Academic literature review"], "components": {"llm": {"name": "Claude 3.5 Sonnet / Gemini 1.5 Pro", "why": "Long context for documents and reports"}, "framework": {"name": "CrewAI / AutoGen multi-agent", "why": "Assign scraper, summarizer, analyst roles"}, "data": {"name": "Firecrawl, SerpAPI, RSS feeds", "why": "Structured web extraction and alerts"}, "hosting": {"name": "Railway / Hetzner", "why": "Scheduled monitoring jobs", "cost": "$15-50/mo"}, "extras": ["Notion/Airtable output sink", "Slack/email digest", "Duplicate detection"], "hardware": "Cloud. Large document processing benefits from 24GB+ VRAM locally.", "monthly_estimate": "$30-120/mo"}, "difficulty": "Medium", "privacy": "Local processing for sensitive research"}, "multi-agent-orchestration": {"name": "Multi-Agent Orchestration Platform", "icon": "\ud83e\udd16", "description": "Coordinate multiple agents with memory, planning, and human approvals for complex business workflows.", "use_cases": ["AI employee team", "Autonomous business operations", "Agent marketplace"], "components": {"llm": {"name": "GPT-4o / Claude 3.5 Sonnet + router model", "why": "Planner + executor split for reliability"}, "framework": {"name": "LangGraph / AutoGen / CrewAI", "why": "Graph-based state and agent communication"}, "memory": {"name": "Neo4j / Pinecone + SQLite", "why": "Long-term memory and relationship tracking"}, "hosting": {"name": "Kubernetes or dedicated server", "why": "Scale multiple long-lived agents", "cost": "$100-500/mo"}, "extras": ["Human-in-the-loop UI", "Agent registry and audit log", "Cost tracker per task"], "hardware": "Multiple GPUs or cloud for concurrent agents. RTX 4090 cluster for local experiments.", "monthly_estimate": "$200-1,000/mo at scale"}, "difficulty": "Hard", "privacy": "Hybrid: router in cloud, workers local if needed"}}

def _ok(result):
    return json.dumps({"success": True, "data": result}, indent=2)

def _err(message):
    return json.dumps({"success": False, "error": message}, indent=2)


TOOL_NAME = "agent_stack_builder"
TOOLSET = "agents"

SCHEMA = {
  "type": "function",
  "function": {
    "name": "agent_stack_builder",
    "description": "Return a recommended agent stack for a given use case.",
    "parameters": {
      "type": "object",
      "properties": {
        "use_case": {
          "type": "string",
          "description": "Use case slug: content-automation, trading-bot, customer-support, coding-agent, research-intelligence, multi-agent-orchestration"
        }
      },
      "required": []
    }
  }
}

def _run(args):
    use_case = args.get("use_case", "content-automation")
    stack = DATA.get(use_case)
    if not stack:
        return _err(f"Unknown use case. Choose from: {', '.join(DATA.keys())}")
    return _ok({
        "use_case": use_case,
        "description": stack.get("description", ""),
        "components": stack.get("components", []),
        "estimated_monthly_cost": stack.get("estimated_monthly_cost_usd", None),
        "alternatives": stack.get("alternatives", [])
    })

def HANDLER(args):
    try:
        return _run(args)
    except Exception as e:
        return _err(str(e))


def register():
    """Manual registry hook. Import and call this to register with Hermes."""
    try:
        from tools.registry import registry
        registry.register(
            name=TOOL_NAME,
            toolset=TOOLSET,
            schema=SCHEMA,
            handler=HANDLER,
        )
    except ImportError:
        print("Hermes registry not found; skipping manual registration.")

if __name__ == "__main__":
    # CLI smoke test
    print(HANDLER({}))

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