Hermes is a visual, hierarchical AI agent framework that uses a Directed Acyclic Graph (DAG) to coordinate multi-agent planning, execution, and security with a drag-and-drop canvas and Docker self-hosting.

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MIT

Last updated

2026-07-26

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FR-AI / ANALYSIS

Why it is worth attention

It balances the rigidity of deterministic workflow tools (like n8n) and the chaos of free-loop agent frameworks (like AutoGen) by combining a visual DAG canvas with dynamic AI planning, enforcing strict cycle-safety and permission intersection security, while supporting local LLMs and a rich set of built-in agents.

Who it is for

  • Developers building multi-agent systems
  • AI enthusiasts who want to self-host agent networks
  • Researchers needing structured agent coordination
  • DevOps professionals seeking secure agent pipelines

Use cases

  • Automating complex multi-step workflows with hierarchical agent delegation
  • Creating a secure research assistant that uses web search, code execution, and data analysis
  • Building a Telegram bot that can plan and execute tasks via visual DAG wiring
  • Prototyping multi-agent systems with a visual canvas before moving to production code

Strengths

  • Visual drag-and-drop DAG prevents infinite loops and ensures deterministic coordination
  • Permission Intersection security model prevents privilege escalation in agent chains
  • Supports any OpenAI-compatible API and local models (Ollama, vLLM) for flexible deployment
  • Built-in agents (code engineer, data analyst, scheduler, etc.) start gracefully even without all API keys

Considerations

  • Requires Docker and some paid API keys (e.g., Serper, OpenRouter) for full functionality
  • Plugin SDK and skills marketplace are still on the roadmap, limiting extensibility today
  • The DAG paradigm may have a learning curve for users unfamiliar with directed acyclic graphs

README quick start

🏛️ Hermes

The Visual, Hierarchical AI Agent Framework

Build networks of AI agents that plan dynamically, coordinate via DAG, and never spin out of control.

git clone https://github.com/pauloberezini/hermes-synapse && cd hermes-synapse
cp .env.example .env  # add your LLM API key
docker compose up -d
# Open → http://localhost:9119

✨ Why Hermes?

Most multi-agent frameworks are either too rigid (n8n: hardwired workflows) or too chaotic (AutoGen: agents talking in circles forever).

Hermes sits in the middle: a visual drag-and-drop canvas where you wire up AI agents in a strict Directed Acyclic Graph (DAG). No infinite loops. No hardcoded pipelines. Just agents that actually coordinate.

Hermesn8n / MakeFlowise / LangFlowAutoGen / CrewAI
Execution🧠 Non-deterministic (AI plans dynamically)🔧 Deterministic (hardcoded steps)🔗 Visual LLM chains💬 Conversational loops
UI🎨 SVG canvas + isolated agent chatsNode editorVisual chain designerCLI / API only
Hierarchy✅ Strict DAG (cycle-safe)↪ Linear / conditionalData-flow graphs⚠️ Free loops (cycle risk)
Security🔐 Permission IntersectionHardcoded authSandbox containerLocal exec by default
Self-hosted✅ Docker, SQLite, local LLMs

📸 Demo

👆 Click to watch — canvas node wiring, Telegram integration, and live DAG planning in 3 minutes.


🚀 Quick Start

Requirements: Docker + Docker Compose. That's it.

# 1. Clone
git clone https://github.com/pauloberezini/hermes-synapse
cd hermes-synapse

# 2. Configure (only OPENROUTER_API_KEY is required — everything else is optional)
cp .env.example .env

# 3. Launch
docker compose up -d --build

# 4. Open dashboard
open http://localhost:9119

Want to use a local model (Ollama)? Set in .env:

LLM_API_BASE=http://host.docker.internal:11434/v1
LLM_MODEL=llama3
OPENROUTER_API_KEY=local  # any non-empty placeholder

🏛️ Architecture

User ──→ Web Dashboard (React Canvas)
           │
           ▼
     🤖 Root Orchestrator (Jarvis)       ← DAG en

Description

Visual multi-agent orchestrator with a drag-and-drop DAG canvas, dynamic LLM planning loops, permission-based tool execution, and local Qdrant RAG.

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