Exploring Multi-Agent Architecture: Brainstorming the Best Route Forward

Posted on Sep 12, 2025

Exploring Multi-Agent Architecture: Brainstorming the Best Route Forward

I’m in the middle of a fascinating architectural exploration, trying to figure out the best path for building scalable AI systems. After diving deep into containerization strategies and orchestration patterns, I’m exploring different approaches and would love to share my current thinking process.

This isn’t a definitive guide—it’s active brainstorming as I work through the pros and cons of various architectural decisions.

Why Current Approaches Are Failing

Most AI projects suffer from the same fundamental problems:

  • Monolithic complexity that becomes unmaintainable as features grow
  • Tight coupling between components that prevents independent scaling
  • Poor developer experience that discourages community contribution
  • Custom orchestration that creates vendor lock-in and technical debt

The solution isn’t just better code—it’s architectural thinking borrowed from the most successful open-source projects.

After analyzing successful GitHub projects with 10k+ stars, four principles emerge:

1. Orchestration is Key

The orchestrator is the brain. It must handle complex, stateful workflows between agents with cycle detection, conditional paths, and state persistence.

2. Modularity First

Each agent should be a separate, self-contained service. This allows independent development, replacement, and scaling—the hallmarks of maintainable systems.

3. API-First Communication

Agents communicate through well-defined HTTP and WebSocket APIs (REST, OpenAPI), not internal language-specific calls. This makes the system language-agnostic and contributor-friendly.

4. Developer Experience Above All

The project must run with a single command (docker compose up) and provide crystal-clear documentation. Complex setup kills community adoption.

The Winning Technology Stack

Based on community adoption and developer familiarity:

🧠 The Orchestrator: LangGraph

LangGraph has emerged as the industry standard for agent workflows. It’s not just a library—it’s a framework for building robust, stateful, multi-agent systems with native support for:

  • Complex workflow graphs with cycles and conditions
  • State persistence across agent interactions
  • Error handling and recovery patterns
  • Visual workflow debugging

Implementation: FastAPI container running LangGraph runtime, exposing clean APIs for task submission and status monitoring.

🤖 The Agent Fleet: FastAPI Services

Each agent runs as an independent FastAPI application with standardized /invoke endpoints:

# Example agent structure
@app.post("/invoke")
async def invoke_agent(request: AgentRequest):
    # Process the request
    result = await agent.process(request.task, request.context)
    return AgentResponse(result=result, status="completed")

Agent Examples:

  • DevAgent: Containerized Aider for code generation
  • GraphitiAgent: Neo4j integration for knowledge graphs
  • RecallAgent: ChromaDB wrapper for long-term memory
  • OpsAgent: System operations and infrastructure management

🚢 The Deployment: Docker Compose

The magic happens in docker-compose.yml—one command launches your entire AI fleet:

version: '3.8'

services:
  # The Captain - LangGraph Orchestrator
  captain-orchestrator:
    build: ./orchestrator
    ports:
      - "8000:8000"
    depends_on:
      - dev-agent
      - graphiti-agent
      - recall-agent
      - neo4j-db
      - chroma-db

  # The Crew - Agent Services
  dev-agent:
    build: ./agents/dev_agent
  
  graphiti-agent:
    build: ./agents/graphiti_agent
    depends_on:
      - neo4j-db
      
  recall-agent:
    build: ./agents/recall_agent
    depends_on:
      - chroma-db

  # Supporting Infrastructure
  neo4j-db:
    image: neo4j:latest
    ports:
      - "7474:7474" # Browser UI
      - "7687:7687" # Bolt protocol
    environment:
      - NEO4J_AUTH=neo4j/pleasechangeme
      
  chroma-db:
    image: chromadb/chroma:latest
    ports:
      - "8001:8000"

Repository Structure for Maximum GitHub Appeal

captains-helm/
├── 📁 orchestrator/          # LangGraph FastAPI Captain
│   ├── app/
│   │   ├── graph.py          # Agent workflow definition
│   │   ├── models.py         # Pydantic API schemas
│   │   └── main.py           # FastAPI entry point
│   ├── requirements.txt
│   └── Dockerfile
├── 📁 agents/
│   ├── 📁 dev_agent/         # Aider API wrapper
│   ├── 📁 graphiti_agent/    # Neo4j knowledge agent
│   ├── 📁 recall_agent/      # ChromaDB memory agent
│   └── 📁 ops_agent/         # System operations
├── 📁 docs/                  # Comprehensive documentation
├── 📁 examples/              # Pre-built workflows
├── docker-compose.yml        # One-command deployment
├── .env.example             # Environment variables
├── LICENSE                  # MIT/Apache 2.0
└── README.md                # Flagship documentation

Why This Architecture Wins

For Developers

  • Familiar technologies: FastAPI, Docker, standard APIs
  • Easy contribution: Add new agents without touching core system
  • Local development: Full stack runs on any machine
  • Clear boundaries: Each agent has defined responsibilities

For Users

  • Simple deployment: docker compose up and everything works
  • Scalable: Run multiple instances of resource-intensive agents
  • Extensible: Plugin architecture for custom agents
  • Maintainable: Update agents independently

For Open Source Success

  • Low barrier to entry: Standard tools, great documentation
  • Modular contribution: Add features without system-wide changes
  • Visual appeal: Clean architecture diagrams and demos
  • Community building: Clear contribution guidelines and examples

Implementation Roadmap

Phase 1: Core Architecture (1-2 weeks)

  • Set up LangGraph orchestrator with FastAPI
  • Create basic agent containers and communication
  • Implement docker-compose configuration
  • Build minimal working demonstration

Phase 2: Agent Development (2-4 weeks)

  • Complete agent implementations with proper APIs
  • Integration testing and error handling
  • Comprehensive API documentation
  • Performance optimization and logging

Phase 3: Open Source Preparation (1-2 weeks)

  • Create comprehensive README with architecture diagrams
  • Add example workflows and video demonstrations
  • Implement contribution guidelines and CI/CD
  • Community engagement strategy

Phase 4: Launch & Growth (Ongoing)

  • GitHub launch with proper marketing and demos
  • Developer content (blog posts, tutorials, videos)
  • Community building and contributor onboarding
  • Feature expansion based on user feedback

The Competitive Advantage

While others build monolithic AI agents, this architecture delivers:

Technical Superiority: Industry-standard tools, proven patterns, scalable design

Developer Experience: 5-minute setup, clear APIs, modular contribution

Community Appeal: Familiar stack, great docs, easy forking and customization

Business Value: Production-ready, maintainable, extensible platform

Current Status: Still Exploring Options

This LangGraph + FastAPI + Docker Compose approach looks promising, but I’m still evaluating multiple paths:

  • Option A: This modern orchestrated approach
  • Option B: Simpler containerization with custom orchestration
  • Option C: Hybrid approaches that blend both strategies
  • Option D: Something completely different I haven’t considered yet

What I’m Looking For

I want to hear from the community:

  • Have you tried similar architectures? What worked? What didn’t?
  • Are there gotchas with LangGraph at scale that I should know about?
  • Alternative orchestration frameworks worth exploring?
  • Deployment complexity - is Docker Compose really the sweet spot?

Next Steps in My Exploration

  1. Prototype testing - Build small versions of each approach
  2. Performance benchmarking - Which scales better?
  3. Developer experience testing - Which is actually easier to contribute to?
  4. Community feedback - What does the real world say?

This is an active exploration, not settled architecture. I’m documenting my thinking process as I work through these decisions. What would you choose? Hit me up with your thoughts—I’m genuinely curious about different perspectives on this architectural challenge.