AssemblyZero

Multi-Agent Orchestration Platform for Enterprise AI Development

Run 12+ AI agents concurrently. One identity. Full governance. Measurable ROI.

Start Here

Production Evidence: 70 Days, 64 Active, 493 Issues Closed

This isn't theoretical. AssemblyZero has been under continuous development for 70 days with contributions on 64 of them (91.4%). Only 6 days off since January 10, 2026.

Daily Activity Log → · Metrics → · March 2026 Velocity →

The Headline: Multi-Agent + Multi-Model

graph TD
    subgraph Intent["HUMAN ORCHESTRATOR"]
        O["Human Intent<br/>& Oversight"]
    end

    subgraph LG["LANGGRAPH WORKFLOWS"]
        W["5 State Machines<br/>SQLite Checkpointing"]
    end

    subgraph Agents["CLAUDE AGENTS (12+)"]
        A["Feature | Bug Fix<br/>Docs | Review"]
    end

    subgraph Gemini["GEMINI VERIFICATION"]
        G["LLD Review | Code Review<br/>Security | Quality"]
    end

    subgraph Gov["GOVERNANCE GATES"]
        M["Requirements | Implementation<br/>Reports | Audit Trail"]
    end

    O --> LG
    LG --> Agents
    Agents --> Gemini
    Gemini --> Gov

What makes AssemblyZero different:

The Problem We Solve

AI coding assistants like Claude Code and GitHub Copilot are transforming development. But enterprise adoption stalls because:

Organizations run pilots. Developers love the tools. Then adoption plateaus at 10-20% because the infrastructure layer is missing.

AssemblyZero is that infrastructure layer.

Multi-Agent Orchestration

The headline feature: run 12+ AI agents concurrently under single-user identity with full coordination.

How It Works

Agent Roles

Result: One engineer orchestrating 12+ agents can accomplish what previously required a team.

Full Architecture Documentation

Gemini Verification Layer

The key differentiator: Claude builds, Gemini reviews.

This isn't just "two models" - it's adversarial verification where one AI checks another's work before humans approve.

Four Review Gates

Why Multi-Model Matters

Model Verification

AssemblyZero detects silent model downgrades:

• Gemini CLI sometimes returns Flash when you request Pro
• Our tools verify the actual model used in the response
• If downgraded, the review is flagged as invalid

Gemini Architecture Details

Technical Architecture

LLM Invocation Patterns

Claude is invoked via claude -p with --tools "" and --strict-mcp-config — no tools, no MCP, deterministic side-effect-free calls. The Anthropic API exists only as a paid fallback for resilience.

LangGraph State Machines

All workflows are LangGraph StateGraph instances with typed state and SQLite checkpointing:

Codebase & Document Intelligence (RAG)

AssemblyZero uses a local RAG system built on ChromaDB and all-MiniLM-L6-v2 embeddings:

• Document retrieval (The Librarian) — injects relevant ADRs, standards, and past LLDs into design prompts
• Codebase retrieval (Hex) — AST-based Python indexing with vector search for implementation context
• Duplicate detection (The Historian) — checks for similar past issues before drafting
• Token budget management — trims context to fit LLM prompt limits
• Local-only — no data leaves the developer machine (see ADR-0212)

Full Technical Architecture → | RAG Architecture →

Governance Gates

Three mandatory checkpoints that cannot be bypassed:

Idea → Issue → LLD Review → Coding → Implementation Review → PR → Report Generation → Merge
                ↑                      ↑                           ↑
           Gemini Gate            Gemini Gate                 Auto-Generated

LLD Review Gate

Before writing ANY code:

1. Design document submitted to Gemini
2. Gemini evaluates completeness, security, testability
3. APPROVE → Proceed to coding
4. BLOCK → Revise design first

Cost of design fix: 1 hour
Cost of code fix: 8 hours
Cost of production fix: 80 hours

Implementation Review Gate

Before creating ANY PR:

1. Implementation report + test report submitted to Gemini
2. Gemini evaluates quality, coverage, security
3. APPROVE → Create PR
4. BLOCK → Fix issues first

Report Generation Gate

Before merge, auto-generate:

• implementation-report.md - What changed and why
• test-report.md - Full test output, coverage metrics

Gate Implementation Details

Permission Friction Management

Permission prompts are the #1 adoption killer.

Every "Allow this command?" prompt breaks flow state. Developers either:

• Click "Allow" without reading (security risk)
• Get frustrated and stop using the tool (adoption failure)

Pattern Detection

AssemblyZero identifies:

• Commands that always get approved → Add to allow list
• Commands that always get denied → Add to deny list
• Novel patterns → Flag for review

Measurable Reduction

Permission Friction Deep Dive

34 Governance Audits

Audits designed with an adversarial philosophy: they exist to find violations, not confirm compliance.

Audit Categories

Key Audits

Full Audit Catalog

Metrics & KPIs

"How do I prove ROI to leadership?"

Adoption Metrics

Friction Metrics

Quality Metrics

Cost Metrics

Full KPI Framework

Architecture: LangGraph State Machines

AssemblyZero workflows run as LangGraph state machines with SQLite checkpointing:

Full Roadmap and Vision

Quick Start

1. Clone and Install

git clone https://github.com/martymcenroe/AssemblyZero.git
cd AssemblyZero
poetry install

2. Configure Your Project

mkdir -p YourProject/.claude
cp AssemblyZero/.claude/project.json.example YourProject/.claude/project.json
# Edit project.json with your project details

3. Generate Configs

poetry run python tools/assemblyzero-generate.py --project YourProject

4. Start Working

The generated configs include:

• Permission patterns that eliminate friction
• Slash commands for common operations
• Hooks for pre/post tool execution

Detailed Setup Guide

Documentation

Wiki

Full documentation at AssemblyZero Wiki (50 pages):

For Different Audiences

See Start Here at the top of this page for persona-based navigation.

About

AssemblyZero was built by Martin McEnroe, applying 29 years of enterprise technology leadership to the emerging challenge of scaling AI coding assistants across engineering organizations.

Relevant Experience

Why This Project Exists

Having led enterprise AI adoption, I know the blockers:

Technical Foundation

• LangChain, LangGraph, LangSmith - Hands-on implementation experience
• 21 US Patents - Innovation track record
• CISSP, CCSP, AWS ML Specialty - Security and ML credentials
• GitHub Copilot, Claude Code - Daily production use

The Bottom Line

This isn't theoretical. It's production infrastructure I use daily to orchestrate 12+ AI agents with full governance.

The code in this repo is the same code that:

• Runs the Gemini verification gates
• Protects secrets via hook-based guard architecture
• Governs PR merges via the Cerberus GitHub App
• Optimizes prompt costs (90% savings via caching and routing)
• Tracks permission friction patterns
• Generates the audit reports
• Bypasses Windows' 32K-char limit with a temp-dir CLAUDE.md hack

If you're scaling AI coding assistants across an engineering organization, this is the infrastructure layer you need.

The Discworld Personas

Workflows are named after Terry Pratchett's Discworld characters. Each persona defines the agent's operational philosophy — making system behavior intuitive and memorable.

graph TB
    subgraph Orch["ORCHESTRATION"]
        Om["Om<br/><i>Human</i>"]
        Moist["Moist<br/><i>Pipeline</i>"]
    end

    subgraph Intel["INTELLIGENCE"]
        Lib["Librarian<br/><i>Doc Retrieval</i>"]
        Hex["Hex<br/><i>Code RAG</i>"]
        Hist["Historian<br/><i>Duplicates</i>"]
        Angua["Angua<br/><i>Scout</i>"]
    end

    subgraph Found["FOUNDATION"]
        Brutha["Brutha<br/><i>Vector Store</i>"]
    end

    subgraph Maint["MAINTENANCE"]
        LuTze["Lu-Tze<br/><i>Janitor</i>"]
        DEATH["DEATH<br/><i>Reconciliation</i>"]
    end

    Om --> Moist
    Moist --> Intel
    Lib --> Brutha
    Hex --> Brutha
    Hist --> Brutha

Full Cast & Architecture → | Architecture Diagrams →

"A man is not dead while his name is still spoken."
GNU Terry Pratchett

License

PolyForm Noncommercial 1.0.0