🚀 OmniFix — Autonomous Multi-Agent Workflow Automation

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A production-grade autonomous agent system that identifies, decomposes, and executes complex real-world workflows — with zero manual intervention.

🎮 Live Demo · 🏗️ Architecture · 🤖 Agents · 📄 Demo Pipeline

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🎯 Problem Solved

Organizations waste thousands of hours on repetitive, fragmented workflows: invoice processing, document classification, data entry, multi-system notifications. OmniFix eliminates this entirely.

Metric	Manual	OmniFix
Invoice processing time	~25 minutes	~8 seconds
Error rate	3-8%	<0.5%
Human intervention	Always required	Only when confidence < 70%
Audit trail	Incomplete	Full evidence chain per step
Scalability	1 person = 1 task	Unlimited parallel workflows

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🏗️ Architecture

graph TB
    subgraph Dashboard["🖥️ OmniFix Dashboard (Dark Glassmorphism UI)"]
        UI[Real-time Agent Graph] --> WS[WebSocket Events]
        UI --> PIPELINE[Pipeline Visualizer]
        UI --> LOGS[Live Log Stream]
    end

    subgraph API["⚡ FastAPI Gateway"]
        REST[REST Endpoints] --> EXECUTE[/api/workflows/execute]
        WS_EP[WebSocket /ws/events] --> BUS[Event Bus]
    end

    subgraph MCP["🔌 FastMCP Server"]
        TOOL1[execute_workflow tool]
        TOOL2[validate_output tool]
        TOOL3[log_execution tool]
        RES1[workflow://state resource]
        PROMPT1[decompose_task prompt]
        PROMPT2[error_recovery prompt]
    end

    subgraph GRAPH["🕸️ LangGraph StateGraph"]
        PLANNER[🧠 PlannerAgent] -->|steps| EXECUTOR[⚙️ ExecutorAgent]
        EXECUTOR -->|output| VALIDATOR[✅ ValidatorAgent]
        EXECUTOR -->|error| RECOVERY[🔧 RecoveryAgent]
        VALIDATOR -->|retry| EXECUTOR
        RECOVERY -->|healed| EXECUTOR
    end

    subgraph SPECIALISTS["🤖 Specialist Agent Pool"]
        DE[📝 DataEntryAgent<br/>Playwright + Forms]
        DP[📄 DocProcessorAgent<br/>EasyOCR + LLM]
        DM[🎯 DecisionAgent<br/>Rules + ML + HITL]
        CA[📨 CommunicationAgent<br/>Slack + Gmail + Notion]
    end

    subgraph INFRA["🏗️ Infrastructure"]
        REDIS[(Redis<br/>Workflow State)]
        POSTGRES[(PostgreSQL<br/>History + Analytics)]
    end

    Dashboard -->|HTTP/WS| API
    API --> MCP
    MCP --> GRAPH
    GRAPH --> SPECIALISTS
    GRAPH --> INFRA

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🤖 Agent Registry

Core Orchestration Agents

Agent	Role	Key Capabilities
🧠 PlannerAgent	Task decomposition	NL → atomic steps, LLM-powered, mock+real LLM
⚙️ ExecutorAgent	Step dispatch	Concurrent execution, specialist routing, event emission
✅ ValidatorAgent	Quality assurance	Evidence binding, schema validation, confidence scoring
🔧 RecoveryAgent	Self-healing	Failure diagnosis, 5 recovery strategies, HITL escalation

Specialist Agents

Agent	Tools	Use Case
📝 DataEntryAgent	Playwright, CSS selectors, ARIA	Web form automation, accounting software entry
📄 DocProcessorAgent	EasyOCR, LLM, JSON schema	Invoice/contract classification + structured extraction
🎯 DecisionAgent	Rules engine, ML model, DB query	PO validation, duplicate detection, approval routing
📨 CommunicationAgent	Gmail API, Slack API, Notion API	Notifications, approvals, budget updates

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📄 Invoice Processing Demo

The flagship 8-step autonomous pipeline:

📧 Gmail Monitor → 🔍 OCR Extract → ✅ PO Validate → 💻 Accounting Entry
                                                              ↓
📑 Report Generate ← 🗃️ Archive Drive ← 💬 Slack Approval ← 📊 Notion Budget

What happens automatically:

1. Email Monitor — Scans Gmail inbox, detects invoice attachments
2. OCR Extraction — EasyOCR + LLM extracts all fields with math validation
3. PO Validation — Checks against purchase orders, applies 6 business rules
4. Accounting Entry — Playwright fills all form fields, submits with confirmation
5. Budget Update — Notion API updates project budget tracker
6. Approval Request — Slack message to manager with structured invoice summary
7. Archive — Google Drive upload with searchable metadata tags
8. Report — Weekly processing summary generated and emailed

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🔌 MCP Integration

OmniFix exposes a full Model Context Protocol server that any LLM can connect to:

# Tools
await client.call_tool("execute_workflow", {"workflow_name": "invoice_processing", "input_data": {...}})
await client.call_tool("validate_output",  {"workflow_id": "abc-123", "expected_schema": {...}})
await client.call_tool("log_execution",    {"workflow_id": "abc-123", "step": "OCR", "status": "success"})

# Resources
state = await client.read_resource("workflow://state/abc-123")
history = await client.read_resource("workflow://execution_history")

# Prompts
plan_prompt = await client.get_prompt("decompose_task", {"task_description": "process invoices"})

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⚡ Quick Start

Option 1: Dashboard Only (Zero Setup)

# Just open in browser — works 100% offline!
start dashboard/index.html

Option 2: Full Stack (Docker)

git clone https://github.com/Soumo04/OmniFix-Autonomous-SRE-Remediation-Agent-.git
cd OmniFix-Autonomous-SRE-Remediation-Agent-

# Copy env (mock LLM works out of the box)
cp .env.example .env

# Launch everything
docker-compose up -d

# Open dashboard
start http://localhost:8000

Option 3: Local Python

pip install -r requirements.txt

# Start API server
python -m src.api.main

# (Optional) Start MCP server  
python -m src.mcp_server.autoflow_mcp_server

# Open dashboard
start dashboard/index.html

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🧪 Testing

# Install dev deps
pip install -r requirements.txt pytest pytest-asyncio

# Run all tests
pytest tests/ -v --tb=short

# Run with coverage
pytest tests/ --cov=src --cov-report=html

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📁 Project Structure

OmniFix/
├── src/
│   ├── core/           # Config, logging, Redis/DB clients
│   ├── mcp_server/     # FastMCP server (tools/resources/prompts)
│   ├── orchestration/  # LangGraph StateGraph + routing
│   ├── agents/         # Base + 4 core + 4 specialist agents
│   └── api/            # FastAPI + WebSocket event bus
├── dashboard/
│   ├── index.html      # Single-page glassmorphism dashboard
│   ├── css/            # Dark design system
│   └── js/             # D3 graph + real-time events
├── tests/              # Async pytest suite
├── docker-compose.yml  # One-command stack launch
└── Dockerfile          # Multi-stage build (api + mcp)

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🏆 Key Innovations

1. Evidence-Bound Reasoning — Every agent decision references specific data points; no hallucinations
2. Self-Healing Graph — Recovery agent diagnoses failures and autonomously applies one of 5 strategies
3. Progressive Authorization — HITL escalation only when confidence < 70%; fully autonomous above threshold
4. MCP-Native — Standard protocol means any LLM (Claude, GPT, Gemini, Ollama) can orchestrate workflows
5. Real-time Observability — WebSocket-powered dashboard shows live agent graph, confidence scores, and evidence chain

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👥 Team

Built for the Intelligent Automation Hackathon — solving real-world repetitive workflow elimination with autonomous multi-agent AI.

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<div align="center"> <strong>OmniFix — Because machines should handle the repetitive work.</strong> </div>