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๐Ÿš€ OmniFix โ€” Autonomous Multi-Agent Workflow Automation

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Python LangGraph FastMCP FastAPI Docker Redis License

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

๐ŸŽฎ Live Demo ยท ๐Ÿ—๏ธ Architecture ยท ๐Ÿค– Agents ยท ๐Ÿ“„ Demo Pipeline


๐ŸŽฏ 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


Related MCP server: Agent Workflow MCP Server

๐Ÿ—๏ธ Architecture

graph TB
    subgraph Dashboard["๐Ÿ–ฅ๏ธ 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["โšก FastAPI Gateway"]
        REST[REST Endpoints] --> EXECUTE[/api/workflows/execute]
        WS_EP[WebSocket /ws/events] --> BUS[Event Bus]
    end

    subgraph MCP["๐Ÿ”Œ 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["๐Ÿ•ธ๏ธ LangGraph StateGraph"]
        PLANNER[๐Ÿง  PlannerAgent] -->|steps| EXECUTOR[โš™๏ธ ExecutorAgent]
        EXECUTOR -->|output| VALIDATOR[โœ… ValidatorAgent]
        EXECUTOR -->|error| RECOVERY[๐Ÿ”ง RecoveryAgent]
        VALIDATOR -->|retry| EXECUTOR
        RECOVERY -->|healed| EXECUTOR
    end

    subgraph SPECIALISTS["๐Ÿค– Specialist Agent Pool"]
        DE[๐Ÿ“ DataEntryAgent<br/>Playwright + Forms]
        DP[๐Ÿ“„ DocProcessorAgent<br/>EasyOCR + LLM]
        DM[๐ŸŽฏ DecisionAgent<br/>Rules + ML + HITL]
        CA[๐Ÿ“จ CommunicationAgent<br/>Slack + Gmail + Notion]
    end

    subgraph INFRA["๐Ÿ—๏ธ Infrastructure"]
        REDIS[(Redis<br/>Workflow State)]
        POSTGRES[(PostgreSQL<br/>History + Analytics)]
    end

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

๐Ÿค– Agent Registry

Core Orchestration Agents

Agent

Role

Key Capabilities

๐Ÿง  PlannerAgent

Task decomposition

NL โ†’ atomic steps, LLM-powered, mock+real LLM

โš™๏ธ ExecutorAgent

Step dispatch

Concurrent execution, specialist routing, event emission

โœ… ValidatorAgent

Quality assurance

Evidence binding, schema validation, confidence scoring

๐Ÿ”ง RecoveryAgent

Self-healing

Failure diagnosis, 5 recovery strategies, HITL escalation

Specialist Agents

Agent

Tools

Use Case

๐Ÿ“ DataEntryAgent

Playwright, CSS selectors, ARIA

Web form automation, accounting software entry

๐Ÿ“„ DocProcessorAgent

EasyOCR, LLM, JSON schema

Invoice/contract classification + structured extraction

๐ŸŽฏ DecisionAgent

Rules engine, ML model, DB query

PO validation, duplicate detection, approval routing

๐Ÿ“จ CommunicationAgent

Gmail API, Slack API, Notion API

Notifications, approvals, budget updates


๐Ÿ“„ Invoice Processing Demo

The flagship 8-step autonomous pipeline:

๐Ÿ“ง Gmail Monitor โ†’ ๐Ÿ” OCR Extract โ†’ โœ… PO Validate โ†’ ๐Ÿ’ป Accounting Entry
                                                              โ†“
๐Ÿ“‘ Report Generate โ† ๐Ÿ—ƒ๏ธ Archive Drive โ† ๐Ÿ’ฌ Slack Approval โ† ๐Ÿ“Š Notion Budget

What happens automatically:

  1. Email Monitor โ€” Scans Gmail inbox, detects invoice attachments

  2. OCR Extraction โ€” EasyOCR + LLM extracts all fields with math validation

  3. PO Validation โ€” Checks against purchase orders, applies 6 business rules

  4. Accounting Entry โ€” Playwright fills all form fields, submits with confirmation

  5. Budget Update โ€” Notion API updates project budget tracker

  6. Approval Request โ€” Slack message to manager with structured invoice summary

  7. Archive โ€” Google Drive upload with searchable metadata tags

  8. Report โ€” Weekly processing summary generated and emailed


๐Ÿ”Œ 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"})

โšก Quick Start

Option 1: Dashboard Only (Zero Setup)

# Just open in browser โ€” 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

๐Ÿงช 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

๐Ÿ“ Project Structure

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

๐Ÿ† Key Innovations

  1. Evidence-Bound Reasoning โ€” Every agent decision references specific data points; no hallucinations

  2. Self-Healing Graph โ€” Recovery agent diagnoses failures and autonomously applies one of 5 strategies

  3. Progressive Authorization โ€” HITL escalation only when confidence < 70%; fully autonomous above threshold

  4. MCP-Native โ€” Standard protocol means any LLM (Claude, GPT, Gemini, Ollama) can orchestrate workflows

  5. Real-time Observability โ€” WebSocket-powered dashboard shows live agent graph, confidence scores, and evidence chain


๐Ÿ‘ฅ Team

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


A
license - permissive license
-
quality - not tested
B
maintenance

Maintenance

โ€“Maintainers
โ€“Response time
โ€“Release cycle
โ€“Releases (12mo)
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