OmniFix MCP Server
Monitors Gmail inbox for incoming emails with attachments (e.g., invoices), extracts relevant data, and sends automated email reports.
Uploads documents to Google Drive with searchable metadata tags for archiving and retrieval.
Updates Notion databases (e.g., project budget trackers) with workflow outputs and status information.
Sends approval requests and notifications to Slack channels with structured summaries of workflow results.
Click on "Install Server".
Wait a few minutes for the server to deploy. Once ready, it will show a "Started" state.
In the chat, type
@followed by the MCP server name and your instructions, e.g., "@OmniFix MCP ServerExecute the automated invoice processing workflow for the latest email attachment"
That's it! The server will respond to your query, and you can continue using it as needed.
Here is a step-by-step guide with screenshots.
๐ OmniFix โ Autonomous Multi-Agent Workflow Automation
A production-grade autonomous agent system that identifies, decomposes, and executes complex real-world workflows โ with zero manual intervention.
๐ฎ Live Demo ยท ๐๏ธ Architecture ยท ๐ค Agents ยท ๐ Demo Pipeline
๐ฏ 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
๐๏ธ Architecture
graph TB
subgraph Dashboard["๐ฅ๏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["โก FastAPI Gateway"]
REST[REST Endpoints] --> EXECUTE[/api/workflows/execute]
WS_EP[WebSocket /ws/events] --> BUS[Event Bus]
end
subgraph MCP["๐ 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["๐ธ๏ธ LangGraph StateGraph"]
PLANNER[๐ง PlannerAgent] -->|steps| EXECUTOR[โ๏ธ ExecutorAgent]
EXECUTOR -->|output| VALIDATOR[โ
ValidatorAgent]
EXECUTOR -->|error| RECOVERY[๐ง RecoveryAgent]
VALIDATOR -->|retry| EXECUTOR
RECOVERY -->|healed| EXECUTOR
end
subgraph SPECIALISTS["๐ค Specialist Agent Pool"]
DE[๐ DataEntryAgent<br/>Playwright + Forms]
DP[๐ DocProcessorAgent<br/>EasyOCR + LLM]
DM[๐ฏ DecisionAgent<br/>Rules + ML + HITL]
CA[๐จ CommunicationAgent<br/>Slack + Gmail + Notion]
end
subgraph INFRA["๐๏ธ Infrastructure"]
REDIS[(Redis<br/>Workflow State)]
POSTGRES[(PostgreSQL<br/>History + Analytics)]
end
Dashboard -->|HTTP/WS| API
API --> MCP
MCP --> GRAPH
GRAPH --> SPECIALISTS
GRAPH --> INFRA๐ค Agent Registry
Core Orchestration Agents
Agent | Role | Key Capabilities |
๐ง PlannerAgent | Task decomposition | NL โ atomic steps, LLM-powered, mock+real LLM |
โ๏ธ ExecutorAgent | Step dispatch | Concurrent execution, specialist routing, event emission |
โ ValidatorAgent | Quality assurance | Evidence binding, schema validation, confidence scoring |
๐ง RecoveryAgent | Self-healing | Failure diagnosis, 5 recovery strategies, HITL escalation |
Specialist Agents
Agent | Tools | Use Case |
๐ DataEntryAgent | Playwright, CSS selectors, ARIA | Web form automation, accounting software entry |
๐ DocProcessorAgent | EasyOCR, LLM, JSON schema | Invoice/contract classification + structured extraction |
๐ฏ DecisionAgent | Rules engine, ML model, DB query | PO validation, duplicate detection, approval routing |
๐จ CommunicationAgent | Gmail API, Slack API, Notion API | Notifications, approvals, budget updates |
๐ Invoice Processing Demo
The flagship 8-step autonomous pipeline:
๐ง Gmail Monitor โ ๐ OCR Extract โ โ
PO Validate โ ๐ป Accounting Entry
โ
๐ Report Generate โ ๐๏ธ Archive Drive โ ๐ฌ Slack Approval โ ๐ Notion BudgetWhat happens automatically:
Email Monitor โ Scans Gmail inbox, detects invoice attachments
OCR Extraction โ EasyOCR + LLM extracts all fields with math validation
PO Validation โ Checks against purchase orders, applies 6 business rules
Accounting Entry โ Playwright fills all form fields, submits with confirmation
Budget Update โ Notion API updates project budget tracker
Approval Request โ Slack message to manager with structured invoice summary
Archive โ Google Drive upload with searchable metadata tags
Report โ Weekly processing summary generated and emailed
๐ 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"})โก Quick Start
Option 1: Dashboard Only (Zero Setup)
# Just open in browser โ works 100% offline!
start dashboard/index.htmlOption 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:8000Option 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.html๐งช 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=html๐ Project Structure
OmniFix/
โโโ src/
โ โโโ core/ # Config, logging, Redis/DB clients
โ โโโ mcp_server/ # FastMCP server (tools/resources/prompts)
โ โโโ orchestration/ # LangGraph StateGraph + routing
โ โโโ agents/ # Base + 4 core + 4 specialist agents
โ โโโ api/ # FastAPI + WebSocket event bus
โโโ dashboard/
โ โโโ index.html # Single-page glassmorphism dashboard
โ โโโ css/ # Dark design system
โ โโโ js/ # D3 graph + real-time events
โโโ tests/ # Async pytest suite
โโโ docker-compose.yml # One-command stack launch
โโโ Dockerfile # Multi-stage build (api + mcp)๐ Key Innovations
Evidence-Bound Reasoning โ Every agent decision references specific data points; no hallucinations
Self-Healing Graph โ Recovery agent diagnoses failures and autonomously applies one of 5 strategies
Progressive Authorization โ HITL escalation only when confidence < 70%; fully autonomous above threshold
MCP-Native โ Standard protocol means any LLM (Claude, GPT, Gemini, Ollama) can orchestrate workflows
Real-time Observability โ WebSocket-powered dashboard shows live agent graph, confidence scores, and evidence chain
๐ฅ Team
Built for the Intelligent Automation Hackathon โ solving real-world repetitive workflow elimination with autonomous multi-agent AI.
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