OmniFix MCP Server
by Soumo04
README.md
# 🚀 OmniFix — Autonomous Multi-Agent Workflow Automation
<div align="center">
[](https://python.org)
[](https://langchain-ai.github.io/langgraph/)
[](https://github.com/jlowin/fastmcp)
[](https://fastapi.tiangolo.com)
[](docker-compose.yml)
[](https://redis.io)
[](LICENSE)
**A production-grade autonomous agent system that identifies, decomposes, and executes complex real-world workflows — with zero manual intervention.**
[🎮 Live Demo](#-quick-start) · [🏗️ Architecture](#%EF%B8%8F-architecture) · [🤖 Agents](#-agent-registry) · [📄 Demo Pipeline](#-invoice-processing-demo)
</div>
---
## 🎯 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** |
---
## 🏗️ Architecture
```mermaid
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
```
---
## 🤖 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 |
---
## 📄 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
---
## 🔌 MCP Integration
OmniFix exposes a full **Model Context Protocol** server that any LLM can connect to:
```python
# 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"})
```
---
## ⚡ Quick Start
### Option 1: Dashboard Only (Zero Setup)
```bash
# Just open in browser — works 100% offline!
start dashboard/index.html
```
### Option 2: Full Stack (Docker)
```bash
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
```bash
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
```
---
## 🧪 Testing
```bash
# 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
```
---
## 📁 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)
```
---
## 🏆 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
---
## 👥 Team
Built for the **Intelligent Automation Hackathon** — solving real-world repetitive workflow elimination with autonomous multi-agent AI.
---
<div align="center">
<strong>OmniFix — Because machines should handle the repetitive work.</strong>
</div>
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