🚀 LLM Task Orchestrator

LLM Task Orchestrator is a modular, extensible autonomous agent framework built around the ReACT (Reasoning + Acting) methodology. It enables structured task execution, tool-based reasoning, multi-agent workflows, human-in-the-loop (HITL) validation, and seamless deployment across CLI, APIs, cloud edge platforms, and chat integrations such as Discord.

This repository provides a complete ecosystem for building, generating, deploying, and orchestrating intelligent LLM-powered agents using both Python and Deno/TypeScript stacks .

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🌟 Highlights

• ReACT-based autonomous reasoning loop
• Multi-agent orchestration and generation system
• Built-in tool ecosystem (search, RAG, file I/O, scraping, math, code execution)
• Streaming responses and real-time execution tracking
• Human-in-the-loop (HITL) validation support
• Discord bot and chat integration support
• Supabase Edge + Serverless deployment ready
• Python + Deno hybrid architecture
• Production-grade modular design

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📁 Repository Structure Overview

LLM-Task-Orchestrator/
│
├── engine/               # Core Python orchestration engine (CrewAI based)
├── agent_factory/        # Dynamic agent generator (Meta-Agent system)
├── standalone_agent/     # Single-file ReACT agent (Deno)
├── chat_integrations/    # Discord & webhook integrations
├── cloud_backend/        # Supabase Edge Functions
├── planning_docs/        # Architecture & planning notes
├── test/                 # Automated tool & pipeline tests
├── setup.py              # Python package installer
├── package.json          # Node/Deno tooling
└── README.md

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⚡ Quick Start

1️⃣ Clone Repository

git clone https://github.com/Parvezkhan0/LLM-Task-Orchestrator.git
cd LLM-Task-Orchestrator

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2️⃣ Install Python Engine Dependencies

cd engine
pip install -r requirements.txt

or

pip install -e .

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3️⃣ Configure Environment Variables

Create a .env file inside the engine/ directory:

# OpenRouter API Key
OPENROUTER_API_KEY=your_api_key_here

# Optional Model Override
OPENROUTER_MODEL=openai/o3-mini-high

# Debug Mode
DEBUG=false

# Human-in-the-loop (HITL)
HITL_ENABLED=false

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4️⃣ Run Core Agent Engine

cd engine
python main.py

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🧠 Architecture Overview

LLM Task Orchestrator is built using a layered architecture:

User Request
     ↓
Task Planner (CrewAI)
     ↓
ReACT Agent Loop
     ↓
Tool Execution Layer
     ↓
Observation Feedback
     ↓
Final Response

Key Layers

Layer	Responsibility
Planning Layer	Task decomposition and workflow logic
ReACT Loop	Reason → Act → Observe → Repeat
Tool Layer	File I/O, RAG, scraping, math, code execution
Memory Layer	Context retention and state tracking
Interface Layer	CLI, REST API, Discord, HTTP endpoints

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🎯 Core Features

🧩 ReACT Reasoning Engine

• Structured thought-action loops
• Multi-step reasoning pipelines
• Tool-triggered execution paths
• Error recovery and retry logic

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🛠 Extensible Tool System

Built-in tools include:

• PDF / DOCX / CSV RAG Search
• Website Scraping
• File Reader / Writer
• Selenium Automation
• YouTube Search
• JSON / Directory Search
• Custom Tool Injection

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👨‍💻 Human-in-the-Loop (HITL)

Enable human validation checkpoints:

• Approve critical actions
• Review generated outputs
• Override automated decisions

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🔄 Streaming Responses

• Real-time progress updates
• Partial response streaming
• Live execution feedback

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🎮 Execution Modes

Mode	Description
Autonomous	Fully automated ReACT execution
HITL Mode	User-validated checkpoints
Streaming	Real-time output streaming
CLI Mode	Local terminal interface
HTTP Mode	REST API service

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🛠 Technical Stack

Component	Technology
Core Engine	Python 3.9+
LLM Provider	OpenRouter API
Agent Framework	CrewAI
Server Layer	FastAPI / HTTP
Edge Deployment	Supabase + Deno
Discord Bots	Deno + Webhooks
Configuration	YAML + ENV
Packaging	pip + setup.py

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📊 Performance Targets

Metric	Target
Standard Response Time	< 2 seconds
Streaming Latency	< 100 ms
Task Success Rate	> 95%
Cold Start (Edge)	< 1 second

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🔐 Security Features

• Environment-based secret management
• Tool input sanitization
• Role-based permission design
• API rate limiting
• Sandbox execution environment

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📚 Documentation Hub

Inside engine/docs/:

Guide	Description
Configuration	System setup and tuning
Templates	Prompt engineering templates
Tools	Custom tool creation guide
Memory	State and storage handling
HITL	Human validation workflows
Advanced	Scaling & orchestration patterns

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🌐 Integration Options

CLI

python main.py

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REST API

Exposed OpenAPI spec:

engine/.well-known/openapi.yaml

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Discord Bot

Located in:

chat_integrations/

Includes:

• Slash commands
• Signature verification
• Interaction handlers
• Supabase edge deployment scripts

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Supabase Edge Functions

Located in:

cloud_backend/functions/

Deploy:

supabase functions deploy agentics-bot --no-verify-jwt

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🧪 Testing Framework

Run automated tests:

pytest test/

Includes coverage for:

• File tools
• Search tools
• Scraping tools
• RAG pipelines

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🧠 Agent Factory (Meta-Agent)

Located in:

agent_factory/

Allows dynamic generation of:

• Custom ReACT agents
• Domain-specific assistants
• Math tutors
• Research bots
• Code reviewers

Example:

./scripts/create_research_assistant.sh

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🚀 Standalone ReACT Agent

Located in:

standalone_agent/

Features:

• Single-file deployment
• Deno-based execution
• Edge optimized
• HTTP API enabled

Run locally:

deno run --allow-net --allow-env agent.ts

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📦 Deployment Options

Platform	Supported
Fly.io	✅
Supabase Edge	✅
Deno Deploy	✅
Railway	✅
Docker	✅
Bare Metal	✅

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📈 Roadmap

Feature	Status
Multi-Agent Federation	🚧 In Progress
GUI Dashboard	🎯 Planned
Vector Memory Store	🎯 Planned
Cloud Orchestration UI	💡 Proposed
Distributed Agent Swarm	💡 Proposed

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🤝 Contributing

We welcome all contributions!

Ways to contribute:

• 🐛 Bug reports
• 💡 Feature ideas
• 🔧 Code improvements
• 📚 Documentation updates
• 🧪 Test coverage

Fork → Create Branch → PR 🚀

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📄 License

MIT License See LICENSE file for details.

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🙏 Credits & Acknowledgments

• Built using CrewAI
• Powered by OpenRouter
• Inspired by ReACT research
• Community-driven architecture