Greentic

Overview

Greentic is an open-source platform for building, deploying, and managing autonomous digital workers written in Rust. It enables developers to create intelligent agents that handle complete tasks end-to-end across multiple communication channels with minimal coding.

Digital workers are autonomous flows that coordinate tools, channels, and sub-agents to accomplish complex objectives in a modular, secure, and language-agnostic way.

Core Concept: Digital Workers

Definition: A digital worker is a complete automation unit that acts autonomously and intelligently to handle full tasks from start to finish.

Examples of Digital Workers:

• Weather bot (fetches weather, responds via Telegram)
• Customer support agent (answers questions, escalates to human)
• Content moderator (reviews submissions, flags inappropriate content)
• Task automation (schedules meetings, sends emails, updates databases)

Key Characteristics:

• Autonomous: Operates without human intervention
• Intelligent: Uses AI to reason and make decisions
• Modular: Composed of reusable components
• Language-agnostic: Built with open standards
• Secure: Runs in isolated WebAssembly environments

Why Rust?

Greentic chose Rust over Python for critical production reasons:

Performance & Concurrency

• Python limitation: Global Interpreter Lock (GIL) prevents true parallelism
• Rust strength: True concurrent execution across all cores
• Practical impact: 500 agents on 64-core machine:
  • Python: Hits ceiling due to GIL
  • Rust: Scales efficiently across all cores

Resource Efficiency

• PyTorch container: 8GB+
• Greentic container: ~100MB
• Result: 80x smaller runtime footprint

Async Runtime

• Tokio: Efficient async execution
• Actix Web: High-performance HTTP handling
• Reqwest: Optimized HTTP client for APIs
• Result: Handles thousands of concurrent requests efficiently

Security

• No GIL vulnerabilities
• Memory safety guaranteed by compiler
• Type safety prevents entire classes of bugs

Platform Components

Flows

Definition: Orchestrated sequences of steps that define how a digital worker operates.

What flows do:

• Link components together
• Define execution logic
• Handle state transitions
• Route between channels and tools

Structure:

Channel (input)   
  → QA Node (question/answer)  
  → Fallback Agent (AI handling)  
  → Tool (external action)  
  → Channel (output)  

Tools

Definition: Modules that perform specific actions or integrate with external services.

Types of tools:

• API tools: Call REST APIs
• Data tools: Query databases
• ML tools: Run inference models
• Action tools: Send emails, create tickets, etc.

Examples:

• WeatherAPI integration
• Slack message posting
• Database queries
• Webhook calls

Agents

Definition: Intelligent components that reason and make decisions.

Capabilities:

• Process natural language
• Make decisions based on context
• Fallback handling
• Complex reasoning

Implementation:

• Can use local models (Ollama, LlamaEdge)
• Can use cloud APIs (OpenAI, Google, etc.)
• Hybrid deployments supported

Channels

Definition: Integration points with external communication platforms.

Current Channels:

• Telegram ✅ (v0.2.0 - working)
• Discord ✅ (v0.2.0 - working)
• Email ✅ (v0.2.0 - working)

Planned Channels:

• Slack (v1.0.0)
• WhatsApp (v1.0.0)
• Microsoft Teams (v1.0.0)

Future Vision: Users request workers via any channel (“Hey Slack, create a customer support bot”) and workers are auto-generated from natural language requests.

Key Features

Open Source

• Full source code transparency
• Community-driven development
• No vendor lock-in
• Free to use and modify

Security

• WebAssembly (Wasm) isolation for plugins
• OAuth integration (Microsoft, Google, etc.)
• Secure secret management
• No credentials embedded in flows

Extensibility

• Build custom flows
• Create custom tools
• Develop channel plugins
• Language-agnostic design

Modularity

• Reusable components
• Mix and match tools
• Compose complex behaviors
• No monolithic design

Performance

• Zero cold-starts
• Fast startup times
• Efficient resource usage
• Scales to thousands of concurrent agents

Real-Time Orchestration

• Server-side agent coordination
• Multi-agent workflows
• Complex pipeline management
• Live updates and state tracking

Installation & Setup

Requirements:

• Rust toolchain (installed via rustup)
• Cargo (Rust’s package manager)
• Command line familiarity

Installation:

cargo install greentic  
greentic init  

Create a flow:

• Define in YAML/config format
• Reference tools and channels
• Deploy with: greentic flow deploy <file>.ygtc
• Start with: greentic flow start <flow_id>

Practical Example: Weather Bot

Flow Structure:

1. Telegram Channel: Listen for messages
2. QA Node: Ask “What city?” (word limit 20)
3. Tool: Call WeatherAPI.com
4. Agent Fallback: If user response complex, use AI agent
5. Output: Send formatted weather to Telegram

Configuration:

• Channels: Telegram
• Tools: WeatherAPI
• Agents: Ollama (local) or OpenAI (cloud)
• QA: Welcome message + question

Result: Fully functional bot with minimal code.

Deployment Options

Current (v0.2.0):

• Local deployment
• Self-hosted servers
• Docker containers (~100MB)

Planned (v0.3.0):

• OAuth integrations
• Enhanced authentication
• More channel options

Planned (v0.4.0):

• Cloud deployment (managed hosting)
• Automatic scaling
• Multi-tenant support

v1.0.0 Vision:

• Auto-generate workers from natural language
• Request workers via messaging platforms
• Fully autonomous agent ecosystem

Roadmap

Version	Features	Status
0.2.0	Telegram, Discord, Email, basic flows	✅ Current
0.3.0	OAuth, more plugins, marketplace	🔄 In progress
0.4.0	Cloud deployment, scaling, multi-tenant	📅 Planned
1.0.0	Auto-generation, Slack/Teams/WhatsApp, full ecosystem	🎯 Vision

Marketplace

Available Now:

• Free flows (templates)
• Plugins (integrations)
• Tools (pre-built actions)
• Starting point for custom development

Strengths

• Performance: Rust means no GIL bottlenecks
• Security: WebAssembly isolation + OAuth
• Extensibility: Easy to add tools and channels
• Open Source: No vendor lock-in
• Resource Efficient: 80x smaller than Python alternatives
• Scalable: Handle thousands of agents concurrently
• Modularity: Mix and match components
• Fast Development: Quick prototyping and deployment

Limitations

Current State:

• Early (v0.2.0), still evolving
• Smaller community than Python tools
• Rust learning curve for developers
• Limited documentation (relative to mature projects)
• Slack/Teams integration not ready

Technical:

• Requires Rust knowledge for custom tools
• WebAssembly understanding needed for advanced plugins
• Less AI library ecosystem than Python
• Fewer integrations than established platforms

Use Cases

Ideal For:

• Autonomous task automation
• Multi-channel bots (Telegram, Discord)
• High-concurrency agent systems
• Resource-constrained deployments
• Production-grade agent systems
• Custom workflow automation
• IoT agent management
• Real-time decision systems

Not Ideal For:

• One-off scripts (too much setup)
• Python-ecosystem dependent work
• Rapid prototyping (Rust learning curve)
• Non-technical teams
• Simple chatbots (overkill)

Comparison with Alternatives

Feature	Greentic	LangChain	LlamaIndex	AutoGen
Language	Rust	Python	Python	Python
Open Source	✅ Yes	✅ Yes	✅ Yes	✅ Yes
Performance	⭐⭐⭐⭐⭐	⭐⭐⭐	⭐⭐⭐	⭐⭐⭐
Concurrency	Native	GIL-limited	GIL-limited	GIL-limited
Channels	Telegram, Discord	N/A	N/A	N/A
Deployment	Self-hosted/local	Self-hosted	Self-hosted	Self-hosted
Learning Curve	High (Rust)	Low (Python)	Low (Python)	Medium
Ecosystem	Growing	Mature	Mature	Growing
Real-Time Agents	✅ Yes	Limited	Limited	✅ Yes

When to Choose Greentic

Choose Greentic if:

• Performance and scaling are critical
• Building production agent systems
• Multi-channel automation needed
• Resource efficiency matters (cloud costs)
• Open source is required
• Team has Rust capability

Choose Python alternative (LangChain, LlamaIndex) if:

• Rapid prototyping needed
• Team only knows Python
• Resources unlimited
• Large ecosystem/integrations needed
• Learning curve is concern

Getting Started

1. Install Rust: curl --proto '=https' --tlsv1.2 -sSf https://sh.rustup.rs | sh
2. Install Greentic: cargo install greentic
3. Initialize: greentic init
4. Create flow: Use weather bot example as template
5. Deploy: greentic flow deploy myflow.ygtc
6. Start: greentic flow start <flow_id>
7. Monitor: Watch logs and agent behavior

Best Practices

Design:

• Keep flows focused and modular
• Reuse tools across flows
• Use fallback agents for complex cases
• Design for error handling

Security:

• Use OAuth for authentication
• Store secrets securely (not in code)
• Isolate sensitive tools
• Regular security updates

Performance:

• Monitor concurrent agent count
• Profile tools and agents
• Optimize API calls
• Cache common queries

Deployment:

• Use Docker containers
• Set resource limits
• Plan for scaling
• Monitor resource usage

Community & Support

Resources:

• GitHub repository (open source)
• Documentation (growing)
• Marketplace (flows and tools)
• Community examples

Getting Help:

• GitHub issues for bugs
• Discussions for questions
• Community marketplace
• Official documentation

Ideal Team Profile

Good Fit:

• Rust engineers
• DevOps/infrastructure teams
• Performance-critical systems
• Production automation needs
• SRE/platform teams

Not Ideal:

• Python-only teams
• No DevOps experience
• Quick MVP needed
• Non-technical teams

Conclusion

Greentic is a powerful, production-grade platform for autonomous digital workers, optimized for performance, security, and scale. It’s best suited for teams that:

1. Need production-grade agent systems
2. Can use or learn Rust
3. Require multi-channel automation
4. Care about resource efficiency
5. Want open-source flexibility

For rapid prototyping and small teams, Python alternatives (LangChain) may be more accessible. For scaling to thousands of agents, Greentic’s Rust foundation provides unmatched performance.

Current Status: Early but mature. Actively developed. Community growing. Production-ready for those comfortable with Rust.

Momentum: Strong. Addressing real pain points (GIL, scalability, resource use) that other platforms ignore.