https://github.com/rahuletto/agent-orchestrator

A sophisticated recursive architecture that spawns and manages LLM-driven agents via a master-delegator model in a form of layers
https://github.com/rahuletto/agent-orchestrator

agents ai artificial-intelligence delegation layers networks

Last synced: 12 months ago
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A sophisticated recursive architecture that spawns and manages LLM-driven agents via a master-delegator model in a form of layers

• Host: GitHub
• URL: https://github.com/rahuletto/agent-orchestrator
• Owner: Rahuletto
• License: gpl-2.0
• Created: 2025-04-23T05:43:05.000Z (about 1 year ago)
• Default Branch: main
• Last Pushed: 2025-07-09T13:25:54.000Z (12 months ago)
• Last Synced: 2025-07-19T06:52:40.693Z (12 months ago)
• Topics: agents, ai, artificial-intelligence, delegation, layers, networks
• Language: Python
• Homepage:
• Size: 3.3 MB
• Stars: 2
• Watchers: 2
• Forks: 1
• Open Issues: 0
• Metadata Files:
  • Readme: README.md
  • License: LICENSE

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README

          
# Autonomous Multi-agent Orchestration

> [!IMPORTANT]

> This branch consist of new proposal of architecture, Instead of parallel agents, I went with Layers of Agents approach. v1 will be available in [old branch](https://github.com/Rahuletto/agent-orchestrator/tree/v1).

A sophisticated recursive architecture that spawns and manages LLM-driven agents via a master-delegator model in a form of layers (just like a neural layers), with task-specific prompt tuning, execution constraints, and multi-agent recursion without human intervention.

> This uses a concept where a primary agent creates specialized sub-agents on demand. Each agent is dynamically generated with task-specific prompting and execution parameters, enabling efficient problem-solving without human intervention while not having a bias and having opinions from various agents instead of deciding on its own. Uses Ensemble technique.

# Architecture

## Here's how it works

![architecture diagram](/assets/architecture_layers.png)

The system follows a recursive workflow:

- `Master Agent` analyzes the input task and generates a comprehensive task breakdown with dependency structure.

- For each subtask, the `Master Agent` creates a specialized `Child Agent` with custom fine-tuned prompts with dependencies.

- ~`Child Agents` work on their assigned tasks and can create their own `Sub-Child Agents` when further specialization is needed~

- `Child Agents` are rearranged based on the dependency graph and co-ordinates with other agents while sharing context

- ~Results flow back up the hierarchy for integration and final output~

- Results flow through layers of agents with context and intreprets a final output.

The key innovation is that agent creation and prompt engineering happen automatically at runtime in _layers_, with no predefined agent structures or human-designed prompts.

## Caveats

As you see, this runs on recrusive layers method. building agents upon agents as it needs. This can result in building an agent layer network as shown below

![agent tree](/assets/layers.png)

The recursive nature of this system creates potential challenges:

- **Resource Management:** Each additional agent consumes computational resources

- **Runtime Concerns:** Deep agent layers can significantly impact completion time as it becomes sequential after a time

While the system offers powerful flexibility by giving control to the `Master Agent`, careful monitoring is recommended for resource-intensive tasks.

## Demo

https://github.com/user-attachments/assets/ec99d37f-c6c9-4fbd-a07e-1997d88aaa99

# Citations and Resources

1. Emergence AI's 2025 Orchestrator

   Automatically creates agents and assembles multi-agent systems with minimal human intervention... continuously refining tools through recursive self-improvement

2. ReDel's Recursive Systems (2024)

   Introduces systems where a root agent "decomposes tasks into subtasks then delegates to sub-agents" rather than using human-defined agent graphs.

3. Beyond Better's Orchestrator (2025)

   Implements "sub-agents created with specialized capabilities for token efficiency and parallel processing" through dynamic task analysis.

# License

This project is licensed under the GNU General Public License (GPL).