Orchestrating Autonomous Agent Networks

Automate complex workflows with task-oriented networks, graph-based execution, and self-optimizing agent formations.

A Python framework for building autonomous agent networks with multi-step reasoning. Automated formation, TaskGraph orchestration, and model-agnostic optimization.

Multi-Agent SystemsAutonomous AgentsTask GraphAgentic FrameworkPython SDKLLM OrchestrationGraph TheoryRAGWorkflow AutomationLiteLLM

Primary Features

Autonomous Agent Formations (Solo, Supervising, Squad, Random)
Graph-Based Task Execution via TaskGraph (Nodes & Edges)
Model-Agnostic LLM Curation with LiteLLM integration
Advanced Memory Management using mem0ai and Chroma DB
Built-in RAG Support and External Tooling via Composio
Automated Workflow Optimization and Dependency Resolution

Agent Network

When multiple agents handle a task, agents will adapt to specific network formation based on the task and network complexity.

You can specify a desired formation or allow the leader to determine it autonomously (default).

	Solo Agent	Supervising	Squad	Random
Formation
Usage	A single agent with tools, knowledge, and memory. When self-learning mode is on - it will turn into Random formation.	Leader agent gives directions, while sharing its knowledge and memory. Subordinates can be solo agents or networks.	Share tasks, knowledge, and memory among network members.	A single agent handles tasks, asking help from other agents without sharing its memory or knowledge.
Use case	An email agent drafts promo message for the given audience.	The leader agent strategizes an outbound campaign plan and assigns components such as media mix or message creation to subordinate agents.	An email agent and social media agent share the product knowledge and deploy multi-channel outbound campaign.	1. An email agent drafts promo message for the given audience, asking insights on tones from other email agents which oversee other clusters. 2. An agent calls the external agent to deploy the campaign.

Architected by Kuriko IWAI

Related Books for Further Understanding

These books cover the wide range of theories and practices; from fundamentals to PhD level.

Linear Algebra Done Right

Foundations of Machine Learning, second edition (Adaptive Computation and Machine Learning series)

Designing Machine Learning Systems: An Iterative Process for Production-Ready Applications

Machine Learning Design Patterns: Solutions to Common Challenges in Data Preparation, Model Building, and MLOps

Share What You Learned

Kuriko IWAI, "Orchestrating Autonomous Agent Networks" in Kernel Labs

https://kuriko-iwai.com/labs/multi-agent-system-framework

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Written by Kuriko IWAI. All images, unless otherwise noted, are by the author. All experimentations on this blog utilize synthetic or licensed data.