agentbyte 0.1.2

A toolkit for designing multiagent systems

Agentbyte: Experimental Multiagent Systems Toolkit

Agentbyte is an experimental Python toolkit for designing scalable multiagent systems. This project is a learning-driven build and is not enterprise-ready.

This project serves a dual purpose:

1. Learning Journey: Master advanced AI engineering concepts by studying picoagents architecture, patterns, and design decisions
2. Knowledge Application: Build Agentbyte step-by-step based on personal knowledge and preferences, deepening expertise in multiagent systems
3. Enterprise Library (future): Create a production-ready library with multi-framework and multi-LLM support
4. Cross-Framework Compatibility: Enable seamless composition of agents from Agentbyte, Microsoft Agent Framework, and Pydantic AI

The development approach is iterative and intentional: understand a pattern → implement it in Agentbyte with custom enhancements → validate with examples → move to next pattern. This methodology builds both library quality and deep AI engineering expertise.

Status

• In progress: Project is actively evolving
• Implemented: LLM client classes built from scratch
• Next step: Create the agent base

Core Stack

• Python 3.12+
• picoagents >= 0.2.3 (reference framework for learning agent orchestration patterns)
• pydantic for data validation and settings management
• LLM Providers:
  • OpenAI API (GPT-4, GPT-5 series models)
  • AWS Bedrock (future integration)
• Frameworks:
  • Microsoft Agent Framework (integration in progress)
  • Pydantic AI (integration in progress)

Architecture

Directory Structure

src/agentbyte/           # Main package
├── agent/              # Agent base classes and abstractions
│   └── base.py         # BaseAgent ABC - extend this for custom agents
├── settings/           # Environment and configuration management
│   └── core.py         # Settings classes (AppSettings, PostgresSettings, AgentbyteBaseSettings)
└── __init__.py         # Package exports

examples/               # Working examples and use patterns
├── pico-agent-test.py  # Simple agent with tool usage
└── round-robin.py      # Multi-agent orchestration with termination conditions

notebooks/              # Development/exploration notebooks
└── pico-init-agent-test.ipynb  # Interactive testing environment

external_lib/           # External reference implementations
└── designing-multiagent-systems/  # picoagents reference (git submodule)

Key Components

1. Settings System (src/agentbyte/settings/core.py)

Purpose: Centralized environment variable management with Pydantic

Key Classes:

• AgentbyteBaseSettings: Base class for all agentbyte configuration
  • Provides .from_env_file(Path) classmethod for notebook/custom environments
  • Automatically prefixes environment variables (e.g., APP_, POSTGRES_)
• AppSettings: Application-level config (log_level, environment)
• PostgresSettings: Database connection with .url property for connection strings

Pattern: All settings classes use Pydantic's SettingsConfigDict with env_prefix to organize environment variables by domain. Settings can be loaded from custom paths—essential for notebooks that don't run from project root.

# In notebooks or custom paths:
settings = PostgresSettings.from_env_file(Path("../../../.env"))

2. Agent Base (src/agentbyte/agent/base.py)

Purpose: Abstract base for custom agent implementations

Current State: Planned work. The agent base will be implemented next.

External Dependency: picoagents (Reference Framework)

picoagents serves as the reference implementation for understanding agent orchestration patterns. Key concepts to study and potentially abstract:

• Agents: Named, instruction-driven with optional tools and model clients
• Orchestrators: RoundRobinOrchestrator for multi-agent conversations
• Termination Conditions: MaxMessageTermination, TextMentionTermination for conversation flow control
• Tools: Functions exposed to agents via function introspection

Learning Goal: Extract generalizable patterns from picoagents that can be reused across Autogen/Pydantic AI frameworks. See external_lib/designing-multiagent-systems/picoagents for full reference source and examples/round-robin.py for application patterns.

License

This project is licensed under the MIT License.

Development Workflows

Environment Setup

# Project uses UV for dependency management (uv.lock present)
# Python 3.12+ required (see .python-version)
# Copy .env template for API keys: OPENAI_API_KEY required, optional: POSTGRES_*, HF_TOKEN, etc.

# Clone with submodules:
git clone --recursive [e-mail]:yourusername/agentbyte.git
cd agentbyte

# Or if already cloned:
git submodule update --init --recursive

Testing & Validation

# Test dependencies available (pytest, pytest-cov)
# Run: pytest tests/ -v --cov=src/agentbyte
# (tests/ directory not yet created—add test files here)

Development in Notebooks

The project uses Jupyter notebooks for interactive development:

• Kernel: ipykernel configured for Python 3.12+
• Pattern: Import agentbyte modules, load settings with .from_env_file(), test agent interactions
• Example: notebooks/pico-init-agent-test.ipynb demonstrates agent initialization and response handling

Running Examples

# Simple agent example (requires OPENAI_API_KEY in .env):
# Supports: gpt-4o, gpt-4-turbo, gpt-4, gpt-5 (as available)
python examples/pico-agent-test.py

# Multi-agent orchestration:
python examples/round-robin.py

References

This library is built from combined learning from the references below and ongoing study of Design Multi-Agent AI Systems Using MCP and A2A.

• Designing Multi-Agent Systems
• Build a Multi-Agent System (from Scratch) (Manning) by Val Andrei Fajardo
• llm-agents-from-scratch