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Basic Information

Agentarium is a Python framework designed to create, manage, and orchestrate multiple AI agents and their interactions in adaptable environments. It provides a programmatic API for defining agents with personalities, roles, and capabilities, enabling direct agent-to-agent communication as well as autonomous decision making. The project includes components for agent creation, custom actions, memory and context management, and a checkpointing system to save and restore agent states. It integrates with external LLM providers through aisuite and supports configuration via a YAML-based LLM settings file. The repository is intended for developers and researchers who need an extensible, reproducible platform to prototype multi-agent scenarios, automate agent behaviors, and coordinate agent workflows across different AI backends.

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App Details

Features
Agentarium exposes a set of core components and features to build multi-agent applications. It includes an Agent core class for creating agents, an Action base class for defining custom behaviors, an AgentInteractionManager to track communications, and a CheckpointManager to persist and restore state. Agents support autonomous act methods, direct talk_to messaging, and pluggable custom actions added at runtime. Memory and contextual state are maintained to inform future responses. The framework integrates with LLM providers through aisuite and accepts YAML configuration for provider and model selection. The package is distributed via pip and emphasizes performance optimization and an extensible architecture to adapt to different use cases and AI backends.
Use Cases
Agentarium helps developers and teams prototype and run coordinated multi-agent experiments and applications without building orchestration plumbing from scratch. It simplifies creating agents with distinct personalities or roles, wiring custom actions, and enabling direct or autonomous interactions for conversational or task-oriented flows. The checkpointing feature makes experiments reproducible by capturing and restoring agent states and interaction history. LLM integration via aisuite and YAML configuration makes it straightforward to switch providers or models for testing. The modular design and action API let users extend capabilities specific to their domain, while the interaction manager and memory support contextual continuity across sessions, making it useful for research, testing, and building agent-driven systems.

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