AnyAgent is a flexible agent framework that lets developers construct intelligent agents capable of reasoning, planning, and executing tasks across diverse domains. It offers a built-in planner for chaining actions, configurable memory stores for long-term context, and easy hookups to external tools and APIs. Through a simple declarative DSL, you can define custom skills, embed event logging, and swap between LLM backends seamlessly. Whether for customer support bots, data analysis assistants, or research prototypes, AnyAgent accelerates agent creation with robust architecture, modular components, and extensibility for real-world automation scenarios.
AnyAgent Core Features
Declarative DSL for agent configuration
Built-in planning and chain-of-action engine
Long-term and short-term memory management
Pluggable external tool and API integration
Multi-LLM backend support
Event logging and analytics
Modular, extensible architecture
AnyAgent Pro & Cons
The Cons
Agent frameworks may introduce complexity and security risks.
Requires more computational power compared to traditional code-defined workflows.
Limited to Python 3.11 or newer, restricting usage in older Python environments.
The Pros
Provides a single interface for multiple agent frameworks, simplifying integration.
Open-source and actively maintained by Mozilla AI.
Supports installation with optional dependencies for flexibility.
Facilitates experimentation with different AI agent frameworks under a unified API.
Minerva is an extensible AI agent framework designed to automate complex workflows using large language models. Developers can integrate external tools—such as web search, API calls, or file processors—define custom planning strategies, and manage conversational or persistent memory. Minerva supports both synchronous and asynchronous task execution, configurable logging, and a plugin architecture, making it easy to prototype, test, and deploy intelligent agents capable of reasoning, planning, and tool use in real-world scenarios.
