Mozilla releases 'Any-Agent', a library that makes it easy to compare and test AI agents, and also supports MCP

Mozilla , known as the developer of the web browser Firefox and the email client Thunderbird , has released a library called ' Any-Agent ' that makes it easier to compare and test AI agents.

Introducing Any-Agent: An abstraction layer between your code and the many agentic frameworks
https://blog.mozilla.ai/introducing-any-agent-an-abstraction-layer-between-your-code-and-the-many-agentic-frameworks/

Since the emergence of ChatGPT, generative AI and large-scale language models (LLMs) have become widespread and rapidly adopted into every aspect of our lives. This is an unexpected development, and the risks of misinformation and automation bias caused by the use of these tools are hotly debated.

Improving the reliability, explainability, and functionality of LLM-based applications is an active area of research, resulting in techniques such as Search Augmentation Generation (RAG) , structured output , and tooling that can be used to improve the quality of LLM-based chat applications and are also integrated into workflow automation.

To give LLM more functionality, it can be integrated into frameworks called 'agents.' The term agent is vague, but Anthropic defines it as 'a fully autonomous system that operates independently over an extended period of time and uses a variety of tools to accomplish complex tasks.' Others use the term agent to refer to more prescriptive implementations that follow a predefined workflow.

Anthropic distinguishes between workflows and agents:

Workflow: A system where LLM and tools are coordinated through predefined code paths
Agent: A system in which LLM dynamically directs the use of its own processes and tools to control how tasks are accomplished.

Implementing an agent is not a trivial task, but the agent must be reliable and have a clearly understood design so that it can be properly evaluated and monitored. To achieve this goal, there are countless frameworks that help build these systems. Some frameworks are model specific, others cloud provider specific, and still others are both model and provider agnostic.

With so many options for building agents, choosing a framework may seem like an arbitrary decision, but in reality it's complicated and agent frameworks, even predefined processing frameworks like ReAct, have quite their own views on how agent logic and routing should be implemented.

The world of AI agents is still in its infancy, and as the options continue to expand, it's important to have a way to choose a framework without being tied to a specific API. While each framework's semantics and underlying code differ, many frameworks are trying to accomplish similar things at a high level. As such, it's useful to provide a common language for building agents regardless of which framework you choose.

Mozilla has developed a library called 'Any-Agent.'

Any Agent
https://mozilla-ai.github.io/any-agent/

Any-Agent allows you to try out the latest frameworks by building your agent once. Switching to new architectures is easy by simply changing the 'AgentFramework' configuration parameter. Additionally, Any-Agent also handles log normalization (utilizing open-inference) so you get consistent output no matter which framework you choose.

It is designed to load and configure the agent framework's MCP server, eliminating the need for developers to understand the semantics of loading the agent framework's MCP server of their choice.

Any-Agent is developed as open source and the source code is available on GitHub.

GitHub - mozilla-ai/any-agent: A single interface to different Agent frameworks
https://github.com/mozilla-ai/any-agent