llamafile Returns

Mozilla.ai is adopting llamafile to advance open, local, privacy-first AI—and we’re inviting the community to help shape its future.

Introducing any-guardrail: A common interface to test AI safety models

Building AI agents is hard, not just due to LLMs, but also because of tool selection, orchestration frameworks, evaluation, safety, etc. At Mozilla.ai, we’re building tools to facilitate agent development, and we noticed that guardrails for filtering unsafe outputs also need a unified interface.

3W for In-Browser AI: WebLLM + WASM + WebWorkers

🤝This is the first "guest post" in Mozilla.ai's blog (congratulations Baris!). His experiment, built upon the ideas of Mozilla.ai’s WASM agents blueprint, extends the concept of in-browser agents with local inference, multi-language runtimes, and full browser-native execution. As we are exploring ways to

Meet mcpd: requirements.txt for agentic systems

mcpd is to agents what requirements.txt is to applications: a single config to declare, pin, and run the tools your agents need, consistently across local, CI, and production.

Standardized Reasoning Content: A first look at using OpenAI’s gpt-oss on multiple providers using any-llm

After OpenAI’s ChatGPT release, the default standard for communication was the OpenAI Completion API. However, with the new “reasoning models”, a critical piece of the output isn’t handled by that OpenAI specification, leaving each provider to decide how to handle the new model capabilities.

Introducing any-llm: A unified API to access any LLM provider

When it comes to using LLMs, it’s not always a question of which model to use: it’s also a matter of choosing who provides the LLM and where it is deployed. Today, we announce the release of any-llm, a Python library that provides a simple unified interface to access the most popular providers.

Wasm-agents: AI agents running in your browser

One of the main barriers to a wider adoption of open-source agents is the dependency on extra tools and frameworks that need to be installed before the agents can be run. In this post, we show how to write agents as HTML files, which can just be opened and run in a browser.

Smarter Prompts for Better Responses: Exploring Prompt Optimization and Interpretability for LLMs

Generative AI models are highly sensitive to input phrasing. Even small changes to a prompt or switching between models can lead to different results. Adding to the complexity, LLMs often act as black-boxes, making it difficult to understand how specific prompts influence their behavior.

The Challenge of Choosing the Right LLM

Imagine you could effortlessly navigate the universe of LLMs, always knowing which one is the perfect fit for your specific query. Today, this is a very difficult challenge. So, how do you efficiently manage and use LLMs for various tasks? This is where LLM Routing emerges as a crucial strategy.

What do you mean by AI testing...?

We recently discussed the increasing need to test applications that make use of AI with tests that target problems specific to AI models. But an immediate follow-up question then arises: What specific problems? How is that testing different?

AIssert: Testing LLM Integrations

Since LLMs exploded into public awareness, we have witnessed their integration into a vast array of applications. However, this also introduces new complexities, especially in testing. At Mozilla.ai, we did some research on the need to introduce formal testing to the end to end app.

Introducing Any-Agent: An abstraction layer between your code and the many agentic frameworks

Since the launch of ChatGPT in 2022, generative AI and LLMs have rapidly entered everyday life. The viral adoption of these tools was unprecedented, and in some ways contentious. In order to grant greater capabilities to LLMs, they can be integrated into a framework that’s referred to as an “Agent”.