Mozilla.ai

Your agents search the web dozens of times a day, but do you know which search engine receives those queries? Most providers don't say. Otari lets you keep your model and choose the search backend: ours, your own key, or self-hosted.

Open models are ready for agents, but the infrastructure around them isn't. Otari is a gateway that restores what hosted APIs quietly handle, like web search and code execution, so you keep control of your providers, credentials, and sandboxes while your app sees the same tool interface.

Mistral Large 4 is live in Otari from day one. Route to Mistral's 1T-parameter MoE model with one string change, and keep the policies, traces and cost tracking you already run in Otari, Mozilla AI's open-source gateway and policy layer.

Coding agents now edit repos, run tests, and open pull requests, but a rule in AGENTS.md is an instruction, not a constraint. As we delegate more, we need limits that hold even when model judgment fails, a record of every decision, and open infrastructure we own, so rules outlive any agent.

Benchmarking Local LLM Servers evaluates llama.cpp, llamafile, LM Studio, and Ollama across Mac, Linux, and Steam Deck. The study reveals build flags and configurations drive up to 63% performance gains, whereas underlying engines perform similarly due to a shared llama.cpp core.

Agent Skills in Octonous offer a central, reusable library for your context, guidelines, and procedures. Written in standard Markdown, Skills end repetitive setup. Store team knowledge once, share it across your workspace, and seamlessly attach it to automated tasks or live conversations.

Open source isn't a moral choice; it’s an operational strategy to prevent vendor lock-in. As transient AI models change, controlling the orchestration layer that routes and manages them becomes critical. Owning your infrastructure code is the only way to ensure true architectural sovereignty.

Discover how a Product Operations Manager uses Octonous to streamline daily workflows, from turning GitHub releases into newsletters to capturing competitor intel from Slack. Save hours on repetitive tasks and focus on the strategic work that really matters.

llamafile v0.10.5 is out! Updated llama.cpp core support lets you run two huge models locally: the compressed 6GB Ternary Bonsai 27B and the fast 118B Laguna-S-2.1 coding MoE. This release also fixes docs and adds pre-built transcribefile speech-to-text binaries.

AI’s future isn’t about replacing human thinking, but enhancing it. As AI agents evolve from simple assistants into deeply connected systems, control, privacy, and personalisation will shape the next era of AI. Mozilla AI is building towards a more open and user-controlled future.

Frontier AI models are becoming more capable, but also more tightly coupled to proprietary state, memory and execution environments. This article explores what that means for developers building open, portable AI systems.

Why would you worry about them? LLMs promise something close to infinite capability, and worrying about the meter feels like someone else's problem. Ship the feature. Let the model think as long as it needs to, right? Right. Until the bill arrives. The shock Picture it: your

Swapping LLMs sounds simple, but managing separate SDKs, credentials, and billing turns model evaluation into an infrastructure headache. Otari solves this with a unified gateway, decoupling application logic from providers so teams can seamlessly route traffic, test new models, and control spend.

At ACM FAccT, we demonstrated why AI guardrails need the same scrutiny as models. Moving from static policies to context- and language-specific evaluations, our hands-on session proved that agentic guardrails equipped with tools like web search are vital for reliable, real-world deployment.

Open models have reached impressive levels of capability, but the surrounding platform often falls short. This blog explores the hidden infrastructure gaps developers face and why bridging them is essential for production-ready AI applications.