Announcement
any-llm now integrates with JupyterLiteAI, LangChain, and Headroom. A single provider-agnostic layer powering notebooks, agents, and context optimization across OpenAI, Anthropic, Mistral, and local models.
Announcement
Run any model, from any provider, like OpenAI, Claude, Mistral, or llamafile from one interface, now in Go. any-llm-go delivers type-safe provider abstraction, channel-based streaming, and normalized error handling across eight providers.
Technical Content
The newest integration with any-guardrail: Alinia AI, whose security models are specifically built to detect threats like prompt injection, data exfiltration, and policy violations by understanding the cultural and linguistic nuances of multilingual AI interactions.
Technical Content
A technical evaluation of multilingual, context-aware AI guardrails, analyzing how English and Farsi responses are scored under identical policies. The findings surface scoring gaps, reasoning issues, and consistency challenges in humanitarian deployments.
Expert Opinion
The State of AI report from OpenRouter and a16z offers valuable insight into API-based model usage. But many small models run locally on CPUs and consumer GPUs, outside managed services. This finding, however, warrants critical examination.
Product Release
Octonous helps people stay in flow while work runs across connected apps. The assistant takes real actions, reads messages, updates tools, and creates items while showing every step and asking for approval before anything changes.
Expert Opinion
Remember early 2025? "Vibe coding" was a meme and seemed mostly a tool for casual builders or those new to coding. It's now 2026, and we find ourselves living in a new reality. Industry leaders like DHH, Karpathy, and Lutke are publicly embracing AI-generated code controlled by human prompting.
Product Release
Secure, encrypted LLM API key management across OpenAI, Anthropic, Google providers. Track costs, set budgets, avoid vendor lock-in. Free beta access now.
Product Release
The new plugin system transforms mcpd from a tool-server manager into an extensible enforcement and transformation layer—where authentication, validation, rate limiting, and custom logic live in one governed pipeline.
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The year 2025 has been a busy one at Mozilla.ai. From hosting live demos and speaking at conferences, to releasing our latest open-source tools, we have made a lot of progress and more exploration this year.
Leverage the JVM's polyglot capabilities to create a self-contained, enterprise-optimized server-side blueprint that combines the performance benefits of WebAssembly with the reliability and maturity of Java's ecosystem.
any-llm managed platform adds end-to-end encrypted API key storage and usage tracking to the any-llm ecosystem. Keys are encrypted client-side, never visible to us, while you monitor token usage, costs, and budgets in one place. Supports OpenAI, Anthropic, Google, and more.
Encoderfile compiles encoders into single-binary executables with no runtime dependencies, giving teams deterministic, auditable, and lightweight deployments. Built on ONNX and Rust, Encoderfile is designed for environments where latency, stability, and correctness matter most.
With mcpd-proxy, teams no longer juggle multiple MCP configs. Run all servers behind one proxy and give every developer the same zero-config access inside their IDE.
Gain visibility and control over your LLM usage. any-llm-gateway adds budgeting, analytics, and access management to any-llm, giving teams reliable oversight for every provider.
AI Agents extend large language models beyond text generation. They can call functions, access internal and external resources, perform deterministic operations, and even communicate with other agents. Yet, most existing guardrails weren’t built to protect these operations.
Run any model like OpenAI, Claude, Mistral, or llama.cpp from one interface. any-llm v1.0 delivers production-ready stability, standardized reasoning output, and auto provider detection for seamless use across cloud and local models.
Mozilla.ai is adopting llamafile to advance open, local, privacy-first AI—and we’re inviting the community to help shape its future.
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.
What if AI agents could run entirely in your browser? Not just the UI part—the actual model inference, agent logic, and response generation, all happening locally without a single API call.
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.
