Code execution with MCP: Building more efficient agents
Direct tool calls consume context for each definition and result. Agents scale better by writing code to call tools instead. Here's how it works with MCP.
Direct tool calls consume context for each definition and result. Agents scale better by writing code to call tools instead. Here's how it works with MCP.
Cohere has implemented model signing for all Cohere Command models hosted on Hugging Face to improve integrity and authenticity efforts.
Today, we release LFM2-ColBERT-350M, a late interaction retriever with excellent multilingual performance. It allows you to store documents in one language (for example, a product description…
On-Policy Distillation by Kevin Lu in collaboration with others at Thinking Machines
At PyTorch Conference 2025 in San Francisco, we unveiled five new projects spanning kernel languages, distributed systems, reinforcement learning, agentic frameworks, and edge AI deployment.
The Production AI Platform.
Together, Liquid AI, AMD, and Robotec.ai have deployed compact foundation models for autonomous agentic robotics: showcasing a specialized 3-billion parameter Liquid vision-language model (LFM2-VL-3B), running efficiently…
When we needed to deploy our hybrid LFM models on-device, we faced a critical challenge: existing inference engines couldn't handle the unique combination of attention and…
We’re excited to release LFM2-VL-3B, the newest and most capable addition to our family of vision LFMs (450M and 1.6B). Built on the LFM2-2.6B backbone, this…
Claude Code's new sandboxing features, a bash tool and Claude Code on the web, reduce permission prompts and increase user safety by enabling two boundaries: filesystem…
Claude is powerful, but real work requires procedural knowledge and organizational context. Introducing Agent Skills, a new way to build specialized agents using files and folders.
Today, we are proud to announce the launch of the Cohere Partner Program, a new initiative designed to help our partners innovate faster, expand market impact,…
We are releasing LFM2-8B-A1B, our first on-device Mixture-of-Experts (MoE) with 8.3B total parameters and 1.5B active parameters per token. By activating only a sparse subset of…
LFM2-Audio defines a new class of audio foundation models: lightweight, multimodal, and real-time. By unifying audio understanding and generation in one compact system, it enables conversational…
LoRA Without Regret by John Schulman in collaboration with others at Thinking Machines
Context is a critical but finite resource for AI agents. In this post, we explore strategies for effectively curating and managing the context that powers them.
We invested in improving Claude's ability to help defenders detect, analyze, and remediate vulnerabilities in code and deployed systems. This work allowed Claude Sonnet 4.5 to…
We’re launching Liquid Nanos — a family of 350M–2.6B parameter foundation models that deliver frontier‑model quality on specialized, agentic tasks while running directly on phones, laptops,…
Expanding ‘xAI For Government’ with more accessible AI tools for the Federal Government
Additional funding supports our growing global operations and development of frontier enterprise AI technology.
Using Cohere Grants to transform students into AI builders.
We're excited to announce LFM2-2.6B, the newest and currently largest model in our Liquid Foundation Model 2 series. Building on our 350M, 700M, and 1.2B models,…
Pushing the Frontier of Cost-Efficient Intelligence
This is a technical report on three bugs that intermittently degraded responses from Claude. Below we explain what happened, why it took time to fix, and…