🤖AI Agents Weekly: Qwen3, mem0, Llama API, Bamba, Qwen2.5-Omni-3B

Qwen3, mem0, Llama API, Bamba, Qwen2.5-Omni-3B

In today’s issue:

• Alibaba’s Qwen team has unveiled Qwen3

• Building production-ready AI Agents with scalable long-term memory

• A new survey of AI Agent protocols

• Building and optimizing email research agents with RL

• Meta announces Llama API (in preview)

• A new MCP authorization specification

• Qwen announces Qwen2.5-Omni-3B

• IBM’s open-source Bamba

• Anthropic announced Integrations

• Top AI devs news, tools, and much more.

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Top Stories

Qwen3

Alibaba’s Qwen team has unveiled Qwen3, a new family of LLMs featuring major architectural, training, and capability upgrades. The flagship MoE model, Qwen3-235B-A22B (235B total, 22B active params), achieves strong benchmark results and is joined by a compact Qwen3-30B-A3B model and six open dense models (0.6B to 32B), all Apache-2.0 licensed. These models outperform earlier Qwen2.5 models across coding, reasoning, and STEM tasks while using fewer active parameters.

Key highlights:

• Hybrid thinking modes let users toggle between “thinking” (CoT-style reasoning) and fast response modes, controllable via prompt tags (/think, /no_think) or programmatically via enable_thinking. This allows flexible budget-controlled inference and task-specific reasoning depth.

• Multilingual reach has expanded to 119 languages across Indo-European, Sino-Tibetan, Afro-Asiatic, and more, enabling robust global applications.

• Training scale-up involved 36T tokens (double Qwen2.5), including curated PDF extractions, synthetic math/code data, and multi-stage pretraining focused on reasoning, long context (up to 128K), and efficient MoE routing.

• Post-training pipeline spans four stages, including CoT fine-tuning, RL on reasoning tasks, fusion of fast and slow modes, and broad general-domain RL. This builds models that are both accurate and versatile.

• Agentic capabilities are improved through support for MCP and integration with Qwen-Agent for tool calling. Users can deploy via vLLM, SGLang, Ollama, or use it in environments like Hugging Face and LMStudio.

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