MagenticBrain

About

What MagenticBrain is

MagenticBrain MagenticBrain is a 14B-parameter orchestration model from Microsoft Research AI Frontiers. It plans multi-step tasks, calls declared tools, and coordinates sub-agents. It does not execute actions itself — every real-world side effect happens inside a host harness. The model is supervised fine-tuned from Qwen/Qwen3-14B on agentic data: function-calling corpora, file-system trajectories, terminal tasks, sub-agent delegation traces, and reasoning data. After supervised fine-tuning, it was further trained with reinforcement learning on in-house-constructed terminal tasks. It's co-designed with MagenticLite, our agentic application and harness, and that's the configuration it has been most thoroughly evaluated in. We're releasing weights only. Inference code, training recipes, and the execution harness are part of MagenticLite. Highlights - Orchestration-first post-training. Specialized for planning, tool selection, multi-turn tool chaining, and sub-agent delegation. Not a general-purpose chat model. - Structured tool calls in JSON. Tool schemas are passed in at inference time. The model selects only from declared tools and never invents new ones. - Sub-agent delegation built in. Trained with explicit handoff traces to Fara1.5-9B, our computer-use sub-agent (browser and desktop control). - Submit-to-terminate protocol. Every task ends with a dedicated submit signal, which is a protocol token, not an action. The harness uses it as the end-of-task indicator. - 32K context. Enough headroom for system prompt + tool schemas + multi-turn trajectory state. - Built on Qwen3-14B. Inherits the base model's reasoning and instruction-following. Model Details Recommended Deployment: MagenticLite MagenticBrain is the orchestration model inside MagenticLite. The training mix, tool-call format, and submit/terminate protocol are calibrated against MagenticLite's execution loop. If you want the behavior MagenticBrain was trained for, run it inside MagenticLite. The weights load in any compatible runtime (Transformers, vLLM, SGLang, TensorRT-LLM). If you integrate the model directly, you're responsible for the execution boundary: declaring the tool schema, parsing the mod

Summarised from the published model card. Read the full card on the HuggingFace links below.

Specifications

What it is

How it works

How language models work

Variants

Sizes & precisions

Open weights ship in several sizes and precisions. One page, all the variants — pick the one that fits your GPU. VRAM figures are estimates from model size.

Using it via the API

Call it like any OpenAI endpoint

Once AxForge deploys magenticbrain for you, it answers on the OpenAI-compatible API — the same base URL and keys as every other model. (magenticbrain below is illustrative; you get the exact model name on deployment.)

$ curl -sS https://api.axforge.ai/v1/chat/completions \
  -H "Authorization: Bearer $AXFORGE_API_KEY" \
  -H "Content-Type: application/json" \
  -d '{"model":"magenticbrain","messages":[{"role":"user","content":"Hello"}]}'

Details

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Open weights

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