| --- |
| license: mit |
| library_name: transformers |
| pipeline_tag: text-generation |
| language: |
| - en |
| base_model: |
| - Qwen/Qwen3-14B |
| tags: |
| - agent |
| - agentic |
| - tool-use |
| - function-calling |
| - orchestration |
| - magentic |
| --- |
| |
| # 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](https://huggingface.co/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 |
|
|
| | | | |
| |---|---| |
| | **Developer** | Microsoft Research AI Frontiers | |
| | **Architecture** | Decoder-only transformer (Qwen3 architecture) | |
| | **Parameters** | ~14B | |
| | **Context length** | 32,768 tokens | |
| | **Inputs** | Text (system prompt, tool schema, user goal, trajectory state) | |
| | **Outputs** | Text and structured JSON tool calls | |
| | **Training period** | January 2026 – April 2026 | |
| | **Release date** | 14 May 2026 | |
| | **License** | MIT | |
| | **Base model** | [Qwen/Qwen3-14B](https://huggingface.co/Qwen/Qwen3-14B) | |
|
|
| ## 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 model's JSON tool calls, executing tools under your own permissions, and handling the `submit` terminator. |
|
|
| ## Quickstart |
|
|
| ### Transformers |
|
|
| Requires `transformers >= 4.51.0`. |
|
|
| ```python |
| import json |
| from transformers import AutoTokenizer, AutoModelForCausalLM |
| |
| model_name = "microsoft/MagenticBrain" |
| tokenizer = AutoTokenizer.from_pretrained(model_name) |
| model = AutoModelForCausalLM.from_pretrained( |
| model_name, |
| torch_dtype="auto", |
| device_map="auto", |
| ) |
| |
| tools = [ |
| { |
| "type": "function", |
| "function": { |
| "name": "read_file", |
| "description": "Read the contents of a file at the given path.", |
| "parameters": { |
| "type": "object", |
| "properties": {"path": {"type": "string"}}, |
| "required": ["path"], |
| }, |
| }, |
| }, |
| { |
| "type": "function", |
| "function": { |
| "name": "submit", |
| "description": "Signal that the task is complete.", |
| "parameters": {"type": "object", "properties": {}}, |
| }, |
| }, |
| ] |
| |
| messages = [ |
| {"role": "system", "content": "You are an orchestration agent. Plan steps and call only the tools declared below."}, |
| {"role": "user", "content": "Summarize the contents of /tmp/report.md."}, |
| ] |
| |
| text = tokenizer.apply_chat_template( |
| messages, |
| tools=tools, |
| tokenize=False, |
| add_generation_prompt=True, |
| enable_thinking=False, # thinking is disabled by default for MagenticBrain |
| ) |
| |
| inputs = tokenizer(text, return_tensors="pt").to(model.device) |
| output_ids = model.generate(**inputs, max_new_tokens=1024)[0][inputs.input_ids.shape[1]:] |
| print(tokenizer.decode(output_ids, skip_special_tokens=True)) |
| ``` |
|
|
| The model emits tool calls as JSON objects. Parse them, execute the tool in your host, feed the result back as a `tool` role message, and call `generate` again. Loop until the model emits `submit`. |
|
|
| ### vLLM |
|
|
| Serve with an OpenAI-compatible endpoint that exposes tool-calling: |
|
|
| ```bash |
| vllm serve microsoft/MagenticBrain \ |
| --enable-auto-tool-choice \ |
| --tool-call-parser hermes \ |
| --max-model-len 32768 |
| ``` |
|
|
| Then call `/v1/chat/completions` with `tools=[...]` as you would with any OpenAI-compatible tool-use model. Pass `chat_template_kwargs={"enable_thinking": false}` to match training-time defaults. |
|
|
| ### Recommended sampling |
|
|
| ``` |
| enable_thinking = false |
| temperature = 0.7 |
| top_p = 0.8 |
| top_k = 20 |
| min_p = 0 |
| presence_penalty = 1 |
| ``` |
|
|
| MagenticBrain runs in non-thinking mode — keep `enable_thinking=False` in the chat template. Do not use greedy decoding; it causes endless repetition on this model family. Lower the temperature if you need stricter, more deterministic tool selection. |
|
|
| ## Training |
|
|
| ### Approach |
|
|
| Post-training has two stages. First, Supervised Fine-Tuning on a heterogeneous agentic data mix. Second, a reinforcement learning stage on in-house-constructed terminal tasks, further specializing the model for multi-step CLI execution. Thinking tokens are disabled by default (`enable_thinking=False`) to control verbosity and reduce looping on long trajectories. |
|
|
| ### Data sources |
|
|
| - **Function-calling and orchestration:** APIGen-MT, ToolACE, xLAM |
| - **MCP environments:** 250+ synthetic Model Context Protocol environments with single- and multi-turn trajectories, generated for tool-orchestration training |
| - **File-system / Cowork:** file rename, organize, delete, and categorize trajectories |
| - **Terminal / CLI:** containerized tasks paired with executable tests |
| - **Sub-agent delegation:** explicit handoff traces (MagenticBrain → Fara1.5-9B for computer use) |
| - **Reasoning and planning:** Kimi-2.5-generated traces (preferred for lower verbosity), with filtered subsets of Dolci, Hermes, and Nemotron data |
|
|
| Total post-training corpus is under 1B tokens. Base-model pretraining is documented in the [Qwen3 technical report](https://arxiv.org/abs/2505.09388). |
|
|
| ### Data processing |
|
|
| The corpus was filtered and normalized before training. Samples ending on non-`submit` tool calls were removed to prevent infinite-loop trajectories. Samples exceeding the 32K context were dropped. JSON tool-call formats were normalized through shared filters. Thinking blocks were stripped or regenerated to control output length. |
|
|
| ### Latest data date |
|
|
| April 30, 2026. The corpus is not collected on an ongoing basis. Future updates will ship as separately versioned models with their own model cards. |
|
|
| ## Intended Use and Limitations |
|
|
| ### Primary use cases |
|
|
| - **Tool orchestration and workflow planning.** Decompose goals into steps, select tools from an explicit schema, populate structured arguments, chain outputs across multiple turns. |
| - **Agentic task coordination inside a host.** File operations, terminal and code execution, and system actions through host-owned tools, plus delegation to sub-agents. |
| - **Research and product experimentation with agentic systems.** Benchmarking agentic reasoning, studying delegation patterns, serving as the orchestrator in multi-agent stacks. |
|
|
| ### Out of scope |
|
|
| - Fully autonomous agents that take unbounded actions or set their own goals |
| - Open-ended consumer chat with broad, unsandboxed tool permissions |
| - Systems that depend on emergent tool invention or self-modification |
| - Authoritative decisions on business outcomes, product logic, or user-facing policies — these remain the responsibility of host systems and humans |
|
|
| Use outside the declared tool schema, without a host-controlled execution boundary, or in deployments that bypass human-in-the-loop confirmation for sensitive actions is unsupported and outside the conditions the model was evaluated under. |
|
|
| ## Responsible AI Considerations |
|
|
| MagenticBrain is a constrained orchestration model with delegated execution. Known risk areas include: |
|
|
| - **Unauthorized or harmful tool actions** if executed without checks |
| - **Over-autonomy and loss of human oversight** if agentic behavior is misinterpreted as self-directed decision-making |
| - **Hallucinated plans or policy-violating suggestions** |
| - **Data provenance, privacy, and IP risk** carried over from base-model pretraining |
|
|
| ### Mitigations |
|
|
| The model has no execution authority on its own. All actions go through host-owned tools, and the tool surface is explicitly scoped — the model cannot call undeclared tools. Inside MagenticLite, additional safety layers apply: tool-output verification, scoped permissions for state-changing actions, and human-in-the-loop confirmation on sensitive operations. |
|
|
| If you're integrating MagenticBrain outside MagenticLite, we recommend: |
|
|
| - Declare the minimum tool surface required (least privilege) |
| - Surface the model's plan and tool calls in your UI for transparency |
| - Gate critical or irreversible tool calls behind explicit human confirmation |
| - Treat the model's plan as a recommendation, not an authoritative decision |
|
|
| MagenticBrain does not claim full autonomy, self-learning, independent policy-setting, or direct execution of real-world actions. Deployments that rely on any of these properties are unsupported. |
|
|
| ### Safety evaluation |
|
|
| Safety evaluation covered harmful content (sexual, violent, hateful, self-harm), copyright and IP content, jailbreaks and UPIA-style prompt injection, ungrounded content, and task adherence. MagenticBrain's safety behavior is comparable to or better than the Qwen3-14B base model on these categories. |
|
|
| ## License |
|
|
| Released under the **MIT License**. Model weights only — inference code, execution harness, training recipes, and hyperparameters are not part of this release. |
|
|
| ## Contact |
|
|
| For information requests under the EU AI Act and related inquiries: **MSFTAIActRequest@microsoft.com** |
|
|
| Authorized representative: Microsoft Ireland Operations Limited, 70 Sir John Rogerson's Quay, Dublin 2, D02 R296, Ireland. |
