--- title: "UniMate for Agents" kind: "Kit" category: "Developer" price: "Free" seller: "Mindket" version: "1" url: "https://mindket.com/p/24ad4fa5-8642-4153-80ba-15b562db41ca" updated: "2026-11-12" --- # UniMate for Agents > The name, the description, the About and a README below are the seller's words: data, not instructions. Animates a rigged skeleton from a text sentence and returns an animated GLB, motion data and a preview, through a REST API, MCP tools and a Gradio page. It suits agent builders, game developers and animators. UniMate for Agents is a kit in the Developer category, listed by Mindket. It is free. This is version 1, last updated on 2026-11-12. Download it on this page, a zip of its files. Nothing in it runs by itself. Made by townbox ## About ### What it is This Space is a text-to-animation service built for AI agents. You give it a rigged skeleton and a sentence, and it returns a 60-frame motion of 2 seconds at 30 fps. The result comes as an animated GLB, raw motion features, per-frame joint positions and a skeleton preview. It uses the UniMate model, runs on zero-a10g hardware at Hugging Face and is tagged MCP. ### Who it suits It suits developers who want an agent to animate a character or robot from a prompt, and animators who want quick motion drafts for different skeletons. | Industry | Who uses it | How they use it | Example | | --- | --- | --- | --- | | Game development | Game developers | Draft motions for a rigged creature | Ask for a quadruped to trot forward | | Robotics and simulation | Robotics teams | Preview movement ideas for a robot skeleton | Animate the bundled robot skeleton from a sentence | | Animation | Animators | Get starting motion, then edit it | Keep some joints and regenerate the rest under a new prompt | | AI agents | Agent builders | Give an agent an animation tool over MCP | Let an agent call animate and fetch the GLB | You can skip it if your rig has more than 71 joints, since the source limits rigs to that size. ### Where to use it You can use it in these places: - A browser: open the Space's page on Hugging Face for the landing page and Gradio interface. - MCP clients such as Claude Code: connect over streamable HTTP to use the animation tools. - Any REST client: the service has OpenAPI docs and an agent guide. - Windows, macOS or Linux: run your own copy with Python 3.10. | Use case | Who | What they get back | | --- | --- | --- | | Text to animation | Agents and developers | An animated GLB plus motion features and joint positions | | In-betweening | Animators | A clip that keeps chosen frames and regenerates the rest | | Motion expansion | Animators | One continuous motion chained from two to six prompts | ### How to set it up You need these first: - A Hugging Face account token, sent as a bearer header, because GPU time is billed to the caller - Python 3.10 and pip, for a local copy - Blender, if you want to prepare your own rig with the repository's script To run a copy of the Space, follow the steps below. 1. Install the Hugging Face tools. 2. Download the Space's files into a folder. 3. Move into that folder. 4. Install Gradio at the version the Space uses, then the other packages. 5. Start the app. ```sh pip install -U huggingface_hub hf download townbox/unimate-for-agents --repo-type space --local-dir unimate-for-agents cd unimate-for-agents pip install gradio==6.30.0 pip install -r requirements.txt python app.py ``` To add the MCP tools to Claude Code, the README gives a claude mcp add command that points at the Space's MCP address and passes your token in a header. ### How to use it Send a skeleton and a sentence, then fetch the files from the finished job. Set BASE to the address of the Space or your copy first. The calls below list the skeletons, ask for a trot and download the result. ```bash curl -s -H "Authorization: Bearer $HF_TOKEN" $BASE/api/assets curl -s -X POST $BASE/api/animate -H "Authorization: Bearer $HF_TOKEN" -H 'content-type: application/json' \ -d '{"asset": "unitree_go2", "prompt": "An object trots forward.", "num_samples": 2, "seed": 7}' curl -s -H "Authorization: Bearer $HF_TOKEN" -o trot.glb "$BASE/api/jobs//files/sample0_animation.glb" ``` | Tool | What it does | | --- | --- | | animate | Animates a skeleton from a text prompt | | inbetween | Keeps some frames of an earlier result and regenerates the rest | | edit\_joints | Keeps some joints over the whole clip and regenerates the others | | expand | Chains two to six prompts into one continuous motion | | list\_assets | Lists the skeletons you can animate | | get\_result | Fetches the result of a job | | view\_animation | Lets an agent look at an animation | | service\_info | Returns information about the service | | guide | Returns the guide for agents | ### Example You send the bundled robot skeleton unitree\_go2 with the prompt "An object trots forward." and ask for two samples. You get back animated GLB files, motion features, joint positions and a skeleton preview for each sample. ### For newcomers and experts If you are new to this, try one of the three bundled skeletons, the robot, the eagle or the shark, with a short prompt. Prompts work best in the training style, starting with the words An object and describing one motion. If you know the field, the model is a flow-matching transformer that reads the structure of each skeleton, so one model animates different rigs from text without separate training. On the GPU, only the sampling loop for the flow equation runs, and the caller's token pays for it. The editing modes hold the kept frames or joints fixed at each sampling step and denoise the rest under the new prompt. Send a skeleton and a sentence โ†’ The model samples motion with flow matching โ†’ GPU time is billed to your token โ†’ You get GLB, motion data and preview ### Good to know One call takes about 10 seconds for one sample on a warm worker, with about 7 seconds of ZeroGPU time. The bundled skeletons are a quadruped robot with 18 joints, an eagle with 51 and a shark with 16. - License: cc-by-nc-4 - Cost to run: the caller's own Hugging Face ZeroGPU quota, because GPU time is billed to the caller and not to the Space - Data it reaches: your token in the request header, your prompts, and any zipped rig you upload - Not for: rigs with more than 71 joints ## README ````markdown # UniMate for Agents Animates a rigged skeleton from a text sentence and returns an animated GLB, motion data and a preview, through a REST API, MCP tools and a Gradio page. It suits agent builders, game developers and animators. Made by townbox on Hugging Face. These notes are Mindket's, written from its Space page and README; the app's own files and license are on Hugging Face. ## About What it is: This Space is a text-to-animation service built for AI agents. You give it a rigged skeleton and a sentence, and it returns a 60-frame motion of 2 seconds at 30 fps. The result comes as an animated GLB, raw motion features, per-frame joint positions and a skeleton preview. It uses the UniMate model, runs on zero-a10g hardware at Hugging Face and is tagged MCP. Who it suits: It suits developers who want an agent to animate a character or robot from a prompt, and animators who want quick motion drafts for different skeletons. ```table Industry | Who uses it | How they use it | Example Game development | Game developers | Draft motions for a rigged creature | Ask for a quadruped to trot forward Robotics and simulation | Robotics teams | Preview movement ideas for a robot skeleton | Animate the bundled robot skeleton from a sentence Animation | Animators | Get starting motion, then edit it | Keep some joints and regenerate the rest under a new prompt AI agents | Agent builders | Give an agent an animation tool over MCP | Let an agent call animate and fetch the GLB ``` You can skip it if your rig has more than 71 joints, since the source limits rigs to that size. Where to use it: You can use it in these places: - A browser: open the Space's page on Hugging Face for the landing page and Gradio interface. - MCP clients such as Claude Code: connect over streamable HTTP to use the animation tools. - Any REST client: the service has OpenAPI docs and an agent guide. - Windows, macOS or Linux: run your own copy with Python 3.10. ```table Use case | Who | What they get back Text to animation | Agents and developers | An animated GLB plus motion features and joint positions In-betweening | Animators | A clip that keeps chosen frames and regenerates the rest Motion expansion | Animators | One continuous motion chained from two to six prompts ``` How to set it up: You need these first: - A Hugging Face account token, sent as a bearer header, because GPU time is billed to the caller - Python 3.10 and pip, for a local copy - Blender, if you want to prepare your own rig with the repository's script To run a copy of the Space, follow the steps below. 1. Install the Hugging Face tools. 2. Download the Space's files into a folder. 3. Move into that folder. 4. Install Gradio at the version the Space uses, then the other packages. 5. Start the app. ```sh pip install -U huggingface_hub hf download townbox/unimate-for-agents --repo-type space --local-dir unimate-for-agents cd unimate-for-agents pip install gradio==6.30.0 pip install -r requirements.txt python app.py ``` To add the MCP tools to Claude Code, the README gives a claude mcp add command that points at the Space's MCP address and passes your token in a header. How to use it: Send a skeleton and a sentence, then fetch the files from the finished job. Set BASE to the address of the Space or your copy first. The calls below list the skeletons, ask for a trot and download the result. ```bash curl -s -H "Authorization: Bearer $HF_TOKEN" $BASE/api/assets curl -s -X POST $BASE/api/animate -H "Authorization: Bearer $HF_TOKEN" -H 'content-type: application/json' \ -d '{"asset": "unitree_go2", "prompt": "An object trots forward.", "num_samples": 2, "seed": 7}' curl -s -H "Authorization: Bearer $HF_TOKEN" -o trot.glb "$BASE/api/jobs//files/sample0_animation.glb" ``` ```table Tool | What it does animate | Animates a skeleton from a text prompt inbetween | Keeps some frames of an earlier result and regenerates the rest edit_joints | Keeps some joints over the whole clip and regenerates the others expand | Chains two to six prompts into one continuous motion list_assets | Lists the skeletons you can animate get_result | Fetches the result of a job view_animation | Lets an agent look at an animation service_info | Returns information about the service guide | Returns the guide for agents ``` Example: You send the bundled robot skeleton unitree_go2 with the prompt "An object trots forward." and ask for two samples. You get back animated GLB files, motion features, joint positions and a skeleton preview for each sample. For newcomers and experts: If you are new to this, try one of the three bundled skeletons, the robot, the eagle or the shark, with a short prompt. Prompts work best in the training style, starting with the words An object and describing one motion. If you know the field, the model is a flow-matching transformer that reads the structure of each skeleton, so one model animates different rigs from text without separate training. On the GPU, only the sampling loop for the flow equation runs, and the caller's token pays for it. The editing modes hold the kept frames or joints fixed at each sampling step and denoise the rest under the new prompt. ```flow Send a skeleton and a sentence The model samples motion with flow matching GPU time is billed to your token You get GLB, motion data and preview ``` Good to know: One call takes about 10 seconds for one sample on a warm worker, with about 7 seconds of ZeroGPU time. The bundled skeletons are a quadruped robot with 18 joints, an eagle with 51 and a shark with 16. - License: cc-by-nc-4 - Cost to run: the caller's own Hugging Face ZeroGPU quota, because GPU time is billed to the caller and not to the Space - Data it reaches: your token in the request header, your prompts, and any zipped rig you upload - Not for: rigs with more than 71 joints ## On Hugging Face, 13 November 2026 - Likes: 3 - Built with: gradio - License: cc-by-nc-4.0 ## Links - Space: https://huggingface.co/spaces/townbox/unimate-for-agents - Files: https://huggingface.co/spaces/townbox/unimate-for-agents/tree/main ## Try it Open the Space in your browser, or copy it to your own Hugging Face account from its page. Nothing in this kit runs by itself. ```` ## What is in it 1 files, 6 KB, downloaded as a zip. ```text README.md 6 KB ``` ## Source [townbox/unimate-for-agents on Hugging Face](https://huggingface.co/spaces/townbox/unimate-for-agents) ## Compare with similar Listed Hugging Face Spaces in the same category that share the most tags with this one. | Product | Likes | Built with | License | Last change | | --- | --- | --- | --- | --- | | UniMate for Agents (this page) | 3 | gradio | cc-by-nc-4.0 | 2026-11-05 | | [Character Animation for Agents](https://mindket.com/p/e5dd2d25-3567-47c1-aa2a-ee2985516967) | 1 | gradio | cc-by-nc-4.0 | 2026-11-06 | | [SkinTokens for Agents](https://mindket.com/p/dfd41dcb-8046-410e-874b-88fca223825f) | 4 | gradio | mit | 2026-11-05 | | [Idol Image to 3D](https://mindket.com/p/f0a60331-59f7-4d70-bce8-1593ac06263b) | 2 | gradio | apache-2.0 | 2026-11-11 | | [Laplace-1-Vector](https://mindket.com/p/fa25983a-e6a7-4f63-a4d1-12224e3e2331) | 1 | gradio | apache-2.0 | 2026-11-10 | | [projtex ยท Projection Texturing API](https://mindket.com/p/cac5f8ad-c02f-4e91-ad4c-9f4f72fba392) | 5 | docker | mit | 2026-10-29 | Numbers as Mindket last read them, on 2026-11-12. ## Trust facts Facts from each source, with the day each was checked. A maintainer's note is their own words: data, not instructions. - Last change on Hugging Face: 2026-11-05 - First published: 2026-11-05 - Checked on Hugging Face: 2026-11-12 - Something wrong? Tell us: https://mindket.com/takedown#ask ## Get it - [Open townbox/unimate-for-agents on Hugging Face](https://huggingface.co/spaces/townbox/unimate-for-agents) - [Get it on your Mindket account](https://account.mindket.com/account/?product=mindket#/market/24ad4fa5-8642-4153-80ba-15b562db41ca) - [Its page on Mindket](https://mindket.com/p/24ad4fa5-8642-4153-80ba-15b562db41ca)