Add OpenEnv blog post

_blog.yml

@@ -6853,3 +6853,15 @@
     - hub
     - partnerships
     - security
+
+- local: openenv
+  title: "Building the Open Agent Ecosystem Together: Introducing OpenEnv"
+  author: spisakjo
+  thumbnail: /blog/assets/openenv/thumbnail.png
+  date: Oct 23, 2025
+  tags:
+    - announcement
+    - open-source
+    - community
+    - agents
+    - rl

openenv.md

@@ -0,0 +1,91 @@
+---
+title: "Building the Open Agent Ecosystem Together: Introducing OpenEnv"
+thumbnail: /blog/assets/openenv/thumbnail.png
+authors:
+- user: spisakjo
+  guest: true
+  org: openenv
+- user: darktex
+  guest: true
+  org: openenv
+- user: zkwentz
+  guest: true
+  org: openenv
+- user: mortimerp9
+  guest: true
+  org: openenv
+- user: Sanyam
+  guest: true
+  org: openenv
+- user: Hamid-Nazeri
+  guest: true
+  org: openenv
+- user: Pankit01
+  guest: true
+  org: openenv
+- user: emre0
+  guest: true
+  org: openenv
+- user: lewtun
+- user: reach-vb
+---
+
+# Building the Open Agent Ecosystem Together: Introducing OpenEnv
+
+With tools like [TRL](https://github.com/huggingface/trl), [TorchForge](https://github.com/meta-pytorch/torchforge) and [verl](https://github.com/volcengine/verl), the open-source community has shown how to scale AI across complex compute infrastructure. But compute is only one side of the coin. The other side is the developer community; the people and tools that make agentic systems possible. That’s why Meta and Hugging Face are partnering to launch the [OpenEnv Hub](https://huggingface.co/openenv): a shared and open community hub for agentic environments.
+
+Agentic environments define everything an agent needs to perform a task: the tools, APIs, credentials, execution context, and nothing else. They bring clarity, safety, and sandboxed control to agent behavior.
+
+These environments can be used for both training and deployment, and serve as the foundation for scalable agentic development.
+
+## The Problem
+Modern AI agents can act autonomously across thousands of tasks. However, a large language model isn’t enough to get those tasks to actually run — it needs access to the right tools. Exposing millions of tools directly to a model isn’t reasonable (or safe). Instead, we need **agentic environments**: secure, semantically clear sandboxes that define exactly what’s required for a task, and nothing more. These environments handle the critical details:
+- Clear semantics about what a task needs
+- Sandboxed execution and safety guarantees
+- Seamless access to authenticated tools and APIs
+
+## The Solution
+To supercharge this next wave of agentic development, Meta-PyTorch and Hugging Face are partnering to launch a [Hub for Environments](https://huggingface.co/openenv): a shared space where developers can build, share, and explore OpenEnv-compatible environments for both training and deployment. The figure below shows how **OpenEnv** fits in the new post-training stack being developed by **Meta**, with integrations for other libraries like **TRL**, **SkyRL**, and **Unsloth** underway:
+
+<p align="center">
+  <img src="https://huggingface.co/datasets/huggingface/documentation-images/resolve/main/blog/openenv/rl-stack.png" alt="rl_stack" width="750"/>
+</p>
+
+Starting next week, developers can:
+- Visit the new [Environment Hub](https://huggingface.co/openenv) on Hugging Face where we will seed some initial environments
+- Interact with environments directly as a Human Agent
+- Enlist a model to solve tasks within the environment
+- Inspect which tools the environment exposes and how it defines its observations
+- Every environment uploaded to the Hub that conforms to the OpenEnv specification automatically gains this functionality — making it fast and easy to validate and iterate before running full RL training.
+
+Alongside this, we’re releasing the [OpenEnv 0.1 Spec (RFC)](https://github.com/meta-pytorch/OpenEnv/blob/main/rfcs/002-env-spec.md) to gather community feedback and help shape the standard.
+
+## The RFCs
+In the current state of the repository, environment creators can create environments using `step()`, `reset()`, `close()` APIs (part of RFCs below). A few examples on how to create such environments can be seen [here](https://github.com/meta-pytorch/OpenEnv/tree/main/src/envs). Environment users can play with local Docker based environments for all environments already available in the repo. Following RFCs are under review:
+
+- [RFC 001:](https://github.com/meta-pytorch/OpenEnv/blob/main/rfcs/001-abstractions.md) Establish architecture for how the core components like Environment, Agent, Task, etc. are related
+- [RFC 002:](https://github.com/meta-pytorch/OpenEnv/blob/main/rfcs/002-env-spec.md) Propose basic env interface, packaging, isolation and communication w/ environment.
+- [RFC 003:](https://github.com/meta-pytorch/OpenEnv/blob/main/rfcs/003-mcp-support.md) Propose encapsulation of MCP tools through environment abstraction and isolation boundaries
+- [RFC 004:](https://github.com/meta-pytorch/OpenEnv/blob/main/rfcs/004-actions-as-tool-calls.md) Extend tool support to cover unified action schema covering tool calling agents as well as CodeAct paradigm.
+
+## Use cases
+- RL Post training: pull in environments across collections and use them to train RL agents with TRL, TorchForge+Monarch, VeRL etc.
+- Environment creation: build an environment and ensure that it interops with popular RL tools in the ecosystem, share with collaborators, etc.
+- Reproduction of SOTA methods: easily replicate methods like those from FAIR's [Code World Model](https://huggingface.co/papers/2510.02387) by integrating environments for agentic coding and software engineering.
+- Deployment: users can create an environment, train on the same environment and then use the same for inference too (the full pipeline)
+
+## What’s Next
+This is just the beginning. We’re integrating the OpenEnv Hub with Meta’s new **TorchForge RL library**, and collaborating with other open-source RL projects such as **verl**, **TRL**, and **SkyRL** to expand compatibility.
+Join us at the PyTorch Conference on Oct 23 for a live demo and walkthrough of the spec, and stay tuned for our upcoming community meetup on environments, RL post-training, and agentic development.
+
+👉 Explore the [OpenEnv Hub](https://huggingface.co/openenv) on Hugging Face and start building the environments that will power the next generation of agents. 
+
+👉 Check out the 0.1 spec which can be found implemented in the [OpenEnv project](https://github.com/meta-pytorch/OpenEnv) → we welcome ideas and contributions to making it better! 
+
+👉 Engage on [Discord](https://discord.gg/YsTYBh6PD9) and talk with the community about RL, environments and agentic development
+
+👉 Try it out yourself - We created a comprehensive [notebook](https://colab.research.google.com/github/meta-pytorch/OpenEnv/blob/main/examples/OpenEnv_Tutorial.ipynb) that walks you through an end to end example and of course you can easily pip install the package via [PyPI](https://pypi.org/project/openenv-core/). This notebook walks you through the abstractions we’ve built, along with an example of how to use existing integrations and how to add yours - Try it out in Google Colab!
+
+👉 Check out supporting platforms - [Unsloth](https://github.com/unslothai/unsloth), [TRL](https://huggingface.co/docs/trl/main/en/openenv), [Lightning.AI](http://Lightning.AI)
+
+Let's build the future of open agents together, one environment at a time 🔥!