diff --git a/_blog.yml b/_blog.yml index 2810405dbd..2ef841eeb3 100644 --- a/_blog.yml +++ b/_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 \ No newline at end of file diff --git a/assets/openenv/thumbnail.png b/assets/openenv/thumbnail.png new file mode 100644 index 0000000000..b52a8296cf Binary files /dev/null and b/assets/openenv/thumbnail.png differ diff --git a/openenv.md b/openenv.md new file mode 100644 index 0000000000..5b97d4e8c9 --- /dev/null +++ b/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: + +

+ rl_stack +

+ +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 🔥!