ROOST Project Roadmap now available: share your feedback!

[cassidyjames]

Hey ROOSTers, community members, partners, and other Internet friends! The ROOST Project Roadmap is now publicly available—and in true open source fashion, it's hosted right here on GitHub, on our Community repo:

https://github.com/roostorg/community/blob/main/roadmap.md

This roadmap is a mostly near-term look at our priorities when it comes to the tools and features we're focused on building.

We're thrilled to share it with you all, and especially to get your input and feedback! Please use this discussion to share any feedback, questions, suggestions, or comments that you might have, and we can work together to discuss them here and file any appropriate issues/pull requests against the community repo when it makes sense.

We look forward to your input! 🐣

[julietshen]

wow so much cool stuff

[joshgay]

Thank you, this is great work and a nice solid bedrock for community building.

[cassidyjames]

Thank you @joshgay!

[vinaysrao1]

@tgthorley Answering your comments here:

I think in general this strikes the balance of providing AI integrations but also ensuring non-AI based capability is available.

However - given the prevalence of AI and ML in Safety (for a decade now) and the increasingly fast adoption of Agentic AI, I wonder if ROOST should be thinking more ambitiously about the role of AI in Safety systems and howAI native capabilities might improve the eco-system.

Our desire to be backwards compatible and provide choice shouldn't limit innovation at the cutting edge.

Thank you for calling this out. We agree that AI will play an increasingly important role in the threat models we have to defend against and in the defenses we will build. This is a relatively short term roadmap and we are looking forward to working with you to build a more ambitious one.

Question in ignorance that this point sparked:
Does Osprey currently support "Sliding Window" type detections?

Yes, this will be one of the features we will put out as a fast follow to the v1 release of Osprey.

One area of exploration that I think is a critical future area for the industry is the integration and feedback in policies and enforcement
guidance so that ML detections can adapt to policy/guidance changes dynamically as well as provide feedback on them (i.e. identifying
policy gaps, outliers that don't fit in existing guidance etc). This tightening of the loop between operations and policy is something
that we should consider.

There is a plan for an ML platform coming that considers this usecase in the context of building prompted LLMs with a feedback mechanism to develop a policy wrt to a dataset with known outcomes. Two outcomes we are looking for is (a) gaps in the current policy (b) changes that will cover the gaps.

Love this and have some thoughts and experience in how we can develop this functionality. I'll see if we can provide some open source
examples. in 2026

Looking forward to hearing about your experience with this.

I'm interested to think about where the line between COOP and Osprey is in this regard. Where does Agentic review live - should it live
in the review system or should it be an extension of the detection systems or is there some new middle layer here for Agentic Review
that essentially feeds into the Human review tool where needed.

This is still in ideas and thinking stage. We had two applications in mind (1) Osprey: offline analysis for missed recall (2) Coop: agentic human assist. Agentic because models will have access to tools to query dbs, do websearch etc to map the full threat network. Given the scope and scale of the investigation, we frame it as human assist rather than automated.

I'd love to think about testing and evaluation suites in this context and how we link that back into COOP.

Yes, this would fit nicely with the eval platform and Coop integration. We will appreciate your inputs.

[marielpovolny]

Thank you for sharing this roadmap! The progress on Osprey alone, especially the feature and stability improvements you’ve delivered in just a few months, is remarkable, especially as someone who new open-source ecosystems and building. Exciting to see this tool mature so quickly into something genuinely usable across different safety workflows.

I have a question about the scope of participation within the Model Community:

The roadmap highlights resources, datasets, prompt-translation support, and model comparison events, and I’m curious whether you also envision the RMC as a place for more practitioner-oriented engagement (e.g. sharing operationally grounded failure modes or insights that emerge during real-world deployment). Or is the intent for the RMC to remain primarily focused on structured evaluation, knowledge resources, and feedback loops to model developers?

Looking forward to seeing how all of this takes shape.

[Celestine22]

Overall, this is awesome stuff. Clarifying how teams should evaluate fit and sequence adoption would make it even easier for potential users and contributors to engage early and effectively. Even a lightweight lifecycle diagram or a short “who this is for” section per project could help.

[ThisIsMissEm]

Support for text input that is markdown formatted would probably be good for applications that support rich text — you probably don't want raw HTML, and many AT Protocol applications have some degree of rich text, though their specifics vary quite a lot (there's no standard for rich text), so converting the text to markdown prior to sending to Coop would allow Coop to render something near correct without supporting custom rich text.

[gbiss]

This is a recommended roadmap item for Coop, which is based on a conversation I had with @julietshen in the coop-internal channel on Discord. It could be useful for Coop to implement "cascading" rules. By that I mean, instead of a rule attempting to match against every media item that stream through the system, the rule could instead be configured to run as follow-up, contingent on an earlier rule being triggered first.

I have the following use-case in mind. Perhaps there exists a more heavy-weight ML service that should only run if an image has first been predicated by a lighter-weight model. Media that trigger the lighter-weight rule would then be put into a queue to be evaluated against the heavy-weight service. This could help avoid filling human moderation queues with many false positives that might arise from a light-weight model, but also avoid performing heavy-weight computation on every media item that streams through the system.

[tgthorley]

Yes! This is the model our detections systems use at GitHub... high recall, low precision rules first, then higher precision (more computationally expensive) rules (including bespoke classifiers and LLMs) run to triage the detections and drive down false positives.

[cassidyjames]

Thanks @gbiss and @tgthorley! I've opened an issue on the Coop repo here: roostorg/coop#75