The first screen of your signup flow asks for "organization" - is that used as a username or as an organization name or both (I can't tell what if anything will be on the next screen)
If your registration process is eventually going to ask me for a username, can the org name and user name be the same?
When you get a chance to work on your login flow, I recommend giving users an opportunity to request the key rather than automatically showing it once only on the first screen.
I created the account from my phone, and don't have access to the dev tools I'd want to paste the key into. I can deal with it, but I don't know if I'll be able to regenerate the key if I lose it, I'd rather not store it on my phone, and I don't trust my accuracy in manually typing it in on my laptop while looking at my phone, so all the options feel not great. Again, not an actual roadblock, but still something I'd encourage fixing.
Edit added: Good thing I copied the key to my phone before writing this message. Jumping over to this page seems to have forced a refresh/logout on the ensure page in the other tab, so my token would (I think? maybe?) be lost at this point if I'd done it in the other order.
Ahh good call. You absolutely can generate a new key from the dashboard, so if you did lose the one generated during the quickstart, you'd be able to generate another when you log in next and go to the API keys tab.
Will make this more clear in the quickstart, thanks for the feedback
It's a more of a black box with claude, at least with this you see the proof strategy and mistakes made by the model when it decomposes the problem. I think instead of Ralph looping you get something that is top-down. If models were smarter and context windows bigger i am sure complex tasks like this one would be simpler, but braking it down into sub agents and having a collective --"we already tried this strategy and it backtracked"-- intelligence is a nice way to scope a limited context window to an independent sub problem.
The Lean angle here is really interesting: most multi-agent demos dodge hard verification, but tying each agent’s output to makes the feedback loop objective.
Curious how you’re handling goal-claim conflicts/duplication when two agents find competing tactic sequences for the same subgoal—do you keep both in memory with some ranking signal (time-to-verify, proof term size, etc.)?
We use TTL-based claim locks so only one agent works on one goal at a time.
Failed strategies + successful tactics all get written to shared memory, so if a claim expires and a new agent picks it up, it sees everything the previous agent tried.
Ranking is first-verified-wins.
For competing decomposition strategies, we backtrack: if children fail, the goal reopens, and the failed architecture gets recorded so the next attempt avoids it.
All of the above. The most frustrating one with the Putnam example with Claude was generating solutions that obviously didn't compile. This feels like plan collapse- not verifying its own work. I'm sure that if you just had a dumb two-model setup, it would eventually get to compiling code after n runs, but that was just for this one failure mode.
For the moment, researching multi-agent orchestration. At first glance, your work looks among the best in class of published work I've seen. Particularly interested to understand the memory/communication/search model you're using, as it sounds like you've trying to think well past the GasTown/Beads/Claude-Code-Swarms concepts.
Very kind of you to say. Our whole vision is that agents can produce way better results, compounding their intelligence, when they lean on shared memory.
I'm curious to see how it feels for you when you run it. I'm happy to help however I can.
Math proofs are really easy to run with this specific harness. Our next experiments are going to be bigger, think full code base refactors. We're working on applying RLM to improve context window limits so we can keep more of the actual code in RAM,
Any workloads you want to see? The best are ones that have ways to measure the output being successful, thinking about recreating the C compiler example Anthropic did, but doing it for less than the $20k in tokens they used.
Yeah we're using Ensue since it already handles the annoying infra pieces you’d otherwise have to build to make this work (shared task state + updates, event streams/subscriptions, embeddings + retrieval over intermediate artifacts). You can run the example with a free key from ensue-network.ai. This repo focuses on the orchestration harness.
claude code doesn't support subscriptions out of the box, so we use the subscription feature to just alert the orchestrator to a single polling file. Not the most elegant thing but still a token save over reading a bunch of sub agent logs. It is as reactive as you can be given the current feature set of claude code.
If your registration process is eventually going to ask me for a username, can the org name and user name be the same?
I created the account from my phone, and don't have access to the dev tools I'd want to paste the key into. I can deal with it, but I don't know if I'll be able to regenerate the key if I lose it, I'd rather not store it on my phone, and I don't trust my accuracy in manually typing it in on my laptop while looking at my phone, so all the options feel not great. Again, not an actual roadblock, but still something I'd encourage fixing.
Edit added: Good thing I copied the key to my phone before writing this message. Jumping over to this page seems to have forced a refresh/logout on the ensure page in the other tab, so my token would (I think? maybe?) be lost at this point if I'd done it in the other order.
Will make this more clear in the quickstart, thanks for the feedback
Failed strategies + successful tactics all get written to shared memory, so if a claim expires and a new agent picks it up, it sees everything the previous agent tried.
Ranking is first-verified-wins.
For competing decomposition strategies, we backtrack: if children fail, the goal reopens, and the failed architecture gets recorded so the next attempt avoids it.
I'm curious to see how it feels for you when you run it. I'm happy to help however I can.
Any workloads you want to see? The best are ones that have ways to measure the output being successful, thinking about recreating the C compiler example Anthropic did, but doing it for less than the $20k in tokens they used.