My setup is Hermes on a beefy machine with running a swarm of pi.devs - connects to GH issues via bot app so I can call it @xxxx in the issue. It can create PR, run actions and all. If I want to see the code I just run kilo code in VS locally.
This setup is so plain and simple that it just works, and it works wonders. I feel like I’ve been running a dev team who actually communicates through issuess and keeps it up to date!
Yep, I think that all harnesses will require native durability, and pi-durable is a step in the right direction. I'm skeptical of bolted-on durability, like vercel's durable-harness, because they have to neuter the harness for things they can't reliably make durable (e.g. https://github.com/vercel-labs/durable-harness/blob/fb056a3c...)
I've built sth similar that acts as a mobile interface to your desktop setup via the multiplexer of your choice (herdr/tmux/zellij).
https://github.com/AltanS/collie
There's also a typescript SDK to connect a durable agent/harness to the app, still experimental though.
Yeah, even as someone building in the space, I sometimes get confused on what and who I'm actually building for.
What we've settled on is that Omnara is a tool for building agents. It's for people putting agents into their own products or workflows using the API. If you're not going to use the API, you probably don't need Omnara. Think of it like Supabase, Supabase gives you a Postgres database behind an API, and the dashboard is there for convenience, but your app talks to the API. Omnara does the same for agents. The UI is handy for watching and poking at agents, or visual analytics, but the API is the main product.
Or more directly, you can think of it like the codex app server or opencode server. Their clients (e.g. the TUI and desktop apps) talk to that server to start and interact with agents. Omnara is the same kind of server, except it runs in the cloud or on your own servers instead of your laptop. We don't provide a client though, you build that.
Nothing special with caching in Omnara, works pretty much the same way as any other harness (we do pretty much what pi/opencode/goose do) since the cache lives on the provider's side. We rebuild the model request by reading from postgres instead of jsonl or sqlite, and the rest is the same as any other harness.
looks cool. how's that different from things like markbase dispatch where you can fire up the agent remotely with some given context. i mean what is the advantage of having the agent run always vs start and stop when you need it but give it a long-term memory context? maybe i'm missing something.
not sure I fully understand the question, but in Omnara, the agent isn't always "running". When it's idle (e.g. waiting for user input, or tool results), the agent is just rows in postgres with nothing happening to them. When a tool result returns or a new input comes in through the API, then a worker is allocated to the agent and starts "running" the agent again.
> The loop runs on stateless workers in the control plane. A worker runs one iteration
> of the loop, either a model call or the tool calls it produced, then releases the
> agent and puts the next iteration on a queue in Postgres.
...
> Also unlike other harnesses, the machines that the workers (our agent loop) run on aren't
> where the agent runs its commands.
It's late but don't think I am grokking this.
Isn't running the commands the heavier part of the work? The agent loop itself is mostly waiting for a remote model and then running the commands?
Why would I need a pool of workers for the loop when the constraining resource is likely to be the machines I am running the commands against (which can't be stateless like the workers are?)
> Why would I need a pool of workers for the loop when the constraining resource is likely to be the machines I am running the commands against
You don't! You can have a single worker running thousands of agents at once, since they're mostly waiting on the model. You can add more workers for scaling to even more agents, and for redundancy (if one worker goes down, another one can keep the agents going)
The machines are the expensive part (after tokens, of course), which is why we split the execution environment machine from the agent loop. Most harnesses run the loop on the same machine as the shell tools, so every agent needs its own VM for its entire life, even though it spends most of that time waiting on the model or tools or external inputs. Because they're decoupled in Omnara, an agent can use 0, 1, or n machines. Agents whose workloads don't need a machine can go without one entirely, and an agent's machine can sleep or go offline without stopping the agent.
i was looking for good self-hostable durable agents to automate a persistent keyword watcher across sm. gonna give this a go. any pointers you recommend?
nice, this is close to something we run ourselves. we put up examples of sm agents that scan socials daily for keywords and DMs us (docs here https://docs.omnara.com/examples/x-signal-agent). probably the fastest starting point is to point your agent to it and ask it to swap in your keywords
My setup is Hermes on a beefy machine with running a swarm of pi.devs - connects to GH issues via bot app so I can call it @xxxx in the issue. It can create PR, run actions and all. If I want to see the code I just run kilo code in VS locally.
This setup is so plain and simple that it just works, and it works wonders. I feel like I’ve been running a dev team who actually communicates through issuess and keeps it up to date!
Can you provide a link to Hermes/a short insight on your setup? It is quite interesting (I like minimal setup)
with a great related talk! https://ai.engineer/talks/UPwGaM2MKHY-log-is-agent
thanks swyx! the log is the agent is a core insight behind this
you beat pi-durable to it!
haha yes! this was the reasoning for making durability native in our harness
feels like everyone is converging on durable agents lately: pi durable, cloudflare agents on durable objects, vercel's durable harness
Yep, I think that all harnesses will require native durability, and pi-durable is a step in the right direction. I'm skeptical of bolted-on durability, like vercel's durable-harness, because they have to neuter the harness for things they can't reliably make durable (e.g. https://github.com/vercel-labs/durable-harness/blob/fb056a3c...)
been following you guys from distance over x. congrats on the launch! excited to see where this goes
appreciate it!
I've built sth similar that acts as a mobile interface to your desktop setup via the multiplexer of your choice (herdr/tmux/zellij). https://github.com/AltanS/collie
There's also a typescript SDK to connect a durable agent/harness to the app, still experimental though.
There is so much stuff in AI space. AI orchestrators, automations, harnesses. I often can't tell what is the difference between projects.
I started thinking is it something like 'ruflo', or 'mirofish', or is it something else totally
Yeah, even as someone building in the space, I sometimes get confused on what and who I'm actually building for.
What we've settled on is that Omnara is a tool for building agents. It's for people putting agents into their own products or workflows using the API. If you're not going to use the API, you probably don't need Omnara. Think of it like Supabase, Supabase gives you a Postgres database behind an API, and the dashboard is there for convenience, but your app talks to the API. Omnara does the same for agents. The UI is handy for watching and poking at agents, or visual analytics, but the API is the main product.
Or more directly, you can think of it like the codex app server or opencode server. Their clients (e.g. the TUI and desktop apps) talk to that server to start and interact with agents. Omnara is the same kind of server, except it runs in the cloud or on your own servers instead of your laptop. We don't provide a client though, you build that.
How does this interact with / take advantage of context caching?
Nothing special with caching in Omnara, works pretty much the same way as any other harness (we do pretty much what pi/opencode/goose do) since the cache lives on the provider's side. We rebuild the model request by reading from postgres instead of jsonl or sqlite, and the rest is the same as any other harness.
looks cool. how's that different from things like markbase dispatch where you can fire up the agent remotely with some given context. i mean what is the advantage of having the agent run always vs start and stop when you need it but give it a long-term memory context? maybe i'm missing something.
not sure I fully understand the question, but in Omnara, the agent isn't always "running". When it's idle (e.g. waiting for user input, or tool results), the agent is just rows in postgres with nothing happening to them. When a tool result returns or a new input comes in through the API, then a worker is allocated to the agent and starts "running" the agent again.
It's late, but I'm not grokking this:
> The loop runs on stateless workers in the control plane. A worker runs one iteration > of the loop, either a model call or the tool calls it produced, then releases the > agent and puts the next iteration on a queue in Postgres. ... > Also unlike other harnesses, the machines that the workers (our agent loop) run on aren't > where the agent runs its commands.
It's late but don't think I am grokking this.
Isn't running the commands the heavier part of the work? The agent loop itself is mostly waiting for a remote model and then running the commands?
Why would I need a pool of workers for the loop when the constraining resource is likely to be the machines I am running the commands against (which can't be stateless like the workers are?)
> Why would I need a pool of workers for the loop when the constraining resource is likely to be the machines I am running the commands against
You don't! You can have a single worker running thousands of agents at once, since they're mostly waiting on the model. You can add more workers for scaling to even more agents, and for redundancy (if one worker goes down, another one can keep the agents going)
The machines are the expensive part (after tokens, of course), which is why we split the execution environment machine from the agent loop. Most harnesses run the loop on the same machine as the shell tools, so every agent needs its own VM for its entire life, even though it spends most of that time waiting on the model or tools or external inputs. Because they're decoupled in Omnara, an agent can use 0, 1, or n machines. Agents whose workloads don't need a machine can go without one entirely, and an agent's machine can sleep or go offline without stopping the agent.
Anthropic has a good post on this architecture: https://www.anthropic.com/research/managed-agents
i was looking for good self-hostable durable agents to automate a persistent keyword watcher across sm. gonna give this a go. any pointers you recommend?
nice, this is close to something we run ourselves. we put up examples of sm agents that scan socials daily for keywords and DMs us (docs here https://docs.omnara.com/examples/x-signal-agent). probably the fastest starting point is to point your agent to it and ask it to swap in your keywords
looks great
Wow Literally what I was looking for.