Very cool to see Pi build a durable agent harness too. I've been building in this space for quite some time myself [0][1] and it is a super interesting place of innovation. Less hype-y that on-your-machine coding agents, but all major players are building products in this space: LangChain Deep Agents, Vercel Eve, OpenAI Agents API, Anthropic Managed Agents, etc.
The main reasons are:
1) they are "durable", i.e. easier to make long-running in an unattended way, and easier to implement recovery, monitoring, etc
2) separating the harness from the compute brings safety and scaling benefits
It's quite an interesting place to hack on, because it's both a well understood problem but with so many wrinkles to it. I can't count how many earlier designs we chewed through before we ended up with the final one and I would not be surprised if we learn even more about it.
Yes, from your release post I can tell that you put a lot of thought into it!
I like your structured concurrency approach with tasks, which is similar to how I do it in Lightspeed too.
Also, the durable state implementation as documents is elegant! Question, though: why directly write/read to the store, why not abstract it and do more of a reducer/redux pattern and hide the persistence of the documents?
> why directly write/read to the store, why not abstract it and do more of a reducer/redux pattern and hide the persistence of the documents?
We tried so many things. At one point it pulls in so much more complexity. At one point we had half of automerge's proxy system in there. In the end we felt like this is a reasonable line to draw, but we will see!
Things are still not settled, and while the API looks like store i/o it's actually more similar to Immer's drafts, just with a different encoding, as JSON patch can't deal with the kinds of data we encounter in our workloads, at least not in a way that keeps memory and perf within some bounds.
The good thing is that this more low level API can be easily papered over with a nice sugary thing.
Cross-post from the 1.0 thread. I’m currently building a harness for Slack to support our on-call and support channels. It’s been working great so far.
The harness is built on top of the Pi SDK. I initially used Codex, but Pi seems more hackable, and I like that it’s vendor-agnostic by default.
Running it on Kubernetes works, but dealing with the JSONL session files and making sure sessions survive pod interruptions adds some complexity. I’m using DBOS for that right now, which works well, although it still feels like overkill.
This came at just the right time. I’m looking forward to removing the pieces I no longer need and simplifying the architecture. Thanks Pi team!
I like the multi-user bit the most. Should make it easier to build my remote control tool, as something I've had to hack around is not being able to use ACP while the TUI is active in an instance.
super interesting. I have so many half-considered questions... like sandboxing (it seems like it is BYO). would love to have some kind of policy engine.. perhaps an integration with https://github.com/NVIDIA/openshell in the form of an extension?
also, I see most of the durability promise comes from persisting JSON documents locally and minimizing the amount of context/data kept in-memory, even during SQLite mode. while this makes sense, my own experiments with a process that relied on a JSONL-based event store have led me to prefer keeping things in-memory to avoid all the friction with I/O.. am I crazy for preferring just a straight .db file being persisted?
Brilliant. I wish the the durable application state wasn't restricted to just the json documents though. There should be some sort of integrated way of implementing the outbox pattern so external stores can be synchronized with the conversation state.
The commit on the root conversation picks out the last agent answer id from the transcript, and durably schedules a task that then syncs it to postgres. inside the task, you fetch the answer by id and send it over to postgres indempotently.
What's missing here is sugar, basically a hook that runs inside each commit so the outbox write is atomic with the state change, with ordered delivery, and possibly a durable change feed with cursors.
Very cool to see Pi build a durable agent harness too. I've been building in this space for quite some time myself [0][1] and it is a super interesting place of innovation. Less hype-y that on-your-machine coding agents, but all major players are building products in this space: LangChain Deep Agents, Vercel Eve, OpenAI Agents API, Anthropic Managed Agents, etc.
The main reasons are:
1) they are "durable", i.e. easier to make long-running in an unattended way, and easier to implement recovery, monitoring, etc
2) separating the harness from the compute brings safety and scaling benefits
3) easier to make multi-player.
[0] https://github.com/smartcomputer-ai/lightspeed
[1] https://github.com/smartcomputer-ai/agent-os/
It's quite an interesting place to hack on, because it's both a well understood problem but with so many wrinkles to it. I can't count how many earlier designs we chewed through before we ended up with the final one and I would not be surprised if we learn even more about it.
Yes, from your release post I can tell that you put a lot of thought into it!
I like your structured concurrency approach with tasks, which is similar to how I do it in Lightspeed too.
Also, the durable state implementation as documents is elegant! Question, though: why directly write/read to the store, why not abstract it and do more of a reducer/redux pattern and hide the persistence of the documents?
> why directly write/read to the store, why not abstract it and do more of a reducer/redux pattern and hide the persistence of the documents?
We tried so many things. At one point it pulls in so much more complexity. At one point we had half of automerge's proxy system in there. In the end we felt like this is a reasonable line to draw, but we will see!
Things are still not settled, and while the API looks like store i/o it's actually more similar to Immer's drafts, just with a different encoding, as JSON patch can't deal with the kinds of data we encounter in our workloads, at least not in a way that keeps memory and perf within some bounds.
The good thing is that this more low level API can be easily papered over with a nice sugary thing.
I'm loving it. Already converted a few of my smaller tools to it, and am ripping out the guts of piclaw to replace them (in time)
Cross-post from the 1.0 thread. I’m currently building a harness for Slack to support our on-call and support channels. It’s been working great so far.
The harness is built on top of the Pi SDK. I initially used Codex, but Pi seems more hackable, and I like that it’s vendor-agnostic by default.
Running it on Kubernetes works, but dealing with the JSONL session files and making sure sessions survive pod interruptions adds some complexity. I’m using DBOS for that right now, which works well, although it still feels like overkill.
This came at just the right time. I’m looking forward to removing the pieces I no longer need and simplifying the architecture. Thanks Pi team!
I like the multi-user bit the most. Should make it easier to build my remote control tool, as something I've had to hack around is not being able to use ACP while the TUI is active in an instance.
What are people using these infinitely-running agents for?
> The entire source code, without tests, is about 15,000 lines, which comes out to about 150,000 tokens with GPT and about 250,000 with Claude.
Woah, that big of a difference when it comes to token counting?
Yeah don't get anyone going on the labs different tokenization schemes lol
super interesting. I have so many half-considered questions... like sandboxing (it seems like it is BYO). would love to have some kind of policy engine.. perhaps an integration with https://github.com/NVIDIA/openshell in the form of an extension?
also, I see most of the durability promise comes from persisting JSON documents locally and minimizing the amount of context/data kept in-memory, even during SQLite mode. while this makes sense, my own experiments with a process that relied on a JSONL-based event store have led me to prefer keeping things in-memory to avoid all the friction with I/O.. am I crazy for preferring just a straight .db file being persisted?
Brilliant. I wish the the durable application state wasn't restricted to just the json documents though. There should be some sort of integrated way of implementing the outbox pattern so external stores can be synchronized with the conversation state.
You can already (sort of, kind of) do that via a task (please excuse the agent slop, it's midnight and it's been a long day):
``` const SyncToPostgres = defineTask<{ entryId: string }, { phase: "send" }, void>({ kind: "app.sync-postgres", version: 1, initial: () => ({ phase: "send" }), phases: { send: async (task, runtime, context) => { const entry = await runtime.read(/* the entry */); await postgres.upsert("messages", { id: task.input.entryId, ...entry }); // idempotent by id await runtime.commit(() => ({ status: "terminal", outcome: { status: "completed" } }), context); }, }, });
```The commit on the root conversation picks out the last agent answer id from the transcript, and durably schedules a task that then syncs it to postgres. inside the task, you fetch the answer by id and send it over to postgres indempotently.
What's missing here is sugar, basically a hook that runs inside each commit so the outbox write is atomic with the state change, with ordered delivery, and possibly a durable change feed with cursors.
Thanks for the input!
Nice! I made my own version of this for Pi but I’m excited to see if I can just replace it lol