lightspeed

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/

:-) https://github.com/smartcomputer-ai/lightspeed/pull/100

Very cool demonstration of managed agents built for the needs of their own business. I think this is where a lot of companies are going to go: on-prem platforms that give various teams access to agents that are as powerful as coding agents, but much more managed and governed. I'm betting on this with my open source project, Lightspeed: https://github.com/smartcomputer-ai/lightspeed

I think this is an important direction: managed agents that control compute. For those who are interested in a self-hosted version of the same concept, I've been working on something like this here: https://github.com/smartcomputer-ai/lightspeed

Im working on one here: https://github.com/smartcomputer-ai/lightspeed The core is there. But there is some work to be done to have a nicer shell and all, which I’m currently focusing on.

Fully agree that in the end sandboxes are required to get frontier performance out of the models. But you can have both: rund the agent outside the vm/sandbox and orchestrate work on it, either directly via shell calls or kicking off an ephemeral subagent on the box. This makes the agent and session that runs outside the vm more durable and opens new orchestration pattern. I’m building the oss version of this here: https://github.com/smartcomputer-ai/lightspeed

A self-hostable Claude Tag or OAI Work [0]. A while ago, I realized that most new agent harnesses being built must be hosted on your machine or on a VM--in other words the agent needs a full OS process at all times. But we do not have good harnesses being built that are multi-tenant, do not use compute while they are paused, but are are still as powerful as, say, Claude Code or Codex, OpenClaw. So I set out to build one. I realized that the best substrate for these kind of agents are durable workflow engines. I'm currently supporting Temporal. AFAIK much of OAI agent infra is built on Temporal too. My harness is decidedly not just another agent SDK, but rather a battery-included product. [0] https://github.com/smartcomputer-ai/lightspeed

I think these kind of semi-coding agents--but hosted--are the future for enterprises. Claude Tag, Claude Cowork, now Work by OAI. Agents-on-your-machine clearly have their place, but for many workflows this is too unruly. Hence, the "long-running agent running on shared infra" pattern. I think this is where the ball is headed. I'm building towards an open source version of this[0]. Still just working on the core, but hopefully soon self-hosted versions can be built on top. [0] https://github.com/smartcomputer-ai/lightspeed

getting downvoted for my other answer. I wasn't clear: yes, there is of course a lot of prior art in pi, and pi specifically does not just store the session events, but adds an abstraction for easy branching which is great. But what I tried to get at is the question what additional events you store to construct more than just the llm session log but also more fine grained events around the entire agent state, which of course depends on what you want out of your agent. The paper here in question is going even further and is event sourcing a larger state than just the session transcript, specifically additional graph structures that are getting built as part of the session. in my agent, specifically, I focus on the event sourcing all the stuff that makes an agent work well as part of a deterministic workflow, which again is prerequisite to

Very cool. I settled on the same/similar design in my agent harness. All relevant events that affect the context window are stored in an event log. Forking agents and sessions is simply setting a pointer to the sequence number of another event log. So if you want to check an implementation of this pattern see: https://github.com/smartcomputer-ai/lightspeed

Agent harness for durable workflows, starting with Temporal. Most agents for durable workflows feel like toy examples. There is no "Codex" or "Claude Code" for, say, Temporal. So I'm building full-featured agent for these runtimes. Why? Because it makes long-running agents easier to operate and scale. Currently, all frontier harnesses need to run inside a guest OS and need a dedicated process, this is quite challenging to orchestrate and maintain. To make it work, I had to figure out what part to run as deterministic workflow code, and what part to run as I/O or side effects (aka activities). I'm using a CAS for most of the payloads to maintain a lightweight footprint in the workflow code. Currently supporting skills, MCP, prompts, a virtual file systems, and soon sandboxes. https://github.com/smartcomputer-ai/lightspeed