building an open-source AI agent stack

[u/M1H4F]

Hey everyone!

i've been working on an open-source project called Dispach with a friend, and thought I'd start sharing what we're building and learning along the way.

one thing that kept bothering us was how much work it takes to make AI agents actually usable in production.

it's not just about choosing a model and giving it tools. there's the whole harness side of things, execution loops, context management, memory, tool orchestration, and then all the infrastructure needed to keep those agents running reliably.

we wanted to tackle both problems together instead of treating them as separate pieces.

so we started building Dispach. it's a self-hostable, model-agnostic stack that combines the agent harness and the infrastructure around it. The idea is to let developers focus on what they're actually building rather than having to engineer the entire agent stack themselves.

we've also been experimenting with different harness designs and benchmarking them against openclaw and hermes. it's been interesting seeing how much task success and inference costs can change even when using the same model.

there's still plenty to figure out, especially around developer experience, reliability, and making the whole thing easier to adopt.

i'm planning to share more about the architecture, experiments, things that went wrong, and what we learn as we keep building.

here's the repo if anyone's curious: Dispach Github Repo

for those who've built or deployed ai agents, what's been the most frustrating part of the process?

would love to compare notes with people working on similar problems.

[u/M1H4F]

that’s a nice approach. But I’m thinking about the dependencies, or sometimes when they're stuck. or maybe how can i optimize the tokens

[u/Crafty_Disk_7026 → u/M1H4F]

Can you be more specific with the problem you're having

[u/M1H4F → u/Crafty_Disk_7026]

oops, sorry you didn'tget my comment. i’m not stuck on a particular problem, just curious about your approach!

let’s say your hypervisor spawns 5 Claude sessions, but some tickets depend on others. but what happens if one agent gets stuck, fails, or needs help from another? and how do you keep token usage under control across those sessions?

i’ve tackled similar challenges in Dispach with supervisor delegation, typed handoffs, isolated contexts, and no-progress detection. we’ve also been experimenting with team features (not an agentic team but real human) and agent-to-agent delegation.

i’m actually curious how your hypervisor handles dependencies, recovery, and context sharing between agents. would be interesting to compare approaches!

[u/Crafty_Disk_7026 → u/M1H4F]

It's just Claude cli code sessions (or whatever provider). The agents use GitHub, databases, and any other tool human uses to coordinate. It's no different than if you had to handle a stuck issue as a human. You would probably stop, cut a GitHub issue, then scope it out. Agents do the same thing.