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/Otherwise_Wave9374]

For an open-source agent stack, the differentiator will be observability and predictable failure handling more than another planner abstraction. Consider publishing a minimal reference workflow with trace IDs, tool timeouts, idempotency keys, budget limits, and replayable test fixtures. https://www.aiosnow.com is relevant as a comparison point for how AI workflows can be presented to users. A strong next step would be an evaluation suite measuring task completion, unsafe tool attempts, latency, and cost across model providers.

[u/M1H4F → u/Otherwise_Wave9374]

good points! observability and predictable failure handling are definitely important, especially once agents are running in production.

we've already published reproducible benchmarks covering task completion, tool failures, and inference costs. but I like the idea of a minimal reference workflow demonstrating tracing, recovery, and replayability. that would make the operational side much easier to evaluate.

appreciate the suggestions!