We're building the runtime for agent teams.
LoopLlama started with a simple frustration: getting a team of AI agents to reliably finish real work meant writing the same brittle glue code over and over. So we built the orchestration layer we wished we had.
Our story
The first wave of AI agents proved the idea: a model with tools and a loop can do real work. The second problem showed up immediately — keeping those loops alive in production. Steps fail, models drift, budgets blow up, and a single run can fan out into dozens of calls that all need to be traced, retried, and merged.
We had built that plumbing more than once, on more than one team, and it was never the interesting part. LoopLlama is the result: describe the work, hand us a crew of agents, and trigger runs over a plain REST API. We sequence the agents, call the models, persist every step, and meter usage so you only pay for the steps your agents actually run.
Today engineering teams use LoopLlama to ship research briefs, code modernization, customer-ops automation, and data-pipeline agents — without standing up an agent framework of their own.
What we value
Ship the hard parts
Planning, retries, checkpoints, and observability are the unglamorous work that makes agents useful. We own them so our customers don't have to.
Boringly reliable
An orchestration layer earns trust by being predictable. We optimize for clear failure modes, honest status, and APIs that behave the same on day 500 as on day 1.
Developer-first
Typed SDKs, readable traces, and docs you can finish in an afternoon. If an engineer can't get a workflow running in five minutes, that's a bug.
Earn the data
Customer inputs and outputs are theirs. We encrypt by default, never train on their data, and design for tenant isolation from the first line of code.
Want to help build it?
We're a small, senior team hiring across engineering, design, and go-to-market.