Kubernetes-native runtime for AI coding agents. You submit a prompt and a git repository. The platform starts an isolated workspace pod, runs the agent in it, and streams the result back over ConnectRPC.
This is not production ready. Run it against repositories you can afford to lose.
The core abstraction is the AgentRun custom resource. Scheduling, LLM routing,
approval gates, and pull-request creation are all built around it.
In scope: running one agent against one workspace, on a cluster you control.
Out of scope: hosting your models, managing your cluster, and acting as a general CI system.
Download the uncworks CLI from
GitHub Releases, then run:
uncworks setup # select a local kube context, install the Helm chart
uncworks open # port-forward and open the web UIThe CLI works against any local Kubernetes distribution: Docker Desktop, OrbStack, k3d, or kind. Allocate 2 CPU and 2 GiB as the minimum. Allocate 4 CPU and 4 GiB for a usable experience.
docs/getting-started.md covers remote clusters and the TUI.
The runs list shows one row per AgentRun. Filter it by stage, mode, and
approval gate.
The logs tab streams the agent's actions: prompts, tool calls, file reads, and bash output.
The files tab shows the workspace tree inside the agent pod, scoped to
/workspace.
The shell tab attaches to the running pod for direct inspection.
The traces tab shows a span timeline for the workflow, the agent's reasoning, and each tool call.
graph LR
User(("User"))
Cloud["OpenRouter / cloud LLMs"]
subgraph K8s["Kubernetes cluster"]
direction TB
subgraph CP["Control plane"]
Web["Web UI"]
API["ConnectRPC API"]
Ctrl["Controller"]
TW["Temporal worker"]
end
subgraph Deps["Deps"]
Temporal["Temporal"]
LiteLLM["LiteLLM"]
Ollama["Ollama"]
Soft["Soft-Serve"]
end
subgraph DP["Agent pod (1 per run)"]
Init["init: hydrate"]
Agent["agent (holds workspace)"]
Sidecar["sidecar: pi-coding-agent"]
end
PVC[("/workspace PVC")]
end
User --> Web --> API --> Temporal --> TW
Ctrl --> API
TW -->|creates| DP
Sidecar --> Agent
Agent --> LiteLLM --> Ollama
LiteLLM --> Cloud
PVC -.- Init
PVC -.- Agent
PVC -.- Sidecar
One run is one Temporal workflow driving one pod. The sidecar fronts
pi-coding-agent. The agent reads and writes inside /workspace. Approval gates
run inside the workflow before a run reaches Succeeded. The default gate is
hybrid, which combines an LLM judge with human approval.
Spec-driven mode runs three stages and retries on failure.
sequenceDiagram
actor U as User
participant W as Workflow
participant M as Manage agent
participant I as Implement agent
U->>W: prompt + repo
W->>M: PLAN, write OpenSpec change
W->>I: EXECUTE, write code against spec
W->>M: VERIFY, task gate and spec validate and LLM judge
alt verify fails
W->>I: retry with failure report
end
W->>U: PR opened (autoPush+autoPR)
Single mode skips Plan and Verify. The agent runs once against the prompt.
| Where | What |
|---|---|
cmd/{apiserver,controller,temporal-worker,uncworks} |
Control plane and CLI |
cmd/sidecar, cmd/hydration |
Pod-side binaries |
internal/server |
ConnectRPC and REST handlers |
internal/temporal |
Workflow, activities, approval gates, LLM judge |
internal/controller |
AgentRun and Project reconcilers |
extensions/aot-determinism.ts |
pi extension loaded into every agent run |
web/ |
React dashboard |
proto/, gen/ |
Service definitions and generated code |
deploy/helm/aot/ |
Helm chart |
devbox shell # enter the toolchain
task install
task cluster:setup # one time: Colima, k3s, and the Helm install
task dev:deploy # rebuild images into k8s.io and roll out
task dev:web # Vite dev server
task test # Go, web, and extension tests
task proto:gen # regenerate after a .proto changeRun task --list for the rest. See CONTRIBUTING.md.
- docs/getting-started.md
- docs/architecture/overview.md
- docs/guides/spec-driven.md, which covers Plan, Execute, and Verify
- docs/reference/api.md and docs/reference/crd.md
Apache License 2.0. See LICENSE. Contributions are welcome under the same terms. See CONTRIBUTING.md.