seekr-hatchery 0.40.1

Task orchestration CLI for AI coding agents

seekr-hatchery

Task orchestration CLI for AI coding agents. Each task gets an isolated git worktree and its own agent session, sandboxed by default inside Docker.

Sandboxing (on by default) — each task runs in full isolation:

• 🐳 Docker sandbox: the agent runs inside a container with carefully scoped filesystem access — read-only repo, write-access only to its own worktree
• 🌿 Isolated worktree: each task gets its own hatchery/<name> git branch and worktree, so parallel work never conflicts

Task management — structured workflow with persistent records:

• 📋 Plan-first workflow: plan → approval → implement → commit; enforced by task files the agent must follow
• 🔄 Resumable sessions: interrupted sessions pick up exactly where they left off via preserved session state
• 📄 Task files as records: each task file becomes a permanent ADR in the repo after completion

Installation

uv tool install seekr-hatchery

To upgrade to the latest release:

uv tool upgrade seekr-hatchery

Note: Do not pin a version on install (e.g. ==0.3.0) — uv stores the constraint and will refuse to upgrade past it.

Requires Python 3.12+ and at least one agent:

• OpenAI Codex: npm install -g @openai/codex — codex on $PATH, OPENAI_API_KEY

Quick start

# Start a new task
hatchery new add-auth

# Start a new task using OpenAI Codex
hatchery new add-auth --agent codex

# Resume an interrupted session
hatchery resume add-auth

# Mark complete and remove the worktree
hatchery done add-auth

# See all tasks for this repo
hatchery list

How it works

hatchery new <name> creates a git worktree on a hatchery/<name> branch, drops a task file there for you to fill in, commits it, then launches an agent session pointed at that worktree. The agent runs inside a Docker sandbox by default — a starter Dockerfile is created automatically on first use. The agent plans, implements, commits, and marks the task complete — all inside the isolated branch. When you're satisfied, hatchery done <name> cleans up the worktree and leaves the branch ready to merge.

Task workflow

When the agent starts a new task it is given a task file at .hatchery/tasks/YYYY-MM-DD-<name>.md. The expected workflow:

1. Plan first — read the task file, ask clarifying questions, propose a numbered implementation plan. No code until the plan is approved.
2. On approval — update the "Agreed Plan" section, then implement step by step.
3. While executing — tick checkboxes in the Progress Log after each step, make a descriptive git commit.
4. If blocked — stop and discuss before proceeding.
5. On completion — mark Status as "complete", add a ## Summary section. The task file is merged into main as the permanent record.

Commands

Command	Description
new <name>	Create worktree + branch, open task file, launch agent
resume <name>	Reattach to the existing session exactly where it left off
done <name>	Remove worktree, retain branch, mark task complete
abort <name>	Remove worktree without marking complete (branch kept)
delete <name>	Remove worktree, delete branch, erase all metadata
list	List all tasks for the current repo
status <name>	Show task metadata and the full task file
self update	Upgrade hatchery to the latest release
config edit	Open ~/.hatchery/config.json in $EDITOR with validation

All new / resume commands accept:

• --no-docker — skip the container even if a Dockerfile is present
• --no-worktree — reuse the current directory instead of creating a new worktree

new also accepts:

• --from <ref> — fork from a specific branch or commit (default: HEAD)
• --editor / --no-editor — force editor or prompt mode for the task objective. By default, hatchery prompts in the terminal; set "open_editor": true in ~/.hatchery/config.json to default to $EDITOR. If the editor is opened and the file is unchanged on close, the task is cancelled.
• --agent [codex] — choose the AI agent (auto-detected from installed agents)

The chosen agent is stored in task metadata and re-used automatically on resume.

Docker sandbox

By default, new and resume build a Docker image from .hatchery/Dockerfile and run the agent inside it. On first new, if no Dockerfile exists, a starter is created for the selected agent and opened for editing.

The container receives:

• Full repo mounted read-only (for context)
• .git/objects and .git/logs read-write (so commits work)
• .git/refs/heads/hatchery/ read-write (own branch ref)
• The task worktree read-write (the only place edits land)
• ~/.codex and a per-task auth config — Codex only
• ~/.gitconfig read-only (commit identity)
• ~/.cache/uv and ~/.ssh if present

A .hatchery/docker.yaml config file is also created alongside the Dockerfile.

Custom mounts (docker.yaml)

.hatchery/docker.yaml controls extra host→container bind-mounts injected on every launch. The file is pre-populated with commented examples — uncomment what you need:

schema_version: 1
mounts:
  # - "~/.kube:/home/hatchery/.kube:ro"
  # - "~/.aws:/home/hatchery/.aws:ro"
  # - "~/.config/gcloud:/home/hatchery/.config/gcloud:ro"
  # - "~/.oci:/home/hatchery/.oci:ro"

Mount format: "host_path:container_path[:mode]" — identical to Docker's own -v syntax.

• ~ is expanded to your home directory.
• mode defaults to ro (read-only) if omitted.
• Invalid entries are a hard error. Paths that do not exist on the host are silently skipped.

The file is tracked in git so every developer on the project gets the same mount configuration. Changes take effect on the next new or resume.

Clipboard image paste

Press Ctrl-V in the agent's TUI to attach an image from your host clipboard to the next prompt. Works on macOS, and on Linux with wl-paste or xclip installed — terminal-agnostic. Enabled by default; set clipboard_images: false in .hatchery/docker.yaml to disable.

API key security

The real API key never enters the container. Hatchery starts a lightweight host-side HTTP reverse proxy on an ephemeral port immediately before launching the container.

Codex (OpenAI):

• OPENAI_API_KEY — a random per-task proxy token
• OPENAI_BASE_URL — pointing to the host proxy (http://host.docker.internal:<port>)

The SDK inside the container uses these transparently. The proxy validates the inbound token, strips whatever credentials the container sends, injects the real API key in the correct format (Authorization: Bearer for OpenAI), and forwards the request over HTTPS. The real key never leaves the host process.

This means a jailbroken or adversarially-prompted agent that reads its API key env var or attempts to exfiltrate it gets only the proxy token — which is worthless outside the session.

The proxy token is stable per-task (persisted across container restarts) so cached credentials stay valid on subsequent resume launches.

Container runtime auto-detection

Hatchery prefers Podman as the sandbox runtime when it is installed, falling back to Docker otherwise. Podman is rootless-native: UID 0 inside the sandbox maps to the calling user on the host — not real root. No daemon required. If you have both installed, podman info is checked first.

Podman-in-Podman (DinD)

DinD enables the agent to run a nested container engine inside its Docker sandbox — useful when your tasks involve building container images, running integration tests with Docker Compose, or any workflow that itself needs a container runtime.

To enable:

1. Uncomment the ── Podman-in-Podman (DinD) block in .hatchery/Dockerfile. This installs podman, fuse-overlayfs, and uidmap, and wires up a passwordless sudo wrapper so the hatchery user can invoke Podman.

2. Set dind: true in .hatchery/docker.yaml:

   schema_version: 1
   dind: true
   mounts: []
   

3. Run hatchery new <name> or resume <name> — the image rebuild is only slow the first time after the Dockerfile change; subsequent runs hit the layer cache.

What you can do inside the container:

# Run as the `hatchery` user inside the sandbox
podman run --rm hello-world
podman build -t my-image .
podman compose up

hatchery automatically provisions .hatchery/seccomp.json the first time DinD is enabled. This seccomp profile allows the extra syscalls required by Podman's user-namespace networking stack.

Session environment

Every agent session launched by new or resume receives two environment variables:

Variable	Value
HATCHERY_TASK	The task name (e.g. add-auth)
HATCHERY_REPO	Absolute path to the repo root

Statusline integration

You can show the active task and its branch in your terminal statusline. Example script:

hatchery_line=""
if [ -n "$HATCHERY_TASK" ]; then
    hatchery_branch=$(git -C "$HATCHERY_REPO" --no-optional-locks \
        rev-parse --abbrev-ref HEAD 2>/dev/null)
    cyan=$(printf '\033[0;36m'); yellow=$(printf '\033[0;33m'); reset=$(printf '\033[0m')
    hatchery_line="├ ${cyan}⬡ ${HATCHERY_TASK}${reset}  ${yellow}${hatchery_branch}${reset}"
fi

Then output "$top\n$hatchery_line\n$bottom" when $hatchery_line is non-empty, otherwise "$top\n$bottom". This renders as:

┌ user@host  ~/path/to/repo  (hatchery/add-auth ●)  Sonnet
├ ⬡ add-auth  hatchery/add-auth
└ [14:32:01]  [████████░░░░░░░░░░░░] 38%

Storage layout

<repo>/
  .hatchery/
    Dockerfile             # optional sandbox definition
    docker.yaml            # optional Docker config (custom mounts, etc.)
    tasks/                 # permanent task records (tracked in git)
    worktrees/             # active worktrees (gitignored)

~/.hatchery/
  meta.json                # DB schema version
  tasks/                   # all per-task state, namespaced by repository
    <repo-id>/             # stable hash of the repo path
      <task-name>/         # one directory per task
        meta.json          # task metadata
        codex_auth.json    # Docker session: proxy-token auth config
        proxy_token        # Docker session: stable API proxy UUID
        COMMIT_EDITMSG     # Docker session: git sentinel file
        ORIG_HEAD          # Docker session: git sentinel file
        git_ptr            # Docker session: container-path .git pointer

Development

uv sync          # install deps and editable package
uv run hatchery --help

uv run ruff format .
uv run ruff check --fix .
uv run pytest tests

Version is derived from git tags via uv-dynamic-versioning. Without a matching v*.*.* tag it resolves to 0.0.0.dev0.

Contributing

PR title format

The PR title must follow Conventional Commits. The CI validates this on every PR.

<type>(<optional scope>)<!>: <description>

Allowed types: feat, fix, docs, chore, style, refactor, perf, test, build, ci, revert, no-bump

Individual commits on your branch are not validated — use whatever messages work for you while developing.

Merging

All PRs are merged via squash commit. The squash commit message is set to the PR title, which is the only commit that lands on main.

Version bumps

On every push to main the CI computes the next version from the squash commit message and creates an annotated git tag. uv-dynamic-versioning derives the package version from that tag.

PR title prefix	Version bump
no-bump:	none — skips tag and release entirely
feat!: / any type with !	major — x.0.0
feat:	minor — 0.x.0
fix:, perf:	patch — 0.0.x
everything else	patch — 0.0.x

Most merges produce a release. Types like chore, docs, refactor etc. result in a patch bump. Use no-bump: to land a commit on main without cutting a release (e.g. for CI tweaks or documentation-only changes that do not warrant a version increment).

Examples

feat(cli): add --dry-run flag
fix: handle missing config file gracefully
chore: update ruff to 0.16
refactor(worktree): extract branch-name validation
feat!: rename `new` command to `start`
no-bump: update CI workflow variables

GitHub repository settings (for maintainers)

• Settings > General > Pull Requests: enable "Allow squash merging", set default commit message to "Pull request title"