docs/en/s07-task-system.md
s01 > s02 > s03 > s04 > s05 > s06 | [ s07 ] s08 > s09 > s10 > s11 > s12
"Break big goals into small tasks, order them, persist to disk" -- a file-based task graph with dependencies, laying the foundation for multi-agent collaboration.
s03's TodoManager is a flat checklist in memory: no ordering, no dependencies, no status beyond done-or-not. Real goals have structure -- task B depends on task A, tasks C and D can run in parallel, task E waits for both C and D.
Without explicit relationships, the agent can't tell what's ready, what's blocked, or what can run concurrently. And because the list lives only in memory, context compression (s06) wipes it clean.
Promote the checklist into a task graph persisted to disk. Each task is a JSON file with status, dependencies (blockedBy), and dependents (blocks). The graph answers three questions at any moment:
pending status and empty blockedBy.completed tasks, whose completion automatically unblocks dependents..tasks/
task_1.json {"id":1, "status":"completed"}
task_2.json {"id":2, "blockedBy":[1], "status":"pending"}
task_3.json {"id":3, "blockedBy":[1], "status":"pending"}
task_4.json {"id":4, "blockedBy":[2,3], "status":"pending"}
Task graph (DAG):
+----------+
+--> | task 2 | --+
| | pending | |
+----------+ +----------+ +--> +----------+
| task 1 | | task 4 |
| completed| --> +----------+ +--> | blocked |
+----------+ | task 3 | --+ +----------+
| pending |
+----------+
Ordering: task 1 must finish before 2 and 3
Parallelism: tasks 2 and 3 can run at the same time
Dependencies: task 4 waits for both 2 and 3
Status: pending -> in_progress -> completed
This task graph becomes the coordination backbone for everything after s07: background execution (s08), multi-agent teams (s09+), and worktree isolation (s12) all read from and write to this same structure.
class TaskManager:
def __init__(self, tasks_dir: Path):
self.dir = tasks_dir
self.dir.mkdir(exist_ok=True)
self._next_id = self._max_id() + 1
def create(self, subject, description=""):
task = {"id": self._next_id, "subject": subject,
"status": "pending", "blockedBy": [],
"blocks": [], "owner": ""}
self._save(task)
self._next_id += 1
return json.dumps(task, indent=2)
blockedBy list, automatically unblocking dependents.def _clear_dependency(self, completed_id):
for f in self.dir.glob("task_*.json"):
task = json.loads(f.read_text())
if completed_id in task.get("blockedBy", []):
task["blockedBy"].remove(completed_id)
self._save(task)
update handles transitions and dependency edges.def update(self, task_id, status=None,
add_blocked_by=None, add_blocks=None):
task = self._load(task_id)
if status:
task["status"] = status
if status == "completed":
self._clear_dependency(task_id)
self._save(task)
TOOL_HANDLERS = {
# ...base tools...
"task_create": lambda **kw: TASKS.create(kw["subject"]),
"task_update": lambda **kw: TASKS.update(kw["task_id"], kw.get("status")),
"task_list": lambda **kw: TASKS.list_all(),
"task_get": lambda **kw: TASKS.get(kw["task_id"]),
}
From s07 onward, the task graph is the default for multi-step work. s03's Todo remains for quick single-session checklists.
| Component | Before (s06) | After (s07) |
|---|---|---|
| Tools | 5 | 8 (task_create/update/list/get) |
| Planning model | Flat checklist (in-memory) | Task graph with dependencies (on disk) |
| Relationships | None | blockedBy + blocks edges |
| Status tracking | Done or not | pending -> in_progress -> completed |
| Persistence | Lost on compression | Survives compression and restarts |
cd learn-claude-code
python agents/s07_task_system.py
Create 3 tasks: "Setup project", "Write code", "Write tests". Make them depend on each other in order.List all tasks and show the dependency graphComplete task 1 and then list tasks to see task 2 unblockedCreate a task board for refactoring: parse -> transform -> emit -> test, where transform and emit can run in parallel after parse