Re-dispatch...

Step 5: Check Progress Anytime

python3 scripts/task_manager.py status my-project

Output:

📋 Project: my-project

🎯 Goal: Build a REST API with tests and docs

📊 Status: active

▶️ Current: test-agent

✅ code-agent: done

Task: Implement REST API with Flask

Output: Created /home/ubuntu/projects/my-project/app.py

🔄 test-agent: in-progress

Task: Write pytest tests for all endpoints

⬜ docs-agent: pending

⬜ monitor-bot: pending

Progress: [██░░] 2/4

Agent Dispatch Details

Session Keys (Dev Team)

Agent

Session Key

code-agent

agent:code-agent:telegram:group:-5189558203

test-agent

agent:test-agent:telegram:group:-5218382533

docs-agent

agent:docs-agent:telegram:group:-5253138320

monitor-bot

agent:monitor-bot:telegram:group:-5193935559

Dispatch Template

When dispatching to an agent, include:

Project context

— what the project is about

Specific task

— what this agent should do

Working directory

— where to create/find files

Previous stage output

— if relevant (e.g., test-agent needs to know what code-agent built)

Example dispatch message:

Project: my-project

Goal: Build a REST API with tests and docs

Your task: Write pytest tests for all endpoints in /home/ubuntu/projects/my-project/app.py

Target: 90%+ coverage, test GET/POST/DELETE /items

Working directory: /home/ubuntu/projects/my-project/

Previous stage (code-agent) output: Created app.py with Flask REST API, 3 endpoints

Delivery Context Fix

⚠️ If an agent's session was first created via

sessions_send

, its

deliveryContext

is

webchat

, not

telegram

. Agent replies won't appear in the Telegram group.

Workaround

After getting the agent's reply via sessions_send , use the message tool to relay key results to the group: message(action="send", channel="telegram", target="-5189558203", message="✅ code-agent 完成: Created app.py") Mode B: DAG Workflow (Parallel Dependencies) Step 1: Initialize DAG Project python3 scripts/task_manager.py init my-project -m dag -g "Build REST API with parallel workstreams" Step 2: Add Tasks with Dependencies TM = "python3 scripts/task_manager.py"

Root tasks (no deps — can run in parallel)

$TM add my-project design -a docs-agent --desc "Write API spec" $TM add my-project scaffold -a code-agent --desc "Create project skeleton"

Tasks with dependencies (blocked until deps are done)

$TM add my-project implement -a code-agent -d "design,scaffold" --desc "Implement API" $TM add my-project write-tests -a test-agent -d "design" --desc "Write test cases from spec"

Fan-in: depends on multiple tasks

$TM add my-project run-tests -a test-agent -d "implement,write-tests" --desc "Run all tests" $TM add my-project write-docs -a docs-agent -d "implement" --desc "Write final docs"

Final gate

$TM add my-project review -a monitor-bot -d "run-tests,write-docs" --desc "Final review" Step 3: View DAG Graph $TM graph my-project ├─ ⬜ design [docs-agent] │ ├─ ⬜ implement [code-agent] │ │ ├─ ⬜ run-tests [test-agent] │ │ │ └─ ⬜ review [monitor-bot] │ │ └─ ⬜ write-docs [docs-agent] │ └─ ⬜ write-tests [test-agent] └─ ⬜ scaffold [code-agent] └─ ⬜ implement (↑ see above) Step 4: Dispatch Ready Tasks $TM ready my-project

Shows all tasks whose deps are met

For each ready task, AGI follows this loop:

1. Get ready tasks: task_manager.py ready --json

2. For each ready task (can dispatch in parallel):

a. Mark in-progress: task_manager.py update in-progress

b. Dispatch agent: sessions_send(sessionKey=..., message=)

3. When agent replies:

a. Save result: task_manager.py result "

"

b. Mark done: task_manager.py update done

c. Check newly unblocked tasks (printed automatically)

4. Repeat until all done

Key DAG Features

Parallel dispatch

:

ready

returns ALL tasks whose deps are satisfied — dispatch them simultaneously

Dep outputs forwarding

:

ready --json

includes

depOutputs

— previous stage results to pass to agents

Auto-unblock notification

When a task completes, shows which tasks are newly unblocked

Cycle detection

:

add

rejects tasks that would create circular dependencies

Partial failure

If one task fails, unrelated branches continue; only downstream tasks block Graph visualization : graph shows tree view with status icons and dedup markers Custom Pipelines Linear (Mode A)

Code + test only

python3 scripts/task_manager.py init quick-fix -g "Hotfix" -p "code-agent,test-agent"

Docs first, then code

python3 scripts/task_manager.py init spec-driven -g "Spec-driven dev" -p "docs-agent,code-agent,test-agent" DAG (Mode B)

Diamond pattern: 2 parallel branches merge for review

$TM init diamond -m dag -g "Parallel dev" $TM add diamond code -a code-agent --desc "Write code" $TM add diamond test -a test-agent --desc "Write tests" $TM add diamond integrate -a code-agent -d "code,test" --desc "Integration" $TM add diamond review -a monitor-bot -d "integrate" --desc "Final review" Choosing Between Modes Mode A (linear) Mode B (dag) When Sequential tasks, simple flows Parallel workstreams, complex deps Dispatch One at a time, auto-advance Multiple simultaneous, dep-driven Setup init -p agents (one command) init -m dag + add per task Best for Bug fixes, simple features Large features, spec-driven dev Data Location Task files: /home/ubuntu/clawd/data/team-tasks/.json ⚠️ Common Pitfalls Mode A: Stage ID is agent name, NOT a number In linear mode, the stage ID is the agent name (e.g., code-agent ), not a numeric index like 1 , 2 , 3 .

❌ WRONG — will error "stage '1' not found"

python3 scripts/task_manager.py assign my-project 1 "Build API" python3 scripts/task_manager.py update my-project 1 done

✅ CORRECT — use agent name as stage ID

python3 scripts/task_manager.py assign my-project code-agent "Build API" python3 scripts/task_manager.py update my-project code-agent done python3 scripts/task_manager.py result my-project code-agent "Created main.py" This applies to all stage-referencing commands: assign , update , result , log , reset . The pipeline order is defined by -p at init time (e.g., -p "code-agent,test-agent,docs-agent" ), and next automatically advances through them in order — but you always reference stages by agent name. Tips One project per task — keep scope focused; create multiple projects for parallel work Meaningful project slugs — rest-api-v2 , bug-fix-auth , refactor-db (not project1 ) Save results — always result before update done ; this is the inter-agent context Log liberally — log is cheap; helps debug failed pipelines Reset and retry — reset --all for clean reruns; reset for targeted retry DAG fan-out — one root task can unblock many parallel tasks DAG fan-in — a task can depend on multiple predecessors (all must complete)