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"nbformat": 4,
"nbformat_minor": 0,
"metadata": {
"colab": {
"provenance": []
},
"kernelspec": {
"name": "python3",
"display_name": "Python 3"
},
"language_info": {
"name": "python"
}
},
"cells": [
{
"cell_type": "markdown",
"source": [
"# \"AI Task Master\" — Multi-Agent Productivity & Execution System\n",
"\n",
"# Upload a Goal / To-Do List / Project Idea → Agents break it down → Prioritize → Create action steps → Assign estimated time → Generate a Daily Execution Plan with smart suggestions & progress tracking logic."
],
"metadata": {
"id": "hMAysKRQphEj"
}
},
{
"cell_type": "markdown",
"source": [
"# 🧱 Step 0: Colab Setup (Install & Imports)"
],
"metadata": {
"id": "IRKP3gswqJSu"
}
},
{
"cell_type": "code",
"source": [
"#!pip install --quiet crewai langchain-openai gradio python-dotenv pydantic -q"
],
"metadata": {
"id": "58wVUNhZnxQh"
},
"execution_count": null,
"outputs": []
},
{
"cell_type": "markdown",
"source": [],
"metadata": {
"id": "sH9afYAjqdCa"
}
},
{
"cell_type": "code",
"source": [
"import os, json, textwrap, datetime as dt\n",
"from typing import Optional, List, Dict, Any\n",
"\n",
"from crewai import Agent, Task, Crew, Process\n",
"from langchain_openai import ChatOpenAI\n"
],
"metadata": {
"id": "EwHFD9RSnsRg"
},
"execution_count": null,
"outputs": []
},
{
"cell_type": "markdown",
"source": [
"# 🔐 Step 1: Configure API Key (OpenAI)\n"
],
"metadata": {
"id": "qiL6nGlGqe_w"
}
},
{
"cell_type": "code",
"source": [
"from google.colab import userdata\n",
"\n",
"# Retrieve the API key from Colab secrets\n",
"os.environ[\"OPENAI_API_KEY\"] = userdata.get('OPENAI_API_KEY')\n",
"\n",
"# Model defaults (you can change)\n",
"MODEL_NAME = \"gpt-4o-mini\" # fast & smart\n",
"TEMPERATURE = 0.2\n",
"llm = ChatOpenAI(model=MODEL_NAME, temperature=TEMPERATURE)"
],
"metadata": {
"id": "3A7Xcn2Qn81O"
},
"execution_count": null,
"outputs": []
},
{
"cell_type": "markdown",
"source": [
"# 🧩 Step 2: Project Inputs Helper"
],
"metadata": {
"id": "aFEecuUqqiiQ"
}
},
{
"cell_type": "code",
"source": [
"# Light helper to bundle inputs\n",
"def default_inputs(\n",
" goal: str,\n",
" deadline_days: int = 14,\n",
" hours_per_day: int = 2,\n",
" start_date: Optional[str] = None,\n",
") -> Dict[str, Any]:\n",
" if not start_date:\n",
" start_date = dt.date.today().isoformat()\n",
" return {\n",
" \"goal\": goal.strip(),\n",
" \"deadline_days\": int(deadline_days),\n",
" \"hours_per_day\": int(hours_per_day),\n",
" \"start_date\": start_date,\n",
" }\n"
],
"metadata": {
"id": "RUHRmzmwoM6E"
},
"execution_count": null,
"outputs": []
},
{
"cell_type": "markdown",
"source": [
"# 🎭 Step 3: Define Agents (Roles + Personalities)\n"
],
"metadata": {
"id": "mt9NZfhlqmEd"
}
},
{
"cell_type": "code",
"source": [
"# Agent: Understand the user's goal & constraints\n",
"goal_understanding = Agent(\n",
" role=\"Goal Understanding Agent\",\n",
" backstory=(\n",
" \"You are a thoughtful strategist. You clarify objectives, constraints, \"\n",
" \"and success criteria. You avoid fluff and get to the essence.\"\n",
" ),\n",
" goal=(\n",
" \"Rewrite the user's goal clearly and identify assumptions, scope, \"\n",
" \"success criteria, hard constraints, and risks.\"\n",
" ),\n",
" allow_delegation=False,\n",
" llm=llm,\n",
")\n",
"\n",
"# Agent: Break the goal into milestones & tasks\n",
"task_breakdown = Agent(\n",
" role=\"Task Breakdown Agent\",\n",
" backstory=(\n",
" \"You are a senior project planner. You decompose work into milestones \"\n",
" \"and concrete tasks that are testable and deliver value.\"\n",
" ),\n",
" goal=(\n",
" \"Produce milestones and detailed subtasks with crisp deliverables.\"\n",
" ),\n",
" allow_delegation=False,\n",
" llm=llm,\n",
")\n",
"\n",
"# Agent: Estimate effort & prioritize\n",
"effort_priority = Agent(\n",
" role=\"Effort & Priority Agent\",\n",
" backstory=(\n",
" \"You are a pragmatic PM. You estimate realistic time and difficulty, \"\n",
" \"sequence tasks, and surface dependencies.\"\n",
" ),\n",
" goal=(\n",
" \"Estimate time per task, difficulty (1-5), dependencies, and propose a priority order.\"\n",
" ),\n",
" allow_delegation=False,\n",
" llm=llm,\n",
")\n",
"\n",
"# Agent: Build a calendar-style plan\n",
"scheduler = Agent(\n",
" role=\"Schedule Maker Agent\",\n",
" backstory=(\n",
" \"You create practical schedules given time budgets per day and deadlines.\"\n",
" ),\n",
" goal=(\n",
" \"Map tasks to days from start_date within deadline_days, respecting hours_per_day.\"\n",
" ),\n",
" allow_delegation=False,\n",
" llm=llm,\n",
")\n",
"\n",
"# Agent: Motivation & execution tips (optional spice)\n",
"motivator = Agent(\n",
" role=\"Motivator Agent\",\n",
" backstory=(\n",
" \"You coach users with specific, actionable tips, accountability tactics, and small rewards.\"\n",
" ),\n",
" goal=\"Provide short, concrete advice to keep momentum high.\",\n",
" allow_delegation=False,\n",
" llm=llm,\n",
")\n",
"\n",
"# Agent: Final synthesis for export\n",
"synthesizer = Agent(\n",
" role=\"Synthesis Agent\",\n",
" backstory=(\n",
" \"You are a concise editor. You assemble a clean, professional plan for download and printing.\"\n",
" ),\n",
" goal=\"Create final Markdown + JSON outputs for the user.\",\n",
" allow_delegation=False,\n",
" llm=llm,\n",
")\n"
],
"metadata": {
"id": "U9Wvk1DqoYwx"
},
"execution_count": null,
"outputs": []
},
{
"cell_type": "markdown",
"source": [
"# 🧠 Step 4: Define Tasks (with JSON-first outputs\n"
],
"metadata": {
"id": "_jgH0_8TqprY"
}
},
{
"cell_type": "code",
"source": [
"def make_tasks(inputs: Dict[str, Any]) -> List[Task]:\n",
" common_vars = (\n",
" f\"GOAL: {inputs['goal']}\\n\"\n",
" f\"START_DATE: {inputs['start_date']}\\n\"\n",
" f\"DEADLINE_DAYS: {inputs['deadline_days']}\\n\"\n",
" f\"HOURS_PER_DAY: {inputs['hours_per_day']}\\n\"\n",
" )\n",
"\n",
" t1 = Task(\n",
" description=textwrap.dedent(f\"\"\"\n",
" Clarify the user's goal.\n",
"\n",
" Context:\n",
" {common_vars}\n",
"\n",
" Return STRICT JSON with keys:\n",
" {{\n",
" \"clarified_goal\": \"...\",\n",
" \"assumptions\": [\"...\"],\n",
" \"scope\": [\"in-scope\", \"...\"],\n",
" \"success_criteria\": [\"...\"],\n",
" \"constraints\": [\"...\"],\n",
" \"risks\": [\"...\"]\n",
" }}\n",
" \"\"\").strip(),\n",
" agent=goal_understanding,\n",
" expected_output=\"JSON only.\",\n",
" )\n",
"\n",
" t2 = Task(\n",
" description=textwrap.dedent(f\"\"\"\n",
" Break the clarified goal into milestones and granular tasks.\n",
"\n",
" Input is the JSON from the previous step.\n",
" Return STRICT JSON:\n",
" {{\n",
" \"milestones\": [\n",
" {{\n",
" \"name\": \"...\",\n",
" \"description\": \"...\",\n",
" \"tasks\": [\n",
" {{\n",
" \"id\": \"T1\",\n",
" \"title\": \"...\",\n",
" \"definition_of_done\": \"...\",\n",
" \"dependencies\": [],\n",
" \"tags\": [\"...\"]\n",
" }}\n",
" ]\n",
" }}\n",
" ]\n",
" }}\n",
" \"\"\").strip(),\n",
" agent=task_breakdown,\n",
" expected_output=\"JSON only.\",\n",
" )\n",
"\n",
" t3 = Task(\n",
" description=textwrap.dedent(f\"\"\"\n",
" Estimate effort and priority for EACH task id from the previous output.\n",
"\n",
" Respect HOURS_PER_DAY={inputs['hours_per_day']} and DEADLINE_DAYS={inputs['deadline_days']}.\n",
" Return STRICT JSON:\n",
" {{\n",
" \"estimates\": [\n",
" {{\n",
" \"id\": \"T1\",\n",
" \"hours\": 2.5,\n",
" \"difficulty_1to5\": 3,\n",
" \"priority_1to5\": 1\n",
" }}\n",
" ]\n",
" }}\n",
" \"\"\").strip(),\n",
" agent=effort_priority,\n",
" expected_output=\"JSON only.\",\n",
" )\n",
"\n",
" t4 = Task(\n",
" description=textwrap.dedent(f\"\"\"\n",
" Create a day-by-day schedule mapping tasks to calendar dates.\n",
"\n",
" Use START_DATE and DEADLINE_DAYS. Do not exceed HOURS_PER_DAY per day.\n",
" Return STRICT JSON:\n",
" {{\n",
" \"schedule\": [\n",
" {{\n",
" \"date\": \"YYYY-MM-DD\",\n",
" \"allocated_hours\": 2,\n",
" \"tasks\": [{{\"id\":\"T1\",\"title\":\"...\",\"hours\":1.5}}]\n",
" }}\n",
" ],\n",
" \"summary\": {{\n",
" \"total_hours\": 0,\n",
" \"days_planned\": 0,\n",
" \"buffer_hours\": 0\n",
" }}\n",
" }}\n",
" \"\"\").strip(),\n",
" agent=scheduler,\n",
" expected_output=\"JSON only.\",\n",
" )\n",
"\n",
" t5 = Task(\n",
" description=textwrap.dedent(f\"\"\"\n",
" Provide 5 short motivation or execution tactics tailored to the plan.\n",
"\n",
" Return STRICT JSON:\n",
" {{\n",
" \"tips\": [\n",
" \"Keep daily sessions short & focused with a single visible deliverable.\",\n",
" \"...\"\n",
" ]\n",
" }}\n",
" \"\"\").strip(),\n",
" agent=motivator,\n",
" expected_output=\"JSON only.\",\n",
" )\n",
"\n",
" t6 = Task(\n",
" description=textwrap.dedent(f\"\"\"\n",
" Synthesize a final MARKDOWN report AND a JSON bundle.\n",
"\n",
" Inputs are the JSONs from all prior tasks. Build:\n",
" 1) Markdown (for printing) with sections:\n",
" - Goal Summary\n",
" - Milestones & Tasks\n",
" - Estimates & Priorities\n",
" - Day-by-Day Plan (table)\n",
" - Tips (bulleted)\n",
" 2) JSON bundle containing all prior JSON merged into one: keys:\n",
" {{\n",
" \"goal_context\": ...,\n",
" \"work_breakdown\": ...,\n",
" \"estimates\": ...,\n",
" \"schedule\": ...,\n",
" \"tips\": ...\n",
" }}\n",
"\n",
" Return STRICT JSON:\n",
" {{\n",
" \"markdown\": \"....\",\n",
" \"bundle\": {{ /* merged JSON object */ }}\n",
" }}\n",
" \"\"\").strip(),\n",
" agent=synthesizer,\n",
" expected_output=\"JSON only.\",\n",
" )\n",
"\n",
" return [t1, t2, t3, t4, t5, t6]\n"
],
"metadata": {
"id": "bULXBMGZoacv"
},
"execution_count": null,
"outputs": []
},
{
"cell_type": "markdown",
"source": [
"# 🏃♂️ Step 5: Run the Crew"
],
"metadata": {
"id": "CBxM7WSqrFzR"
}
},
{
"cell_type": "code",
"source": [
"def run_planner(goal: str, deadline_days: int = 14, hours_per_day: int = 2, start_date: Optional[str] = None):\n",
" inputs = default_inputs(goal, deadline_days, hours_per_day, start_date)\n",
"\n",
" tasks = make_tasks(inputs)\n",
" crew = Crew(\n",
" agents=[goal_understanding, task_breakdown, effort_priority, scheduler, motivator, synthesizer],\n",
" tasks=tasks,\n",
" process=Process.sequential, # run in order\n",
" verbose=True,\n",
" memory=True,\n",
" cache=True,\n",
" )\n",
"\n",
" result = crew.kickoff(inputs=inputs)\n",
"\n",
" # The final task returns JSON with 'markdown' and 'bundle'\n",
" # crewai often returns extra text around json; ensure it's valid JSON:\n",
" def coerce_json(text: str) -> Dict[str, Any]:\n",
" try:\n",
" return json.loads(text)\n",
" except Exception:\n",
" # Try to extract JSON block heuristically\n",
" start = text.find(\"{\")\n",
" end = text.rfind(\"}\")\n",
" if start != -1 and end != -1:\n",
" return json.loads(text[start:end+1])\n",
" raise\n",
"\n",
" final = coerce_json(str(result))\n",
" md = final.get(\"markdown\", \"# Plan\\n\\n(No markdown received)\")\n",
" bundle = final.get(\"bundle\", {})\n",
"\n",
" # Save artifacts\n",
" out_dir = \"/content/task_master_outputs\"\n",
" os.makedirs(out_dir, exist_ok=True)\n",
" stamp = dt.datetime.now().strftime(\"%Y%m%d_%H%M%S\")\n",
"\n",
" md_path = os.path.join(out_dir, f\"plan_{stamp}.md\")\n",
" json_path = os.path.join(out_dir, f\"plan_{stamp}.json\")\n",
"\n",
" with open(md_path, \"w\", encoding=\"utf-8\") as f:\n",
" f.write(md)\n",
" with open(json_path, \"w\", encoding=\"utf-8\") as f:\n",
" json.dump(bundle, f, indent=2, ensure_ascii=False)\n",
"\n",
" return {\n",
" \"markdown_path\": md_path,\n",
" \"json_path\": json_path,\n",
" \"markdown_preview\": md[:1500] + (\"\\n\\n...[truncated]...\" if len(md) > 1500 else \"\"),\n",
" }\n"
],
"metadata": {
"id": "wikuoefWoc0Z"
},
"execution_count": null,
"outputs": []
},
{
"cell_type": "markdown",
"source": [
"# Step 6: Minimal Gradio UI"
],
"metadata": {
"id": "bbs5keN2rOAh"
}
},
{
"cell_type": "code",
"source": [
"import gradio as gr\n",
"\n",
"def gradio_run(goal, deadline_days, hours_per_day, start_date):\n",
" out = run_planner(goal, int(deadline_days), int(hours_per_day), start_date or None)\n",
" md = \"\"\n",
" with open(out[\"markdown_path\"], \"r\", encoding=\"utf-8\") as f:\n",
" md = f.read()\n",
" return md, out[\"markdown_path\"], out[\"json_path\"]\n",
"\n",
"with gr.Blocks(title=\"CrewAI Task Master\") as demo_ui:\n",
" gr.Markdown(\"# 🧠 CrewAI Task Master\\nPlan any goal into a day-by-day schedule.\")\n",
" with gr.Row():\n",
" goal = gr.Textbox(label=\"Your Goal\", placeholder=\"e.g., Build an AI portfolio website in 10 days\", lines=3)\n",
" with gr.Row():\n",
" deadline = gr.Number(label=\"Deadline (days)\", value=14, precision=0)\n",
" hours = gr.Number(label=\"Hours per day\", value=2, precision=0)\n",
" start = gr.Textbox(label=\"Start Date (YYYY-MM-DD, optional)\", placeholder=\"Leave blank for today\")\n",
" run_btn = gr.Button(\"Run Agents 🚀\")\n",
"\n",
" md_out = gr.Markdown(label=\"Plan (Markdown)\")\n",
" md_file = gr.File(label=\"Download Markdown\")\n",
" json_file = gr.File(label=\"Download JSON\")\n",
"\n",
" run_btn.click(\n",
" gradio_run,\n",
" inputs=[goal, deadline, hours, start],\n",
" outputs=[md_out, md_file, json_file]\n",
" )\n",
"\n",
"demo_ui.launch(share=False)\n"
],
"metadata": {
"colab": {
"base_uri": "https://localhost:8080/",
"height": 599
},
"id": "HDJKz5r4oisy",
"outputId": "1c91172e-6ac4-4d3e-8074-1ad8ced4fc9a"
},
"execution_count": null,
"outputs": [
{
"output_type": "stream",
"name": "stdout",
"text": [
"Colab notebook detected. To show errors in colab notebook, set debug=True in launch()\n",
"Note: opening Chrome Inspector may crash demo inside Colab notebooks.\n",
"* To create a public link, set `share=True` in `launch()`.\n"
]
},
{
"output_type": "display_data",
"data": {
"text/plain": [
"<IPython.core.display.Javascript object>"
],
"application/javascript": [
"(async (port, path, width, height, cache, element) => {\n",
" if (!google.colab.kernel.accessAllowed && !cache) {\n",
" return;\n",
" }\n",
" element.appendChild(document.createTextNode(''));\n",
" const url = await google.colab.kernel.proxyPort(port, {cache});\n",
"\n",
" const external_link = document.createElement('div');\n",
" external_link.innerHTML = `\n",
" <div style=\"font-family: monospace; margin-bottom: 0.5rem\">\n",
" Running on <a href=${new URL(path, url).toString()} target=\"_blank\">\n",
" https://localhost:${port}${path}\n",
" </a>\n",
" </div>\n",
" `;\n",
" element.appendChild(external_link);\n",
"\n",
" const iframe = document.createElement('iframe');\n",
" iframe.src = new URL(path, url).toString();\n",
" iframe.height = height;\n",
" iframe.allow = \"autoplay; camera; microphone; clipboard-read; clipboard-write;\"\n",
" iframe.width = width;\n",
" iframe.style.border = 0;\n",
" element.appendChild(iframe);\n",
" })(7860, \"/\", \"100%\", 500, false, window.element)"
]
},
"metadata": {}
},
{
"output_type": "execute_result",
"data": {
"text/plain": []
},
"metadata": {},
"execution_count": 11
}
]
},
{
"cell_type": "code",
"source": [],
"metadata": {
"id": "YC9b0G8romnv"
},
"execution_count": null,
"outputs": []
}
]
} |