BusyBrain Multi-Agent Planner

BusyBrain turns an overwhelming week into a humane plan. Instead of producing a generic to-do list, one public orchestrator coordinates three independently registered Fetch.ai uAgents that reason about study workload, calendar logistics, and sustainable pacing. The final answer includes the invisible work that ordinary calendars miss: setup, travel, food, transitions, breaks, and recovery.

Why agents?

The specialists have intentionally different goals. The Study Planner protects learning progress, the Calendar Planner protects time constraints, and the Wellness Planner protects the student's capacity. The orchestrator must reconcile their recommendations into one usable plan. This makes agent coordination central to the product rather than an eligibility wrapper around a chatbot.

Agent network

Agent	Responsibility	Port	Address
BusyBrain Orchestrator	ASI:One Chat Protocol entry point and final synthesis	8001	agent1q2qrnqd6v20qx0ltq2pnt2r85fadkekgs2js07w84h9jj87lhgm2crewv4j
Study Planner	Focus rhythms, workload, deadlines, and daily study limits	8002	agent1qdchqqjrvhyvx9cxxtmgwy498zwscn9ac9yqja43cqv90w3q3asq70rjrww
Calendar Planner	Fixed events, conflicts, travel, preparation, and transitions	8003	agent1qvv9585yydz2r9p3n245t9y6l46kqpsa0e299n93nl5y60f6lgfwx64wygz
Workload & Social Context	Calendar-pressure signals, fair meetup timing, recovery, and rationale	8004	agent1q0pkvdgsxv4w8r5lqh2chmlhantxtlk28e5yh9g06ah5xwnuvqznqzenkwe

End-to-end flow

1. A user sends a natural-language request from ASI:One.
2. BusyBrain acknowledges the Chat Protocol message and creates a request ID.
3. It dispatches a typed PlanningRequest to the Study, Calendar, and Workload & Social specialists.
4. Each specialist returns a signed SpecialistResponse.
5. BusyBrain verifies each sender, waits for all three, resolves conflicts, and returns ranked options through the Chat Protocol.
6. The user replies choose option 1, choose option 2, or choose option 3.
7. BusyBrain persists a confirmed-event action receipt and returns a populated Google Calendar action link—all inside the ASI:One conversation.

If a specialist is unavailable, the orchestrator finalizes after 45 seconds with the verified partial results and names the missing specialist instead of hanging.

Local setup

Python 3.12 or 3.13 is recommended.

python3.13 -m venv .venv
source .venv/bin/activate
pip install -r requirements.txt
cp .env.example .env

Add an ASI:One key and four unique private seeds to .env. Never commit .env; changing a seed after registration changes that agent's address.

Run each agent in its own terminal:

make orchestrator
make study
make calendar
make workload

Open each Inspector URL, choose Connect → Mailbox, and keep all four processes running for the live demo. Each agent needs its own mailbox.

Before testing in ASI:One, validate the exact same Chat Protocol route locally in a fifth terminal:

make smoke-test

The client prints TEST PASSED after the orchestrator has collected all three specialist responses and returned the final plan. Press Ctrl+C after the result.

ASI:One demo prompt

Find me 45 minutes with Maya in the next seven days, preferably in the afternoon and not right after class. Compare both calendars, give me the three best options, and explain why the top time is fair to both of us.

Then reply:

choose option 2

The terminal logs show the orchestrator dispatching the request and receiving three specialist responses before returning the synthesized plan. The Workload & Social agent calls the deterministic ranking engine against seeded calendars, so the times and fit scores are evidence rather than invented availability. The second turn produces a durable confirmation receipt and a Google Calendar action.

Pika invitation handoff

After the user approves a meetup time, the Streamlit experience prepares a consent-gated Pika invitation using a saved cartoon avatar rather than a real person's face. The creative concept turns her wrist calendar into a glowing jellyfish portal whose bubbles assemble into the confirmed date, time, and place.

The Pika MCP workflow is deliberately multi-step: generate a short spoken invitation, animate the approved avatar to that audio, and render the confirmed event details as stable mobile-safe text. The output is an 8-second vertical clip displayed beside the calendar confirmation. Scheduling remains independent, so a media-provider delay can never lose or invalidate the confirmed event. This combines an original visual story, privacy-aware generation, and a shareable action outcome instead of treating video as decoration.

Existing product prototype

The repository also contains a Streamlit interface and deterministic scheduling engine:

streamlit run app.py
python main.py

These modules demonstrate the broader product direction; the Agentverse workflow is implemented by agent.py, study_agent.py, calendar_agent.py, and wellness_agent.py.

For the complete browser demo (calendar UI + interactive Pika message), run:

.venv/bin/uvicorn api:app --reload --port 8000
python3 -m http.server 8080

Then open http://localhost:8080/. Keep both processes running during the demo.

To start the website, API, and all four Fetch.ai agents from one terminal instead:

make app

Press Ctrl+C once to stop every Busy Brain process.

Safety and scope

BusyBrain provides planning support, not medical diagnosis, therapy, or professional academic advice. Specialist responses are treated as recommendations, and missing schedule information is surfaced as an assumption rather than silently invented.

Hackathon submission checklist

• Public GitHub repository
• Four Agentverse profile links documented
• Shared ASI:One chat session
• Three-to-five-minute demo video
• Devpost explanation and architecture

Tests

python -m unittest discover -s tests -v

The suite covers natural-language constraints, collision prevention, recovery-gap ranking, option selection, durable confirmation, and Google Calendar handoff URLs.