shrey150 / cloud-cooked

Never get cooked by a flight cancellation again 🙈

cooked

Inspiration

After missing many flights of our own and having friends deal with the situation of missing/cancelled/delayed flights interfering with plans, we decided to build cooked.

When a flight is missed or delayed, the possible alternatives are very open-ended, and traditional travel search engines aren't sufficient. Under the constraints of a missed/delayed flight situation, the scope of possibile options expands to the extent that an AI-agent model might actually be the best solution — a dynamic tool that can think of options like driving to nearby airports, taking public transit or buses, or even repositioning to a completely new city.

What it does

1. Enter in your flight number

2. We call fetch.ai's DELTA-V Chat API with the prompt in cloud-cooked/convex/actions.ts

3. Given agents that we've already set up on fetch.ai's agentverse, we ask DELTA-V to respond to our prompt, finding you alternative routings for your flight including buses, trains, public transit, etc. There is no coding needed on our end besides the prompt — DELTA-V automatically determines the best agents to complete the task, then queries the APIs it needs to come up with a plan.

   • Agent code is in agents/ folder

4. The alternate plan is displayed to the user.

5. If the user likes the alternate plan, we have another prompt that is sent to DELTA-V which allows the user to book all the tickets (stretch goal).

How we built it

• fetch.ai to determine which agents to use
• Convex to keep track of fetch.ai DELTA-V chatbot sessions on a per-tab basis
• Next.js/Vercel for hosting
• Figma to design the frontend
• Tailwind to style the frontend

Challenges we ran into

• Finding good APIs for data
• Dealing with DELTA-V API expiration

Accomplishments that we're proud of

• Very nice looking frontend, with design files in Figma
• We were able to write a relatively stable wrapper around the DELTA-V API with nice abstractions to make accessing messages easier
• Diverse set of agents able to complete a wide variety of tasks
• Well-engineered prompt for DELTA-V

What we learned

• Prompt engineering
• Advanced Tailwind classes
• Convex and fetch.ai concepts

What's next

• Getting flight delay prediction working with a weather/airport delay agent
• Getting non-flight alternatives working with a public transit/train agent

For developers

Getting started

1. Install dependencies: bun install
2. Start Next.js dev server: bun dev
3. Start Convex dev server: bunx convex dev

Debugging

• If the call to fetch.ai fails, you probably need to get a new authentication token. Log out and log back into the fetch.ai API docs, then copy the JWT and replace the FETCH_AI_AUTH_TOKEN environment variable in Convex (in the dev environment)