"Keep My Job" App

Are you struggling to keep your job? Want to impress your boss and appear marginally more competent? You are in the right place!

Keep My Job app is an AI multi-agent system that helps you work faster and get more done—without staring at a blank architecture diagram for hours.

Given a problem statement, the agents collaborate to propose an optimal solution architecture and automatically generate C4 system context and container diagrams.

Demo

Watch this 2-minute demo of the app in action.

10,000-Foot View

flowchart TD
    B:::userClass@{ shape: processes, label: "fa:fa-user Manager" }
    U["fa:fa-user Employee"]:::userClass
    AR["fa:fa-robot Root Agent"]:::agentClass
    AS:::agentClass@{ shape: processes, label: "fa:fa-robot Solution Architecture Agent" }
    AC:::agentClass@{ shape: processes, label: "fa:fa-robot C4 Agent" }
    DM:::artifactClass@{ shape: lin-cyl, label: "fa:fa-file 1 x Solution Architecture\nDocument" }
    DD:::artifactClass@{ shape: lin-cyl, label: "fa:fa-file 1 x C4 Context Diagram \n 2 x C4 Container Diagrams" }
    TL:::toolClass@{ shape: processes, label: "fa:fa-hammer Local Tool" }
    TM("fa:fa-hammer Mermaid MCP Tool"):::toolClass

    U--impresses-->B
    B--gives job back-->U
    U e0@--interacts-->AR
    AR e1@--> AS
    AR e2@--> AC
    AS e3@--writes--> DM
    AS --uses--> TL
    AC --uses--> TL
    AC --uses--> TM
    AC e4@--writes--> DD

    e0@{ animate: true }
    e1@{ animate: true }
    e2@{ animate: true }
    e3@{ animate: true }
    e4@{ animate: true }

    classDef userClass fill:#FFFFFF,stroke:#666666,color:#666666
    classDef toolClass fill:lightblue,stroke:blue,color:#666666
    classDef agentClass fill:lightgreen,stroke:green,color:#666666
    classDef artifactClass fill:#CCCCCC,stroke:#666666,color:#666666

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5,000-Foot View

1,000-Foot View

STEP 1: INSTALL PYTHON DEPENDENCIES

• Install Python dependencies using uv.

cd app/
uv venv
source .venv/bin/activate
uv sync

STEP 2: SETUP MODEL

Using Gemini in Vertex AI

• Log in to Google Cloud:

gcloud auth login && gcloud auth application-default login

• Create an .env file.

cp adk/.env.gemini.sample adk/.env

# replace `[PROJECT_ID]` in .env with your GCP project ID.

Using Ollama-Hosted Local Model

• Install and restart Ollama service:

brew install ollama
brew services restart ollama

• Pull a local model:

ollama pull qwen3:8b

• Create an .env file.

cp adk/.env.ollama.sample adk/.env

# Update MODEL in .env accordingly

STEP 3: RUN THE BAD BOY

• Run the web UI.

cd app/
uv run main.py  

• Navigate to http://127.0.0.1:8000 and run the agent.

STEP 4: ????

STEP 5: PROFIT!