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layout default
title Chapter 1: Getting Started
nav_order 1
parent Devika Tutorial

Chapter 1: Getting Started

Welcome to Chapter 1: Getting Started. In this part of Devika Tutorial: Open-Source Autonomous AI Software Engineer, you will build an intuitive mental model first, then move into concrete implementation details and practical production tradeoffs.

This chapter walks through installing Devika, configuring API keys, and running a first autonomous coding task end-to-end.

Learning Goals

  • clone and install Devika with all required system dependencies
  • configure LLM provider credentials and environment variables
  • launch the Devika web UI and backend services
  • submit a first task and verify agent output in the workspace

Fast Start Checklist

  1. clone the repository and install Python and Node.js dependencies
  2. install Playwright browsers and Qdrant vector store
  3. set API keys in config.toml for at least one LLM provider
  4. start the backend and frontend servers, then submit a hello-world task

Source References

Summary

You now have a working Devika installation and have executed your first autonomous software engineering task from prompt to generated code.

Next: Chapter 2: Architecture and Agent Pipeline

Depth Expansion Playbook

Source Code Walkthrough

devika.py

The test_connect function in devika.py handles a key part of this chapter's functionality:

# initial socket
@socketio.on('socket_connect')
def test_connect(data):
    print("Socket connected :: ", data)
    emit_agent("socket_response", {"data": "Server Connected"})


@app.route("/api/data", methods=["GET"])
@route_logger(logger)
def data():
    project = manager.get_project_list()
    models = LLM().list_models()
    search_engines = ["Bing", "Google", "DuckDuckGo"]
    return jsonify({"projects": project, "models": models, "search_engines": search_engines})


@app.route("/api/messages", methods=["POST"])
def get_messages():
    data = request.json
    project_name = data.get("project_name")
    messages = manager.get_messages(project_name)
    return jsonify({"messages": messages})


# Main socket
@socketio.on('user-message')
def handle_message(data):
    logger.info(f"User message: {data}")
    message = data.get('message')
    base_model = data.get('base_model')
    project_name = data.get('project_name')
    search_engine = data.get('search_engine').lower()

This function is important because it defines how Devika Tutorial: Open-Source Autonomous AI Software Engineer implements the patterns covered in this chapter.

devika.py

The data function in devika.py handles a key part of this chapter's functionality:

# initial socket
@socketio.on('socket_connect')
def test_connect(data):
    print("Socket connected :: ", data)
    emit_agent("socket_response", {"data": "Server Connected"})


@app.route("/api/data", methods=["GET"])
@route_logger(logger)
def data():
    project = manager.get_project_list()
    models = LLM().list_models()
    search_engines = ["Bing", "Google", "DuckDuckGo"]
    return jsonify({"projects": project, "models": models, "search_engines": search_engines})


@app.route("/api/messages", methods=["POST"])
def get_messages():
    data = request.json
    project_name = data.get("project_name")
    messages = manager.get_messages(project_name)
    return jsonify({"messages": messages})


# Main socket
@socketio.on('user-message')
def handle_message(data):
    logger.info(f"User message: {data}")
    message = data.get('message')
    base_model = data.get('base_model')
    project_name = data.get('project_name')
    search_engine = data.get('search_engine').lower()

This function is important because it defines how Devika Tutorial: Open-Source Autonomous AI Software Engineer implements the patterns covered in this chapter.

devika.py

The get_messages function in devika.py handles a key part of this chapter's functionality:

@app.route("/api/messages", methods=["POST"])
def get_messages():
    data = request.json
    project_name = data.get("project_name")
    messages = manager.get_messages(project_name)
    return jsonify({"messages": messages})


# Main socket
@socketio.on('user-message')
def handle_message(data):
    logger.info(f"User message: {data}")
    message = data.get('message')
    base_model = data.get('base_model')
    project_name = data.get('project_name')
    search_engine = data.get('search_engine').lower()

    agent = Agent(base_model=base_model, search_engine=search_engine)

    state = AgentState.get_latest_state(project_name)
    if not state:
        thread = Thread(target=lambda: agent.execute(message, project_name))
        thread.start()
    else:
        if AgentState.is_agent_completed(project_name):
            thread = Thread(target=lambda: agent.subsequent_execute(message, project_name))
            thread.start()
        else:
            emit_agent("info", {"type": "warning", "message": "previous agent doesn't completed it's task."})
            last_state = AgentState.get_latest_state(project_name)
            if last_state["agent_is_active"] or not last_state["completed"]:

This function is important because it defines how Devika Tutorial: Open-Source Autonomous AI Software Engineer implements the patterns covered in this chapter.

devika.py

The handle_message function in devika.py handles a key part of this chapter's functionality:

# Main socket
@socketio.on('user-message')
def handle_message(data):
    logger.info(f"User message: {data}")
    message = data.get('message')
    base_model = data.get('base_model')
    project_name = data.get('project_name')
    search_engine = data.get('search_engine').lower()

    agent = Agent(base_model=base_model, search_engine=search_engine)

    state = AgentState.get_latest_state(project_name)
    if not state:
        thread = Thread(target=lambda: agent.execute(message, project_name))
        thread.start()
    else:
        if AgentState.is_agent_completed(project_name):
            thread = Thread(target=lambda: agent.subsequent_execute(message, project_name))
            thread.start()
        else:
            emit_agent("info", {"type": "warning", "message": "previous agent doesn't completed it's task."})
            last_state = AgentState.get_latest_state(project_name)
            if last_state["agent_is_active"] or not last_state["completed"]:
                thread = Thread(target=lambda: agent.execute(message, project_name))
                thread.start()
            else:
                thread = Thread(target=lambda: agent.subsequent_execute(message, project_name))
                thread.start()

@app.route("/api/is-agent-active", methods=["POST"])
@route_logger(logger)
def is_agent_active():

This function is important because it defines how Devika Tutorial: Open-Source Autonomous AI Software Engineer implements the patterns covered in this chapter.

How These Components Connect

flowchart TD
    A[test_connect]
    B[data]
    C[get_messages]
    D[handle_message]
    E[is_agent_active]
    A --> B
    B --> C
    C --> D
    D --> E
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