AutoGen-Coder is an innovative application based on the AutoGen framework by Microsoft, designed to streamline the software development life cycle (SDLC) through multi-agent conversations. This tool automates the process of generating code and deployment templates from requirement or architecture documents.

• Multi-Agent Conversation: Engages users in a conversational manner, involving expert and critic personas at each stage of the SDLC.
• Comprehensive SDLC Stages: Includes Plan, Design, Develop, Integrate, and Deploy stages, each equipped with specific roles and functions.
• Automated Code and Template Generation: Transforms requirement and architecture documents into executable code and deployment templates.

1. To install dependencies, run the following command:

pip install -r requirements.txt

2. Create "notebook" directory
3. Create a file OAI_CONFIG_LIST (you can add additional models as required)

[
  {
    "model": "gpt-3.5-turbo",
    "api_key": "<KEY>"
  },
  {
    "model": "gpt-3.5-turbo-16k",
    "api_key": "<KEY>"
  }
]

3. In each SDLC/*.py file can modify

self.config_list = autogen.config_list_from_json(
    "notebook/OAI_CONFIG_LIST",
    filter_dict={
        "model": ["gpt-3.5-turbo-16k"]
    },
)

4. Setup your AWS access

5. Run the python script to start the model.

6. Enter exit to continue when the model prompts for feedback.

AutoGen-Coder is operated through a series of commands, corresponding to different stages of the SDLC:

1. Plan and Design Phase:

   • Engages Chief Product Officer and Product Manager for the Plan phase.
   • Involves Chief Technology Officer and Software Architect for the Design phase.

   main.py --config awslambda --workspace /path/to/dir --project-name name --task "develop a simple HelloWorld API"

   main.py --config awslambda --workspace /path/to/dir --project-name name --requirement-doc /path/to/word.docx

2. Direct Architecture Implementation:

   • This command expects a technical document and details the resources to be deployed.
   • Skips the Plan and Design stages.

   main.py --config awslambda --workspace /path/to/dir --project-name name --architecture-doc /path/to/architecture.md