WisLLM

LLM Agentic Development Team, designed to support business-as-code platforms, services, and data.

MVP Goals

• Develop comprehensive requirements documentation for a project.
• Copilot instructons for each LLM/Agent should be generated from the requirements.
• Write the initial stories necesary to implement the project.
• Develop a plan and priorities to tackle the project in an agile manner.
• Scrum Master LLM should be able to delegate tasks to the appropriate agents.
• Each LLM developer should focus on their specific expertise and delivery only on the next iterative task

Design Philosophy

• Locally Capable: Each agent and LLM should be capable of running on local computers.
• Energy Efficiency: The system should be designed to minimize energy consumption, allowing for efficient operation on local hardware.
• Feedback Required: Every agent should demand and require feedback from one another or the user.
• Agentic: Agents should be able to operate independently, but also collaborate when necessary.
• Collaborative: Agents should be able to work together to achieve a common goal.
• Agile Development: The system should be designed to support agile development practices, allowing for rapid iteration and improvement.
• Complexity: LLM Context use-cases and windows should be minimized to reduce complexity and improve performance.
• Modular: The system should be modular, allowing for easy integration or swapping of new agents and services.

WisLLM Design Architecture

flowchart TD
    subgraph Users
        User[Business Users]
    end

    subgraph Design[Design LLMs]
        Scrum[Scrum Master]
        Architect[Solutions Architect]
        DataScientist[Data Scientist]
        Researcher[Requirements Researcher]
    end

    subgraph Collab[Collaboration Protocols]
        LangChain[LangChain State Management]
    end

    subgraph Dev[Developer LLMs]
        UXUI[UX/UI Dev]
        Backend[Backend Developer]
        Data[Data Engineer]
    end

    subgraph Ops[Operations LLMs]
        DevSecOps[DevSecOps Developer]
        Infrastructure[Infrastructure Automator]
        Reporter[Reporting Operator]
    end

    subgraph Int[Interface Protocols]
        WisLLM[WisLLM API]
    end

    User <--> Scrum
    User <--> Architect

    Architect <--> Scrum
    Architect <--> DataScientist
    
    Architect <--> Researcher
    DataScientist <--> Researcher

    Design <--> Collab
    Collab <--> Dev
    Collab <--> Ops

    Dev --> Int
    Ops --> Int

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Folder Structure

WisLLM/
├── pyproject.toml                    # Root workspace configuration
├── shared/                           # Shared libraries
│   ├── pyproject.toml
│   └── shared/
│       ├── state/                    # State management
│       │   ├── schemas.py           # Pydantic schemas
│       │   ├── store.py             # State store interface
│       │   └── sync.py              # Synchronization
│       ├── tools/                    # Common tools
│       ├── agents/                   # Base agent classes
│       │   └── base_agent.py
│       └── comms/                    # Inter-agent communication
│           └── handoffs.py
│
├── agents/                           # Individual agents
│   ├── supervisor/
│   │   ├── pyproject.toml
│   │   ├── langgraph.json
│   │   └── supervisor_agent/
│   │       ├── graph.py
│   │       ├── nodes.py
│   │       └── prompts.py
│   ├── research_agent/
│   ├── data_analyst/
│   ├── content_creator/
│   ├── code_reviewer/
│   ├── qa_agent/
│   ├── security_agent/
│   ├── performance_agent/
│   ├── integration_agent/
│   └── notification_agent/
│
└── infrastructure/
    ├── docker/
    └── kubernetes/

TODO

• Update Data Team Roles (Data Scientist, Data Architect, Data Warehousing)
• Fix Folder Structure
• Add LangGraph Studio integration
• Create LangGraph for Supervisor Agent
• Debug Dockerfile and Docker Compose

Getting Started

uv sync
uv run main.py

%pip install --upgrade --quiet vllm -q

nanonets/Nanonets-OCR-s

Notes

API: http://localhost:2024

Docs: http://localhost:2024/docs

LangGraph Studio Web UI: https://smith.langchain.com/studio/?baseUrl=http://127.0.0.1:2024

https://github.com/langchain-ai/langgraph-studio

https://langchain-ai.github.io/langgraph/cloud/how-tos/studio/quick_start/

https://github.com/langchain-ai/langgraph

https://langchain-ai.github.io/langgraph/cloud/deployment/setup_pyproject/#specify-dependencies

https://langchain-ai.github.io/langgraph/reference/supervisor/

https://langchain-ai.github.io/langgraph/reference/agents/

https://langchain-ai.github.io/langgraph/reference/graphs/

https://langchain-ai.github.io/langgraph/cloud/reference/cli/

https://langchain-ai.github.io/langgraph/cloud/deployment/setup_pyproject/#specify-environment-variables

https://docs.smith.langchain.com/

https://langchain-ai.github.io/langgraph/tutorials/rag/langgraph_agentic_rag/