LLM Agentic Development Team, designed to support business-as-code platforms, services, and data.
- 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
- 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.
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[Software Development LLMs]
UXUI[UX/UI Engineer]
Backend[Backend Engineer]
Data[Data Engineer]
end
subgraph Ops[Operations LLMs]
DevSecOps[DevSecOps Engineer]
Infrastructure[Automation Engineer]
Reporter[Reporting Analyst]
end
subgraph Int[Interface Protocols]
WisLLM[WisLLM API]
end
User <--> Scrum
User <--> Architect
User <--> DataScientist
Architect <--> Scrum
Architect <--> DataScientist
Architect <--> Researcher
DataScientist <--> Scrum
DataScientist <--> Researcher
Design <--> Collab
Collab <--> Dev
Collab <--> Ops
Dev --> Int
Ops --> Int
These LLMs are currently targeted for MVP deployment, and are subject to change based on evolving projects needs, technology advancements, and team feedback.
-
Reasoning & Architecture Agents: Manages the project, prioritizes tasks, and ensures smooth collaboration between agents.
- NVIDIA Nemotron-H family. The 2/4.8/9bn hybrid Mamba-Transformer models achieve instruction following and code-generation accuracy comparable to dense 30bn LLMs of the same generation at an order-of-magnitude fraction of the inference FLOPs.
- Microsoft Phi series.
- https://huggingface.co/microsoft/Phi-3.5-mini-instruct - Solutions Architect/Scrum Master
- https://huggingface.co/microsoft/Phi-4-mini-reasoning - Data Scientist
-
Code & Data Development Agents: Develops and maintains the project codebase, ensuring high-quality software development practices.
- Falcon-3-10B-1.58bit-Instruct - https://huggingface.co/tiiuae/Falcon3-10B-Instruct-1.58bit
-
Operations Engineering Agents:
- Salesforce xLAM-2-8B. The 8bn model achieves state-of-the-art performance on tool calling despite is relatively modest size, surpassing frontier models like GPT-4o and Claude 3.5.
- https://arxiv.org/pdf/2506.02153 - Small Language Models are the Future of Agentic AI
- https://arxiv.org/pdf/2402.17764 -
- https://github.com/microsoft/BitNet/blob/main/gpu/README.md - BitNet GPU Inference
- https://arxiv.org/abs/2504.12285 - BitNet Technical Report
- https://llm-stats.com/
WisLLM/
├── pyproject.toml # Root workspace configuration
├── scripts/
│ ├── build.sh # Build script for the project
│ ├── deploy.sh # Deployment script
│ └── run-local.sh # Run script for the project
├── src/
│ ├── __init__.py # Package initialization
│ ├── main.py
│ ├── agents/ # Main entry point for the applicatio
│ | ├── scrum_master/ # Scrum Master Agent
│ | │ ├── langgraph.json
│ | | ├── graph.py
│ | | ├── nodes.py
│ | | └── prompts.py
│ | ├── researcher/ # Requirements Researcher Agent
│ | ├── data_engineer/ # Data Engineer Agent
│ | ├── data_scientist/ # Data Scientist Agent
│ | ├── uiux_engineer/ # UX/UI Engineer Agent
│ | ├── devsecops_engineer/ # DevSecOps Engineer Agent
│ | ├── automation_engineer/ # Automation Engineer Agent
│ | ├── architect/ # Solutions Architect Agent
│ | ├── report_analyst/
│ | └── notification_agent/
│ └── shared/ # Shared libraries for all agents
│ ├── state/ # State management
│ │ ├── schemas.py # Pydantic schemas
│ │ ├── store.py # State store interface
│ │ └── sync.py # Synchronization
│ ├── tools/ # Common tools used by the agent
│ │ ├── cli/ # Command-line interface tools
│ │ ├── chat/ # Chat interface
│ │ └── networking/ # Network interface (services/integration?)
│ ├── agents/ # Base agent classes
│ │ └── base_agent.py
│ └── comms/ # Inter-agent communication
│ └── handoffs.py
│
└── infrastructure/
├── docker/
└── kubernetes/
- Update Data Team Roles (Data Scientist, Data Architect, Data Warehousing)
- Fix Folder Structure
- Add LangGraph Studio integration
- Create LangGraph for Scrum Agent
- Debug Dockerfile and Docker Compose
uv sync
uv run main.py%pip install --upgrade --quiet vllm -q
nanonets/Nanonets-OCR-s
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://docs.smith.langchain.com/
https://langchain-ai.github.io/langgraph/tutorials/rag/langgraph_agentic_rag/