Instructions to use tflux2011/contextual-engineering-framework with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use tflux2011/contextual-engineering-framework with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-generation", model="tflux2011/contextual-engineering-framework")# Load model directly from transformers import AutoModel model = AutoModel.from_pretrained("tflux2011/contextual-engineering-framework", dtype="auto") - Notebooks
- Google Colab
- Kaggle
- Local Apps
- vLLM
How to use tflux2011/contextual-engineering-framework with vLLM:
Install from pip and serve model
# Install vLLM from pip: pip install vllm # Start the vLLM server: vllm serve "tflux2011/contextual-engineering-framework" # Call the server using curl (OpenAI-compatible API): curl -X POST "http://localhost:8000/v1/completions" \ -H "Content-Type: application/json" \ --data '{ "model": "tflux2011/contextual-engineering-framework", "prompt": "Once upon a time,", "max_tokens": 512, "temperature": 0.5 }'Use Docker
docker model run hf.co/tflux2011/contextual-engineering-framework
- SGLang
How to use tflux2011/contextual-engineering-framework with SGLang:
Install from pip and serve model
# Install SGLang from pip: pip install sglang # Start the SGLang server: python3 -m sglang.launch_server \ --model-path "tflux2011/contextual-engineering-framework" \ --host 0.0.0.0 \ --port 30000 # Call the server using curl (OpenAI-compatible API): curl -X POST "http://localhost:30000/v1/completions" \ -H "Content-Type: application/json" \ --data '{ "model": "tflux2011/contextual-engineering-framework", "prompt": "Once upon a time,", "max_tokens": 512, "temperature": 0.5 }'Use Docker images
docker run --gpus all \ --shm-size 32g \ -p 30000:30000 \ -v ~/.cache/huggingface:/root/.cache/huggingface \ --env "HF_TOKEN=<secret>" \ --ipc=host \ lmsysorg/sglang:latest \ python3 -m sglang.launch_server \ --model-path "tflux2011/contextual-engineering-framework" \ --host 0.0.0.0 \ --port 30000 # Call the server using curl (OpenAI-compatible API): curl -X POST "http://localhost:30000/v1/completions" \ -H "Content-Type: application/json" \ --data '{ "model": "tflux2011/contextual-engineering-framework", "prompt": "Once upon a time,", "max_tokens": 512, "temperature": 0.5 }' - Docker Model Runner
How to use tflux2011/contextual-engineering-framework with Docker Model Runner:
docker model run hf.co/tflux2011/contextual-engineering-framework
Use Docker images
docker run --gpus all \
--shm-size 32g \
-p 30000:30000 \
-v ~/.cache/huggingface:/root/.cache/huggingface \
--env "HF_TOKEN=<secret>" \
--ipc=host \
lmsysorg/sglang:latest \
python3 -m sglang.launch_server \
--model-path "tflux2011/contextual-engineering-framework" \
--host 0.0.0.0 \
--port 30000# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:30000/v1/completions" \
-H "Content-Type: application/json" \
--data '{
"model": "tflux2011/contextual-engineering-framework",
"prompt": "Once upon a time,",
"max_tokens": 512,
"temperature": 0.5
}'Contextual Engineering Patterns: Architecting Adaptable AI Agents
Reference implementations for the architectural patterns defined in the book "Contextual Engineering: Architecting Adaptable AI Agents for the Real World" by Tobi Lekan Adeosun.
π Overview
Standard AI agents are designed for the "Abundance Baseline" of Silicon Valleyβperfect internet, unlimited power, and institutional trust. When deployed in the Global South, these agents fail due to the "Agentic Gap" between their reasoning capabilities and environmental realities.
This repository contains the Python reference implementations for the three core adaptation layers introduced in the book:
- Infrastructure Adapter: Handling offline states and compute scarcity.
- Cultural Adapter: Managing semantic drift and high-context communication.
- Safety Adapter: Enforcing constitutional guardrails and Human-in-the-Loop (HITL) workflows.
β‘ Quick Start (Hybrid Router)
How to use the Infrastructure Adapter to route traffic based on connectivity:
from src.infrastructure.inference_router import HybridRouter
# Initialize router with cost/latency preferences
router = HybridRouter(preference="economy", offline_fallback=True)
# The router automatically checks network status (N(t))
model_choice = router.select_model(
prompt="Summarize this contract",
complexity_score=0.85
)
print(f"Routing to: {model_choice}")
# Output: "Llama-3-8B-Local" (if offline) or "GPT-4o" (if online)
## π Repository Structure
The code is organized by the "Adapter Layer" it serves, matching the chapters of the manuscript.
```text
βββ src
β βββ infrastructure
β β βββ sync_manager.py # (Chapter 3) The "Sync-Later" Architecture & Offline Queue
β β βββ inference_router.py # (Chapter 4) The Hybrid Router (Local vs. Cloud)
β βββ safety
β β βββ sentinel.py # (Chapter 9) Constitutional Safety Checks & Kill Switches
β β βββ escalation_ladder.py # (Chapter 10) Human-in-the-Loop Risk Evaluation Logic
β βββ culture
β βββ context_injector.py # (Chapter 6) Dynamic Few-Shot Prompting logic
βββ README.md
## Citation
If you use this framework in your research, please cite the associated whitepaper:
```bibtex
@article{adeosun2026contextual,
title={Contextual Engineering: Architectural Patterns for Resilient AI Agents},
author={Adeosun, Tobi},
journal={AfricArXiv},
year={2026},
url={[https://osf.io/preprints/africarxiv/](https://osf.io/preprints/africarxiv/)[YOUR_HANDLE]}
}
Install from pip and serve model
# Install SGLang from pip: pip install sglang# Start the SGLang server: python3 -m sglang.launch_server \ --model-path "tflux2011/contextual-engineering-framework" \ --host 0.0.0.0 \ --port 30000# Call the server using curl (OpenAI-compatible API): curl -X POST "http://localhost:30000/v1/completions" \ -H "Content-Type: application/json" \ --data '{ "model": "tflux2011/contextual-engineering-framework", "prompt": "Once upon a time,", "max_tokens": 512, "temperature": 0.5 }'