bmasterai 0.2.3

A comprehensive Python framework for building multi-agent AI systems with advanced logging, monitoring, and integrations

BMasterAI • Agent Learning Lab

Learn how to build, instrument, and ship AI agents through polished, real-world examples that you can run end to end.

Need the deep technical reference? Jump to README.content.md.

BMasterAI reframes multi-agent development as a hands-on learning studio. Instead of burying features in raw engineering docs, we surface real-world playbooks, storytelling assets, and telemetry hooks that help you understand how production-grade agents come together.

Whether you're new to agents or leveling up a team, every asset is designed to teach best practices while you ship polished experiences.

Why Builders Choose BMasterAI

• Story-first blueprints – Each example pairs code with narrative context so you can see how an agent solves a real problem.
• Telemetry-ready agents – Track outcomes, reasoning, and costs out of the box to learn how production monitoring is done.
• Enterprise-ready launchpad – From laptop experiments to Kubernetes rollouts, the same agents scale without rework.

Pick Your Learning Track

• Start Fast → docs/getting-started.md, lessons/ walkthroughs, and examples/basic_usage.py get a chatbot live in minutes.
• Build Skills → follow the lessons/ workshops and remix prompts inside examples/ folders to learn agent patterns hands-on.
• Explore Scenarios → the examples/ catalog curates industry narratives (finance, real estate, growth) to study end-to-end flows.
• Scale the Story → k8s/, helm/, and telemetry packages show how to operate agents in real deployments.

Featured Agent Playbooks

• Launch in Minutes – examples/basic_usage.py, examples/minimal-rag/, examples/enhanced_examples.py
• Industry Spotlights – examples/ai-real-estate-agent-team/, examples/ai-stock-research-agent/, examples/ai-sports_betting_analysis/
• Executive Insights – examples/ai-stress-linkedin-reasoning/, examples/reasoning_logging_example.py
• Interactive Launch Assets – examples/gemini-reasoning-streamlit/, examples/mcp-github-streamlit/, examples/streamlit-app/
• Operations & Telemetry – examples/kubernetes-telemetry/, examples/agno-telemetry/, examples/openclaw-telemetry/ (OpenClaw AI agent observability), bmasterai_telemetry/

Use these playbooks to study prompts, orchestration patterns, and telemetry practices. Rebuild them locally, experiment with your own data, then adapt the flows to your projects when you're ready.

Getting Started

# Set up a local studio
git clone https://github.com/travis-burmaster/bmasterai.git
cd bmasterai
python3 -m venv .venv && source .venv/bin/activate
pip install -e .[dev]

# Run your first agent
python examples/basic_usage.py

Add telemetry (optional): pytest --cov=src/bmasterai, python examples/reasoning_logging_example.py, or stream data into the dashboards under examples/kubernetes-telemetry/.

Build, Measure, Scale

• Document what you learn – Update docs/ with runbooks, troubleshooting notes, and walkthroughs for future learners.
• Instrument outcomes – Use src/bmasterai/logging.py and bmasterai_telemetry/ to capture success metrics and decision trails.
• Deploy with confidence – Follow README-k8s.md and docs/kubernetes-deployment.md to graduate from demo to production.

Share What You Discover

We welcome new tutorials, walkthroughs, and refined playbooks. Open a PR with:

• A clear learning objective for the agent or workflow
• Screenshots, Looms, or Streamlit share links that reinforce the lesson
• Lessons learned so others can replicate (or extend) your approach

Let’s build the go-to showcase for learning AI agent excellence—one playbook at a time.