🤖 AI Agents Multi-agent, Context-Aware AI, Single-agent, MCP-based, browser agents, voice agents, image segmentation

📦 Ready-to-Run Examples Clone → install → run

⚙️ Developer Workflows Agent structure, tool integration, logging, evaluations, planning

🔎 RAG Systems Simple RAG → agent-driven retrieval → hybrid (Keyword+Vector) → local & private RAG

💬 Chat-with-Anything Apps GitHub, Gmail, PDFs, videos, research papers

What RAG Is Good At

• Grounding LLMs with private or fresh data
• Question answering over documents and code
• Reducing hallucinations with good retrieval

Where RAG Fails

• Bad chunking or embeddings
• Too much retrieved data causing noise
• No understanding of relationships
• Poor multi-step reasoning across documents

What Is Possible

• ✅ Fact lookup and summarization
• ⚠️ Complex reasoning needs structure
• ❌ Deep relational reasoning with vectors alone

I use Graph Databases when relationships matter.

• Entity and relationship modeling
• Multi-hop reasoning
• Graph-augmented RAG (Graph + Vector + Keyword)
• Long-term agent memory

Works well where vector-only RAG fails.

• Fine-tune Gemma, Llama, and other OSS models
• Know when fine-tuning is needed — and when not
• Combine fine-tuning with RAG and tools

• Local-first and cloud-native LLM apps
• Kubernetes-based deployments
• Scalable and reproducible agent systems

Common Failure Modes

• Wrong answers that sound correct
• Partial or broken reasoning
• Incorrect or unnecessary tool calls
• Missing steps in workflows
• No explicit error thrown

Output Quality

• Expected vs actual output
• Factual correctness
• Completeness of response

Reasoning & Planning

• Step-by-step reasoning quality
• Plan adherence in multi-step tasks
• Hallucinated or skipped steps

Tool Usage

• Correct tool selection
• Correct input schema
• Proper response handling
• Avoiding unnecessary calls

RAG Quality

• Retrieval relevance
• Context grounding
• Faithfulness to retrieved documents

Execution Reliability

• Task completion success/failure
• Partial success detection
• Retry and recovery behavior

Observability & Feedback

• Structured logging for agents
• Tracing agent decisions and tool calls
• Capturing silent failures
• Feedback loops for improvement

Minimal, Opinionated Starters Clean templates designed to get you from zero → running agent fast, without boilerplate or abstractions.

Advanced LLM Demonstrations Agentic RAG, hybrid search, Graph-based retrieval, long-running agents, failure-aware execution.

LLM Integration Patterns Practical patterns for OpenAI, local models, and OSS LLMs (Gemma, LLaMA) — prompt design, tool schemas, memory, context control.

Agent Reliability & Evaluation Measuring output quality, reasoning correctness, tool-calling accuracy, and retrieval faithfulness.

Scalable Project Structure Recommended folder layouts, logging strategies, tracing, and evaluation hooks so demos can evolve into production systems.

The goal is simple: help developers skip the noise and start building.