✨ A comprehensive course exploring context engineering using Redis and LangChain by building a progressively more complex agent ✨
| Technology | Purpose |
|---|---|
| Redis | Vector storage, semantic search, caching |
| RedisVL | Vector search library with FilterQuery |
| LangGraph | Stateful agent workflows |
| LangChain | LLM application framework |
| Redis Agent Memory Server | Working and long-term memory management for agents |
| OpenAI | Language model for reasoning |
The progressive_agents/ directory contains a learning path from basic RAG to production-ready agents:
graph LR
S0[Stage 0: <br/>System Context] -->
S1[Stage 1: <br/>Baseline RAG] --> S2[Stage 2: <br/>Context Engineered RAG]
S2 --> S3[Stage 3: <br/>From RAG to Agent]
S3 --> S4[Stage 4: <br/>React Agent + Hybrid Search]
S4 --> S5[Stage 5<br/>Working Memory]
S5 --> S6[Stage 6<br/>Long-term Memory]
| Stage | Key Feature | Overview |
|---|---|---|
| Intro | Getting Acquainted | An overview of context engineering |
| 0 | System Context | Constructing effective system prompts |
| 1 | Baseline RAG | Exploring a basic RAG that consumes Raw JSON context |
| 2 | Data Engineering | Data engineered RAG with 50% less token usage |
| 3 | Full Agent | A full LangGraph-based agent with intent classification, quality and eval |
| 4 | Hybrid Search + ReAct | Visible reasoning trace and hybrid search |
| 5 | Working Memory | Session-based conversation history |
| 6 | Long-term Memory | Complete agent: memory + reasoning + tools |
