✨ A comprehensive course exploring context engineering using Redis and LangChain by building a progressively more complex agent ✨
📢 Note: This course is currently in Development. Additional components and stages will be released in the near future, so please check back regularly for updates. This course will eventually be migrated as a lab to Redis University.
| Technology | Purpose |
|---|---|
| Redis | Vector storage, semantic search, caching |
| RedisVL | Vector search library with FilterQuery |
| LangGraph | Stateful agent workflows |
| LangChain | LLM application framework |
| LangSmith | Agent Observability platform |
| 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/>NER + Hybrid Search]
S4 --> S4R[Stage 4R: <br/>Upgrade to ReAct Agent]
S4R --> S5[Stage 5<br/>Working Memory]
S5 --> S6[Stage 6<br/>Long-term Memory]
| Stage | Key Feature | Overview | Status |
|---|---|---|---|
| Intro | Getting Acquainted | An overview of context engineering | Available |
| 0 | System Context | Constructing effective system prompts | Coming Soon |
| 1 | Baseline RAG | Exploring a basic RAG that consumes Raw JSON context | Agent Code Avaliable, Notebook Coming Soon |
| 2 | Context Engineering | Context engineered RAG with 50% less token usage | Agent Code Avaliable, Notebook Coming Soon |
| 3 | Full Agent | A full LangGraph-based agent with intent classification, quality and eval | Coming Soon |
| 4 | Hybrid Search | Adding NER + FilterQuery for exact course codes | Coming Soon |
| 4R | + ReAct | Visible reasoning trace | Coming Soon |
| 5 | Working Memory | Session-based conversation history | Coming Soon |
| 6 | Long-term Memory | Complete agent: memory + reasoning + tools | Coming Soon |
To begin exploring context engineering with Redis and LangChain:
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Prerequisites: You'll need an OpenAI API key. Get one at platform.openai.com.
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Start with the first notebook: Start with the first notebook (01) in the
section-1-context-engineering-fundamentalsdirectory.