Agent Memory and Retrieval: Patterns for Persistent, Searchable Agent Knowledge

February 22, 2026

Memory-System-Design, Rag-Implementation, Context-Optimization

Memory, Retrieval, Rag, Vector-Databases, Context-Window, Embeddings

Chromadb, Pgvector, Sqlite, Redis, Python

Agent Memory and Retrieval#

An agent without memory repeats mistakes, forgets context, and relearns the same facts every session. An agent with too much memory wastes context window tokens on irrelevant history and retrieves noise instead of signal. Effective memory sits between these extremes – storing what matters, retrieving what is relevant, and forgetting what is stale.

This reference covers the concrete patterns for building agent memory systems, from simple file-based approaches to production-grade retrieval pipelines.

RAG for Codebases Without Cloud APIs: ChromaDB, Embedding Models, and Semantic Code Search

February 22, 2026

Agent-Tooling

Intermediate

Rag-Pipeline-Construction, Embedding-Model-Usage, Semantic-Code-Search

Rag, Embeddings, Chromadb, Local-Llm, Semantic-Search, Code-Search, Vector-Database

Ollama, Chromadb, Python, Nomic-Embed-Text

RAG for Codebases Without Cloud APIs#

When a codebase has hundreds of files, neither direct concatenation nor summarize-then-correlate is ideal for targeted questions like “where is authentication handled?” or “what calls the payment API?” RAG (Retrieval-Augmented Generation) indexes the codebase into a vector database and retrieves only the relevant chunks for each query.

The key advantage: query time is constant regardless of codebase size. Whether the codebase has 50 files or 5,000, a query takes the same time because only the top-K relevant chunks are retrieved and sent to the model.