AI memory and reasoning systems enable large language models and agents to retain information across interactions and solve complex problems through structured thought processes. Memory systems range from short-term conversation buffers to persistent knowledge graphs, while reasoning techniques guide models through step-by-step problem decomposition, verification, and refinement. In 2026, context engineering has emerged as the overarching discipline for managing what information lives in the context window at each step, underpinning everything from multi-million-token long-context models to agentic memory architectures. Understanding the interplay between memory architecture, reasoning strategy, and efficient context management is critical for building production agents that maintain context, reduce hallucinations, and execute multi-step workflows reliably.
What This Cheat Sheet Covers
This topic spans 15 focused tables and 109 indexed concepts. Below is a complete table-by-table outline of this topic, spanning foundational concepts through advanced details.
Table 1: Memory Types and Architecture
| Type | Example | Description |
|---|---|---|
messages = [{"role": "user", "content": "Hi"}, {"role": "assistant", "content": "Hello"}] | • Stores recent conversation turns within the current session • limited by context window size and discarded when the session ends. | |
vector_db.store(embedding, metadata, user_id) | • Persists knowledge across sessions using external storage • retrieved dynamically based on relevance rather than loaded wholesale. | |
{"timestamp": "2026-04-01", "event": "user asked about X", "outcome": "provided Y"} | • Records specific events and interactions with temporal context • allows the agent to recall what happened when and learn from past experiences. | |
knowledge_graph.add_fact("Paris", "capital_of", "France") | • Stores general facts and relationships independent of when they were learned • declarative knowledge without time markers. | |
workflow = ["analyze_input", "generate_plan", "execute_steps", "verify_output"] | • Encodes learned skills, workflows, and action sequences • guides how to perform tasks rather than storing facts, often implemented as tool patterns or code. | |
results = vector_db.search(query_embedding, top_k=5) | • Embeds memories as vectors • enables semantic similarity search to retrieve contextually relevant information from large memory stores. | |
graph.add_edge("user_123", "prefers", "dark_mode") | • Represents knowledge as entities and relationships in a graph structure • captures complex associations and multi-hop reasoning paths. |