Context Engineering
Deciding what goes in the context window and what belongs in memory — retrieval, compression, prompting and token spend.
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How to Reduce LLM Token Costs: The Persistent Memory Approach
Most LLM token costs come from re-sending conversation history. Here's how persistent memory cuts that by 60-90% with token counts and working code.
8 min read
Loop Engineering for AI Agents: Memory-First Design
Learn what loop engineering is, token-rich vs token-poor loops, and how Mem0 solves core memory challenges for production AI agents.
10 min read
How to Build Context Queries for AI Agents with Mem0
Learn how context queries power production AI agents, patterns for retrieval, limitations, and how Mem0 provides durable, queryable memory for agents.
12 min read
Context Engineering for AI Agents: How to Route Queries to Memory
Learn how to detect context queries in AI agents, route them to memory, and integrate Mem0 for reliable retrieval, storage, and personalization.
14 min read
Context Engineering in Multi-Turn AI Agents
Context engineering keeps AI agents coherent across long conversations. Learn sliding window, summarization, and memory-augmented context strategies.
12 min read
How To Reduce Context Cost With Smart Context Construction
Context window costs compound fast in multi-turn agents. Learn smart context construction techniques to reduce token usage without losing relevant context.
9 min read
Context Compression vs Memory in AI Agents
Context compression shrinks what is in the window. Memory stores what is worth keeping long-term. Learn how both techniques work and when to use each.
7 min read
Agent Memory Staleness: How Recency-Aware Ranking Fixes Retrieval Drift
Long-running agents surface stale memories because retrieval ignores recency. Mem0 Memory Decay fixes this: real A/B results, 0.15 score gap, copy-paste harness.
16 min read
Memory Retrieval Strategies for AI Agents
The multiple retrieval strategies for AI agent memory, their tradeoffs, failure modes, and how to pick one for your usecase
11 min read
Memory vs Context Window for LLM and AI Agents | Mem0
Explore the differences between context windows and persistent memory, common AI agent failure modes, and best practices for building production-ready agents.
15 min read
Context Window vs Persistent Memory: Why 1M Tokens Isn't Enough
A 1M context window sounds like a lot. Here's why persistent memory beats context-stuffing for production AI agents in the real world.
12 min read
What Is Agentic RAG? How It Works and When to Use It
Agentic RAG adds autonomous AI agents to traditional RAG pipelines, enabling multi-step planning, validation, and tool use. Learn how it works, when to use it, and what tradeoffs to expect in production.
14 min read
Context Engineering AI: How To Build Smarter LLM Agents In 2026
Context engineering AI helps teams build smarter agents in 2026. Learn what context engineering is and apply context engineering for AI agents with LLM best practices
12 min read
Agentic RAG vs Traditional RAG: Complete Guide
Learn how agentic RAG systems with intelligent memory outperform traditional RAG by 26% accuracy and 90% fewer tokens. Complete implementation guide for December 2025.
8 min read
LLM Summarization Techniques For Managing Chat History 2026
LLM summarization techniques enable compression of long chat history token loads. Apply LLM context management to keep AI context accurate and cost efficient
10 min read