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66 articles on AI memory, agent engineering and production AI, newest first.
TencentDB Agent Memory, Read From the Source: It Needs a Second LLM and Searches by Keyword by Default
The guides and news pages that rank for TencentDB Agent Memory repeat three claims its source code does not support: that it builds memory without any model API, that it runs hybrid vector search out of the box, and that no conversation data leaves the machine.
Local Embedding Models in 2026: Which to Run, What Hardware They Need, and What They Cost
A local embedding model turns your text into vectors on hardware you control, so no document or query leaves your machine to get embedded.
Context Language Models: What the UW and Meta Paper Changes for Agent Builders, and What It Leaves to Memory
A Context Language Model is an existing model that rewrites its own context like a file. It manages working context inside a task better than summarisation, with less compute, and throws that context away when the task ends.
Harness And Agents
Every LLM call is stateless, and a model on its own cannot act; the software that turns it into an agent is the harness.
How to Add Conversational Memory to a LangChain App (2026 Guide)
To add conversational memory to a LangChain app today, pass a checkpointer to create_agent and reuse one thread_id per conversation; to remember a person across conversations, add a LangGraph store or plug in Maximem Synap through the maximem-synap-langchain package.
Short-Term vs Long-Term Memory in LLMs, and What "Context Memory" Actually Means
Short-term memory in an LLM is whatever sits in the context window on the current call: the system prompt, the recent conversation, tool results and any retrieved text, all re-sent by the application on every request and gone when the request ends.
LLM Context Windows in 2026: GPT-4o Is 128K, the Frontier Is 1M, and What Happens When a Chat Runs Out
GPT-4o has a 128,000-token context window and can write at most 16,384 tokens in a single reply, with the reply counted inside the same 128,000.
Best Embedding Models in 2026: Open-Source Picks, Semantic Search, and How to Choose for RAG
As of 26 September 2026, the best open-source embedding model for most teams is Qwen3-Embedding, which is Apache 2.0, reads 32K tokens and ranks 5th on MTEB English v2 and 4th on MTEB Multilingual v2.
How to Give an LLM Long-Term Memory, and Why RAG Alone Is Not It
You give an LLM long-term memory by adding a layer outside the model that decides what is worth keeping from each conversation, stores it against the right person, updates it when a fact changes, and puts only the relevant slice back into the prompt on the next call.
What Is RAG? How Retrieval-Augmented Generation Works, and Where It Stops
RAG, short for retrieval-augmented generation, is a technique that lets a large language model answer from information it was never trained on: when a question arrives, the application searches a knowledge source for the most relevant passages, places them in the prompt next to the question, and the model writes its answer from that material.
Knowledge Graph vs Vector Search for Agent Memory: When to Add a Graph, and Why Time Matters
For agent memory, neither wins on its own: vector search finds past statements that mean something close to the current question, and a knowledge graph connects facts through the same person or company and tells which of two conflicting facts is current.
What Is a Graph Database, and When Should You Use One?
A graph database stores data as nodes and relationships and answers questions by following those relationships instead of joining tables; use one when your main questions are about paths across connected data, and keep a relational database for records and reporting.
How to Reduce LLM Token Costs in Long Conversations: What Caching Saves, and Where It Stops
A long conversation costs far more than its number of turns suggests, because every request carries the system prompt and the entire history again, so total input grows with the square of the number of turns.
Claude API Pricing in 2026: Every Model per Million Tokens, and What Pro and Max Cost
The Claude API costs between $1 and $10 per million input tokens and between $5 and $50 per million output tokens on Anthropic's current models.
What is WebMCP? When and How to Use WebMCP in a Browser Agent?
The decision is per step, not per site: a page can expose a clean search tool and leave its account settings as ordinary DOM controls, so an agent that picks one mode per domain gets the worst of both.
Best Chatbot Memory Layer Plugins for Persistent User Data Retention and Memory
The best chatbot memory layer plugin captures durable user information, stores it with the right scope, retrieves only the relevant part of it, and updates or deletes it reliably.
The Best AI Agent Memory Layers in 2026, Ranked on Axes You Can Verify
No single AI agent memory layer is best in 2026. The right one depends on tenant isolation, contradiction handling, where you can run it, and what a retrieval costs, all of which you can check in an afternoon.
How To Use Jev In Your AI Agent: A Map Of Decision Seams
Jev cannot write a single word, which is the reason to care about it, so the question worth asking is never whether it should be your agent but which decisions inside your agent it should own.
AI Agent Evals: The Judge Is The Part Nobody Measures
An LLM judge scoring the same agent output three times will often hand back three different answers, and almost no guide to agent evaluation asks whether the judge agrees with itself or what it costs to run on everything rather than a sample.
Agent Memory Is a Real Problem. Most Builders Just Have Not Hit It Yet.
Every LLM call is stateless; agent frameworks solve that within a single run, which is why the memory problem only shows up once the run ends.
Best Claude Code Skills in 2026: 12 New Skills and What Changed
Important Claude Code skills shipped since February 2026, what each replaces, install commands, and the security problem that came with the marketplace.
MCP 2026-07-28: 20 Breaking Changes and the Errors They Cause
MCP 2026-07-28 removed sessions, the initialize handshake, and the ability for servers to initiate requests at all. It is wire-incompatible in both directions, so nothing breaks until a client upgrades underneath you. This is the full diff, every error you will hit with the fix for each, the HTTP+SSE deadline that two official sources disagree about, and a scorecard of what the release left alone.
Agentic Context Management: Agent Memory Is Not Merely a Storage & Retrieval Problem, It Is an Architecture Problem
We argue in our latest paper, that agent memory and cost is a lifecycle and architecture problem
Why OpenAI shipped an Official Plugin for Claude Code
OpenAI ships Codex plugin for ClaudeCode harness