Almost every agent project now claims to provide “long-term memory.”
For one project, that means embedding chat history. For another, it means maintaining a user profile. A third lets the model edit Markdown files. A fourth builds a bitemporal knowledge graph. All four use the word memory, but they are not the same system and should not be placed on one undifferentiated leaderboard.
To decide whether a system genuinely remembers, I would rather ask three questions:
Aliyun’s CAP has a piece on picking an inference engine that narrows the field to four: Ollama, vLLM, SGLang, and Hugging Face Pipeline. In 2024, that framing was fine.
By 2026, it’s missing half the map. NVIDIA’s TensorRT-LLM has completed its “PyTorch-ification,” SGLang became famous as the first open-source project to reproduce DeepSeek’s large-scale deployment, Hugging Face slapped a “maintenance mode” banner on TGI and told you to switch to vLLM — and the real throughline of the entire 2025 inference landscape can be summed up in one word: disaggregate.
OpenClaw’s vector retrieval silently failed — but BM25 text search kept the memory system running for two weeks unnoticed. Should you even bother fixing it? Here’s how I used NVIDIA’s free embedding API to complete the picture at zero cost.
OpenClaw’s daily-ai-news cron job kept timing out. The root cause: a missing absolute path in the SKILL.md caused the Agent to spend 15 exec calls searching for a tool every run. Messages 165→54, exec calls 44→7 — one file path beat any algorithm optimization.
The important change is not “two more tool features.” It is the movement of multi-step orchestration into a code-execution environment, with only a compact result returning to model context. Background: The Cost Problem in Agent Tool Calling # In traditional agent tool-calling, every tool invocation requires a full cycle of “model inference → tool execution → result return → model re-inference.” This seemingly natural loop breaks down at scale in three ways:
A full-chain production battle log: from startup failures and Feishu message silent drops to production stability — compaction safeguard, five-layer debugging, model-harness fit, and memory system comparison.
In April 2026, we migrated seo-project’s task queue from Celery to Temporal. We dropped exactly one dependency (celery), wrote 11 new files (src/infrastructure/temporal/), and renamed our containers from api/worker/beat to api/temporal_worker_blue/green with blue-green deployment. The most common question afterward: why not just keep using Celery? If it’s already running, what’s the point?
This article is the answer. It doesn’t come from documentation comparisons. It comes from production bugs we hit running Agent pipelines at scale.
Correction — July 29, 2026
The first version of this article combined three observations—a company name inside a promo code, a Stripe-hosted checkout, and community reposts—into a story that was too certain: Stripe’s partner network issued the codes and employees of those companies leaked them. The available evidence does not establish that chain.
Every Agent builder hits this question eventually: where do I store user data so the agent remembers it next session? Three approaches dominate the landscape: RAG (vector retrieval), LLM Wiki (structured knowledge injection), and plain-text context memory (the CLAUDE.md / Cursor Rules pattern). Each has vocal advocates. But picking wrong is expensive — do RAG too light and it’s a noise generator; do plain text too heavy and it’s a token incinerator.
Hugo shortcodes make it easy to embed live code demos. Here are three ways: The diagram first separates their boundaries: a native shortcode, a CodePen iframe, and a CodeSandbox app serve different complexity levels and execute code in different places.
Three embed paths: prefer a native shortcode for simple, versioned effects; CodePen for shareable snippets; and CodeSandbox for a complete interactive application. 1. Inline CSS Demo (No External Service) # A spinning loader animation, right in the article:
