The Four Kinds of Agent Memory: Working, Episodic, Semantic, Procedural
Most teams buy one vector store and call it 'memory.' It solves exactly one of the four problems — which is why the agent still loses the thread and repeats yesterday's mistake.
5 min
Agent Memory and State
Nine repositories tackling the hardest unsolved problem in agent design — remembering, retrieving, and forgetting across the lifetime of a conversation.
4 min
Agent Memory vs RAG: What's Actually Different
Both embed a query and pull matching text into the prompt, so they look like the same trick. The difference is who writes the index — and that single fact moves the hard problem from retrieval to write discipline.
4 min
Three Places to Keep an Agent's Memory
The memory libraries aren't competing on accuracy. They're competing on geography — where the remembering happens relative to your agent's loop. Pick the place, not the benchmark.
4 min
Filesystem vs Vector Database for Agent Memory: Why 2026 Agents Write to Files
The year's quietest architecture shift is agents moving their memory out of vector stores and into plain files. It isn't that memory got better — it's that teams stopped using a retrieval tool for a state problem.
5 min
On-Device Vector Search for Agent Memory: sqlite-vec, ObjectBox, and Qdrant Edge
A hosted vector database is the right home for a shared knowledge base and the wrong home for one agent's private memory. Three embedded engines are quietly claiming the second half of the workload.
4 min
Mem0 vs Zep vs Letta: Choosing a Memory Layer for Your AI Agent
Three popular open-source memory frameworks that look like rivals but are actually three different bets on where memory lives — and how much of your architecture you hand over.
5 min
LangMem vs Mem0: Memory You Program vs Memory You Call
They get compared like rivals, but one is memory you program and the other is memory you call — and the benchmark leaderboard only measures one of them.
4 min
TeleMem vs Mem0: When a Drop-In Memory Layer Is Really a Different Bet
TeleMem ships as a one-line replacement for Mem0 — import telemem as mem0 — and claims a 16-point accuracy edge. Read where that number comes from and you learn exactly which agent it's for.
4 min
Why the Top Agent-Memory System on LongMemEval Stores Everything Verbatim — and Runs No LLM
MemPalace tops the long-term memory benchmark by refusing to summarize. That's not a trick — it's an argument that the field spent two years solving the wrong half of the problem.
5 min
Memora vs Wiki Memory: Two Bets on What Agent Memory Should Be
Microsoft and LangChain shipped agent-memory frameworks a day apart in June. They disagree on the one axis the benchmarks don't measure — whether you should be able to read what your agent remembers.
4 min
How AI Agents Decide What to Forget: Memory Consolidation in Mem0, Zep, and the Memory Tool
Every serious agent-memory system is really a forgetting system. The hard part was never storing what the agent learns — it's pruning the contradictions and stale facts that quietly poison retrieval.
5 min
What Anthropic's 'Dreaming' Does to Agent Memory — and Why a Bad Dream Doesn't Wash Out
Claude's new consolidation loop replays an agent's day and writes down what it learned. The same mechanism that lifted one customer's task completion ~6x is the one that makes a poisoned lesson permanent.
6 min
When Agent Memories Contradict: Don't Let the LLM Decide Which One Is Fresh
Your agent stores the same fact twice with different values. The intuitive fix — ask the model which is newer — is the one 2026's benchmarks say to avoid.
4 min
How to Evaluate AI Agent Memory: LoCoMo, LongMemEval, and Why Long Context Isn't Enough
Bigger context windows don't fix forgetting. The benchmarks that actually test agent memory — LoCoMo and LongMemEval — and what their question categories reveal about where it breaks.
4 min
How to Read an Agent-Memory Benchmark: The LoCoMo and LongMemEval Number Wars
Mem0 says 92.5% on LoCoMo. Mastra says 95% on LongMemEval. Zep corrected its own 84% to 58%. They can't all be right — and the baseline that beats them all is the one no vendor charts.
5 min
Agent Memory Benchmarks: LoCoMo vs LongMemEval vs BEAM
The benchmarks that grade an agent's memory just moved the finish line from 9,000 tokens to 10 million — and the new one proves a million-token context window doesn't buy you long-term memory.
4 min
Mem0 vs Zep vs Letta: Why Agent-Memory Benchmarks Don't Agree
The whole agent-memory leaderboard war — 84% vs 58% vs 75% — is being fought over a ten-conversation dataset called LOCOMO. Once you see how the numbers are made, you stop shopping on accuracy.
4 min
How Many Tokens Does an Agent Memory Layer Use? From 7K to 3.26M per Query
A June 2026 paper clocks three popular memory frameworks on the same benchmark: 118K, 632K, and 3.26M tokens per query. The 500x spread isn't noise — it's a design choice most teams never realize they're making.
5 min
Everyone Ships Agents. Almost No One Ships Memory.
The industry has standardized how agents reach out to the world and ignored the harder question of what they keep — and that asymmetry is not an accident.
5 min
Memory Stopped Being a Layer
The hard problem of agent memory was never remembering. It's knowing when a remembered fact has quietly stopped being true.
5 min