The LibraryField notes on AI agent memory
Evidence-first deep-dives on evaluation, context engineering, and the science of how AI agents remember.
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Puts named products or approaches side by side against the same question, so a reader can choose. Needs at least two named things and one shared criterion.Explains how something works or what it is, with worked examples. One system may be the subject; it is not being weighed against another.What we ran, measured or hit ourselves, with the numbers and the conditions. First-hand evidence, not a survey of other people's claims.Source-backed deep dive into the literature or the mathematics, with citations. Argues from published work rather than from a product surface.
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All 113 articles
What cross-IDE persistent context platforms are available for engineering teams?
Three kinds answer it: shared memory stores a team writes to, code and company context served over MCP, and team rules rendered into each tool's files.
ReadAgent Memory
What cross-IDE persistent context platforms are available for engineering teams?
Three kinds answer it: shared memory stores a team writes to, code and company context served over MCP, and team rules rendered into each tool's files.
What options exist for connecting an AI agent memory layer across both ChatGPT web interfaces and localized coding environments?
Hosted remote MCP, a browser extension, a tunnel to a local server, or OpenAI's own bridges: how one memory layer reaches ChatGPT on the web and coding tools.
AI assistant keeps giving outdated answers after codebase refactoring, how to update its learned knowledge?
After a refactor the code moved and the memory did not. Which coding assistants tie a note to code, check it before use, or re-read instead, per their docs.
Video, 14:50
Explain how an importance gate prevents noise accumulation in persistent agent memories
How an agent memory write gate filters noise and duplicate context: what six systems test on the write path, and what a refused write becomes.
How to handle continuous context retention for autonomous software engineering bots?
How AI agents catch up on memory they missed: a queryless newest-first feed, cursor paging while others write, and empty answers that name their scope.
Video, 14:48
How can I implement long-term associative memory in Python for autonomous agents?
Long-term associative memory in Python: run a standard-library example, compare six memory APIs, and test persistence, expansion, and feedback.
Secrets & Trust
Shared Memory Poisoning: One Bad Write, Many Agents
Shared memory poisoning can expose many agents to one bad write. Use a three-trust matrix and consumer-side hygiene to limit the risk.
How Do AI Agents Trust Each Other? A2A, OAuth and mTLS
A2A and MCP hand identity to OAuth, OIDC and mTLS. A signed Agent Card proves origin, not trust; RFC 8693 scopes delegation; prompt injection stays open.
Least Privilege for AI Agents
Least privilege bounds what a tricked AI agent can do — not whether it's tricked: authz policy, JIT tokens, and the confused-deputy ceiling it can't cross.
Prompt Injection Is a Credential-Exfiltration Attack
Prompt injection is a 3-stage credential kill chain: injection lands, the agent reads a secret, it leaves via an allowed channel. Three defenses matter.
The Trust-Model Spectrum for AI Agent Secrets
Six rungs of protecting an AI agent's secret, weakest to strongest—each defeats a different threat, but none stops a tricked agent misusing what it unlocks.
Credential the LLM Never Sees for MCP Tools
Credential the LLM never sees: resolve secrets below the model, inject them on the wire, and account for MCP, logs, and confused deputies.
Memory Science
Continual learning algorithms: EWC, GEM, iCaRL
Continual learning algorithms explained: what EWC, GEM, A-GEM and iCaRL actually fixed about catastrophic forgetting, and what transfers to AI agent memory.
Why Your AI Agent Repeats the Same Mistakes
AI coding agents repeat corrected mistakes because nothing records whether past suggestions worked. Outcome memory attaches results to recalled facts.
How to implement context decay so an AI agent forgets stale or incorrect instructions over time?
Vector stores return deprecated APIs and dead conventions forever. Closing, decay, and suppression keep agent memory current without destroying history.
Attention as a Hopfield Network
Transformer attention is equivalent to one update step of a modern Hopfield network under specific query, key, value substitutions.
Memory, From DRAM to Agents: One Word, Twelve Worlds
Types of memory in computing, mapped from DRAM and virtual memory to KV caches, model weights, context windows, and persistent agent memory.
Transactive Memory: Who Remembers What in a Team
Transactive memory divides the labor of remembering across a group — a directory of who knows what — mapped, with its limits, to multi-agent AI memory.
Benchmark Wars
How Are AI Code Review Tools Benchmarked, and Can You Trust the Results?
AI code review benchmarks explained: answer keys, LLM judges, counting rules and live leaderboards, and what to ask before trusting a #1 claim.
RAGAS Metrics Explained: Reference-Free RAG Evaluation
RAGAS metrics explained: faithfulness, response relevancy, context precision and recall. Two of the six text RAG metrics need no reference; none spans sessions.
BLEU vs ROUGE vs F1 vs SARI: Pick the Right Metric
BLEU, ROUGE, F1, accuracy, and SARI explained by metric question, Evaluate I/O shape, failure mode, and routing.
Can You Trust an LLM Judge? A Field Manual
LLM-as-judge reliability explained: assess position bias, verbosity, self-preference, rubrics, retrieval recall, and benchmark comparability.
AI Memory Benchmarks: A Field Guide
A map of how AI-agent memory is actually measured — LoCoMo, LongMemEval, BEAM, long-context tests — what each checks, what it misses, and which to trust.
LLM-as-Judge Variance in AI Memory Benchmarks
Swap only the grading prompt on the same answers and an AI-memory benchmark score moves ~40 points — you can't trust a leaderboard without the recipe.
Context Builder & Orchestration
Video, 16:37
Why does my vector DB retrieval keep returning duplicate noise for my chat agent, and how to fix it?
Duplicate results in vector search have three causes: look-alikes, one document's chunks, one fact stored twice. The setting that fixes each, store by store.
Video, 19:02
Cursor Memory Bank: What Actually Loads
The canonical Cursor Memory Bank ships fifty five rule files. Exactly one of them is guaranteed to reach a session, and it tells the agent where to write, not what to read. Checked against the repository, the vendor's own pages and the forum threads where it fails.
Memory Coherence in MCP: What Happens When Two Clients Write
MCP defines no ordering, conflict detection or merge semantics for memory. Coherence is a property each server chooses, and shipped servers choose differently.
Video, 13:45
AGENTS.md: What Supported Actually Means, Tool by Tool
The format has no required fields and one sentence about precedence. Seven tools that list themselves as supporting it describe seven different mechanisms, and one of them says outright that a closer file overrides an earlier one because it comes later in the combined prompt.
Video, 11:12
Why does my code editor AI keep overriding custom repository guidelines every session?
A rules file is text placed in a context window. All three vendors say so on their own pages, in their own words. What delivers a rule, what the wrapper around it says, and what does enforce, because something does.
Video, 11:53
What Is an MCP Memory Server? Protocol, Tools, Tokens
MCP standardises how a client discovers a tool, calls it, and reads the answer. It does not standardise memory. Walking the whole path with the specification and the public reference server open.
World Representation
Agent Memory Deduplication: The Missing Error Rate
Six agent-memory systems, read at pinned commits: hardcoded similarity thresholds, no published error rate, and no way to undo a merge.
Is Memory Hyperbolic? What Neuroscience Shows
Is memory hyperbolic? Cognitive maps are real, but one narrow CA1 result and indirect odor evidence do not establish a universal memory geometry.
The Independence Illusion: When AI Agents Agree
AI agent consensus can be weak evidence when agents share models, sources, prompts, or memory. Count independent provenance chains instead.
Who Wins When Agents Disagree? The Authority Problem
When voting, reputation, and consensus can fail for multi-agent memory — and the published alternatives: outcomes, meta-knowledge, independence, reasons.
Bitemporal Memory for AI Agents: The Missing Axis
Bitemporal memory tracks valid time and transaction time — settled engineering. One reviewed agent system ships it; none of seven benchmarks tests it.
Agent Memory Provenance: Who Wrote It, Not Only When
Two agents wrote conflicting facts: a timestamp shows which came last, not whose claim should win. Record writer, evidence and derivation, and keep both.
Light Reading
What are the leading dedicated AI agent memory layer platforms?
What is the best AI agent memory in 2026? A decision map across Mem0, Zep, Letta, Cognee, Supermemory, LangMem, EverOS, and Mnemoverse, with the vendor position flagged up front.
Mem0 vs Zep vs Letta vs Cognee vs Supermemory (September 2026)
Mem0, Zep, Letta, Cognee and Supermemory compared with Mnemoverse, the publisher of this page: integration posture, self-host reality, temporal handling, and what benchmark scores hide.
Ontology vs Schema vs Topology: One Idea, Many Dialects
Ontology vs schema vs topology: one shared commitment, five different contracts, and what missing data means — false, unknown, or forbidden.
Workflow Intelligence Ships as a Primitive
OpenAI's Record & Replay for Codex and Anthropic's composable skills arrived the same week. Two stacks, one shift: encoding how work is done and making the procedure callable. The ownership question follows.
Memory Is Becoming a Procurement Decision
Cursor's valuation, Anthropic's dreaming preview, Google's Memory Bank: different architectures, same shift. Persistent memory has stopped being a research conversation and become a choice builders can no longer defer.
Agent Memory Is Not a Database
Orogat and Mansour name four failure modes of treating agent memory as storage, and propose GEM: four state-level operations instead of record-level CRUD. Why the abstraction, not the engine, is the problem.
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