Agent-Memory

A memory layer fails quietly in one specific way that matters more than any benchmark score: the agent asks for context, receives nothing usable, and answers anyway. Nothing raises in the application. The model’s confidence is unchanged, the user gets a plausible answer with no memory behind it, and no alert fires because from the client’s point of view the call returned. Cognee is a graph-and-vector memory library for agents, and its maintainers publish dated fleet telemetry as public GitHub issues. That telemetry is unusual: it gives per-release, per-day denominators for exactly this failure, so the silence can be measured rather than described. This article maps each visible symptom to one cause, one probe, and one fix, and keeps every number tied to the release and date printed in the issue it came from. ...

Your agent stored user prefers aisle seats with an expiration date. The date passed. The user says it again. You call add() with a fresh expiration_date, get no exception, get an empty results list, and get_all() shows nothing. The fact never lands, and it will never land as long as the expired copy is what the search returns. On mem0ai 2.2.1 — the Python OSS package, self-hosted — the cause is a read/write asymmetry. Expiration is applied as a filter on the read paths but not on the ADD path. The expired row is still handed to the extraction LLM as an “existing memory”, and its hash is still loaded into the dedup set, so the fresh write is dropped as a duplicate of a record the user can no longer see. The same write machinery adds a second dependency: an update that only touches metadata or the expiry date still calls the embedding provider, and a provider error stops that update before anything is persisted. ...

Coding agents like Claude Code and Codex remember nothing once a session ends, which is why you keep re-asking the same questions and re-discovering the same fixes. Lore is a local-first desktop app that reads the session files these agents already save, connects them to your repositories and Git history, and puts everything into a single searchable SQLite archive — with no accounts, no telemetry, no cloud database, and no LLM calls. Because it only reads what Claude Code and Codex already write on disk, Lore adds searchable long-term memory to your coding agents without changing how they behave or locking you into another vendor. ...

Coding agent experience management is the practice of turning the session history your local coding agents generate into a searchable, migratable, and reusable asset instead of leaving it as scattered log files. Open-source tools such as AgentHist bring sessions from Codex, Claude Code, OpenCode, and Pi into one library for browsing, searching, exporting, and selective importing — including cross-machine migration and cross-agent conversion. AgentHist is a TypeScript CLI, MIT-licensed, supporting Linux, macOS, and Windows, created August 2026 (source: GitHub API). ...

Portable Handoff: How to Keep Context Between AI Chats, Models, and Coding Agents AI coding agents start every session with a fresh context window, so anything you do not persist is re-learned from scratch. A portable handoff file solves this: you write your instructions, preferences, and project conventions once, then symlink or import that single source of truth into every agent you use — Claude, Gemini, Cursor, or Codex — so context follows you across chats, models, and tools without drifting. ...

Coding agents lose context across repositories because a session is scoped to one repo while the system under investigation spans many. WCTX is a local-first MCP server that solves this by importing finished coding-agent sessions into a SQLite + FTS5 store, linking repositories with typed relations, and exposing seven tools that let a fresh session search prior evidence with git-based freshness verdicts. No cloud account, no embeddings, no transcript upload. ...

ContextNest vs Mem0 vs Zep is the wrong framing: these three are complementary layers of the same memory stack, not competing products. Zep provides session log memory, Mem0 handles personalization memory, and ContextNest governs corporate knowledge — and production agents typically need all three. A single memory database is the most common architectural pitfall, because it cannot serve session continuity, user personalization, and compliance-ready knowledge at the same time. This guide dissects each layer, compares them head-to-head, and shows how to stack them. ...

Open-source memory for coding agents synced over SSH lets you carry your AI assistant’s context, preferences, and project knowledge across every machine you work on — without a cloud service. The most portable approach is a git-backed markdown store: symlink your agent’s memory files, push and pull over SSH, and let auto-sync hooks handle the rest. This guide walks through the three proven methods and how to pick the right one. ...

Heimdall is an open-source, CPU-only knowledge layer that gives AI coding agents a verified, self-healing memory across every repository you work in. Instead of returning plausible-but-unverified matches, every search result carries a trust verdict — STRONG, WEAK, REBUILT, or STALE — re-checked against your live filesystem at query time. It indexes with tree-sitter and local embeddings, spends zero tokens on memory maintenance, and re-anchors moved files automatically. What is Heimdall and why does it exist? Heimdall is a trust-verified knowledge layer built specifically for AI coding agents. It was released on Hacker News on August 22, 2026, and is published on npm as @ariantdeva/heimdall at version 0.2.1. The project’s core claim is simple: most agent memory tools return results that look right but have not been checked against the actual state of your codebase. Heimdall exists to close that gap. ...

AgentBrain is a local-first, long-term memory tool for AI agents that stores everything as a plain Markdown vault. Instead of locking your agent’s memory into a proprietary database or cloud API, it gives you an append-only Markdown folder you can read, edit, grep, and version with Git. It uses index-first retrieval with CJK-aware BM25 to stay token-efficient, and enforces human-approved consolidation to prevent multi-agent write conflicts. What Is AgentBrain? A Local-First Markdown Memory Vault for AI Agents AgentBrain is an open-source Python tool (Python 3.10+) that gives AI agents a durable, local-first memory layer stored as plain Markdown files. The core idea is simple: an agent’s long-term knowledge should live in a human-readable folder on your own machine, not inside a black-box database or a third-party cloud service. ...