A comparison of the company brain and organizational memory platforms that give human teams and AI agents one shared memory layer — Sentra, Mem0, Zep, Glean, Coworker, and Granola.
A developer comparison of the best codebase context memory tools for AI coding agents - Sentra Code Memory, Augment Code, Sourcegraph Cody, CodeAlive, Repomix, and Repowise.
Why retrieval-augmented generation breaks down as AI agent memory - similarity is not correctness, no temporal awareness - and what write-time, bi-temporal memory does differently.
A plain-English explainer of bi-temporal knowledge graphs - valid time vs transaction time - and why AI agent memory needs them to avoid stale, deprecated answers.
Why AI agents lose context and forget across sessions - context windows, per-agent scope, retrieval limits - and how a shared write-time memory layer fixes it.
The company brain explained - what it is, the layers of organizational memory, and how it differs from enterprise search, RAG, and per-agent memory.
RAG vs knowledge-graph memory for AI agents compared across accuracy, freshness, multi-hop reasoning, and token cost - and when to use each.
Sentra vs Glean compared - enterprise search vs a write-time, bi-temporal memory layer for teams and AI agents. Where each wins, and how they complement.
Sentra vs Mem0 compared - org-wide shared memory vs per-agent recall. Scope, write-time comprehension, bi-temporal awareness, and when to use each.
Sentra vs Zep compared - one org-wide bi-temporal graph for humans and agents vs per-entity agent memory graphs. Scope, temporal modeling, and fit.
The best Glean alternatives for teams and AI agents - Sentra, Mem0, Zep, Dust, Coworker.ai, and Onyx - across enterprise search, memory layers, and agent orchestration.
Sentra vs Coworker.ai compared - an org-wide shared memory layer for teams and agents vs a personal AI coworker app. Scope, persistence, and which to pick.
The best organizational memory software for teams and AI agents - Sentra, Mem0, Zep, Glean, Cognee, and Letta - compared on scope, memory model, and temporal awareness.
A buyer's guide to enterprise AI memory - what it is, why it matters now, the requirements that separate it from RAG and search, and the governance bar.
A developer how-to for giving Cursor persistent, long-term memory across sessions with an MCP memory server - and why bi-temporal memory avoids deprecated patterns.
Why coding agents burn tokens re-crawling your repo, how persistent memory cuts that cost, and how write-time bi-temporal codebase memory stops deprecated-pattern errors.
Sentra vs Anthropic's Claude Tag - a model-agnostic, bi-temporal company memory layer vs an AI coworker inside Slack. Memory scope, lock-in, governance, and how they run together.
Subprocessors include Amazon Web Services, GitHub, Slack, Google Cloud Platform, and OpenAI.