guide
Connect ChatGPT and Perplexity to Hindsight Memory
Connect ChatGPT and Perplexity to Hindsight with MCP, OAuth, and custom instructions so both tools can retain context instead of starting cold.
Fix OpenClaw Retention and Recall on Default Main Sessions
Fix OpenClaw retention on default main sessions by aligning bank granularity defaults, validating config, and checking new skip logs when recall seems missing.
Reduce Hindsight Consolidation Memory Fan-Out Safely
Reduce Hindsight consolidation memory fan-out by tuning recall budget, source fact limits, and FlashRank memory settings on large banks in production.
Run Hindsight CLI on Linux ARM64 Without Workarounds
Run Hindsight CLI on Linux ARM64 using the new release asset, then configure profiles, API access, and daily memory commands on Pi or ARM hosts.
Share Hindsight Memory Across ChatGPT and Perplexity
Share Hindsight memory across ChatGPT and Perplexity by pointing both connectors at the same bank, then test cross-tool recall on real work.
Size Hindsight Memory Footprint for Real Deployments
Size Hindsight deployments with the new memory footprint guidance for full and slim images, workers, the control plane, and PostgreSQL capacity planning.
Use Mental Model Tags in Hindsight's New List View
Use Hindsight mental model tags with the new List view, server-side tag suggestions, and safer filtering for refresh and reflect workflows today.
将 ChatGPT 和 Perplexity 连接到 Hindsight 内存
使用 MCP、OAuth 和自定义指令将 ChatGPT 和 Perplexity 连接到 Hindsight，使两个工具都能保留上下文而不是从零开始。
修复 OpenClaw 默认主会话上的保留和回忆
通过对齐内存库粒度默认值、验证配置以及在回忆似乎缺失时检查新的跳过日志，修复 OpenClaw 默认主会话上的保留。
安全地减少 Hindsight 合并内存扇出
通过调整回忆预算、源事实限制和大型库上的 FlashRank 内存设置，减少 Hindsight 合并内存扇出。
在 Linux ARM64 上运行 Hindsight CLI 而无需解决方法
使用新的发布资产在 Linux ARM64 上运行 Hindsight CLI，然后在 Pi 或 ARM 主机上配置配置文件、API 访问和日常内存命令。
在 ChatGPT 和 Perplexity 之间共享 Hindsight 内存
通过将两个连接器指向同一个库在 ChatGPT 和 Perplexity 之间共享 Hindsight 内存，然后测试实际工作中的跨工具回忆。
为实际部署调整 Hindsight 内存占用空间
使用完整和精简映像、workers、控制平面和 PostgreSQL 容量规划的新内存占用空间指导来调整 Hindsight 部署。
在 Hindsight 的新列表视图中使用心理模型标签
使用 Hindsight 心理模型标签与新列表视图、服务器端标签建议以及更安全的刷新和反射工作流过滤。
Beginner's Guide to Persistent Memory for AI Agents
A beginner's guide to persistent memory for AI agents, including what it is, why it matters, and how to think about setup, recall, and retention clearly.
Context Windows Are Not Memory
Context windows are not memory. Learn why bigger prompts help only temporarily, and what real persistent memory adds for reliable agents over time.
Designing AI Agents That Remember What Matters
A practical guide to designing AI agents that remember what matters without storing everything, polluting recall, or overwhelming the active prompt.
How Agent Memory Reduces Repetition and Rework
How agent memory reduces repetition and rework by carrying forward facts, choices, and preferences that users should not have to repeat every session.
How AI Agents Learn Across Sessions
How AI agents learn across sessions when memory captures durable preferences, facts, and outcomes instead of resetting from scratch every time.
How Memory Helps AI Agents Stay Consistent
Learn how memory helps AI agents stay consistent across sessions, tools, and repeated tasks without forcing users to restate critical context.
How Persistent Memory Changes Agent Behavior
See how persistent memory changes agent behavior by improving continuity, reducing repetition, and making agents more adaptive across sessions.
Short-Term vs Long-Term Memory for AI Agents
Understand short-term vs long-term memory for AI agents, including what each layer does and why useful systems need both working together well.
Stateless Agents vs Memory-Powered Agents
Compare stateless agents vs memory-powered agents so you can decide when memory is essential, and when a simpler agent design is enough today.
The Difference Between Memory, Retrieval, and Context
Understand the difference between memory, retrieval, and context so you can design agent systems with clearer responsibilities and fewer blind spots.
The Hidden Cost of Memoryless AI Agents
The hidden cost of memoryless AI agents includes rework, repeated prompting, weak continuity, and poor handoffs across sessions and tools today.
What Agent Memory Really Means
Learn what agent memory really means, how it differs from chat history and retrieval, and what a useful memory layer should actually do in practice.
What Makes Agent Memory Actually Useful
What makes agent memory actually useful: good retention, reliable recall, clear scope, and enough visibility to trust the system in real workflows.
When Do AI Agents Need Memory?
When do AI agents need memory? Use this guide to tell whether your workflow needs durable recall, or whether a simpler approach is enough today.
Why AI Agents Forget, and What to Do About It
Why AI agents forget, the most common memory failures behind that behavior, and what to do if you want more reliable continuity over time today.
Why Chat History Is Not Enough for AI Agents
Why chat history is not enough for AI agents, and what a real memory layer adds when the task needs continuity, recall, and structure over time.
Why Multi-Step Tasks Break Without Memory
Why multi-step tasks break without memory, especially when agents need to preserve goals, intermediate results, and prior decisions accurately.
Why Reliable AI Agents Need More Than Prompts
Why reliable AI agents need more than prompts, especially when long-lived tasks require memory, retrieval, and stronger operational structure.
Why Tool-Using Agents Need Shared Memory
Why tool-using agents need shared memory when several assistants, editors, or surfaces should build on the same durable context together well.
Why Your AI Agent Needs Memory
Why your AI agent needs memory, what breaks without it, and how persistent recall helps agents stay useful across sessions, tasks, and tools.
Building Multi-Agent Systems with Shared Memory Guide
Multi-agent memory works when agents share the right bank boundaries. This guide covers shared agent context, isolation patterns, and when Hindsight fits best.