Your agents can get smarter over time with Hindsight. The most powerful AI memory system.
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Author: Better Stack · Watch on YouTube
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Original language: English
Original video: Your agents can get smarter over time with Hindsight. The most powerful AI memory system. · Channel: Better Stack · 01:32 · Watch on YouTube
Topics: AI agents, Enterprise AI applications, Data, databases and embeddings, Prompt and context engineering
Summary
Section titled “Summary”The video introduces Hindsight, an agent memory system designed to reduce repeated mistakes by helping agents store and recall information. It can run as a central server or in an embedded setup, with data stored in Postgres and vector search support. Hindsight turns incoming information into structured facts, connects related facts, and uses background consolidation to maintain observations and mental models. Its shared-memory setup can let agents across an organization benefit from information gathered by other team members.
Key points
Section titled “Key points”- Hindsight is presented as an agent memory system intended to help prevent agents from repeating mistakes.
- It can run as a central server inside Docker or in an embedded mode with no server.
- Both modes store data in Postgres with PGVector support.
- The embedded version uses PGZero, described as a zero-config Postgres binary with PGVector available.
- An LLM rewrites incoming data into short facts organized by what, when, where, who, and why.
- Facts are connected through shared entities, similar meaning, time, and cause.
- Search can follow links to relevant facts even when their wording does not match the query.
- Background consolidation merges facts into observations and keeps mental models up to date.
- A central server can provide shared memory for agents across an organization.
Chapters
Section titled “Chapters”| Start | Chapter | Summary |
|---|---|---|
| 00:00 | Introducing Hindsight | The video presents Hindsight as a way to reduce repeated agent mistakes and introduces its shared-memory capabilities. |
| 00:31 | How Hindsight stores data | Hindsight offers central-server and embedded deployment options, both backed by Postgres and vector search. |
| 01:01 | Fact extraction and memory consolidation | The system structures incoming data as connected facts and uses background LLMs to consolidate and maintain its memory. |
| 01:32 | Shared memory across a team | The video explains how a central Hindsight server can make one team member’s information accessible to other agents and points viewers to the GitHub repo. |
Quotes
Section titled “Quotes”Hindsight is an agent memory system — 00:00
Hindsight can run in one of two ways — 00:31
an LLM rewrites the input into short facts — 01:01
everyone in your organization can benefit from that same shared memory — 01:32
Concepts and entities
Section titled “Concepts and entities”Hindsight, Claude, Zap, Supermemory, PostgreSQL, pgvector, PGZero, Docker, Better Stack
Full transcript (original language)
Section titled “Full transcript (original language)”00:00 Have you ever noticed how annoying it is to keep telling your agents the same thing over and over again even with built-in systems like Claude’s memory and default, you’ll still see the same mistakes come up repeatedly. Here’s a system that can fix all of that for you. Hindsight is an agent memory system that calls itself the most accurate agent memory system ever tested. Compared to other tools like Zap and Supermemory, it comes out way ahead and even lets you share memory between your entire team. So, let me explain how all of this works. Most memory systems use knowledge
00:31 graphs, but Hindsight instead runs a custom server that gives you complete control in storing and recalling knowledge. Hindsight can run in one of two ways, as a central server which can run inside Docker or embedded using no server at all. In both cases, data is stored inside a Postgres database backed by PGVector for full vector database support. The embedded version does this via PGZero, a zero-config Postgres binary with PGVector already available. But, it’s so much more than just a
01:01 vector database. As data comes in, an LLM rewrites the input into short facts using what, when, where, who, and why fields. Facts connect through shared entities, similar meaning, time, and cause. So, later search can follow these links to facts that do not match the query words themselves. Background consolidation merges facts into observations and keeps mental models up-to-date. So, it combines a traditional Postgres database, vector, and full-text search with background LLMs. And this results in superior memory that can automatically clean
01:32 itself. This gives us some of the most accurate memory management available today. And because it can run inside a central server, it means everyone in your organization can benefit from that same shared memory. So, one team member could work through a problem and the data from that can now be accessed by the agents of every other team member. You can check out the GitHub repo to get started today. And if you want to stay up-to-date with tech and AI, then subscribe to Better Stack.