Makers of Hindsight. Agent memory that lets your agents learn over time. https://xitter.cf/t.co/JkzXgOQg74
Boulder, CO
Joined December 2023
- Tweets1.4K
- Following331
- Followers1.5K
- Likes967
Vectorize retweeted
I tried them all. Simplicity won for me:
1. @Linear = source of truth/context layer/tasks/project management
2. @Vectorizeio Hindsight = Agent memory/journals
Any question?
Vectorize retweeted
Yep. You don’t need so many apps. I hate this. I use linear als source of truth and project management tool. And hindsight as agent memory for all agents. That’s it. Don’t over complicate
Vectorize retweeted
TITLE
Your Agent Forgets You Tonight
DESCRIPTION
Tomorrow morning the agent does not know who Alice is. You told it yesterday. Hindsight, from Vectorize, is an open memory layer that says it is built to learn, not just replay the chat.
This film uses their README and a GitHub measurement taken October 24, 2026. I did not run Hindsight, and I did not rerun their benchmark.
What their README says
- Most agent memory recalls the conversation. Hindsight says it is focused on agents that learn, not just remember.
- Three operations: retain writes a fact into a bank, recall searches that bank, and reflect answers from the whole pile.
- Their own example: Alice works at Google as a software engineer.
- Four kinds of memory: a world fact, an experience, an observation that keeps its quotes, and a mental model that is rewritten as the bank learns.
- A bank is one brain for one user, one agent, or one project. They say isolation is strict. Nothing leaks across banks.
- Memory Defense is off until you turn it on. Then every retain is scanned against 45 patterns for secrets and personal data. A match is redacted, or blocked, before it is stored.
- They say it leads the LongMemEval benchmark, and that Virginia Tech and The Washington Post reproduced that result. Other scores, they say, are self-reported. I did not rerun it.
- They say Fortune 500 companies use it. That sentence is theirs.
- Their support table lists Windows, with Docker or pip.
Measured on GitHub, October 24, 2026
- Language: Python. License: MIT.
- Created October 30, 2025. Last push 2:44 p.m. Pacific, the same day.
- 53,425 stars. 142 open issues. 6,237 forks.
- Stars are attention, not a test.
Comment the one thing you would never retain: a password, a client name, or yesterday's work.
Sources
github.com/vectorize-io/hind…
api.github.com/repos/vectori…
hindsight.vectorize.io/
Chapters
0:00 Forgets tonight
0:09 Built to learn
0:22 Write it down
0:37 Four kinds
0:56 One brain
1:17 Not a test
2:01 You keep
HASHTAGS
#Hindsight #AIAgents #AgentMemory #AINews #Vectorize #AITools #Python #OpenSource
TAGS
Hindsight, AI agents, agent memory, Vectorize, retain, recall, reflect, AI news, AI tools, Python, MIT, LongMemEval
See the Exact Prompt Before You Spend a Token.
You write a mission for your memory bank. Something like "focus on billing decisions and who approved them." You save it, and the extraction gets better, or it doesn't.
Now: where did that sentence go?
If you guessed the system prompt, you're wrong for two of the three operations that use it.
TL;DR
- A retain or consolidation mission lands in the user message, not the system prompt. Reflect's lands in the system prompt, where it replaces the agent's role line rather than adding to it.
- POST /banks/{id}/prompts/preview returns the exact messages retain, consolidation or reflect would send. No LLM call, no writes.
- Active blocks concatenate byte-for-byte into the real prompt, and there's a test pinning that against the actual builder.
- A setting you switched off comes back as an inactive block, positioned where it would have landed.
- The endpoint refuses to accept your sample text or a candidate mission, on purpose.
- Shipped in v0.10.0 for self-hosted, and coming soon to Hindsight Cloud.
Know more: linkedin.com/pulse/see-exact…
Vectorize retweeted
me on the phone looking at shared memories from claude and chatgpt via @Vectorizeio ; spine router is my primary ai skill installed, medical analyzer is an extremely interesting way my chats analyze stuff
Vectorize retweeted
DOT agent just orchestrated a custom plugin to give my phone chatgpt shared memory with @Vectorizeio !!!
xitter.cf/stevenmathern/status/2…
chatGPT work was able to create a custom plug-in for ChatGPT mobile to connect to the hindsight memory system @Vectorizeio as you can see in the screenshots and we have confirmed that it is functional. This was orchestrated by the dot agent running two different chats said it's sol high. It is now assisting Claude in connecting to the shared memory bank while I just talk on my phone, only occasionally clicking a confirmation on the Mac.
Vectorize retweeted
Part 69 in amazing repos
Agent memory that learns
The diagram shows the retain, recall and reflect loop
Repo: github.com/vectorize-io/hind…
Vectorize retweeted
Asked Hermes to consult my Hindsight memory first. Its verdict: “You have a technician’s nose for bullshit, an architect’s appetite for interconnected systems, and the browsing restraint of a raccoon with an all-access badge.” Lmao
Vectorize retweeted
chatGPT work was able to create a custom plug-in for ChatGPT mobile to connect to the hindsight memory system @Vectorizeio as you can see in the screenshots and we have confirmed that it is functional. This was orchestrated by the dot agent running two different chats said it's sol high. It is now assisting Claude in connecting to the shared memory bank while I just talk on my phone, only occasionally clicking a confirmation on the Mac.
8 Agent Memory Use Cases (With Real Examples).
"Agent memory" sounds abstract until you notice it is the same fix applied to a dozen different problems. Any time an agent should remember something past the current session, and today starts from a blank slate, that is a memory-shaped hole. Hindsight fills it with the same three operations everywhere: retain to write, recall to read, reflect to reason. What changes from one use case to the next is not the primitive, it is who the memory belongs to and where it surfaces.
Here are the agent memory use cases people actually build, each with a concrete example: lnkd.in/gqe9TWR2
Vectorize retweeted
Memory should reduce repetition, not replace verification. We pair Hermes + Hindsight with live-source checks. How it works: tevpro.com/blog/how-we-use-h…
Vectorize retweeted
Turning Jev's typed answers into an ordering for candidate memories is a smart fit for Hindsight. hevmind.com/writing/jev-has-…
We Added Jev as a Reranker. Here's What We Learned.
Hindsight 0.10.1 adds Jev as a reranker provider. Jev is an unusual model: it produces no text at all. You send it a passage and a typed question, and you get back a typed answer with a calibrated probability. TypeSafe calls this class of thing a System One model, after Kahneman's fast, automatic kind of thinking.
That shape is a good fit for reranking, which is not a writing task. There is a pile of candidate memories and a question about which ones matter, and the only thing you want back is an ordering.
Getting there took longer than we expected, and two of the designs we tried failed outright. Here is what we learned.
Know more: linkedin.com/pulse/we-added-…
Fastest growing agent repo in October: Hindsight
10 repos de GitHub para agentes IA que más han crecido en Octubre:
1. Hindsight
Memoria para tus agentes que aprende de lo que hacen.
+28.5K estrellas en el último mes.
github.com/vectorize-io/hind…
2. Paperclip
Una app para gestionar a todos tus agentes en el trabajo, como si fueran un equipo.
+24.8K, ya pasa de 105.9K.
github.com/paperclipai/paper…
3. ECC
Skills, memoria y seguridad para Claude Code, Codex, OpenCode y Cursor.
+22.5K, va por 286K.
github.com/affaan-m/ECC
4. Impeccable
Un lenguaje de diseño para que lo que diseña tu agente no parezca hecho por una IA.
+15.1K.
github.com/pbakaus/impeccabl…
5. FreeLLMAPI
34 proveedores gratis y 635 modelos detrás de un solo endpoint. Si uno cae, salta al siguiente.
+11.2K.
github.com/tashfeenahmed/fre…
6. OpenShell de NVIDIA
Un entorno seguro y privado donde dejar trabajar a tus agentes.
+8.3K.
github.com/NVIDIA/OpenShell
7. Plugins oficiales de Claude Code
El directorio de plugins que mantiene la propia Anthropic.
+7.2K.
github.com/anthropics/claude…
8. CLI-Anything
Convierte cualquier programa en algo que tu agente puede manejar.
+6.4K.
github.com/HKUDS/CLI-Anythin…
9. text-to-cad
Tu agente diseña piezas en 3D a partir de texto.
+6.1K.
github.com/earthtojake/text-…
10. Knowledge Work Plugins de Anthropic
Plugins para usar Claude en ventas, legal, finanzas y marketing.
+5.8K.
github.com/anthropics/knowle…
Todos open source y gratis.
We Added Jev as a Reranker. Here's What We Learned.
Hindsight 0.10.1 adds Jev as a reranker provider. Jev is an unusual model: it produces no text at all. You send it a passage and a typed question, and you get back a typed answer with a calibrated probability. TypeSafe calls this class of thing a System One model, after Kahneman's fast, automatic kind of thinking.
That shape is a good fit for reranking, which is not a writing task. There is a pile of candidate memories and a question about which ones matter, and the only thing you want back is an ordering.
Getting there took longer than we expected, and two of the designs we tried failed outright. Here is what we learned.
Know more: linkedin.com/pulse/we-added-…
Document search used to match only the document id. It now searches document text and tells you which fields matched, so the search box finds a document by what it actually says.
Knowledge pages and mental models also download as PDF or Markdown.
Most of this release is reliability work:
→ deadlocks between consolidation, retain and delete
→ large graph reads freezing the API
→ retains replayed after an LLM key was rejected
→ a failed chunk replacing a stored document
→ workers exiting cleanly on one SIGTERM
Read the blog here: hindsight.vectorize.io/blog/…
And the changelog: hindsight.vectorize.io/chang…
