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Hindsight Memory

OrganizationPopular
vectorize-io
hindsight-memory

Long-term memory for the agent via Hindsight. Use to recall relevant past context before answering, retain durable facts as you learn them, and reflect over accumulated memory for the "why" behind a decision. Load whenever continuity across sessions matters — the user refers to earlier work, states a lasting preference, or asks a question that prior context could answer.

Overview

Publishervectorize-io
Repositoryhindsight
Skill namehindsight-memory
Stars
44K
Forks
5.8K
Bundled files
Instructions only
LicenseMIT
Links
  • Markdown instructions

    A SKILL.md file the model loads on demand, so it only costs tokens when a request actually matches.

  • Works with any LLM

    AI skills are plain Markdown, not provider-specific code, so this works with GPT, Claude, Gemini, Grok, or a local model.

  • Self-contained

    Everything the model needs lives in the instructions — no extra files to sync.

  • Open source

    Published by vectorize-io on GitHub. Read the source before you install it.

Installation

Install the Hindsight Memory AI skill in TypingMind to use it with any LLM, or drop it into another agent that reads SKILL.md.

1

Install in TypingMind

TypingMind installs a skill straight from its GitHub folder — it reads SKILL.md, bundles the resource files, and stores the result locally.

  1. Open the app and go to Plugins → Skills.
  2. Choose "Install from GitHub".
  3. Paste the skill folder URL below and confirm.
    https://github.com/vectorize-io/hindsight/tree/main/hindsight-integrations/agent-plugin/skills/hindsight-memory
  4. Enable the skill in any chat where you want it available.
Plugins → Skills → Add skill → From GitHub URL, then paste the folder URL and press Continue.
2

Install in another agent

Any agent that reads the Agent Skills format can use this skill — copy the folder into that agent's skills directory.

Claude Code — .claude/skills
git clone --depth 1 https://github.com/vectorize-io/hindsight.git /tmp/hindsight
mkdir -p .claude/skills
cp -r /tmp/hindsight/hindsight-integrations/agent-plugin/skills/hindsight-memory .claude/skills/hindsight-memory
Restart Claude Code after copying so it picks up the new skill.

Use it in TypingMind

Enable Hindsight Memory in any TypingMind chat and the model takes it from there. Its name and description sit in the system prompt, and the moment a request matches, the model loads the full instructions itself — you never invoke it by hand, and it costs no tokens until it is actually used.

The model loads Hindsight Memory on its own as soon as a request matches it.

Works with any AI model

AI skills are plain Markdown instructions rather than provider-specific code, so Hindsight Memory is not tied to the model it was written for. Install it once in TypingMind and use it with GPT-5, Claude, Gemini, Grok, DeepSeek, Mistral, Llama, or a local model you run yourself — all on your own API keys.

  • Loaded only when it is needed

    The system prompt carries just the name and description. The instructions are fetched on the first matching request, so an idle skill costs nothing.

  • Switch models mid-chat

    Because the skill is instructions rather than code, changing model does not break it — the next model reads the same SKILL.md.

Skill instructions

This is the SKILL.md content the model loads. Read it before installing — a skill is instructions your model will follow.

Hindsight long-term memory

This plugin connects the agent to Hindsight, a long-term memory engine. Memory persists across sessions in a bank (scoped by HINDSIGHT_BANK_ID), so what you retain now is available to recall in future conversations.

The hindsight MCP server exposes the tools below. Prefer these over guessing from scratch when the answer might live in past context.

When to recall (read memory)

Call recall at the start of a task, or whenever the user:

  • refers to earlier work, a past decision, or "the thing we set up",
  • states a preference or constraint that may already be recorded,
  • asks a question that accumulated project/user context could answer.
recall(query: "how do we deploy the API and which region")

recall runs semantic + keyword + graph + temporal retrieval and returns the most relevant memories. Ground your answer in what comes back, and say when nothing relevant was found rather than inventing continuity.

When to retain (write memory)

Call retain when you learn something durable and reusable — worth having in a future session, not just this one:

  • stable user preferences ("prefers pnpm; deploys from main only"),
  • project facts and decisions ("staging DB is Postgres 16 on Neon"),
  • outcomes and gotchas ("the flaky test was a timezone bug, fixed in #482").
retain(content: "The user deploys the API to us-east-1 via GitHub Actions on push to main.")

Do not retain transient chatter, secrets, or anything the user asked you to keep out of memory. Retain the fact, not the whole transcript.

When to reflect (reason over memory)

Call reflect when a single recall is too shallow and you need synthesized reasoning over everything remembered — the why behind a behavior, or a judgment that weighs many facts together:

reflect(query: "What has repeatedly caused our CI to flake, and what should we standardize?")

reflect is slower and disposition-aware; use it deliberately, not for lookups.

Bank scope

All tools operate on the bank selected by the connection (HINDSIGHT_BANK_ID, default default). One bank = one memory store — keep a project's or a user's memory in its own bank so context stays isolated and relevant. You never pass a bank id to a tool; it is implicit from the endpoint.

Frequently asked questions

What does the Hindsight Memory AI skill do?

Long-term memory for the agent via Hindsight. Use to recall relevant past context before answering, retain durable facts as you learn them, and reflect over accumulated memory for the "why" behind a decision. Load whenever continuity across sessions matters — the user refers to earlier work, states a lasting preference, or asks a question that prior context could answer.

Why use Hindsight Memory on TypingMind?

Because you install it once and use it with any model. Hindsight Memory is plain Markdown rather than provider-specific code, so the same skill runs on GPT-5, Claude, Gemini, Grok, or a local model — and you can switch model mid-chat without it breaking. TypingMind runs on your own API keys, so you pay providers directly instead of a per-seat subscription, and your skills and chats stay in your own storage.

How do I install Hindsight Memory in TypingMind?

Open Plugins → Skills → Install from GitHub in TypingMind and paste https://github.com/vectorize-io/hindsight/tree/main/hindsight-integrations/agent-plugin/skills/hindsight-memory. TypingMind reads its SKILL.md and installs it as a skill you can enable per chat.

Which AI models can use Hindsight Memory?

Any model you connect in TypingMind. AI skills are plain Markdown instructions rather than provider-specific code, so GPT, Claude, Gemini, Grok, and local models can all load this skill when a request matches it.

How many AI models can I use with Hindsight Memory?

As many as you like. As long as a model supports skills, you can use Hindsight Memory with it — GPT, Claude, Gemini, Grok, DeepSeek, Mistral, Llama and more — all on TypingMind with your own API keys.

Is the Hindsight Memory AI skill free?

Yes. It is published on GitHub by vectorize-io under the MIT license. You only pay your own AI provider for the tokens you use.

What are AI skills?

An AI skill is a reusable instruction bundle that teaches an AI model how to do one specific task. It follows the open Agent Skills format: a SKILL.md file with a name and description, plus any scripts, templates or reference files the model may need. The model reads the instructions only when your request matches the skill, so an installed skill costs nothing until it is used.

How are AI skills different from plugins or MCP servers?

A plugin or MCP server gives a model new tools to call — code that runs somewhere and returns a result. An AI skill gives the model knowledge and process instead: how to approach a task, which steps to follow, what good output looks like. Skills are plain Markdown, so they need no server, no API key and no runtime, and they work with any model.

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