vectorize-io/hindsight is an agent memory system that stores experiences and helps AI agents learn over time instead of only recalling conversation history. It is used by developers adding long-term memory to agents, including coding agents. Catalogue add-ons connect agents to Hindsight and its memory workflows.
Getting it into your agent
One page per mod, every tool's command on it. A separate URL per tool would split the same page into five that compete with each other.
npx skills add vectorize-io/hindsight --skill hindsight-memorygit clone --depth 1 https://github.com/vectorize-io/hindsightWrote this? Show the measurements
A badge with what this costs and how it scanned, read live from this page, so it follows the numbers instead of freezing them. Markdown for a README, HTML for a documentation site or a project page.
[](https://agentmods.dev/skills/vectorize-io/hindsight/hindsight-memory)<a href="https://agentmods.dev/skills/vectorize-io/hindsight/hindsight-memory"><img src="https://agentmods.dev/badge/skills/vectorize-io/hindsight/hindsight-memory/github.svg" alt="Measured on agentmods" height="20"></a>Or the 80×15 button, for a site that already has a row of RSS and ATOM ones. Only the verdict fits; the numbers stay here.
<a href="https://agentmods.dev/skills/vectorize-io/hindsight/hindsight-memory"><img src="https://agentmods.dev/badge/skills/vectorize-io/hindsight/hindsight-memory.svg" alt="Reviewed on agentmods" width="80" height="20"></a>- Snyk pass
- NVIDIA SkillSpector pass
What it costs to keep this loaded
Counted locally with the o200k_base tokenizer, which is exact for GPT models; Claude uses its own tokenizer and its counts differ. Treat this as one consistent yardstick across the catalogue rather than a bill. Prices are per million input tokens.
| Model | Per session | Once invoked |
|---|---|---|
| Fable 5.1 | $0.00074 | $0.00620 |
| Opus 5.5 | $0.00030 | $0.00248 |
| Sonnet 5.5 | $0.00015 | $0.00124 |
| Haiku 4.5 | $0.00007 | $0.00062 |
Grade A, and why
hindsight-memory scanned grade A with 0 findings against 26 rules in 11 categories — prompt injection, anti-refusal, data exfiltration, privilege escalation, supply chain, agent snooping, system-prompt leakage, SSRF and excessive agency — measured 1mo ago.
A static scan of the body, not an audit. Every finding is printed with the line that produced it so you can judge whether it matters here. A mod is markdown that instructs an agent; that is exactly why what it instructs is worth reading.
Nothing flagged
None of the 26 patterns this scan looks for appear in this file: no shell pipes, no recursive deletes, no credential paths, no hidden text, no instruction-override or anti-refusal phrasing, no agent-config snooping. That is not a guarantee, it is the absence of the things that are checkable.
How it starts
The opening of the file, as written. The whole thing — 65 lines — stays where its author put it; the contents beside it link to each section on GitHub.
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
mainonly"), - 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.
What this file has done since we first saw it
Hashed on every crawl. A supply-chain change to an agent config is a question of when, not whether, so the history is kept rather than the latest state alone.
- 1mo ago First seen · 65 lines · 74 tokens per session scan A 736dec06d414
hindsight-memory is a skill published in the GitHub repository vectorize-io/hindsight (51,231 stars, last pushed 2d ago), licensed MIT. It adds 74 tokens to every session and 620 once invoked, about $0.0003 per session on Opus 5.5. A static security scan graded it A with 0 findings. No closer match exists in the catalogue, so it is treated as the original; first seen 2026-08-30.
Other skills, from other repositories
persistent-notes
Save notes locally to /mnt/workspace/notes.json file. Use when user wants to "save a note" or "remember something".
session-summaries
What the chat right-panel session summary shows, what it costs, and how to make a session summarize well. Load when the user asks about the session summary panel, why a summary looks wrong or empty, or how to turn it on.
semantix
Install and use the semantix memory kernel as a middleware in your agent: extract user preferences / workflows / experience from past sessions, retrieve and inject them on demand. One binary + your agent's own tools.
search-shared-sessions
Search an Agent Relay workspace's shared coding-agent sessions for prior work, decisions, fixes, or the context behind a task or pull request. Use when team history could answer the user's question or prevent repeating an investigation.
memory-audit-belief-duel
A guided review process for conflicting beliefs or memories. It examines cases where two conclusions cannot both be true, including conflicts between a general rule and a more specific memory.
memory-audit-discoverability
A review guide for checking whether stored memories can be found at the right time. It focuses on where memories are attached, when they are triggered, whether aliases are missing, and whether a parent has too many children.