Hindsight: Agent Memory for Claude Code That Learns From Your Corrections
The agent made these same mistakes three days ago, and today it makes them again. CLAUDE.md seems to be invented for exactly this, but a month into the work it turns into a list of fifty rules, half of them outdated, and finding the right line in it is harder than explaining the task to the agent again.
There is an open project that solves this very problem a different way: not with a file of rules, but with memory that learns from your decisions on its own. It's called Hindsight, the repository is open, github.com/vectorize-io/hindsight, MIT license, more than 43 thousand stars on GitHub as of the end of September 2026.
How this differs from CLAUDE.md
CLAUDE.md stays static text: you write and edit it by hand. The agent reads it at the start of a session, and that's it. After that the file doesn't change by itself until you open it again.
Hindsight works differently: every decision you made in your conversation with the agent goes into memory automatically, with no separate command. If the agent did something wrong before and you corrected it, then on a similar task it already sees that this should not be done. The developers describe it this way: memory draws conclusions from what it has accumulated, instead of piling facts into an archive without sorting.
What the memory consists of
According to the project documentation, the memory bank stores four types of records: facts about the world (objective knowledge about the project or subject area), agent experience (its own attempts and their results), synthesized profiles (summaries of people, projects, or entities built from repeating facts and experience), and opinions (the agent's subjective judgments with a confidence score, which can change over time).
Search in this memory runs four ways in parallel: semantic search, classic full-text search, search by links between records, and search by time. The developers name three main operations: saving, searching, and a separate step of reflection, where the system reviews what it has accumulated and builds new links between facts.
How to connect it to Claude Code
The connection goes through Agent Skills, the built-in skill mechanism of Claude Code. The install command:
curl -fsSL https://hindsight.vectorize.io/get-skill | bash -s -- --app claude
The skill goes into ~/.claude/skills/, and after that Claude Code decides on its own when to use it: you don't need to call a command explicitly, and the agent saves and recalls as the situation requires.
Then there is a fork. Local mode keeps the data on your disk, brings up a built-in PostgreSQL, and asks you to run uvx hindsight-embed configure once with a key from your own LLM. Cloud mode sends the memory to Hindsight Cloud: you don't need to run your own server, but you need a separate cloud key and usage-based billing.
Who it suits and who it's too early for
It suits people who work with Claude Code (or Cursor, Cline, Aider, and similar agents) on long projects and are tired of CLAUDE.md growing faster than they can clean it. Especially where the same agent mistakes repeat from session to session.
It doesn't suit those who do short one-off tasks with no history: memory is needed where there is something to remember. And honestly, I have no data on how Hindsight behaves on truly large projects over months, only the description from the documentation and an independent check of the LongMemEval benchmark results by third-party teams.
Bottom line
Hindsight doesn't fully replace CLAUDE.md, but it removes its main weakness: a file that has to be cleaned by hand because it doesn't understand on its own what in it is already outdated. Open code, MIT license, installation with one command in Skills.
I write about the tools I test for my own Claude Code workflow in the channel «Растём с ИИ» ("Growing with AI"). Another breakdown of setups for Claude Code: 5 MCP servers that close five different gaps.
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