ИИ-агенты
honcho
Honcho — Plastic Labs' memory library for stateful agents that learn about their users over time.
★ 7,5 тыс. звёздAGPL-3.0
Смотреть руководство →hindsight — проект с открытым исходным кодом из категории «ИИ-агенты» от vectorize-io (vectorize-io/hindsight на GitHub). У него 47 008 звёзд на GitHub и 5 967 форков, он распространяется по лицензии MIT и последний раз обновлялся на GitHub 7 октября 2026.
Agent memory that learns — so your agents get smarter over time.
Decide what you want the project to do before installing anything.
Grab the source and skim the README for setup requirements.
Install dependencies and add any API keys or settings it needs.
Launch it and follow the example workflow from the docs.
Recall that improves instead of just accumulating.
Agents remember across sessions.
Drop it into Python agent stacks.
Designed to slot into existing agent builds.
From the team behind vectorize-io tooling.
Free for commercial use.
Download the source and move into the project folder.
git clone https://github.com/vectorize-io/hindsight.git
cd hindsightIsolate dependencies, then install requirements.
python -m venv .venv && source .venv/bin/activate
pip install -r requirements.txtLaunch using the entry point shown in the README.
python -m hindsight # check README for the exact commandIt is an open-source project at vectorize-io/hindsight with 5967 forks. Check the contributors tab and recent commits to judge how active maintenance is.
Yes — it's MIT licensed: free to use, modify, and ship commercially. Just keep the license notice in copies.
It persists interaction history and refines recall over time — the docs at hindsight.vectorize.io cover the architecture.
It's a memory layer, not just storage: retrieval plus learning. You may still use a vector DB underneath.
Clone the repo and follow the README quickstart — the docs at https://hindsight.vectorize.io/ walk through setup.
Honcho — Plastic Labs' memory library for stateful agents that learn about their users over time.
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