Honeycomb
legioncodeinc
Persistent, shareable memory for AI coding agents across sessions and tools.
What is Honeycomb?
An AGPL-3.0 daemon that gives coding agents durable memory: it captures agent turns, distills them into a three-tier model (keys, summaries, raw sessions), and serves them back to any harness (Claude Code, Cursor, Codex) over a CLI, a dashboard, or MCP/SDK. Storage is built on Activeloop Deeplake, and it can run fully self-hosted against the open-source pg_deeplake Postgres extension with no Activeloop account.
Pros & Cons
Pros
- Substantial, actively maintained project (500+ commits, v0.11.0, pushed the same day it was checked) with three interfaces: CLI, dashboard, and MCP/SDK
- Genuinely self-hostable and free: runs against the open pg_deeplake Postgres extension, no cloud account required
- Harness-agnostic - the same memory is recalled across Claude Code, Cursor, and Codex instead of being siloed per tool
Cons
- AGPL-3.0 is strong copyleft: anything you ship on top of it, including over a network, must be released under AGPL, which many companies avoid
- Semantic recall is opt-in and pulls a ~600 MB embedding model on first use; the headline distillation/skill-mining pipeline is disabled by default to avoid model costs
- Cross-device and team sharing bind to loopback and route through Deeplake org/workspace mediation rather than direct peer-to-peer
License
AGPL-3.0 (OSI-open)
Whole codebase is AGPL-3.0-or-later with no open-core split, but the copyleft is network-triggering - relevant if you embed it in a hosted product.
When it is interesting
You want one self-hostable memory layer that follows you across Claude Code, Cursor, and Codex.
When it is too early
AGPL copyleft is a blocker for your product, or you do not want to run a daemon plus a Postgres/Deeplake backend.
This repo featured in the 2026-07 edition of the Open-Source AI Radar.
claude-mem
thedotmack
Persistent memory layer across agent sessions with automatic semantic summaries and token-cost transparency.
graphify
safishamsi
AI coding skill that turns a folder of code, docs and images into a queryable knowledge graph across 20+ agents.
memU
NevaMind-AI
Memory framework for proactive AI agents - typed memory graph from chats, docs and media.