cognee vs headroom
headroom is much bigger: 69.0k stars against 30.5k. Over the days we have tracked them cognee moved +19.7% and headroom +15.9%, so cognee is growing faster right now.
They split the axes: cognee leads on context depth, headroom on setup ease.
Stars and commit dates come from our own daily tracking. The six axes are read off each project's documentation by our review pipeline, so they describe what a project says about itself, not what we measured in its code.
Where they stand today
Runs the full memory layer (graph, vectors, sessions) on a single self-hosted Postgres-backed stack, combining vector search and evolving knowledge graphs.
- Stars
- 30.5k
- Tracked growth
- +19.7%
- Maturity
- ●●●●●
- Last commit
- 54m ago
- Language
- Python
- License
- Apache-2.0
- Cost to run
- Your LLM API key, or use Cognee Cloud (managed)
Local-first, reversible context compression that both shrinks prompts and actively reduces model output tokens via a drop-in proxy and cross-agent memory.
- Stars
- 69.0k
- Tracked growth
- +15.9%
- Maturity
- ●●●●●
- Last commit
- 6h ago
- Language
- Python
- License
- Apache-2.0
- Cost to run
- Local software is free; LLM calls use your API key.
Six axes, head to head
Each axis runs 0 to 5. The label under a score is what that project's own docs claim, not a category average.
| Axis | cognee | headroom |
|---|---|---|
Context depth How much of your codebase it sees before it answers: the open diff, the diff plus related files, or the whole repository. | ●●●●● Whole-graph memory | ●●●●● Diff + related files |
Noise control How it keeps output volume down — severity thresholds, deduplication, incremental runs over new commits only. | ●●●●● Feedback & routing | ●●●●● Dedup & shaping |
Customization How far it bends to your team: custom rules, prompts, style guides, per-path config. | ●●●●● Config + plugins | ●●●●● Rich CLI & config |
Privacy Whether your code stays on your own infrastructure: fully local, self-hostable, or cloud API only. | ●●●●● Self-hostable | ●●●●● Self-hostable |
Model freedom Whether you can point it at any provider, or it is wired to one. | ●●●●● Bring-your-key | ●●●●● Bring-your-key |
Setup ease What it takes to get a first useful run out of it. | ●●●●● API key required | ●●●●● One-command start |
Which one to pick
Pick cognee if…
Self-hostable — run a unified, persistent agent memory on a single Postgres instance with minimal extra services and strong integration options.
- Context depth: Whole-graph memory (5/5 against 3/5)
Pick headroom if…
Easy setup — wrap agents or run a local proxy in one command to get large token savings, reversible originals, and shared memory across agents without code changes.
- Setup ease: One-command start (5/5 against 3/5)
What people want from each one
topoteretes/cognee
Hacker News: Show HN: Cognee – Open-Source AI Memory Layer That Remembers Context drew 9 points and 2 comments.
- [Hackathon] Vellum integration — cognee memory nodes for Vellum Workflows 👍 0
- [Bug]: recall ratifies affirmative/keyword queries as stored facts — and can harden them (e.g. 'added to watch list' becomes 'merged') 👍 0
- [Bug] GET /datasets/{id}/data and /data/{id}/raw return 500 (IndexError) instead of 404 when dataset is not found 👍 0
headroomlabs-ai/headroom
Hacker News: Headroom – The context compression layer for AI agents drew 3 points and 0 comments.
Questions people ask
Is cognee better than headroom?
They split the axes: cognee leads on context depth, headroom on setup ease. cognee is worth picking when self-hostable — run a unified, persistent agent memory on a single Postgres instance with minimal extra services and strong integration options.
Which of cognee and headroom keeps my code private?
cognee: Self-hostable (4/5). headroom: Self-hostable (4/5).
What does each one cost to run?
cognee: Your LLM API key, or use Cognee Cloud (managed). headroom: Local software is free; LLM calls use your API key..
Full profiles: topoteretes/cognee and headroomlabs-ai/headroom. Everything else in Memory & context.