headroom vs mem0
The two are close in size: 69.0k stars for headroom, 64.7k for mem0. Over the days we have tracked them headroom moved +15.9% and mem0 +6.1%, so headroom is growing faster right now.
headroom leads on context depth and model freedom. mem0 does not take any axis by a clear margin.
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
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.
Provides a production-ready, self-hostable memory layer with a token-efficient memory algorithm, entity linking, and agent skills for easy integration into AI assistants.
- Stars
- 64.7k
- Tracked growth
- +6.1%
- Maturity
- ●●●●●
- Last commit
- 18h ago
- Language
- Python
- License
- Apache-2.0
- Cost to run
- Your LLM/API key costs; self-host or paid cloud available
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 | headroom | mem0 |
|---|---|---|
Context depth How much of your codebase it sees before it answers: the open diff, the diff plus related files, or the whole repository. | ●●●●● Diff + related files | ●●●●● User/session only |
Noise control How it keeps output volume down — severity thresholds, deduplication, incremental runs over new commits only. | ●●●●● Dedup & shaping | ●●●●● Multi-signal retrieval |
Customization How far it bends to your team: custom rules, prompts, style guides, per-path config. | ●●●●● Rich CLI & config | ●●●●● Plugins & 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 | ●●●●● Multiple LLMs |
Setup ease What it takes to get a first useful run out of it. | ●●●●● One-command start | ●●●●● One-command start |
Which one to pick
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.
- Context depth: Diff + related files (3/5 against 1/5)
- Model freedom: Bring-your-key (5/5 against 3/5)
Pick mem0 if…
Self-hostable — one-command bootstrap and SDKs let teams add a scalable, token-efficient memory layer while keeping data under their control.
What people want from each one
headroomlabs-ai/headroom
Hacker News: Headroom – The context compression layer for AI agents drew 3 points and 0 comments.
mem0ai/mem0
Hacker News: Show HN: Mem0 – open-source Memory Layer for AI apps drew 201 points and 61 comments.
Questions people ask
Is headroom better than mem0?
headroom leads on context depth and model freedom. mem0 does not take any axis by a clear margin. headroom is worth picking when 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.
Which of headroom and mem0 keeps my code private?
headroom: Self-hostable (4/5). mem0: Self-hostable (4/5).
What does each one cost to run?
headroom: Local software is free; LLM calls use your API key.. mem0: Your LLM/API key costs; self-host or paid cloud available.
Full profiles: headroomlabs-ai/headroom and mem0ai/mem0. Everything else in Memory & context.