agentmemory vs headroom
headroom is much bigger: 69.0k stars against 28.0k. Over the days we have tracked them agentmemory moved +25.3% and headroom +15.9%, so agentmemory is growing faster right now.
Neither one leads on the six capability axes, so the choice comes down to which of them fits the way you already work.
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
Provides a local, multi-agent persistent memory server with hybrid retrieval and confidence scoring that shares memories across any MCP/HTTP agent.
- Stars
- 28.0k
- Tracked growth
- +25.3%
- Maturity
- ●●●●●
- Last commit
- 5d ago
- Language
- TypeScript
- License
- Apache-2.0
- Cost to run
- Free to run locally; optional cloud API keys for agent LLMs.
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 | agentmemory | 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. | ●●●●● Diff + related files | ●●●●● Diff + related files |
Noise control How it keeps output volume down — severity thresholds, deduplication, incremental runs over new commits only. | ●●●●● Confidence scoring | ●●●●● Dedup & shaping |
Customization How far it bends to your team: custom rules, prompts, style guides, per-path config. | ●●●●● Config file options | ●●●●● Rich CLI & config |
Privacy Whether your code stays on your own infrastructure: fully local, self-hostable, or cloud API only. | ●●●●● Fully local | ●●●●● Self-hostable |
Model freedom Whether you can point it at any provider, or it is wired to one. | ●●●●● Provider-agnostic | ●●●●● Bring-your-key |
Setup ease What it takes to get a first useful run out of it. | ●●●●● One-command setup | ●●●●● One-command start |
Which one to pick
Pick agentmemory if…
Easy setup — one-command local memory server with a demo and adapters for many coding agents, preserving session memory across restarts.
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.
What people want from each one
rohitg00/agentmemory
headroomlabs-ai/headroom
Hacker News: Headroom – The context compression layer for AI agents drew 3 points and 0 comments.
Questions people ask
Is agentmemory better than headroom?
Neither one leads on the six capability axes, so the choice comes down to which of them fits the way you already work. agentmemory is worth picking when easy setup — one-command local memory server with a demo and adapters for many coding agents, preserving session memory across restarts.
Which of agentmemory and headroom keeps my code private?
agentmemory: Fully local (5/5). headroom: Self-hostable (4/5).
What does each one cost to run?
agentmemory: Free to run locally; optional cloud API keys for agent LLMs.. headroom: Local software is free; LLM calls use your API key..
Full profiles: rohitg00/agentmemory and headroomlabs-ai/headroom. Everything else in Memory & context.