headroom vs hindsight
headroom is much bigger: 73.8k stars against 30.5k. Over the days we have tracked them headroom moved +24.1% and hindsight +72.7%, so hindsight 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
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
- 73.8k
- Tracked growth
- +24.1%
- Maturity
- ●●●●●
- Last commit
- 15h ago
- Language
- Python
- License
- Apache-2.0
- Cost to run
- Local software is free; LLM calls use your API key.
Hindsight provides a learning-focused agent memory with biomimetic data structures and a reflect operation to build mental models, going beyond simple RAG-style recall.
- Stars
- 30.5k
- Tracked growth
- +72.7%
- Maturity
- ●●●●●
- Last commit
- 14h ago
- Language
- Python
- License
- MIT
- Cost to run
- Your LLM API key (or self-hosted models); Hindsight itself can be self-hosted
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 | hindsight |
|---|---|---|
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 | ●●●●● Related context |
Noise control How it keeps output volume down — severity thresholds, deduplication, incremental runs over new commits only. | ●●●●● Dedup & shaping | ●●●●● Rerank & filtering |
Customization How far it bends to your team: custom rules, prompts, style guides, per-path config. | ●●●●● Rich CLI & config | ●●●●● Config API & metadata |
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-own / local |
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.
Pick hindsight if…
Learning-focused — choose Hindsight when you need agents that form and reflect on long-term memories (mental models) with easy self-hosted deployment and multi-provider model support.
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.
vectorize-io/hindsight
- Feature Request: Team, agent, and memory-level access control 👍 5
- reranker(typesafe): every recall fails over on banks with realistic-size memories — the request ships full candidate text against Jev's ~32k budget, and nothing bounds it 👍 2
- [Feature]: I'd like to benefit from disk savings with Embedding Quantization 👍 1
Questions people ask
Is headroom better than hindsight?
Neither one leads on the six capability axes, so the choice comes down to which of them fits the way you already work. 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 hindsight keeps my code private?
headroom: Self-hostable (4/5). hindsight: Self-hostable (4/5).
What does each one cost to run?
headroom: Local software is free; LLM calls use your API key.. hindsight: Your LLM API key (or self-hosted models); Hindsight itself can be self-hosted.
Full profiles: headroomlabs-ai/headroom and vectorize-io/hindsight. Everything else in Memory & context.