headroom
★ 77.5k
vs
engram
★ 163
headroom vs engram
headroom: Headroom compresses everything your AI agent reads — tool outputs, logs, RAG chunks, files, and conversation history — before it reaches the LLM. It achieves the same answers with a fraction of the tokens.; engram: Engram is a local identity layer for AI tools that stores your profile, preferences, lessons learned, and key decisions as local JSON files. It exposes them through MCP so every AI tool reads the same context, persisting across sessions, tool updates, and switches. 100% local and open source.
01
TL;DR
headroomMemory & Context · ★ 77.5k
When to choose:
- Long tool output, logs, JSON responses or RAG chunks are driving up cost or hurting answers.
- You want compression without code changes: run it as a local proxy, an MCP server, or by wrapping Claude Code, Codex or Cursor.
- Compression must run locally, with originals cached so the model can retrieve the full text.
When to skip:
- Your agent's problem is missing or outdated docs, not too much context: a docs server fits better.
- You cannot add a component that must keep running and may add latency.
- Your tasks cannot tolerate lossy compression: verify answer quality on your own work first.
engramMemory & Context · ★ 163
Developers: Maintain quality standards, architecture decisions, and lessons across projects.
02
Side-by-Side Comparison
Field
headroomengramCategory
Memory & Context
Memory & Context
Stars
★ 77.5k
★ 163
License
Apache-2.0
Apache-2.0
Install Command
pip install "headroom-ai[all]"
pip install piia-engram
Updated
1d ago
1d ago
Open Source
Yes
Yes
Tags
Context Compression, Token Optimization, AI Agents
ai-agent, ai-identity, ai-memory
03
Features
headroom01In-app library for compression (Python/TypeScript)
02Zero-code-change proxy mode
03One-command agent wrapping for various AI agents
04Cross-agent shared memory and auto-deduplication
05Reversible compression (CCR) with original content retrieval
engram01Cross-tool identity sharing
02Local-first, private data
03MCP-compatible
04Knowledge inheritance across projects
05Passive knowledge capture
04
Use Cases
headroom↳Reduce LLM token usage and API costs for AI agents.
↳Enable shared context and memory across multiple AI agents.
↳Optimize coding agents by compressing tool outputs, logs, and RAG chunks.
↳Maintain full data fidelity with reversible context compression.
engram↳Developers: Maintain quality standards, architecture decisions, and lessons across projects.
↳Investment analysts: Store reasoning chains for decisions across analyses.
↳System architects: Keep Architecture Decision Records that persist across companies.
05
Best For
headroomMost PopularEssential
engram—
FAQ
FAQ
What is the difference between headroom and engram?
Both headroom and engram are in the Memory & Context category. headroom has 77.5k stars, while engram has 163 stars.
Which is better, headroom or engram?
The best choice depends on your use case. Choose headroom if Reduce LLM token usage and API costs for AI agents., and engram if Developers: Maintain quality standards, architecture decisions, and lessons across projects..
Is headroom free or open source?
Yes, headroom is open source on GitHub (Apache-2.0).
Is engram free or open source?
Yes, engram is open source on GitHub (Apache-2.0).
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