hmem
★ 24
vs
headroom
★ 77.5k
hmem vs headroom
hmem: hmem is a memory system for AI agents that persists project context across sessions, devices, and AI providers. It uses a 5-level lazy loading architecture to minimize token usage while retaining full details. It supports auto-logging, background checkpoints, and cross-platform sync.; 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.
01
TL;DR
hmemMemory & Context · ★ 24
AI coding assistants that need to maintain project context across sessions
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- Your agent's problem is missing or outdated docs, not too much context: a docs server fits better.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.
02
Side-by-Side Comparison
Field
hmemheadroomCategory
Memory & Context
Memory & Context
Stars
★ 24
★ 77.5k
License
MIT
Apache-2.0
Install Command
npm install -g hmem-mcp && npx hmem init
pip install "headroom-ai[all]"
Updated
3mo ago
1d ago
Open Source
Yes
Yes
Tags
ai-agents, ai-tools, claude
Context Compression, Token Optimization, AI Agents
03
Features
hmem015-level lazy loading to minimize token usage
02Automatic session logging and background checkpoints via Haiku
03Cross-provider and cross-tool support
04Project-based memory organization
05Encrypted sync across devices
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
04
Use Cases
hmem↳AI coding assistants that need to maintain project context across sessions
↳Multi-device workflow where different agents continue the same project
↳Knowledge management for personal AI assistants
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.
05
Best For
hmem—
headroomMost PopularEssential
FAQ
FAQ
What is the difference between hmem and headroom?
Both hmem and headroom are in the Memory & Context category. hmem has 24 stars, while headroom has 77.5k stars.
Which is better, hmem or headroom?
The best choice depends on your use case. Choose hmem if AI coding assistants that need to maintain project context across sessions, and headroom if Reduce LLM token usage and API costs for AI agents..
Is hmem free or open source?
Yes, hmem is open source on GitHub (MIT).
Is headroom free or open source?
Yes, headroom is open source on GitHub (Apache-2.0).
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