headroom
★ 77.5k
vs
ditto
★ 298
headroom vs ditto
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.; ditto: Ditto is a tool that analyzes your past AI agent coding sessions to automatically create a personalized working profile. This profile helps AI agents understand your specific work habits, design preferences, and writing style, making them act more like you.
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- Your agent's problem is missing or outdated docs, not too much context: a docs server fits better.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.
dittoMemory & Context · ★ 298
Customize AI agents to understand your definition of 'done,' preferred debugging methods, and UI design principles.
02
Side-by-Side Comparison
Field
headroomdittoCategory
Memory & Context
Memory & Context
Stars
★ 77.5k
★ 298
License
Apache-2.0
MIT
Install Command
pip install "headroom-ai[all]"
npx skills add ohad6k/ditto@ditto
Updated
1d ago
1w ago
Open Source
Yes
Yes
Tags
Context Compression, Token Optimization, AI Agents
AI Agents, Personalization, Context Management
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
ditto01Mines real coding-agent session logs (e.g., Claude Code, Codex, Copilot CLI) to extract implicit user behaviors.
02Generates a private, local working profile (`you.md`) that AI agents read before every task.
03Distinguishes and loads separate layers for work, design, and writing profiles based on the task.
04Ensures privacy by processing logs locally with best-effort redaction of secrets.
05Renders a shareable 'profile card' summarizing key laws and traits.
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.
ditto↳Customize AI agents to understand your definition of 'done,' preferred debugging methods, and UI design principles.
↳Improve agent communication to match your actual working voice and avoid repetitive explanations.
↳Enhance agent rejections to align with what you typically reject on sight (e.g., 'fix the one thing,' not rewriting).
↳Develop a consistent persona for multi-agent systems without explicitly writing down all rules.
05
Best For
headroomMost PopularEssential
ditto—
FAQ
FAQ
What is the difference between headroom and ditto?
Both headroom and ditto are in the Memory & Context category. headroom has 77.5k stars, while ditto has 298 stars.
Which is better, headroom or ditto?
The best choice depends on your use case. Choose headroom if Reduce LLM token usage and API costs for AI agents., and ditto if Customize AI agents to understand your definition of 'done,' preferred debugging methods, and UI design principles..
Is headroom free or open source?
Yes, headroom is open source on GitHub (Apache-2.0).
Is ditto free or open source?
Yes, ditto is open source on GitHub (MIT).
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