headroom
★ 77.5k
vs
sense
★ 40
headroom vs sense
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.; sense: Sense is an MCP server designed to provide AI coding agents with a deep structural understanding of codebases. It indexes repositories locally, offering tools like symbol graph, semantic code search, and convention detection to enhance agent efficiency and accuracy.
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- Your agent's problem is missing or outdated docs, not too much context: a docs server fits better.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.
senseMemory & Context · ★ 40
Enable AI agents to efficiently answer structural code questions like 'who calls this function?' by using pre-computed graphs.
02
Side-by-Side Comparison
Field
headroomsenseCategory
Memory & Context
Memory & Context
Stars
★ 77.5k
★ 40
License
Apache-2.0
MIT
Install Command
pip install "headroom-ai[all]"
curl -fsSL https://luuuc.github.io/sense/install.sh | sh
Updated
1d ago
1d ago
Open Source
Yes
Yes
Tags
Context Compression, Token Optimization, AI Agents
AI Coding Agents, Codebase Context, Symbol Graph
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
sense01Provides symbol relationships, callers, callees, inheritance, tests, and dead code detection via `sense_graph`.
02Offers hybrid semantic code search (`sense_search`) with bi-encoder, cross-encoder, keyword, and text fallback.
03Calculates blast radius, affected code, affected tests, and risk score using `sense_blast`.
04Detects project-specific coding conventions from source via `sense_conventions` for style adherence.
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.
sense↳Enable AI agents to efficiently answer structural code questions like 'who calls this function?' by using pre-computed graphs.
↳Reduce AI agent tool calls, token usage, and session time, leading to significant cost and time savings for code-related tasks.
↳Help AI agents write code that adheres to specific project conventions and styles, improving code quality and consistency.
↳Perform advanced code analysis such as identifying dead code or understanding the blast radius of changes.
05
Best For
headroomMost PopularEssential
sense—
FAQ
FAQ
What is the difference between headroom and sense?
Both headroom and sense are in the Memory & Context category. headroom has 77.5k stars, while sense has 40 stars.
Which is better, headroom or sense?
The best choice depends on your use case. Choose headroom if Reduce LLM token usage and API costs for AI agents., and sense if Enable AI agents to efficiently answer structural code questions like 'who calls this function?' by using pre-computed graphs..
Is headroom free or open source?
Yes, headroom is open source on GitHub (Apache-2.0).
Is sense free or open source?
Yes, sense is open source on GitHub (MIT).
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