headroom
★ 77.5k
vs
enquire-mcp
★ 34
headroom vs enquire-mcp
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.; enquire-mcp: enquire-mcp transforms your Obsidian vault into persistent, queryable long-term memory for various AI agents, eliminating the need to re-explain context in every session. It provides best-in-class hybrid retrieval, including semantic search, reranking, and HNSW, all running locally with zero cloud calls during serving.
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- Your agent's problem is missing or outdated docs, not too much context: a docs server fits better.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.
enquire-mcpMemory & Context · ★ 34
Long-term memory for AI agents: Provides durable, semantic recall for agents across sessions, models, and providers, grounded in your Obsidian notes.
02
Side-by-Side Comparison
Field
headroomenquire-mcpCategory
Memory & Context
Memory & Context
Stars
★ 77.5k
★ 34
License
Apache-2.0
MIT
Install Command
pip install "headroom-ai[all]"
npm install -g @oomkapwn/enquire-mcp
Updated
1d ago
1d ago
Open Source
Yes
Yes
Tags
Context Compression, Token Optimization, AI Agents
AI Agents, Long-term Memory, RAG
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
enquire-mcp01Vendor-neutral memory: Your memory lives in .md files, ensuring portability across different AI agents and providers.
02Best-in-class hybrid retrieval: Features BM25, multilingual embeddings, BGE cross-encoder reranker, HNSW, and int8 quantization for superior search quality and performance.
03Zero cloud calls during serve: All models are cached locally, and vault content never leaves your machine, ensuring privacy and air-gap safety.
04Freshness-aware recall: Search hits report note age and a `stale` flag, with optional recency-weighted ranking to prioritize fresh knowledge.
05PDFs with OCR & GraphRAG-light: Integrates PDFs (including OCR for scanned docs) into hybrid search and offers GraphRAG-light for wikilink community detection.
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.
enquire-mcp↳Long-term memory for AI agents: Provides durable, semantic recall for agents across sessions, models, and providers, grounded in your Obsidian notes.
↳Personal knowledge base / second brain: Offers hybrid retrieval across 50+ languages, wikilink graph-boost, and GraphRAG-light to surface relevant notes and discover connections.
↳Agentic RAG / context engineering: Exposes per-signal scores, HyDE pre-rewrites, and sub-question decomposition for advanced retrieval, plus a built-in evaluation harness.
05
Best For
headroomMost PopularEssential
enquire-mcp—
FAQ
FAQ
What is the difference between headroom and enquire-mcp?
Both headroom and enquire-mcp are in the Memory & Context category. headroom has 77.5k stars, while enquire-mcp has 34 stars.
Which is better, headroom or enquire-mcp?
The best choice depends on your use case. Choose headroom if Reduce LLM token usage and API costs for AI agents., and enquire-mcp if Long-term memory for AI agents: Provides durable, semantic recall for agents across sessions, models, and providers, grounded in your Obsidian notes..
Is headroom free or open source?
Yes, headroom is open source on GitHub (Apache-2.0).
Is enquire-mcp free or open source?
Yes, enquire-mcp is open source on GitHub (MIT).
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