headroom
★ 78.0k
vs
deja-vu
★ 1.3k
headroom vs deja-vu
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.; deja-vu: Deja-vu is a zero-dependency, local-first binary that indexes and makes searchable the historical sessions of various AI coding agents. It acts as a universal memory layer, allowing agents to recall past solutions and context efficiently, even from before its installation.
01
TL;DR
headroomMemory & Context · ★ 78.0k
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- Your agent's problem is missing or outdated docs, not too much context: a docs server fits better.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.
deja-vuMemory & Context · ★ 1.3k
Reusing solutions from past agent conversations to avoid re-debugging or re-implementing.
02
Side-by-Side Comparison
Field
headroomdeja-vuCategory
Memory & Context
Memory & Context
Stars
★ 78.0k
★ 1.3k
License
Apache-2.0
MIT
Install Command
pip install "headroom-ai[all]"
curl -fsSL https://raw.githubusercontent.com/vshulcz/deja-vu/main/install.sh | sh
Updated
1d ago
1d ago
Open Source
Yes
Yes
Tags
Context Compression, Token Optimization, AI Agents
AI Agent Memory, Session Search, Code Assistant
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
deja-vu01Fast, Retroactive Session Search
02Automatic AI Agent Memory Recall
03Cross-Machine Memory Synchronization
04Seamless Agent Context Handoff
05Automated Sensitive Data Redaction
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.
deja-vu↳Reusing solutions from past agent conversations to avoid re-debugging or re-implementing.
↳Debugging complex issues by accessing comprehensive past debugging sessions and design decisions.
↳Maintaining persistent context across different AI agents and development machines.
↳Sharing sanitized session digests with colleagues for onboarding or knowledge transfer.
↳Understanding file change history and decision-making via session blame.
05
Best For
headroomMost PopularEssential
deja-vu—
FAQ
FAQ
What is the difference between headroom and deja-vu?
Both headroom and deja-vu are in the Memory & Context category. headroom has 78.0k stars, while deja-vu has 1.3k stars.
Which is better, headroom or deja-vu?
The best choice depends on your use case. Choose headroom if Reduce LLM token usage and API costs for AI agents., and deja-vu if Reusing solutions from past agent conversations to avoid re-debugging or re-implementing..
Is headroom free or open source?
Yes, headroom is open source on GitHub (Apache-2.0).
Is deja-vu free or open source?
Yes, deja-vu is open source on GitHub (MIT).
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