headroom
★ 77.5k
vs
remindb
★ 129
headroom vs remindb
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.; remindb: remindb stores agentic memory in a single SQLite file. It parses notes into a structured tree with temperature tracking, enables efficient retrieval via FTS5 search, and supports versioning and summarization. This reduces token costs compared to re-reading raw files every session.
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- Your agent's problem is missing or outdated docs, not too much context: a docs server fits better.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.
remindbMemory & Context · ★ 129
Prevent coding agents from re-reading the same notes every session
02
Side-by-Side Comparison
Field
headroomremindbCategory
Memory & Context
Memory & Context
Stars
★ 77.5k
★ 129
License
Apache-2.0
MIT
Install Command
pip install "headroom-ai[all]"
curl -fsSL https://raw.githubusercontent.com/radimsem/remindb/main/install.sh | bash
Updated
1d ago
2mo ago
Open Source
Yes
Yes
Tags
Context Compression, Token Optimization, AI Agents
agent-memory, ai-agents, ast
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
remindb01Structured memory tree with typed nodes (heading, list, code, etc.)
02Temperature mechanism that tracks node usage and promotes hot content
03Automatic summarization of cold nodes via agent-driven compaction
04Git-style versioning with per-node diffs and snapshot cursors
05Full-text search (FTS5) with token budget control
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.
remindb↳Prevent coding agents from re-reading the same notes every session
↳Maintain persistent, cross-session memory for MCP-capable agents
↳Share memory between different agents or devices via a portable .db file
05
Best For
headroomMost PopularEssential
remindb—
FAQ
FAQ
What is the difference between headroom and remindb?
Both headroom and remindb are in the Memory & Context category. headroom has 77.5k stars, while remindb has 129 stars.
Which is better, headroom or remindb?
The best choice depends on your use case. Choose headroom if Reduce LLM token usage and API costs for AI agents., and remindb if Prevent coding agents from re-reading the same notes every session.
Is headroom free or open source?
Yes, headroom is open source on GitHub (Apache-2.0).
Is remindb free or open source?
Yes, remindb is open source on GitHub (MIT).
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