headroom
★ 77.5k
vs
state-memory-mcp
★ 49
headroom vs state-memory-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.; state-memory-mcp: @putervision/state-memory-mcp is a zero-infrastructure, deterministic Model Context Protocol (MCP) server designed to provide AI coding assistants with a structured, persistent SQLite graph for tracking workflow state. It offers efficient context management, reduces latency by offloading to a local database, and supports multi-agent collaboration with a shared blackboard.
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- Your agent's problem is missing or outdated docs, not too much context: a docs server fits better.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.
state-memory-mcpMemory & Context · ★ 49
Providing persistent workflow state to AI coding assistants like Cursor and Copilot
02
Side-by-Side Comparison
Field
headroomstate-memory-mcpCategory
Memory & Context
Memory & Context
Stars
★ 77.5k
★ 49
License
Apache-2.0
MIT
Install Command
pip install "headroom-ai[all]"
npm install -g @putervision/state-memory-mcp && state-memory-mcp init
Updated
1d ago
2w ago
Open Source
Yes
Yes
Tags
Context Compression, Token Optimization, AI Agents
State Management, Context Management, SQLite
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
state-memory-mcp01Deterministic State Memory & Compact TaskSlices for fast evaluation
02Efficient Context Management & Decision Thresholding to prevent graph bloat
03Multi-Agent Blackboard for shared context and parallel subagent updates
04100% Local & Private architecture, ensuring all state stays within your workspace
05Interactive 3D Visualizer for graph exploration
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.
state-memory-mcp↳Providing persistent workflow state to AI coding assistants like Cursor and Copilot
↳Managing context and decision-making for autonomous agents
↳Facilitating multi-agent collaboration on complex tasks with a shared context store
↳Auditing and tracing agent activities with a tamper-evident event ledger
↳Improving agent efficiency and reducing latency in task retrieval and context processing
05
Best For
headroomMost PopularEssential
state-memory-mcp—
FAQ
FAQ
What is the difference between headroom and state-memory-mcp?
Both headroom and state-memory-mcp are in the Memory & Context category. headroom has 77.5k stars, while state-memory-mcp has 49 stars.
Which is better, headroom or state-memory-mcp?
The best choice depends on your use case. Choose headroom if Reduce LLM token usage and API costs for AI agents., and state-memory-mcp if Providing persistent workflow state to AI coding assistants like Cursor and Copilot.
Is headroom free or open source?
Yes, headroom is open source on GitHub (Apache-2.0).
Is state-memory-mcp free or open source?
Yes, state-memory-mcp is open source on GitHub (MIT).
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