mori
★ 32
vs
headroom
★ 77.5k
mori vs headroom
mori: Mori is a shared memory layer for AI coding agents that allows multiple instances to learn from each other, ensuring coherence and preventing 'cold starts'. It distills session activity into durable knowledge, enabling agents to start informed and benefit from collective intelligence.; 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.
01
TL;DR
moriMemory & Context · ★ 32
Enabling AI coding agents across different machines or teams to share learned knowledge and maintain consistency.
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- Your agent's problem is missing or outdated docs, not too much context: a docs server fits better.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.
02
Side-by-Side Comparison
Field
moriheadroomCategory
Memory & Context
Memory & Context
Stars
★ 32
★ 77.5k
License
AGPL-3.0
Apache-2.0
Install Command
git clone https://github.com/fjwood69/mori.git && cd mori && cp deploy/homelab/.env.example deploy/homelab/.env && docker compose -f deploy/homelab/docker-compose.yml up -d
pip install "headroom-ai[all]"
Updated
3d ago
1d ago
Open Source
Yes
Yes
Tags
AI Agents, Shared Memory, Knowledge Base
Context Compression, Token Optimization, AI Agents
03
Features
mori01Multi-Instance Coherence: Shared memory ensures all AI agent instances are informed by others' decisions.
02Dream Pipeline: Automatically distills session events into structured memories.
03Universal Ingestion: Feed various data types (PDFs, images, git history) into the memory store.
04Strategic Consultation: Provides LLM guidance grounded in project context and team standards.
05Inter-Agent Messaging: Enables agents to send tasks, questions, and decisions across the network.
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
04
Use Cases
mori↳Enabling AI coding agents across different machines or teams to share learned knowledge and maintain consistency.
↳Onboarding new AI agents or team members by rapidly bootstrapping the memory store with existing project documentation and history.
↳Obtaining context-aware strategic advice and reviews (e.g., architecture, security) from LLMs based on project specifics.
↳Orchestrating complex tasks by delegating work and sharing decisions among multiple AI agent instances.
↳Persistent knowledge base for AI development projects, preventing repetitive learning and ensuring continuity.
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.
05
Best For
mori—
headroomMost PopularEssential
FAQ
FAQ
What is the difference between mori and headroom?
Both mori and headroom are in the Memory & Context category. mori has 32 stars, while headroom has 77.5k stars.
Which is better, mori or headroom?
The best choice depends on your use case. Choose mori if Enabling AI coding agents across different machines or teams to share learned knowledge and maintain consistency., and headroom if Reduce LLM token usage and API costs for AI agents..
Is mori free or open source?
Yes, mori is open source on GitHub (AGPL-3.0).
Is headroom free or open source?
Yes, headroom is open source on GitHub (Apache-2.0).
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