AgentIndex icon
AgentIndex
rlm-claude logo
rlm-claude
★ 39
vs
cortex logo
cortex
★ 35

rlm-claude vs cortex

rlm-claude: RLM is an MCP server designed to provide Claude Code with persistent memory, preventing loss of conversation history and context after `/compact` operations. It offers advanced memory management features including insights, chunking, and intelligent retention, enabling more productive and context-aware AI interactions.; cortex: Cortex is a private, free, and local memory engine for personal AI agents, built with Rust. It provides multi-tier, self-evolving long-term memory that persists across sessions and devices, offering sub-millisecond performance and end-to-end encrypted cloud synchronization.

01

TL;DR

rlm-claude logo
rlm-claudeMemory & Context · ★ 39

Preventing context and decision loss during long Claude Code development sessions.

Read rlm-claude full review →
cortex logo
cortexMemory & Context · ★ 35

Providing persistent, private long-term memory for AI agents and LLMs across conversation sessions.

Read cortex full review →
02

Side-by-Side Comparison

Field
rlm-claude logorlm-claude
cortex logocortex
Category
Memory & Context
Memory & Context
Stars
★ 39
★ 35
License
MIT
MIT
Install Command
pip install mcp-rlm-server[all]
brew tap gambletan/tap && brew install cortex-mcp-server
Updated
2w ago
6d ago
Open Source
Yes
Yes
Website
↗ Visit
↗ Visit
GitHub
↗ GitHub
↗ GitHub
Tags
Persistent Memory, Claude Code Integration, Context Management
AI Agent Memory, Local Storage, Privacy-Preserving AI
03

Features

rlm-claude logorlm-claude
01Automatic snapshot saving before context loss during `/compact`.
02Dual memory systems: 'Insights' for key decisions/facts and 'Chunks' for full conversation segments.
03Hybrid search combining BM25 keyword matching and semantic cosine similarity.
04Smart retention lifecycle with active, archive, and purge zones, and immunity for critical data.
05Multi-project and multi-domain organization for memory and search filtering.
cortex logocortex
01Private by Default: Memories reside in a local SQLite file, ensuring zero telemetry and data privacy.
02Real, Multi-Tier Memory: Features 4 memory tiers (Working, Episodic, Semantic, Procedural), multi-signal retrieval, Bayesian beliefs, and a cross-channel people graph.
03Sub-millisecond Performance: Achieves 156µs ingest and 568µs search speeds, significantly faster than cloud memory APIs.
04Cross-Device End-to-End Encrypted Sync: Optionally syncs memories across devices through your own cloud storage (iCloud, Google Drive, etc.) with AES-256-GCM encryption.
05Drop-in Integration: Serves as an MCP server for Claude, offers Python bindings, and integrates with frameworks like LangGraph, DeerFlow, and OpenClaw.
04

Use Cases

rlm-claude logorlm-claude
↳Preventing context and decision loss during long Claude Code development sessions.
↳Efficiently recalling past project details, architectural decisions, and technical findings.
↳Organizing and searching through AI-generated knowledge across different projects and domains.
↳Revisiting debug sessions and past solutions for problem-solving and code refactoring.
↳Maintaining universal rules and preferences that apply across all AI interactions.
cortex logocortex
↳Providing persistent, private long-term memory for AI agents and LLMs across conversation sessions.
↳Enabling AI assistants to remember user preferences, facts, and past interactions without sending data to external servers.
↳Building personal AI systems where user data stays entirely on-device or synced via user's own encrypted cloud storage.
↳Integrating advanced memory capabilities (e.g., people graph, Bayesian beliefs, contradiction detection) into agent orchestration platforms like LangGraph or DeerFlow.
↳Developing AI applications that require low-latency, real-time memory access and context generation for prompt injection.
FAQ

FAQ

What is the difference between rlm-claude and cortex?
Both rlm-claude and cortex are in the Memory & Context category. rlm-claude has 39 stars, while cortex has 35 stars.
Which is better, rlm-claude or cortex?
The best choice depends on your use case. Choose rlm-claude if Preventing context and decision loss during long Claude Code development sessions., and cortex if Providing persistent, private long-term memory for AI agents and LLMs across conversation sessions..
Is rlm-claude free or open source?
Yes, rlm-claude is open source on GitHub (MIT).
Is cortex free or open source?
Yes, cortex is open source on GitHub (MIT).
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