AgentIndex icon
AgentIndex
rlm-claude logo
rlm-claude
★ 39
vs
Yggdrasil logo
Yggdrasil
★ 22

rlm-claude vs Yggdrasil

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.; Yggdrasil: Yggdrasil provides a local, always-on memory for AI agents like Claude Code and Codex, allowing them to retain project context, decisions, and lessons across different sessions and projects. It ensures AI assistants never forget past interactions, offering cross-project recall and persistent knowledge without sending data to the cloud.

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 →
Yggdrasil logo
YggdrasilMemory & Context · ★ 22

Prevent AI agents from forgetting project context in new sessions.

Read Yggdrasil full review →
02

Side-by-Side Comparison

Field
rlm-claude logorlm-claude
Yggdrasil logoYggdrasil
Category
Memory & Context
Memory & Context
Stars
★ 39
★ 22
License
MIT
AGPL-3.0
Install Command
pip install mcp-rlm-server[all]
uvx --from yggdrasil-memory ygg install
Updated
2w ago
1d ago
Open Source
Yes
Yes
Website
↗ Visit
↗ Visit
GitHub
↗ GitHub
↗ GitHub
Tags
Persistent Memory, Claude Code Integration, Context Management
Local Memory, AI Agent, Persistent Context
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.
Yggdrasil logoYggdrasil
01Persistent memory across sessions, tools, and projects.
02Shared memory for Claude Code, Codex, and other MCP agents.
03Cross-project recall to reuse past solutions and decisions.
04100% local operation with no cloud dependency or telemetry.
05Zero-dependency default mode using SQLite + FTS5 for keyword search.
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.
Yggdrasil logoYggdrasil
↳Prevent AI agents from forgetting project context in new sessions.
↳Allow AI agents to leverage decisions and lessons learned from other projects.
↳Consolidate existing knowledge sources (e.g., transcripts, Obsidian vaults) into a unified memory.
↳Provide a consistent identity and persona for AI assistants across tools.
FAQ

FAQ

What is the difference between rlm-claude and Yggdrasil?
Both rlm-claude and Yggdrasil are in the Memory & Context category. rlm-claude has 39 stars, while Yggdrasil has 22 stars.
Which is better, rlm-claude or Yggdrasil?
The best choice depends on your use case. Choose rlm-claude if Preventing context and decision loss during long Claude Code development sessions., and Yggdrasil if Prevent AI agents from forgetting project context in new sessions..
Is rlm-claude free or open source?
Yes, rlm-claude is open source on GitHub (MIT).
Is Yggdrasil free or open source?
Yes, Yggdrasil is open source on GitHub (AGPL-3.0).
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