AgentIndex icon
AgentIndex
rekal logo
rekal
★ 53
vs
engram-rs logo
engram-rs
★ 28

rekal vs engram-rs

rekal: rekal provides long-term memory for LLMs via an MCP server. It stores memories in a local SQLite file and retrieves them using hybrid search (BM25 keywords + vector semantics + recency decay). It works with any MCP-capable agent like Claude Code, Codex CLI, and OpenCode.; engram-rs: engram-rs is a memory engine for AI agents that organizes knowledge along two axes: time (three-layer decay & promotion) and space (self-organizing topic tree). It intelligently promotes important memories, fades noise, and automatically clusters related knowledge, providing a more effective alternative to flat memory stores.

01

TL;DR

rekal logo
rekalRAG / Knowledge Base · ★ 53

Persistent memory for coding agents across sessions to remember user preferences and decisions

Read rekal full review →
engram-rs logo
engram-rsRAG / Knowledge Base · ★ 28

Enhancing AI agents with structured, evolving memory

Read engram-rs full review →
02

Side-by-Side Comparison

Field
rekal logorekal
engram-rs logoengram-rs
Category
RAG / Knowledge Base
RAG / Knowledge Base
Stars
★ 53
★ 28
License
MIT
MIT
Install Command
pip install rekal
curl -fsSL https://raw.githubusercontent.com/kael-bit/engram-rs/main/install.sh | bash
Updated
1mo ago
7mo ago
Open Source
Yes
Yes
Website
↗ Visit
↗ Visit
GitHub
↗ GitHub
↗ GitHub
Tags
llm, mcp, mcp-server
AI Memory Engine, Rust, Semantic Search
03

Features

rekal logorekal
01Local SQLite storage, no cloud or API keys
02Hybrid search combining BM25 keywords, vector semantics, and recency decay
03MCP server compatible with various coding agents
04Comprehensive tool-based memory management (store, search, update, delete, prune)
05Smart write operations with supersede and linking for knowledge evolution
engram-rs logoengram-rs
01Three-Layer Lifecycle (Buffer, Working, Core memory management)
02LLM Quality Gate for intelligent memory promotion
03Self-Organizing Topic Tree for automatic knowledge clustering
04Semantic Dedup & Merge of similar memories
05Activity-driven Automatic Decay of importance
04

Use Cases

rekal logorekal
↳Persistent memory for coding agents across sessions to remember user preferences and decisions
↳Storing project-specific knowledge and conventions without external services
↳Maintaining a searchable history of agent interactions and decisions
engram-rs logoengram-rs
↳Enhancing AI agents with structured, evolving memory
↳Managing context and long-term knowledge for AI applications
↳Automating knowledge retention and recall for intelligent systems
↳Providing a self-organizing memory for LLM-powered assistants
FAQ

FAQ

What is the difference between rekal and engram-rs?
Both rekal and engram-rs are in the RAG / Knowledge Base category. rekal has 53 stars, while engram-rs has 28 stars.
Which is better, rekal or engram-rs?
The best choice depends on your use case. Choose rekal if Persistent memory for coding agents across sessions to remember user preferences and decisions, and engram-rs if Enhancing AI agents with structured, evolving memory.
Is rekal free or open source?
Yes, rekal is open source on GitHub (MIT).
Is engram-rs free or open source?
Yes, engram-rs is open source on GitHub (MIT).
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Related

Alternatives to rekal →Alternatives to engram-rs →rekal details →engram-rs details →