AgentIndex icon
AgentIndex
rekal logo
rekal
★ 53
vs
deep-research logo
deep-research
★ 4.7k

rekal vs deep-research

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.; deep-research: Deep Research is an AI-powered tool designed to generate comprehensive research reports rapidly, leveraging advanced AI models and internet connectivity. It prioritizes user privacy by processing and storing all data locally.

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 →
deep-research logo
deep-researchRAG / Knowledge Base · ★ 4.7k

Rapidly generate in-depth research reports for personal and professional use.

Read deep-research full review →
02

Side-by-Side Comparison

Field
rekal logorekal
deep-research logodeep-research
Category
RAG / Knowledge Base
RAG / Knowledge Base
Stars
★ 53
★ 4.7k
License
MIT
MIT
Install Command
pip install rekal
docker run -d --name deep-research -p 3333:3000 xiangfa/deep-research
Updated
1mo ago
4mo ago
Open Source
Yes
Yes
Website
↗ Visit
↗ Visit
GitHub
↗ GitHub
↗ GitHub
Tags
llm, mcp, mcp-server
AI Research, LLM, Privacy-focused
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
deep-research logodeep-research
01Rapidly generates comprehensive research reports in minutes.
02Ensures privacy by processing and storing all data locally in the browser.
03Supports a wide range of mainstream large language models (LLMs).
04Integrates with various web search engines for enhanced information gathering.
05Allows uploading and processing local files to build a knowledge base.
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
deep-research logodeep-research
↳Rapidly generate in-depth research reports for personal and professional use.
↳Serve as a deep research as a service (SaaS) platform via SSE API.
↳Integrate deep research functionality into other AI applications using MCP.
FAQ

FAQ

What is the difference between rekal and deep-research?
Both rekal and deep-research are in the RAG / Knowledge Base category. rekal has 53 stars, while deep-research has 4.7k stars.
Which is better, rekal or deep-research?
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 deep-research if Rapidly generate in-depth research reports for personal and professional use..
Is rekal free or open source?
Yes, rekal is open source on GitHub (MIT).
Is deep-research free or open source?
Yes, deep-research is open source on GitHub (MIT).
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