AgentIndex icon
AgentIndex
ken logo
ken
★ 33
vs
deep-research logo
deep-research
★ 4.7k

ken vs deep-research

ken: Ken is a pure Go port of semble, providing fast hybrid code search for agents with drop-in MCP compatibility. It offers a single static binary distribution, approximately 44x fewer tokens than grep+Read at recall@10, and supports indexing source code and documentation. The retrieval algorithm is a verbatim port of semble's hybrid BM25 + Model2Vec approach.; 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

ken logo
kenRAG / Knowledge Base · ★ 33

Agent code search: quickly find relevant code chunks across large codebases

Read ken 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
ken logoken
deep-research logodeep-research
Category
RAG / Knowledge Base
RAG / Knowledge Base
Stars
★ 33
★ 4.7k
License
MIT
MIT
Install Command
go install github.com/townsendmerino/ken/cmd/ken@latest && go install github.com/townsendmerino/ken/cmd/ken-mcp@latest && ken download-model
docker run -d --name deep-research -p 3333:3000 xiangfa/deep-research
Updated
1d ago
4mo ago
Open Source
Yes
Yes
Website
↗ Visit
↗ Visit
GitHub
↗ GitHub
↗ GitHub
Tags
agents, bm25, code-search
AI Research, LLM, Privacy-focused
03

Features

ken logoken
01Pure Go, no cgo; single static binary with free cross-compilation
02Drop-in MCP-compatible with semble (same tool schemas and output format)
03Verbatim port of semble's hybrid BM25 + Model2Vec retrieval algorithm
04Measured ~44x fewer tokens than grep+Read at recall@10 on agent queries
05Fast cold start (~10–20ms for small indexes)
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

ken logoken
↳Agent code search: quickly find relevant code chunks across large codebases
↳Embedded-corpus distribution: ship documentation as a single static binary with zero infrastructure
↳Database schema indexing: index live Postgres/SQLite/MySQL schemas for agent context
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 ken and deep-research?
Both ken and deep-research are in the RAG / Knowledge Base category. ken has 33 stars, while deep-research has 4.7k stars.
Which is better, ken or deep-research?
The best choice depends on your use case. Choose ken if Agent code search: quickly find relevant code chunks across large codebases, and deep-research if Rapidly generate in-depth research reports for personal and professional use..
Is ken free or open source?
Yes, ken 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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