Reka

Memory that flows. Knowledge that stays.

Self-hosted RAG infrastructure for AI coding assistants.
Give Claude Code, Cursor, or Windsurf persistent memory across sessions.

Plugin · Quick Start · How It Works · Features · Website · Issues

The Problem

Your AI coding assistant forgets everything between sessions. Every new conversation starts from zero -- no memory of past decisions, no knowledge of your architecture, no recall of what was already tried and failed.

Reka fixes this. It gives your AI assistant persistent memory, semantic code search, and a knowledge graph of your codebase -- all running on your machine.

Claude Code Plugin

The fastest way to use Reka. Two commands inside Claude Code:

/plugin marketplace add getreka/reka-plugin
/plugin install reka@reka-plugins

The plugin gives you the full Reka experience without any manual MCP configuration:

What you get	Details
10 commands	`/reka:code`, `/reka:investigate`, `/reka:review`, `/reka:arch`, `/reka:debate`, `/reka:start`, `/reka:end`, `/reka:onboard`, `/reka:memory-review`, `/reka:restart-api`
5 agents	feature-builder, code-reviewer, test-writer, rag-researcher, rag-ops -- all with persistent memory
4 hooks	Auto session lifecycle, context checks before edits, Prettier + tsc on save, memory consolidation on stop
MCP server	`@getreka/mcp` auto-configured with your API key (stored in OS keychain)

Team auto-install

Add to your project's .claude/settings.json and every team member gets the plugin automatically:

{
  "extraKnownMarketplaces": {
    "reka-plugins": {
      "source": { "source": "github", "repo": "getreka/reka-plugin" }
    }
  },
  "enabledPlugins": {
    "reka@reka-plugins": true
  }
}

Plugin repo: getreka/reka-plugin -- full docs, commands reference, and architecture overview.

Quick Start

Option A: Claude Code Plugin (recommended)

/plugin marketplace add getreka/reka-plugin
/plugin install reka@reka-plugins
/reka:start

Option B: MCP Server + CLI

# 1. Start the infrastructure
git clone https://github.com/getreka/reka.git
cd reka && docker-compose up -d

# 2. Initialize your project (generates API key + .mcp.json)
npx @getreka/cli init --project my-app

That's it. Open your AI assistant -- it now has memory.

Try the demo (no install needed)

npx @getreka/cli init --demo --project my-app

This connects to a live Reka instance so you can try memory, search, and indexing before deploying your own. Demo data may be reset periodically.

What `reka init` does

Creates an API key mapped to your project
Writes .mcp.json with the correct MCP server config
Your AI assistant auto-discovers the MCP server on next launch

The generated .mcp.json:

{
  "mcpServers": {
    "reka": {
      "command": "npx",
      "args": ["-y", "@getreka/mcp"],
      "env": {
        "REKA_API_KEY": "rk_myapp_a3f8b2..."
      }
    }
  }
}

One API key = one project. No extra headers or config needed. The key tells Reka which project you're working on.

How It Works

 Claude Code + Reka Plugin
        │
        │  10 commands · 5 agents · 4 hooks
        │
        ▼
 @getreka/mcp            ← npm package, auto-configured by plugin
        │
        │ HTTP + API Key
        ▼
 Reka API (:3100)        ← Express server
        │
   ┌────┼────┬────┐
   ▼    ▼    ▼    ▼
 Qdrant Ollama BGE-M3 Redis
 vectors  LLM  embed  cache

Each project gets isolated collections in Qdrant:

myapp_codebase -- indexed source code
myapp_memory -- decisions, patterns, insights
myapp_graph -- import/dependency relationships
myapp_symbols -- function/class/type index

No cross-contamination between projects. One backend serves them all.

Features

Search

Hybrid search -- vector similarity + keyword matching
Code graph -- traverse imports, dependencies, blast radius
Symbol index -- instant lookup of functions, classes, types
Smart dispatch -- LLM routes your query to the best search strategy

Memory

Persistent memory -- decisions, patterns, ADRs survive across sessions
Memory governance -- quarantine, promotion, decay (not everything deserves permanence)
Consolidation -- duplicate memories merge and evolve over time
Spreading activation -- associative recall finds related memories

Indexing

Multi-language -- TypeScript, Python, Go, Rust, Java, C#, and more
Incremental -- hash-based change detection, only re-indexes what changed
Confluence -- index your team's docs alongside code
Knowledge graph -- auto-extracts import/export relationships

Platform

Claude Code plugin -- 10 commands, 5 agents, 4 hooks, auto-configured MCP
MCP native -- works with Claude Code, Cursor, Windsurf out of the box
35 tools -- search, memory, indexing, agents, architecture, review
Dashboard -- Vue 3 web UI for memory review and analytics

LLM & Embedding

Fully local -- Ollama + BGE-M3, zero API keys needed
Or hybrid -- local for fast tasks, Claude/GPT-4 for complex analysis
Your choice -- Ollama, OpenAI, or Anthropic for LLM; BGE-M3, OpenAI, or Ollama for embeddings

Deployment Options

	Self-Hosted	Hybrid	Cloud
Price	Free	$12/dev/mo	$35/dev/mo
Infrastructure	Your machine	Local + Reka Cloud	Fully managed
Data residency	100% local	Code local, vectors in cloud	Cloud
Setup	`docker-compose up`	Coming soon	Coming soon
Best for	Privacy-first, solo devs	Teams wanting zero-ops search	Enterprise, no infra

Self-hosted is the full platform with zero limitations. Hybrid and Cloud are coming soon -- join the waitlist.

Requirements

Component	Minimum	Recommended
Docker	24+	Latest
RAM	8 GB	16 GB
Node.js	22+	Latest LTS
Disk	~2 GB	SSD recommended
GPU	Not required	NVIDIA for Ollama acceleration

For the Claude Code plugin only: Claude Code 1.0.33+, Node.js 22+. Docker required only for self-hosted backend.

Configuration

Key environment variables for rag-api/.env:

Variable	Default	Description
`EMBEDDING_PROVIDER`	`bge-m3-server`	`bge-m3-server`, `ollama`, `openai`
`LLM_PROVIDER`	`ollama`	`ollama`, `openai`, `anthropic`
`OLLAMA_MODEL`	`qwen3.5:35b`	Model for LLM completions
`QDRANT_URL`	`http://localhost:6333`	Vector database
`REDIS_URL`	`redis://localhost:6380`	Cache (optional, recommended)
`OPENAI_API_KEY`	--	Required if using OpenAI
`ANTHROPIC_API_KEY`	--	Required if using Anthropic

Default config runs fully local -- no API keys needed.

MCP Tools

Reka exposes ~35 tools to your AI assistant. The most used:

Tool	What it does
`search_codebase`	Semantic search across your indexed code
`hybrid_search`	Vector + keyword search combined
`find_symbol`	Fast lookup: functions, classes, types
`search_graph`	Traverse imports, find dependencies
`ask_codebase`	Natural language Q&A over your code
`context_briefing`	Multi-strategy lookup before making changes
`remember`	Store a decision, insight, or pattern
`recall`	Retrieve relevant memories
`record_adr`	Record an architecture decision
`index_codebase`	Index or re-index the project
`start_session` / `end_session`	Session lifecycle with continuity
`run_agent`	Launch an autonomous sub-agent

Dashboard

cd dashboard && npm install && npm run dev
# Open http://localhost:3000

Memory explorer, search playground, project analytics, indexing status, system health.

Comparison

	Reka	Cursor Memory	Cody (Sourcegraph)	Continue.dev	Greptile
Self-hosted	Fully	Cloud only	Partial	Yes	Cloud only
Claude Code plugin	Yes	No	No	No	No
Memory governance	Full lifecycle	No	No	No	No
Multi-project	Isolated	Per-workspace	Yes	Limited	Yes
Code graph	Yes	No	Yes	No	Yes
MCP native	Yes	No	No	Yes	No
LLM choice	Any	Locked	Locked	Any	Locked
Data privacy	100% local	Cloud	Partial	Local	Cloud
Cost	Free	$/seat	$/seat	Free	$/repo

Project Structure

reka/
├── rag-api/          # Core API server (Express, TypeScript)
├── mcp-server/       # MCP server (@getreka/mcp)
├── cli/              # CLI tool (@getreka/cli)
├── dashboard/        # Web dashboard (Vue 3)
├── docker/           # Dockerfiles, Grafana, Prometheus configs
└── docker-compose.yml

Plugin: getreka/reka-plugin -- Claude Code plugin (separate repo)

Contributing

We welcome contributions. See CONTRIBUTING.md for setup and guidelines.

Good first issues are labeled good first issue.

License

Reka is source-available under the Business Source License 1.1.

Self-host freely -- no restrictions for internal use
Build integrations -- plugins, extensions, custom tools
Converts to Apache 2.0 after 3 years

The only restriction: you cannot offer Reka as a managed service competing with Reka Cloud.

Give your AI assistant the memory it deserves.

Install Plugin · Get started · getreka.dev · Report an issue

Name		Name	Last commit message	Last commit date
Latest commit History 117 Commits
.claude		.claude
.github/workflows		.github/workflows
cli		cli
dashboard		dashboard
docker		docker
mcp-server		mcp-server
rag-api		rag-api
.eslintignore		.eslintignore
.gitignore		.gitignore
.mcp.json.example		.mcp.json.example
CLAUDE.md		CLAUDE.md
CONTRIBUTING.md		CONTRIBUTING.md
LICENSE		LICENSE
README.md		README.md
docker-compose.yml		docker-compose.yml
install.sh		install.sh

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Reka

The Problem

Claude Code Plugin

Team auto-install

Quick Start

Option A: Claude Code Plugin (recommended)

Option B: MCP Server + CLI

Try the demo (no install needed)

What `reka init` does

How It Works

Features

Search

Memory

Indexing

Platform

LLM & Embedding

Deployment Options

Requirements

Configuration

MCP Tools

Dashboard

Comparison

Project Structure

Contributing

License

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Reka

The Problem

Claude Code Plugin

Team auto-install

Quick Start

Option A: Claude Code Plugin (recommended)

Option B: MCP Server + CLI

Try the demo (no install needed)

What reka init does

How It Works

Features

Search

Memory

Indexing

Platform

LLM & Embedding

Deployment Options

Requirements

Configuration

MCP Tools

Dashboard

Comparison

Project Structure

Contributing

License

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