th0th
Semantic search with 98% token reduction for AI assistants.
README
<img src="https://i.imgur.com/WP7ivBc.png" alt="th0th" style="visibility: visible; max-width: 60%; display: block; margin: 0 auto;" />
th0th
Ancient knowledge keeper for modern code
Semantic search with 98% token reduction for AI assistants.
Como reduzi 98% do uso de contexto (e custos) de IA no meu workflow / How I reduced AI context usage (and costs) by 98% in my workflow https://www.tabnews.com.br/S1LV4/como-reduzi-em-98-por-cento-o-uso-de-contexto-e-os-custos-de-ia-no-meu-workflow
Quick Start
One-line install (recommended)
curl -fsSL https://raw.githubusercontent.com/S1LV4/th0th/main/install.sh | bash
Installs interactively. Three modes:
| Mode | Requires | Best for |
|---|---|---|
| Docker (default) | Docker | Production, quick start |
| Docker build | Docker + Git | Custom builds, local changes |
| Source | Git + Bun | Development, contributors |
Non-interactive (CI/scripted):
# Docker mode, custom port, skip start
TH0TH_MODE=docker TH0TH_API_PORT=4000 TH0TH_NO_START=1 \
curl -fsSL https://raw.githubusercontent.com/S1LV4/th0th/main/install.sh | bash
Manual setup (from source)
# 1. Clone and install
git clone https://github.com/S1LV4/th0th.git
cd th0th
bun install
# 2. Setup (100% offline with Ollama)
./scripts/setup-local-first.sh
# - Installs/starts Ollama
# - Pulls bge-m3 embedding model (1024 dimensions)
# - Creates .env with defaults
# - Runs bun run diagnose to validate the stack
# 3. Build and start
bun run build
bun run start:api
Verify: curl http://localhost:3333/health
Tip: Run
bun run diagnoseat any time to validate Ollama connectivity, database access, embedding generation, and migration status.
Integration
OpenCode (recommended)
File: ~/.config/opencode/opencode.json
Via MCP package:
{
"mcp": {
"th0th": {
"type": "local",
"command": [
"bunx",
"@th0th-ai/mcp-client"
],
"environment": {
"TH0TH_API_URL": "http://localhost:3333"
},
"enabled": true
}
}
}
Via Plugin:
{
"plugin": ["@th0th-ai/opencode-plugin"]
}
From source (development):
{
"mcpServers": {
"th0th": {
"type": "local",
"command": ["bun", "run", "/path/to/th0th/apps/mcp-client/src/index.ts"],
"enabled": true
}
}
}
VSCode / Antigravity
Create .vscode/mcp.json in your workspace:
{
"servers": {
"th0th": {
"command": "bunx",
"args": ["@th0th-ai/mcp-client"],
"env": {
"TH0TH_API_URL": "http://localhost:3333"
}
}
}
}
Or run ./scripts/setup-vscode.sh for automatic configuration.
Docker
{
"mcpServers": {
"th0th": {
"type": "local",
"command": ["docker", "compose", "run", "--rm", "-i", "mcp"],
"enabled": true
}
}
}
Available Tools
| Tool | Description |
|---|---|
th0th_index |
Index a project directory for semantic search |
th0th_search |
Semantic + keyword search with filters |
th0th_remember |
Store important information in persistent memory |
th0th_recall |
Search stored memories from previous sessions |
th0th_compress |
Compress context (keeps structure, removes details) |
th0th_optimized_context |
Search + compress in one call (max token efficiency) |
th0th_analytics |
Usage patterns, cache performance, metrics |
REST API
# Development
bun run dev:api
# Production
bun run start:api
Swagger docs: http://localhost:3333/swagger
Endpoints
# Index a project
curl -X POST http://localhost:3333/api/v1/project/index \
-H "Content-Type: application/json" \
-d '{"projectPath": "/home/user/my-project", "projectId": "my-project"}'
# Search
curl -X POST http://localhost:3333/api/v1/search/project \
-H "Content-Type: application/json" \
-d '{"query": "authentication", "projectId": "my-project"}'
# Store memory
curl -X POST http://localhost:3333/api/v1/memory/store \
-H "Content-Type: application/json" \
-d '{"content": "Important decision...", "type": "decision"}'
# Compress context
curl -X POST http://localhost:3333/api/v1/context/compress \
-H "Content-Type: application/json" \
-d '{"content": "...", "strategy": "code_structure"}'
Configuration
Config file: ~/.config/th0th/config.json (auto-created on first run)
Quick Config Commands
# Show current configuration
npx @th0th-ai/mcp-client --config-show
# Show config file path
npx @th0th-ai/mcp-client --config-path
# Show config directory
npx @th0th-ai/mcp-client --config-dir
# Initialize configuration
npx @th0th-ai/mcp-client --config-init
# Show help
npx @th0th-ai/mcp-client --help
Embedding Providers
| Provider | Model | Cost | Quality |
|---|---|---|---|
| Ollama (default) | qwen3-embedding, bge-m3, nomic-embed-text | Free | Good-Excellent |
| Mistral | mistral-embed, codestral-embed | $$ | Great |
| OpenAI | text-embedding-3-small | $$ | Great |
Advanced Configuration
For detailed configuration management, use the config CLI:
# Initialize with specific provider
npx @th0th-ai/mcp-client --config-init # Ollama (default)
npx @th0th-ai/mcp-client --config-init --mistral your-api-key # Mistral
npx @th0th-ai/mcp-client --config-init --openai your-api-key # OpenAI
# Switch provider
npx @th0th-ai/mcp-client --config-init --mistral your-api-key
npx @th0th-ai/mcp-client --config-init --ollama-model qwen3-embedding
# Set specific configuration values
npx @th0th-ai/mcp-client --config-set embedding.dimensions 4096
Scripts
| Command | Description |
|---|---|
bun run build |
Build all packages |
bun run dev |
Development (all apps) |
bun run dev:api |
REST API with hot reload |
bun run dev:mcp |
MCP server with watch |
bun run start:api |
Start REST API |
bun run start:mcp |
Start MCP server |
bun run test |
Run tests |
bun run lint |
Lint code |
bun run type-check |
Type checking |
bun run diagnose |
Validate full stack (Ollama, database, embeddings) |
Architecture
th0th/
├── apps/
│ ├── mcp-client/ # MCP Server (stdio)
│ ├── tools-api/ # REST API (port 3333)
│ └── opencode-plugin/ # OpenCode plugin
├── packages/
│ ├── core/ # Business logic, search, embeddings, compression
│ └── shared/ # Shared types & utilities
└── scripts/
| Component | Description |
|---|---|
| Semantic Search | Hybrid vector + keyword with RRF ranking |
| Embeddings | Ollama (local) or Mistral/OpenAI API |
| Compression | Rule-based code structure extraction (70-98% reduction) |
| Memory | Persistent SQLite storage across sessions |
| Cache | Multi-level L1/L2 with TTL |
License
MIT
Recommended Servers
playwright-mcp
A Model Context Protocol server that enables LLMs to interact with web pages through structured accessibility snapshots without requiring vision models or screenshots.
Magic Component Platform (MCP)
An AI-powered tool that generates modern UI components from natural language descriptions, integrating with popular IDEs to streamline UI development workflow.
Audiense Insights MCP Server
Enables interaction with Audiense Insights accounts via the Model Context Protocol, facilitating the extraction and analysis of marketing insights and audience data including demographics, behavior, and influencer engagement.
VeyraX MCP
Single MCP tool to connect all your favorite tools: Gmail, Calendar and 40 more.
graphlit-mcp-server
The Model Context Protocol (MCP) Server enables integration between MCP clients and the Graphlit service. Ingest anything from Slack to Gmail to podcast feeds, in addition to web crawling, into a Graphlit project - and then retrieve relevant contents from the MCP client.
Kagi MCP Server
An MCP server that integrates Kagi search capabilities with Claude AI, enabling Claude to perform real-time web searches when answering questions that require up-to-date information.
E2B
Using MCP to run code via e2b.
Neon Database
MCP server for interacting with Neon Management API and databases
Qdrant Server
This repository is an example of how to create a MCP server for Qdrant, a vector search engine.
Exa Search
A Model Context Protocol (MCP) server lets AI assistants like Claude use the Exa AI Search API for web searches. This setup allows AI models to get real-time web information in a safe and controlled way.