
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
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
# 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.
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
}
}
}
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.
{
"mcpServers": {
"th0th": {
"type": "local",
"command": ["docker", "compose", "run", "--rm", "-i", "mcp"],
"enabled": true
}
}
}
| Tool | Description |
|---|---|
th0th_index |
Index a project directory with semantic embeddings |
th0th_index_status |
Poll background indexing job progress |
th0th_search |
Hybrid semantic + keyword search with RRF ranking. Supports responseMode=enriched for full content + imports + parentSymbol in one call |
th0th_reindex |
Force full reindex after a large refactor |
th0th_reset_project |
Delete all indexed data for a project (vectors, symbols, memories) |
th0th_list_projects |
List all indexed projects with status and file counts |
th0th_project_map |
One-shot project summary: stats, top files by PageRank, symbol distribution |
| Tool | Description |
|---|---|
th0th_search_definitions |
Find function/class/type definitions by name |
th0th_get_references |
Find all usages of a symbol across the project |
th0th_go_to_definition |
Jump to definition with file + line context |
th0th_symbol_snippet |
Get raw code snippet by file + line range |
th0th_read_file |
Read a file with symbol metadata and imports |
| Tool | Description |
|---|---|
th0th_remember |
Store important information in persistent memory |
th0th_recall |
Semantic search over stored memories |
th0th_memory_list |
Browse memories by type/importance (audit mode) |
th0th_compress |
Compress context (keeps structure, removes detail) |
th0th_optimized_context |
Search + compress in one call (max token efficiency) |
th0th_analytics |
Usage patterns, cache performance, metrics |
Synapse is an optional post-retrieval modulation layer that improves result quality over a session by tracking task context, agent affinity, and working-memory. Enable by creating a session and passing sessionId to th0th_search.
| Tool | Description |
|---|---|
th0th_synapse_session |
Create/resume a cognitive session scoped to a task |
th0th_synapse_prime |
Seed working-memory buffer with recalled memories |
th0th_synapse_access |
Record file access to boost that file in future searches |
Environment variables for fine-tuning retrieval (all optional):
| Variable | Default | Description |
|---|---|---|
SEARCH_DISABLE_KEYWORD |
false |
Pure vector-only mode (+44% MRR on NL→code) |
RRF_KEYWORD_BOOST |
2.5 |
Keyword weight multiplier for code queries |
RRF_VECTOR_WEIGHT |
0.3 |
Vector similarity weight in final score blend |
RRF_MAX_CHUNKS_PER_FILE |
2 |
Diversity cap — prevents one file monopolising results |
SEARCH_MIN_SCORE |
0.3 |
Score threshold below which results are dropped |
OLLAMA_EMBED_DELAY_MS |
0 |
Delay between Ollama embed calls (set >0 for CPU) |
# Development
bun run dev:api
# Production
bun run start:api
Swagger docs: http://localhost:3333/swagger
# 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"}'
Config file: ~/.config/th0th/config.json (auto-created on first run)
# 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
| 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 |
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
| 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) |
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, enriched response mode |
| Synapse | Post-retrieval cognitive modulation: task alignment, agent affinity, working-memory buffer |
| Symbol Graph | PageRank-based centrality, definitions, references, go-to-definition |
| Embeddings | Ollama (local) or Mistral/OpenAI API |
| Compression | Rule-based code structure extraction (70-98% reduction) |
| Memory | Persistent SQLite/PostgreSQL storage across sessions |
| Cache | Multi-level L1/L2 with TTL |
MIT
$ claude mcp add th0th \
-- python -m otcore.mcp_server <graph>