Sirchmunk: Raw data to self-evolving intelligence, real-time.

Quick Start · Key Features · Web UI · How it Works · FAQ

🇨🇳 中文

🔍 Agentic Search  •  🧠 Knowledge Clustering  •  📊 Monte Carlo Evidence Sampling
⚡ Indexless Retrieval  •  🔄 Self-Evolving Knowledge Base  •  💬 Real-time Chat

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🌰 Why “Sirchmunk”？

Intelligence pipelines built upon vector-based retrieval can be rigid and brittle. They rely on static vector embeddings that are expensive to compute, blind to real-time changes, and detached from the raw context. We introduce Sirchmunk to usher in a more agile paradigm, where data is no longer treated as a snapshot, and insights can evolve together with the data.

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✨ Key Features

1. Embedding-Free: Data in its Purest Form

Sirchmunk works directly with raw data -- bypassing the heavy overhead of squeezing your rich files into fixed-dimensional vectors.

• Instant Search: Eliminating complex pre-processing pipelines in hours long indexing; just drop your files and search immediately.
• Full Fidelity: Zero information loss —- stay true to your data without vector approximation.

2. Self-Evolving: A Living Index

Data is a stream, not a snapshot. Sirchmunk is dynamic by design, while vector DB can become obsolete the moment your data changes.

• Context-Aware: Evolves in real-time with your data context.
• LLM-Powered Autonomy: Designed for Agents that perceive data as it lives, utilizing token-efficient reasoning that triggers LLM inference only when necessary to maximize intelligence while minimizing cost.

3. Intelligence at Scale: Real-Time & Massive

Sirchmunk bridges massive local repositories and the web with high-scale throughput and real-time awareness.
It serves as a unified intelligent hub for AI agents, delivering deep insights across vast datasets at the speed of thought.

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Traditional RAG vs. Sirchmunk

Dimension	Traditional RAG	✨Sirchmunk
💰 Setup Cost	High Overhead (VectorDB, GraphDB, Complex Document Parser...)	✅ Zero Infrastructure Direct-to-data retrieval without vector silos
🕒 Data Freshness	Stale (Batch Re-indexing)	✅ Instant & Dynamic Self-evolving index that reflects live changes
📈 Scalability	Linear Cost Growth	✅ Extremely low RAM/CPU consumption Native Elastic Support, efficiently handles large-scale datasets
🎯 Accuracy	Approximate Vector Matches	✅ Deterministic & Contextual Hybrid logic ensuring semantic precision
⚙️ Workflow	Complex ETL Pipelines	✅ Drop-and-Search Zero-config integration for rapid deployment

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Demonstration

Access files directly to start chatting

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🎉 News

• 🚀 Feb 5, 2026: Release v0.0.2 — MCP Support, CLI Commands & Knowledge Persistence!

  • MCP Integration: Full Model Context Protocol support, works seamlessly with Claude Desktop and Cursor IDE.
  • CLI Commands: New sirchmunk CLI with init, config, serve, and search commands.
  • KnowledgeCluster Persistence: DuckDB-powered storage with Parquet export for efficient knowledge management.
  • Knowledge Reuse: Semantic similarity-based cluster retrieval for faster searches via embedding vectors.

• 🎉🎉 Jan 22, 2026: Introducing Sirchmunk: Initial Release v0.0.1 Now Available!

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🚀 Quick Start

Prerequisites

• Python 3.10+
• LLM API Key (OpenAI-compatible endpoint, local or remote)
• Node.js 18+ (Optional, for web interface)

Installation

# Create virtual environment (recommended)
conda create -n sirchmunk python=3.13 -y && conda activate sirchmunk 

pip install sirchmunk

# Or via UV:
uv pip install sirchmunk

# Alternatively, install from source:
git clone https://github.com/modelscope/sirchmunk.git && cd sirchmunk
pip install -e .

Python SDK Usage

import asyncio

from sirchmunk import AgenticSearch
from sirchmunk.llm import OpenAIChat

llm = OpenAIChat(
        api_key="your-api-key",
        base_url="your-base-url",   # e.g., https://api.openai.com/v1
        model="your-model-name"     # e.g., gpt-4o
    )

async def main():
    
    searcher = AgenticSearch(llm=llm)
    
    result: str = await searcher.search(
        query="How does transformer attention work?",
        search_paths=["/path/to/documents"],
    )
    
    print(result)

asyncio.run(main())

⚠️ Notes:

• Upon initialization, AgenticSearch automatically checks if ripgrep-all and ripgrep are installed. If they are missing, it will attempt to install them automatically. If the automatic installation fails, please install them manually.
  • References: https://github.com/BurntSushi/ripgrep | https://github.com/phiresky/ripgrep-all
• Replace "your-api-key", "your-base-url", "your-model-name" and /path/to/documents with your actual values.

Command Line Interface

Sirchmunk provides a powerful CLI for server management and search operations.

Installation

pip install "sirchmunk[web]"

# or install via UV
uv pip install "sirchmunk[web]"

Initialize

# Initialize Sirchmunk with default settings (Default work path: `~/.sirchmunk/`)
sirchmunk init

# Initialize with WebUI frontend build (requires Node.js 18+)
sirchmunk init --ui

# Alternatively, initialize with custom work path
sirchmunk init --work-path /path/to/workspace

Configure

# Show current configuration
sirchmunk config

# Regenerate configuration file if needed (Default config file: ~/.sirchmunk/.env)
sirchmunk config --generate

Start Server

# Start backend API server only
sirchmunk serve

# Start with WebUI on a single port (requires prior `sirchmunk init --ui`)
sirchmunk serve --ui

# Development mode: backend + Next.js dev server with hot-reload
sirchmunk serve --ui --dev

# Custom host and port
sirchmunk serve --host 0.0.0.0 --port 8000

Search

# Search in current directory
sirchmunk search "How does authentication work?"

# Search in specific paths
sirchmunk search "find all API endpoints" ./src ./docs

# Quick filename search
sirchmunk search "config" --mode FILENAME_ONLY

# Output as JSON
sirchmunk search "database schema" --output json

# Use API server (requires running server)
sirchmunk search "query" --api --api-url http://localhost:8584

Available Commands

Command	Description
sirchmunk init	Initialize working directory and configuration
sirchmunk init --ui	Initialize with WebUI frontend build
sirchmunk config	Show or generate configuration
sirchmunk serve	Start the API server (backend only)
sirchmunk serve --ui	Start with embedded WebUI (single port)
sirchmunk serve --ui --dev	Start with Next.js dev server (hot-reload)
sirchmunk search	Perform search queries
sirchmunk version	Show version information

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🔌 MCP Server

Sirchmunk provides a Model Context Protocol (MCP) server that exposes its intelligent search capabilities as MCP tools. This enables seamless integration with AI assistants like Claude Desktop and Cursor IDE.

Quick Start

# Install MCP package
pip install sirchmunk-mcp

# Initialize and configure
sirchmunk-mcp init
sirchmunk-mcp config --generate

# Edit ~/.sirchmunk/.mcp_env with your LLM API key

# Test with MCP Inspector
npx @modelcontextprotocol/inspector sirchmunk-mcp serve

Features

• Multi-Mode Search: DEEP mode for comprehensive analysis, FILENAME_ONLY for fast file discovery
• Knowledge Cluster Management: Automatic extraction, storage, and reuse of knowledge
• Standard MCP Protocol: Works with stdio and Streamable HTTP transports

📖 For detailed documentation, see Sirchmunk MCP README.

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🖥️ Web UI

The web UI is built for fast, transparent workflows: chat, knowledge analytics, and system monitoring in one place.

_{Home — Chat with streaming logs, file-based RAG, and session management.}

_{Monitor — System health, chat activity, knowledge analytics, and LLM usage.}

Option 1: Single-Port Mode (Recommended)

Build the frontend once, then serve everything from a single port — no Node.js needed at runtime.

# Initialize with WebUI build (requires Node.js 18+ at build time)
sirchmunk init --ui

# Start server with embedded WebUI
sirchmunk serve --ui

Access: http://localhost:8584 (API + WebUI on the same port)

Option 2: Development Mode

For frontend development with hot-reload:

# Start backend + Next.js dev server
sirchmunk serve --ui --dev

Access:

• Frontend (hot-reload): http://localhost:8585
• Backend APIs: http://localhost:8584/docs

Option 3: Legacy Script

# Start frontend and backend via script
python scripts/start_web.py 

# Stop all services
python scripts/stop_web.py

Configuration:

• Access Settings → Envrionment Variables to configure LLM API, and other parameters.

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🏗️ How it Works

Sirchmunk Framework

Core Components

Component	Description
AgenticSearch	Search orchestrator with LLM-enhanced retrieval capabilities
KnowledgeBase	Transforms raw results into structured knowledge clusters with evidences
EvidenceProcessor	Evidence processing based on the MonteCarlo Importance Sampling
GrepRetriever	High-performance indexless file search with parallel processing
OpenAIChat	Unified LLM interface supporting streaming and usage tracking
MonitorTracker	Real-time system and application metrics collection

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Data Storage

All persistent data is stored in the configured SIRCHMUNK_WORK_PATH (default: ~/.sirchmunk/):

{SIRCHMUNK_WORK_PATH}/
  ├── .cache/
    ├── history/              # Chat session history (DuckDB)
    │   └── chat_history.db
    ├── knowledge/            # Knowledge clusters (Parquet)
    │   └── knowledge_clusters.parquet
    └── settings/             # User settings (DuckDB)
        └── settings.db

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🔗 HTTP Client Access (Search API)

When the server is running (sirchmunk serve or sirchmunk serve --ui), the Search API is accessible via any HTTP client.

API Endpoints

Method	Endpoint	Description
POST	/api/v1/search	Execute a search query
GET	/api/v1/search/status	Check server and LLM configuration status

Interactive Docs: http://localhost:8584/docs (Swagger UI)

cURL Examples

# Basic search (DEEP mode)
curl -X POST http://localhost:8584/api/v1/search \
  -H "Content-Type: application/json" \
  -d '{
    "query": "How does authentication work?",
    "search_paths": ["/path/to/project"],
    "mode": "DEEP"
  }'

# Filename search (fast, no LLM required)
curl -X POST http://localhost:8584/api/v1/search \
  -H "Content-Type: application/json" \
  -d '{
    "query": "config",
    "search_paths": ["/path/to/project"],
    "mode": "FILENAME_ONLY"
  }'

# Full parameters
curl -X POST http://localhost:8584/api/v1/search \
  -H "Content-Type: application/json" \
  -d '{
    "query": "database connection pooling",
    "search_paths": ["/path/to/project/src"],
    "mode": "DEEP",
    "max_depth": 10,
    "top_k_files": 20,
    "keyword_levels": 3,
    "include_patterns": ["*.py", "*.java"],
    "exclude_patterns": ["*test*", "*__pycache__*"],
    "return_cluster": true
  }'

# Check server status
curl http://localhost:8584/api/v1/search/status

Python Client Examples

Using requests:

import requests

response = requests.post(
    "http://localhost:8584/api/v1/search",
    json={
        "query": "How does authentication work?",
        "search_paths": ["/path/to/project"],
        "mode": "DEEP"
    },
    timeout=300  # DEEP mode may take a while
)

data = response.json()
if data["success"]:
    print(data["data"]["result"])

Using httpx (async):

import httpx
import asyncio

async def search():
    async with httpx.AsyncClient(timeout=300) as client:
        resp = await client.post(
            "http://localhost:8584/api/v1/search",
            json={
                "query": "find all API endpoints",
                "search_paths": ["/path/to/project"],
                "mode": "DEEP"
            }
        )
        data = resp.json()
        print(data["data"]["result"])

asyncio.run(search())

JavaScript Client Example

const response = await fetch("http://localhost:8584/api/v1/search", {
  method: "POST",
  headers: { "Content-Type": "application/json" },
  body: JSON.stringify({
    query: "How does authentication work?",
    search_paths: ["/path/to/project"],
    mode: "DEEP"
  })
});

const data = await response.json();
if (data.success) {
  console.log(data.data.result);
}

Request Parameters

Parameter	Type	Default	Description
query	string	required	Search query or question
search_paths	string[]	required	Directories or files to search (min 1)
mode	string	"DEEP"	DEEP or FILENAME_ONLY
max_depth	int	null	Maximum directory depth
top_k_files	int	null	Number of top files to return
keyword_levels	int	null	Keyword granularity levels
include_patterns	string[]	null	File glob patterns to include
exclude_patterns	string[]	null	File glob patterns to exclude
return_cluster	bool	false	Return full KnowledgeCluster object

Note: FILENAME_ONLY mode does not require an LLM API key. DEEP mode requires a configured LLM.

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❓ FAQ

How is this different from traditional RAG systems?

Sirchmunk takes an indexless approach:

1. No pre-indexing: Direct file search without vector database setup
2. Self-evolving: Knowledge clusters evolve based on search patterns
3. Multi-level retrieval: Adaptive keyword granularity for better recall
4. Evidence-based: Monte Carlo sampling for precise content extraction

What LLM providers are supported?

Any OpenAI-compatible API endpoint, including (but not limited too):

• OpenAI (GPT-4, GPT-4o, GPT-3.5)
• Local models served via Ollama, llama.cpp, vLLM, SGLang etc.
• Claude via API proxy

How do I add documents to search?

Simply specify the path in your search query:

result = await searcher.search(
    query="Your question",
    search_paths=["/path/to/folder", "/path/to/file.pdf"]
)

No pre-processing or indexing required!

Where are knowledge clusters stored?

Knowledge clusters are persisted in Parquet format at:

{SIRCHMUNK_WORK_PATH}/.cache/knowledge/knowledge_clusters.parquet

You can query them using DuckDB or the KnowledgeManager API.

How do I monitor LLM token usage?

1. Web Dashboard: Visit the Monitor page for real-time statistics
2. API: GET /api/v1/monitor/llm returns usage metrics
3. Code: Access searcher.llm_usages after search completion

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📋 Roadmap

• Text-retrieval from raw files
• Knowledge structuring & persistence
• Real-time chat with RAG
• Web UI support
• Web search integration
• Multi-modal support (images, videos)
• Distributed search across nodes
• Knowledge visualization and deep analytics
• More file type support

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🤝 Contributing

We welcome contributions !

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📄 License

This project is licensed under the Apache License 2.0.

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✨ Sirchmunk: Raw data to self-evolving intelligence, real-time.

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