llama-searcher 0.1.0

A powerful, modular RAG-orchestrator that aggregates 10+ search engines (Google, Bing, Firecrawl, Exa, Tavily) into LLM-ready markdown.

🦙 llama-searcher

llama-searcher is a professional-grade search orchestration framework designed for AI Agents and RAG (Retrieval-Augmented Generation) pipelines. It unifies traditional SEO/SERP APIs with modern neural search engines, transforming raw web data into clean, LLM-ready markdown.

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🚀 Key Features

• Deca-Engine Support: Seamlessly switch between or aggregate results from 10+ providers:
  • Neural AI Search: Firecrawl, Exa (Metaphor), Tavily, Perplexity Sonar.
  • Traditional SERP: Google Custom Search, Bing, SerpApi, Serper.dev, Brave Search, Zenserp.
• RAG-Ready Output: Automatically cleans HTML, removes boilerplate (nav, footers, scripts), and returns structured markdown optimized for context windows.
• Smart Orchestration: Concurrent search execution, link deduplication, and intelligent content merging.
• Event Extraction: Built-in logic for parsing sports, calendars, and community events from structured search metadata.
• Professional Architecture: Production-ready modular design with standardized logging, error handling, and dynaconf configuration management.

📚 Documentation

For a deep dive into the project structure, configuration, and how to extend it, check out our Tutorial.

🛠️ Tech Stack

• Core: Python 3.10+, Asynchronous execution with asyncio & aiohttp.
• Scraping: Headless Playwright (for dynamic content) & httpx (for performance).
• AI/LLM: LangChain, OpenAI/Gemini compatible API integration, Tiktoken for token optimization.
• Storage: In-memory vector stores and RAG-based similarity search.

🚦 Quick Start

1. Configuration

The project uses dynaconf. Populate your API keys in .secrets.toml:

[default]
GOOGLE_API_KEY = "your_key"
CSE_ID = "your_cse_id"
BING_API_KEY = "your_key"
FIRECRAWL_API_KEY = "your_key"
# ... add other keys as needed

2. Basic Usage

from llama_searcher.api.search import get_events

# Use multiple engines at once
result = get_events(
    search_qeury="Music festivals in Europe 2024",
    engine="google,exa,tavily"
)
print(result)

3. Running the API

uv run python -m llama_searcher.api.app

4. Running the MCP Server (for AI Agents)

uv run python -m llama_searcher.mcp_server

🏗️ Architecture

├── agents/             # LLM Analysis & Summarization Agents
├── api/                # Unified Search Entry point
├── core/               # Fetchers, Cleaners, RAG, and SearchProviders
├── services/           # Orchestration & Domain services
├── utils/              # Configuration (Dynaconf) & Logging
└── main.py             # Entry point

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Created with ❤️ for the Advanced Agentic Coding community.