Local Search Agent
A framework that replace traditional RAG pipelines
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“Local Search Agent A framework that replace traditional RAG pipelines”its own headline
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How it runs2
“# Or use Ollama for a fully local, zero-cost setup (no key needed) # Install from https://ollama.com # Download a”
Runs offline
“AGENT — Watch the Terminal document querying video demo Python API — Watch the Local Search Agent API Integration video demo Install pip inst”
API
What it offers as proof1
“README.md -> https://github.com/wiss84/local-search-agent/blob/main/README.md”
What it names4
“Local Search Agent takes a different approach: BM25 keyword search via Meilisearch, structured metadata, and a LangGraph agent loop with tools .”
NamesMeilisearchBuilt onMeilisearch
“Multi-provider LLM — Google, Ollama (local), OpenAI, Anthropic”
NamesGoogle · Ollama · OpenAI · AnthropicIntegrates withGoogle · Ollama · OpenAI · Anthropic
“Wrap an indexed workspace as a tool and plug it into any external AI agent — LangChain, LangGraph, Google Gemini SDK, or any framework that calls a function.”
NamesLangChainIntegrates withLangChain
“Installing Tesseract enables a faster OCR path (~5 second per page vs. minutes without it).”
NamesTesseractIntegrates withTesseract
About
A framework that replace traditional RAG pipelines. Ingest any number of documents in multiple workspaces (channels, departments, etc.), index it with BM25, and let the agent search, fetch, and reason over it, exactly like searching the web, but entirely on your machine. No vector store, no embedding needed.