OmniSLM
The production framework for Small Language Models. Build secure, local-first AI applications without the API overhead.
The Problem
Current AI tooling forces a binary choice: either lock your enterprise data into expensive, opaque cloud APIs (like OpenAI), or spend months gluing together brittle, experimental open-source scripts to run models locally.
OmniSLM bridges this gap. It provides a robust, Python-first architecture that treats local inference, vector databases, and multi-agent orchestration as first-class citizens.
Core Architecture
Runtime Agnostic
Hot-swap between Ollama, vLLM, or Llama.cpp with a single configuration flag. No need to rewrite your agent logic when you change your inference engine.
Native RAG Engine
Built-in document ingestion, chunking strategies, and hybrid search integrations with FAISS and Qdrant.
Privacy First
Designed for VPCs and air-gapped environments. Zero telemetry, zero external API calls by default.
Agent SDK
Tools for building autonomous agents capable of tool-use and multi-step reasoning using standard Python functions.
Roadmap
v0.1: Core Inference Engine
Basic support for Ollama and initial RAG pipelines using FAISS.
v0.5: Agent Orchestration (Current)
Introduction of tool-calling, multi-agent communication, and advanced memory management.
v1.0: Enterprise Readiness
Multi-tenant vector isolation, continuous batching integration, and comprehensive observability hooks.
Technical Deep Dives
View All Notes →Lessons Learned Building OmniSLM
The technical hurdles and architectural decisions behind the framework.