The full Token Factory Cookbook — 50 recipes across 17 categories, 28 notebooks rendered with their outputs. The runnable backing for the reference architectures on the ecosystem page. Built on open models through one OpenAI-compatible base_url.
Core Token Factory notebooks — adding a LoRA, batch inference, guided JSON, image LoRA.
Point one base_url at Token Factory — the OpenAI-compatible on-ramp, plus aisuite, LiteLLM, and LlamaIndex SDKs.
Production agents across every major framework — LangChain, CrewAI, Agno, AWS Strands, Google ADK, LlamaIndex, Pydantic, and more.
This folder has the following agents:
source ↗A production-grade AI Data Analyst Chatbot workflow for n8n, leveraging Nebius Token Factory's DeepSeek-V3-0324-fast LLM for advanced, structured data analysis and Google Workspace automation.
source ↗A simple demonstration of using the Strands library with Nebius Token Factory's API to create an AI assistant that can fetch weather information.
source ↗A benchmarking tool built with the CAMEL framework that compares the performance of various AI models, including OpenAI and Nebius models. This tool measures and visualizes the speed of different models in terms of tokens processed per seco
source ↗A LangChain Deep Agent that produces decision-grade competitive briefs, powered by an LLM served by Nebius Token Factory, and real-time web research via Tavily.
source ↗A simple yet powerful AI research crew built with CrewAI that leverages multiple specialized agents to discover and analyze groundbreaking technologies.
source ↗Production use case: support teams need agents that combine ticket text, order state, policies, and safe workflow recommendations.
source ↗A SaaS-style customer support agent that answers product questions, looks up real order status, and escalates to a human ticket when it can't confidently resolve the issue.
source ↗Production use case: data teams need read-only agents that can inspect pipelines, query governed datasets, and recommend next actions when metrics break.
source ↗A powerful AI agent built with Agno that analyzes and provides insights about HackerNews content. This agent uses the Nebius AI model to deliver intelligent analysis of tech news, trends, and discussions.
source ↗Production use case: SRE teams need fast, evidence-backed triage during incidents without giving an LLM the ability to mutate infrastructure directly.
source ↗This is a small natural-language-to-SQL data agent inspired by the architecture of eosho/langchaindataagent, but implemented from scratch for this cookbook.
source ↗Two starter examples of a LangChain Deep Agent powered by an LLM served by Nebius Token Factory.
source ↗This folder contains production-oriented LangChain examples powered by Nebius Token Factory. Each example is self-contained with its own requirements.txt, .env.example, source files, and sample data.
source ↗A powerful AI agent template built with LlamaIndex that demonstrates how to create intelligent agents using the LlamaIndex framework. This starter project implements a Task Management Assistant using the Nebius AI model to showcase LlamaInd
source ↗PitchLoop is a conversational voice-agent example powered by Gradium, Nebius Token Factory, and LangChain.
source ↗A powerful AI agent built with PydanticAI that provides real-time weather information for any city. This agent uses the Nebius AI model to deliver accurate weather forecasts and insights.
source ↗This example shows a sample 'tool calling' agent, built using Google's Agent Development Kit (ADK) framework.
source ↗This agent demonstrates a multi‑step research pipeline powered by Nebius Token Factory models (OpenAI‑compatible) with a FastAPI backend and a Next.js frontend. It uses the model moonshotai/Kimi-K2-Instruct by default.
source ↗Production use case: security, privacy, and procurement teams need fast vendor reviews grounded in internal controls and contract evidence.
source ↗A Streamlit travel planning app built with LangChain and Nebius. It is inspired by AI travel-agent demos, but it uses a different app structure and planning flow:
source ↗Function calling and live web tools (Tavily) on open models.
Retrieval-augmented generation over PDFs and vector stores — Milvus, Qdrant, Weaviate, LlamaIndex.
A powerful RAG (Retrieval-Augmented Generation) chat application built with Streamlit, LlamaIndex, and Nebius AI's Qwen3 model. This application allows users to upload PDF documents and interact with them through an AI-powered chat interfac
source ↗This example demonstrates how to build a end-to-end RAG pipeline using open source stack.
source ↗Internal Support Agent is a Retrieval-Augmented Generation (RAG) assistant combining web search, document processing, vector retrieval, and intelligent support tools. It features multi‑agent orchestration, automatic support assessment, and
source ↗An AI-powered content generation platform that helps in creating social media posts, articles, and demo applications using a RAG pipeline built with Nebius and Qdrant Cloud vector database. !Qdrant-Nebius RAG
source ↗This example shows querying a PDF using llama index framework and running LLM on Nebius Token Factory
source ↗Train and serve LoRA adapters on open models.
Full fine-tuning pipelines on Token Factory.
Generate training data, distill, and evaluate smaller models.
Model-specific quickstarts — GLM-4.5, GPT-OSS, Qwen3, Nemotron.
Token Factory inside OpenClaw, Tavily, and more.
OpenClaw is a self-hosted AI assistant that connects chat apps (WhatsApp, Telegram, Discord, and more) to LLM providers. Because Nebius Token Factory serves open models through an OpenAI-compatible API, you can point OpenClaw at Token Facto
source ↗Tavily is an LLM-optimized search API built specifically for agents. It delivers clean, structured, and context-aware web results — making it ideal for research, competitive intelligence, and multi-step agentic workflows. Tavily handles que
source ↗End-to-end applications built on Token Factory.
Coding-agent and code-generation recipes.
Hands-on workshop material.
A hands-on workshop for building a practical AI agent system with two MCP servers: a Deep Research Agent and a LinkedIn Writing Workflow. The current implementation uses Nebius AI Studio for text LLM calls, Exa for real-time research, Gemin
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source ↗Written deep-dives.
Community-contributed guides.
Pelicans on bicycles, snake games — open models having fun.
This is inpired by Simon Willison's experiment called pelican riding a bicycle. It has become a cool vibe benchmark to evaluate LLMs. Original github repo: simonw/pelican-bicycle
source ↗A visual snake battle game where two LLMs compete against each other! Watch as AI models control their snakes in a battle of survival.
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