Turns local documents into a private, self-hosted ChatGPT-style assistant with no-code agents for web browsing and workflow automation. Runs across LLM providers — OpenAI, Anthropic, Ollama — and routes tools smartly to cut token use.
Calls 100+ LLM providers — OpenAI, Anthropic, Gemini, Bedrock, Azure — through one OpenAI-compatible API, as a Python SDK or self-hosted proxy. The proxy adds virtual keys, spend tracking, rate limits, and load balancing across models and providers.
Runnable Jupyter notebooks for building with the Claude API: tool use, RAG, vision, prompt caching, sub-agents, classification, summarization, and integrations like Pinecone and Voyage embeddings. Copy-paste recipes that drop into real projects.
Chat with your documents via retrieval-augmented generation; each answer carries inline citations and a built-in viewer highlights the cited PDF passage. Pairs full-text with vector search and runs on OpenAI, Azure, Cohere, Ollama, or local models.
Gives AI agents persistent long-term memory: ingests documents in any format and continuously builds a self-hosted knowledge graph fusing vector embeddings, graph reasoning, and ontology grounding, so agents recall and reason over connected facts.
Provides a memory-first library and managed service that stores, reasons about, and serves long-term state for agents and users — offering continual representations, session context, vector search, and a chat-style API for personalized behavior.
Build and deploy enterprise-grade conversational agents with integrated RAG pipelines, workflow orchestration, multi-modal IO, and model-agnostic integrations (private and public LLMs). Designed for self-hosted production with vector stores and tooling integrations.
Provides a self-hosted inference engine that serves all models an agent needs—embeddings, retrieval/reranking, document-to-markdown OCR, structured extraction, content-safety scoring, and LLM generation—through an OpenAI-compatible API. Bundles a 100+ model catalog, SDKs, and production deployment tooling.
Lets AI agents place and answer business phone calls, holding spoken conversations to collect structured data, answer questions, and escalate to humans. Built on Azure Communication Services and Azure OpenAI, with RAG over your own documents.
Curated developer resources that demonstrate building RAG systems, multi-agent workflows, and memory-augmented AI using Oracle AI Database and OCI — includes end-to-end reference apps, notebooks, guides, and workshops for hands-on prototyping.
Builds a knowledge graph from a text corpus by extracting entities and relations, clusters it into communities with the Leiden algorithm, and summarizes them — so queries can synthesize across scattered documents instead of retrieving isolated chunks.
Generates Wikipedia-style articles from web search using LLMs: it researches topics, produces multi-perspective outlines via perspective-guided question asking and simulated conversations, then drafts full articles with citations. Supports human-AI collaboration (Co-STORM) and grounding with multiple retrievers.