Wraps a local, OpenAI-compatible inference server in one messages API so you can build private AI apps with no data leaving your network: document ingestion, retrieval with inline citations, and built-in tools (web search, code execution, MCP).
Evaluates and tests LLM apps — RAG pipelines, agents, and workflows — using objective metrics that mix LLM-as-judge scoring with deterministic measures. Auto-generates synthetic test datasets and integrates with LangChain and tracing tools.
Provides end-to-end observability, evaluation, and optimization for LLM-based applications by tracing model calls, running automated evaluations, and surfacing production metrics. Ships SDKs, broad framework integrations, LLM-as-a-judge metrics, and dashboards to support development, CI, and production monitoring.
Self-hostable chat client that unifies many LLM providers (OpenAI, Claude, Gemini, Ollama, DeepSeek) behind one UI. Adds file-upload knowledge-base RAG, vision/TTS, an MCP plugin system, and an agent marketplace, with one-click Vercel or Docker deployment.
Runs retrieval-augmented Q&A over your own documents on local hardware, so files never leave your machine. Blends semantic, keyword, and late-chunking retrieval, with a router that picks RAG or a direct LLM answer per query and verifies it.
Teaches generative AI app development through 21 lessons covering LLM basics, prompting, chat, search, image generation, agents, RAG, fine-tuning, small models, and responsible AI.
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.
Framework for unit-testing, evaluating and benchmarking LLM systems with ready-made metrics (G‑Eval, hallucination, task completion), support for local judge models and synthetic datasets, plus CI-friendly integrations for LangChain/OpenAI/Anthropic.
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.
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.
Self-hostable platform for building enterprise GenAI apps with visual workflow orchestration — loops, parallelism, human-in-the-loop — plus RAG, agents, unified model management, and in-house OCR for handwriting and rare characters.
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.