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Visual canvas for composing, testing, and deploying LLM-based pipelines and multi-agent workflows. Supports major LLMs and vector databases, exports flows as APIs or MCP servers, and offers a desktop bundle for local experimentation and iteration.
Contains tech-blog posts scraped from Habr (primarily Russian, some English) in Parquet format with ~100K–1M records. Suited for multilingual text-generation and language-model fine-tuning; license is not specified, so verify before redistribution.
Reimplements OpenAI's Whisper speech-to-text on the CTranslate2 inference engine, running up to 4x faster at the same accuracy while using less memory. Adds a batched pipeline, 8-bit quantization, VAD filtering, and word-level timestamps.
Runs AI-generated code in secure, isolated cloud sandboxes you control via Python or JavaScript SDKs; supports self-hosting (Terraform) and AWS/GCP, enabling agents and code-interpreting workflows to execute real-world tools safely.
Streamlines post-training and fine-tuning for large language and multimodal models with a single YAML-driven pipeline. Supports LoRA/QLoRA, full fine-tuning, preference tuning, RL methods, multi-GPU/FSDP/DeepSpeed, and many model backends (Hugging Face, local checkpoints).
Compiles one LLM into device-native binaries running on CUDA, ROCm, Metal, Vulkan, WebGPU, and CPU — same model from server to browser to phone. On Apache TVM, it ships MLCEngine with an OpenAI-compatible API across Python, JS, REST, iOS, and Android.
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).
Probes LLMs for failure modes — prompt injection, jailbreaks, data leakage, toxicity, hallucination — the way nmap scans a network. Ships 20+ attack probes that run against Hugging Face, OpenAI, Bedrock, Cohere, or any REST endpoint.
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.
Enterprise-grade multi-agent orchestration framework that builds, runs, and scales autonomous agent swarms for production. Offers modular swarm architectures, protocol support (MCP, AOP), a marketplace, multi-model provider integrations and observability.
Edits code across an existing repo from the terminal: you describe a change in plain English, it maps the whole codebase, applies edits to the right files, and auto-commits each change as a reviewable git commit. Works with most LLMs.
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.