Ask questions directly against your documents — powered by local LLMs, no cloud required.

A document Q&A system built on a full RAG pipeline: ingest PDFs, chunk and embed them into a vector store, then retrieve and answer with a locally-running LLM via Ollama. Fast, private, and runs entirely on your machine. Includes MLflow-based observability — query traces, retrieval quality metrics, and latency are tracked per run for easy debugging and iteration.

Benchmark and compare small language models side-by-side — entirely offline, zero cloud.

Run up to 3 models simultaneously with real-time token streaming and per-model metrics. A built-in benchmark suite covers 18 prompts across 10 categories (reasoning, code, math, safety). Interactive Plotly charts visualize throughput, TTFT, RAM usage, and quality-vs-speed trade-offs — all on local hardware.

The official Python client for the Clarifai AI platform — models, workflows, datasets, and deployments via a clean API.

Drove significant growth through CLI improvements, new API surface coverage, and DX enhancements — contributing to a 10x increase in monthly downloads.

Decompose complex queries into subtasks and coordinate specialized agents to research topics end-to-end.

Production-grade multi-agent pipeline: Orchestrator → Search Agent → Summarizer → Critic, connected via A2A typed messaging and MCP servers for tools, memory, and web search. Tracks token usage, latency, cost, and confidence scores per agent. Streamlit dashboard with Plotly charts for visual analytics.