Kiln

Overview

Kiln is a free, desktop‑first platform that lets developers design, evaluate, and deploy AI systems without writing boilerplate code. By exposing a rich Model Context Protocol (MCP) interface, Kiln turns complex model pipelines—retrieval‑augmented generation, agentic workflows, fine‑tuning, and synthetic data creation—into configurable tasks that any AI assistant can invoke. This unifies disparate tooling under a single, well‑documented API surface, allowing assistants to request model inference, data augmentation, or evaluation metrics on demand.

The core value of Kiln lies in its zero‑code fine‑tuning and evaluation workflows. Developers can upload a dataset, launch an automated training job on any supported backend (Ollama, OpenAI, Fireworks, etc.), and immediately expose the resulting model as an MCP endpoint. Evaluations are equally streamlined: built‑in metrics such as BLEU, ROUGE, or custom LLM‑based scorers can be run against a live model and the results returned to the assistant in real time. This tight integration removes the friction of manual CI/CD pipelines and lets AI assistants orchestrate end‑to‑end experiments with a single request.

Key capabilities include:

• Retrieval‑Augmented Generation (RAG): Attach document stores to a model, enabling the assistant to fetch context before generating answers.
• Agentic Orchestration: Define multi‑actor workflows where each actor is a separate MCP tool, allowing assistants to delegate tasks and aggregate responses.
• Synthetic Data Generation: Interactively create large evaluation or fine‑tuning corpora by prompting the model, then automatically format and ingest them into training pipelines.
• Structured JSON Output: Enforce schema‑constrained responses, making it trivial for assistants to parse results into downstream systems.
• Comprehensive Model Library: Pre‑tested compatibility with over 100 models across vendors, ensuring that an MCP client can call any model without manual adapter code.

Real‑world use cases span from rapid prototyping of customer support bots—where a single Kiln endpoint can fetch relevant knowledge base articles, generate an answer, and evaluate sentiment—to production deployments of compliance‑aware agents that must audit every response against regulatory text. Because Kiln’s MCP surface is declarative, AI assistants can treat it like any other API: they request a prompt, specify the desired tool (e.g., “fine‑tune”, “evaluate”), and receive a structured response. This abstraction empowers developers to focus on business logic while the assistant handles model orchestration, data management, and quality assurance behind the scenes.