Promptic - Trace, evaluate, and optimize AI agents and LLM prompts

Promptic is an optimization platform for GenAI applications. It benchmarks and tunes every layer of your stack — model selection, prompts, tools, and agent architecture — against your specific business metrics like quality, cost, and latency, then ships the configuration that performs best on your data. Instead of trial-and-error prompt engineering, you get data-driven optimization that systematically finds and validates the best-performing setup for your use case.

Promptic follows a systematic, data-driven loop: you bring your use-case data and define the business metrics that matter — quality, cost, and latency — and Promptic iteratively optimizes every layer of your stack, from model selection and prompts to tool use and agent architecture. The optimization is powered by our own state-of-the-art Promptic Optimizer, with additional strategies like GEPA and DSPy optimizers coming soon, and we keep adding the best techniques as the research evolves. Each iteration is scored against your metrics, so the configuration you ship is validated to deliver measurable improvements on your data instead of being a guess.

Yes — Promptic is built for agentic workflows. The Python SDK and the promptic CLI expose traces, evaluations, datasets, and runs as structured, machine-readable output (every list command supports --json). That means a coding agent can pull failing traces, run an evaluation, read the structured insights, apply fixes to your prompts or tool schemas, and re-run the evaluation to verify the improvement — autonomously.

Agent evaluations systematically analyze your agent's traces to find failure patterns automatically. Heuristic checks detect loops, frequent tool errors, unused tools, cost hotspots, and abnormal terminations, while LLM judges score qualitative dimensions like plan adherence, reasoning coherence, and efficiency. Every finding is a structured insight with severity, the share of runs affected, cited evidence spans, and a concrete suggested fix — so you know exactly what to change.

Add two lines of Python — import promptic_sdk and promptic_sdk.init() — and Promptic captures every LLM call, tool call, and workflow step in your application. Tracing is built on OpenTelemetry and auto-instruments OpenAI, Anthropic, AWS Bedrock, Vertex AI, Mistral, LangChain, LangGraph, the OpenAI Agents SDK, the Claude Agent SDK, PydanticAI, and more. Each trace shows full inputs and outputs, token counts, cost in USD, latency, and a span waterfall of your agent's execution.

No, Promptic is designed to be intuitive and accessible to business users across your team. You don't need coding skills or technical expertise to optimize your prompts. Simply upload your data, provide your initial prompt, and let Promptic handle the complex optimization process automatically.

Promptic supports multiple LLM providers including OpenAI, Claude, Gemini, and other popular foundation models. You can optimize prompts for any provider and easily convert your optimized prompts to work with different LLM providers, giving you maximum flexibility in your AI implementation.

Manual prompt engineering is trial and error on a single layer. Promptic is data-driven, automated optimization across your whole stack — model selection, prompts, tools, and agent architecture — measured against your specific business metrics. Every change is backed by tracing and automated evaluations, so instead of guessing you can see exactly which version wins on quality, cost, and latency, and why. You also get detailed analytics and visualization of your optimization progress, making each improvement easy to track.

Most LLM tools are observability platforms — they stop at tracing and evaluations, so you can see what's happening but the actual fixing is left to you. Promptic is the opposite: our focus is optimization, not observability. Tracing and evaluations matter to us only as the data foundation — the ground truth Promptic needs to automatically benchmark and tune every layer of your stack (model selection, prompts, tools, and agent architecture) and ship the configuration that performs best on your metrics. That optimization focus is what sets us apart. On top of it, Promptic is vendor-independent, so you can benchmark and switch between LLM providers freely, and it's accessible to the people who know what good results look like: business users get a no-code workflow with real-time analytics and visual progress tracking, while developers and their coding agents get a Python SDK and CLI with structured, machine-readable output. Tools like DSPy are powerful for programmatic prompt optimization but require technical expertise; Promptic makes the whole optimization loop accessible to your entire team.

The optimization time depends on the complexity of your task and the size of your evaluation dataset. However, most Promptic optimizations complete within minutes. You can monitor the progress in real-time through our dashboard and see performance improvements with each iteration.

Promptic works with a wide variety of AI tasks. It currently is most optimized for all classification tasks (e.g email-routing, intent-classification, hallucination-detection, hate-speech-detection, etc.). We currently also support text generation tasks and information extraction in beta as well as MCP Tool Optimization. So whether you're working on customer service automation, content creation, or data analysis, Promptic can help optimize your prompts for better performance.

Promptic runs on our own Promptic Optimizer — a state-of-the-art algorithm that systematically searches for the configuration that performs best against your metrics. Support for additional optimizers like GEPA and DSPy optimizers is on the way. Our ambition is to build and integrate the best automated optimization algorithms and make them easily accessible, so we continuously expand our optimizer portfolio to reflect the latest research and best practices.

Getting started with Promptic is simple! Sign up for early access on our website, then bring your use-case data and the KPIs that matter — quality, cost, and latency. From there you can trace your existing agent, run evaluations to see where it breaks down, and let Promptic benchmark and optimize every layer — models, prompts, tools, and architecture — to ship the best-value configuration for your use case.