Agent Development Toolkit | Arthur
Stop Guessing. Start Shipping Agents.
An open-source toolkit for building, testing, and monitoring AI agents in production.
How it works
One workflow for the whole agent lifecycle.
Step 1
Manage
Keep prompts versioned, tagged, and promotable across environments. Roll back in seconds when something regresses.
Step 2
Experiment
Test prompt changes, model swaps, and RAG configs against real data before anything ships. Know what changed and why it mattered.
Step 3
Monitor
Trace every agent run end to end. Catch hallucinations, failures, and drift in production before your users do.
Get Started Today
Ship Reliable AI Agents. Fast.
Prompts that behave like code.
Most teams treat prompts like config files — unversioned, untracked, and painful to roll back. One bad change can quietly break production.
- Version and promote prompts across environments without redeploying your agent
- Roll back in seconds when performance drops — no firefighting, no guesswork
- Template prompts to control structure and variables at runtime, across teams or tenants
Test changes before they reach users.
Swapping a model or tweaking a prompt is a gamble without structured tests. Most teams ship first and find out what broke second.
- A/B test prompts, models, and RAG configs against real production data — not synthetic examples
- Test full agent workflows — tool use, reasoning paths, and output formatting, not just single completions
- Score results automatically or with human review — and see exactly what changed and why
See exactly what your agent did.
When an agent fails, you shouldn't have to piece together logs and hope for the best.
- Inspect every step — inputs, tool calls, reasoning paths, and outputs across every run
- Filter by prompt version, user, outcome, or cost to find the source of a failure fast
- Built on OpenTelemetry — works with LangChain, LlamaIndex, OpenAI, Anthropic, and anything else in the OpenInference ecosystem
Know before your users do.
Quality problems in production are invisible until someone complains. By then it's too late.
- Run evals on live traffic — hallucination, PII, prompt injection, toxicity, and correctness, continuously
- Set alerts the moment quality drifts — not after a user escalation
- Validate before you ship with curated datasets and pre-deployment test runs
Works with what you already have.
You shouldn't have to rebuild your stack to get observability.
- Use any model — OpenAI, Anthropic, Cohere, or open-source
- Bring any framework — LangChain, LangGraph, LlamaIndex, Vercel AI SDK, and more
- Deploy your way — Docker, CloudFormation, or Helm. Your environment, your data.
| Agent Framework | Eval Platform | Arthur Engine + Toolkit | |
|---|---|---|---|
| Build and run agents | |||
| Prompt versioning & management | Basic | ||
| Structured A/B experiments | |||
| Real-time guardrails (hallucination, PII, injection) | |||
| End-to-end trace debugging | |||
| Traditional ML model eval | |||
| Self-hosted / open-source | Varies | SaaS | MIT licensed |
How It Fits Into the Arthur Engine
The Agent Toolkit is part of the Arthur Engine — Arthur's free, open-source AI evaluation and monitoring platform. The Engine provides the foundation: real-time guardrails, LLM eval infrastructure, and flexible deployment. The Toolkit builds on top of that with the full agent development workflow.
Works with every model and framework
Ready to turn your AI into real-world impact?
We'll help you move from pilots and prototypes to production-grade applications, with evaluation every step of the way.