Maxim AI

The playground is where prompt work happens. You test and change prompts, models, tools and context without touching code, version prompts outside the codebase, chain pieces into a workflow in a low-code view, and deploy a chosen version with a rule set. For teams juggling several prompt edits a week, the versioning keeps track of what changed and who shipped it.

Maxim runs a simulation and evaluation engine that tests agents across thousands of scenarios using metrics you define. Simulations push an agent through varied situations, while evaluations score the quality of each run against built-in or custom metrics. Automations connect the engine to CI/CD workflows, and the analytics view turns the results into reports you can share.

Once an agent is live, Maxim logs traces, so you can see multi-step workflows and long chains of tool calls laid out step by step. The same log stream feeds LLM observability dashboards for latency, cost and quality. Online evaluations score real interactions, covering generation, tool calls and retrieval, and alerts fire on regressions. This is what separates a one-time test from continuous quality tracking.

A single library holds pre-built and custom evaluators across several styles: LLM-as-a-judge, statistical, programmatic and human scoring. Because the same evaluators serve both simulations and live monitoring, a score means the same thing at test time and in production.

Maxim supports tool definitions and structured outputs natively. You can create and test tools that are either code-based or API-based, then watch how an agent picks and calls them. This matters for agents that lean on external APIs, since a wrong tool call often breaks a whole run.

The platform handles synthetic and custom multimodal datasets with easy import, and it connects through Python and TypeScript SDKs. Teams on a supported stack can move results into their own pipelines, and custom pricing rules keep reported token costs in line with negotiated rates.