Maxim now supports over 6500 models through Ollama, enabling you to test prompts and workflows on your local machine with ease. Key benefits:
OpenAI's research preview model, said to be the largest and most capable chatbot model to date, is now available on Maxim.
Start using this model on Maxim via the OpenAI provider:
✅ Go to Settings > Models > OpenAI and add GPT 4.5 Preview.
Claude 3.7 Sonnet - Anthropic's latest reasoning-focused model is now available on Maxim. Design custom evaluators and prompt experiments leveraging the analytical capabilities of this model.
Start using this model on Maxim via the Anthropic provider:
✅ Go to Settings > Models > Anthropic and add Claude 3.7 Sonnet.
Leverage the capabilities of this OpenAI o1 competitor on Maxim to design custom AI evaluators for your workflows and experiment with your prompts.
Enable DeepSeek-R1 on Maxim via the Together AI provider:
✅ Go to Settings > Models > Together AI and select DeepSeek R1.
OpenTelemetry (OTel) is an open-source observability framework that provides standardized protocols and tools for collecting and routing telemetry data in a unified format.
Maxim is fully OpenTelemetry compliant, enabling you to seamlessly relay/forward application logs to New Relic or any observability platform of your choice that supports OTel.
Key Benefits:
🌍 Unified Observability: Collect and route telemetry data, including production logs, in a standardized format—no more juggling multiple protocols.
🧩 Enhanced Flexibility: Avoid vendor lock-in and choose the observability tools that best fit your enterprise needs.
🖥️ Enterprise-Grade Monitoring: Integrate your connectors with just one step and leverage Maxim's observability and evaluation stack to maintain production quality and seamless AI operations.
Stay ahead of operational challenges with Maxim's robust and flexible monitoring framework. Learn more.
To ensure the quality of AI agents, evaluation needs to happen at each step, i.e., for LLM generation, planner action, tool call, etc. With Maxim’s observability and evaluation suite, you can attach custom evaluators to each level of your logging hierarchy (trace, span, or component within the span).
Key features:
🔍 Granular monitoring: Log and evaluate each step of your agentic workflow in real time. Continuously monitor quality at each step and define alerts for proactive issue resolution.
💻 SDK support: Integrate tracing and evaluation directly from your code using programming language support offered by Maxim.
📊 Metrics: Track key metrics such as cost, latency, and evaluator scores for each node.
This ensures you gain actionable insights into every part of your AI agent's operations, helping you debug faster and maintain production-grade performance. Learn more.
The Maxim SDK empowers developers to test their AI workflows directly from their local environment, eliminating the need for repetitive data uploads and back-and-forth interactions with the platform.
Our SDK support gives you the following benefits:
📊 Flexible data sources: Use local CSV files or other data sources as test datasets.
💻 Local testing: Trigger test runs directly on your local machine without uploading data to Maxim.
📈 Seamless monitoring: Track the status of test runs in the Maxim dashboard, just like regular runs.
This makes quality assurance faster, more flexible, and developer-friendly. Learn more.
Integrating Maxim CLI into your CI/CD pipelines enables you to evaluate features thoroughly before deployment, ensuring that only high-quality updates reach users.
Key benefits of Maxim CLI: ⚙️ Custom evaluators: Leverage Maxim’s robust evaluators and define tailored evaluation criteria to suit your unique requirements.
🚦 Pass/fail criteria: Use evaluation scores to automate deployment decisions.
🔗 Deployment mapping: Easily trace test runs back to deployments for faster debugging.
Get started with Maxim CLI to ensure the speed and reliability of your AI deployments.
Analytics dashboard provides powerful tools to help you gain insights from your data.
Create detailed comparison reports between existing runs to easily analyze, share, and export data as needed. This feature enables you to make data-driven decisions by comparing different test runs side by side.
Create dynamic dashboards based on a filter logic of recent runs to monitor progress over time. This upcoming feature will allow you to visualize and track your data in real-time through customizable dashboards.
Human annotations enable you to incorporate manual feedback into your log evaluation pipeline. This feature is crucial for validating model outputs, gathering training data, and maintaining quality control.
Learn how to set up your first annotation queue here.
