Hamming
Prompt optimization, automated voice testing, monitoring, and more. Test your AI voice agent against 1000s of simulated users in minutes. AI voice agents are hard to get right. A small change in prompts, function call definitions or model providers can cause large changes in LLM outputs. We're the only end-to-end platform that supports you from development to production. You can store, manage, version, and keep your prompts synced with voice infra providers from Hamming. This is 1000x more efficient than testing your voice agents by hand. Use our prompt playground to test LLM outputs on a dataset of inputs. Our LLM judges the quality of generated outputs. Save 80% of manual prompt engineering effort. Go beyond passive monitoring. We actively track and score how users are using your AI app in production and flag cases that need your attention using LLM judges. Easily convert calls and traces into test cases and add them to your golden dataset.
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PromptLayer
The first platform built for prompt engineers. Log OpenAI requests, search usage history, track performance, and visually manage prompt templates. manage Never forget that one good prompt. GPT in prod, done right. Trusted by over 1,000 engineers to version prompts and monitor API usage. Start using your prompts in production. To get started, create an account by clicking “log in” on PromptLayer. Once logged in, click the button to create an API key and save this in a secure location. After making your first few requests, you should be able to see them in the PromptLayer dashboard! You can use PromptLayer with LangChain. LangChain is a popular Python library aimed at assisting in the development of LLM applications. It provides a lot of helpful features like chains, agents, and memory. Right now, the primary way to access PromptLayer is through our Python wrapper library that can be installed with pip.
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ZenLLM
ZenLLM is an AI cost optimization platform for engineering teams running LLM applications in production. It connects provider invoices to the application behavior behind them, showing which prompts, workflows, models, customers, retries, and request paths are driving spend. Teams send request-level telemetry through the ZenLLM SDK and can attach business context such as workflow, owner, customer, team, or product feature without storing prompt or response content. It monitors token usage, model selection, latency, errors, retries, and cost, then surfaces the waste patterns hidden by aggregate provider dashboards. It detects context accumulation when conversations or agents resend growing histories, premium-model overuse on low-risk work, retry loops that repeat expensive context, stale system prompts, routing mistakes, anomalies, and weak cost ownership.
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PromptIDE
The xAI PromptIDE is an integrated development environment for prompt engineering and interpretability research. It accelerates prompt engineering through an SDK that allows implementing complex prompting techniques and rich analytics that visualize the network's outputs. We use it heavily in our continuous development of Grok.
We developed the PromptIDE to give transparent access to Grok-1, the model that powers Grok, to engineers and researchers in the community. The IDE is designed to empower users and help them explore the capabilities of our large language models (LLMs) at pace. At the heart of the IDE is a Python code editor that - combined with a new SDK - allows implementing complex prompting techniques. While executing prompts in the IDE, users see helpful analytics such as the precise tokenization, sampling probabilities, alternative tokens, and aggregated attention masks.
The IDE also offers quality of life features. It automatically saves all prompts.
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