QueryCraft is an open-source framework by Rakuten Technology for crafting, debugging, and optimizing AI agent prompts. It offers a modular prompt pipeline, integrates with popular LLM APIs, and provides built-in evaluation metrics for response quality, token usage, and cost tracking. Developers can simulate agent workflows, compare prompt variations, and iterate quickly to enhance performance and reduce operational expenses.
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QueryCraft
QueryCraft is an open-source framework by Rakuten Technology for crafting, debugging, and optimizing AI agent prompts. It offers a modular prompt pipeline, integrates with popular LLM APIs, and provides built-in evaluation metrics for response quality, token usage, and cost tracking. Developers can simulate agent workflows, compare prompt variations, and iterate quickly to enhance performance and reduce operational expenses.
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What is QueryCraft?
QueryCraft is a Python-based prompt engineering toolkit designed to streamline the development of AI agents. It enables users to define structured prompts through a modular pipeline, connect seamlessly to multiple LLM APIs, and conduct automated evaluations against custom metrics. With built-in logging of token usage and costs, developers can measure performance, compare prompt variations, and identify inefficiencies. QueryCraft also includes debugging tools to inspect model outputs, visualize workflow steps, and benchmark across different models. Its CLI and SDK interfaces allow integration into CI/CD pipelines, supporting rapid iteration and collaboration. By providing a comprehensive environment for prompt design, testing, and optimization, QueryCraft helps teams deliver more accurate, efficient, and cost-effective AI agent solutions.
Who will use QueryCraft?
- AI developers
- Prompt engineers
- Data scientists
- Research teams
- ML engineers
How to use the QueryCraft?
- Step1: Install QueryCraft via pip: pip install querycraft
- Step2: Configure API keys for desired LLM providers
- Step3: Define prompt modules and pipelines using the QueryCraft SDK or YAML
- Step4: Set up evaluation metrics for response quality and cost
- Step5: Execute queries and collect results with the CLI or Python code
- Step6: Analyze results, compare prompt variations, and refine prompts iteratively
Platform
- Linux
- Mac
- Windows
QueryCraft's Core Features & Benefits
The Core Features
- Modular prompt pipeline design
- Multi-LLM API integration
- Built-in evaluation metrics
- Token usage and cost tracking
- Response debugging and visualization
- CLI and Python SDK interfaces
- Workflow benchmarking
- Custom metric support
The Benefits
- Accelerated prompt iteration
- Improved agent performance
- Reduced development costs
- Seamless LLM integration
- Enhanced collaboration
- Data-driven optimization
QueryCraft's Main Use Cases & Applications
- Prompt engineering and optimization
- LLM performance benchmarking
- Cost-efficient AI agent development
- Automated response quality evaluation
- Workflow debugging and visualization
FAQs of QueryCraft
QueryCraft Company Information
- Rakuten Technology
QueryCraft Reviews
5/5
QueryCraft's Main Competitors and alternatives?
LangChain
PromptLayer
LangSmith
LlamaIndex
Promptable
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