LAMBDA

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Basic Information

LAMBDA is an open-source, code-free multi-agent data analysis system that leverages large language models to perform complex data analysis via natural language instructions. It is designed to let users and researchers run iterative generative workflows without writing code by orchestrating specialized data agents. The system emphasizes human-in-the-loop operation through a user interface that permits direct intervention while two complementary agent roles, the programmer and the inspector, generate and debug analysis code. The repository provides installation and configuration guidance including Conda environment setup, dependency installation, an IPython kernel for a local code interpreter, and a config.yaml to set API keys, models and runtime options. It supports exporting reproducible Jupyter Notebooks, integrates external models or local LLM backends, includes demo case studies and documentation, and is released under the MIT License.

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App Details

Features
Code-free data analysis driven by natural language instructions that lets non-programmers specify analysis tasks. A multi-agent architecture with distinct programmer and inspector roles that iteratively generate, test and debug code. A user interface that supports direct human intervention in the operational loop. Flexible model integration supporting OpenAI-style APIs and local LLM backends and frameworks mentioned in the README. Automatic report generation to reduce time spent on formatting and writeups. Jupyter Notebook export for reproducibility and further analysis. Configurable runtime and model settings in config.yaml including streaming, caching, max attempts and execution time, and an optional retrieval/knowledge integration toggle. Demo case studies and a docs site. Planned improvements like logging, preinstalled kernels, UI replacement and Docker packaging are listed.
Use Cases
LAMBDA helps users perform complex data science tasks without coding by converting natural language goals into analysis code via coordinated agents, lowering the barrier for data-driven workflows. The programmer and inspector roles automate code generation and self-correction, which reduces debugging time and guides iterative exploration. The GUI enables human oversight and intervention, balancing automation with control. Exporting to Jupyter Notebooks promotes reproducibility and allows experts to refine results further. Support for cloud APIs or local LLM backends offers deployment flexibility and privacy options. Configuration options like caching, execution limits and knowledge retrieval allow users to tailor performance and cost. The repository includes demos and documentation to help researchers and educators evaluate and extend the system.

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