# LAMBDA: A Large Model Based Data Agent

arXiv: https://arxiv.org/abs/2407.17535 · HTML: https://mlsanity.com/paper/2407.17535/

## Key facts

- Cited by 30 papers in the map (data through 14 Oct 2026).
- Placed in Web, search, and research agents, one of 16 directions in LLM agents and tool use.
- Builds on 38 of its 71 references that are in the map.
- First posted on arXiv 24 Jul 2024.

## Placed in

- LLM agents and tool use / [Web, search, and research agents](https://mlsanity.com/agents/web-search-and-research-agents/)

## Builds on (most cited first, 30 of 38)

- [Language Models are Few-Shot Learners](https://mlsanity.com/paper/2005.14165/) (2020)
- [GPT-4 Technical Report](https://mlsanity.com/paper/2303.08774/) (2023)
- [LLaMA: Open and Efficient Foundation Language Models](https://mlsanity.com/paper/2302.13971/) (2023)
- [Retrieval-Augmented Generation for Knowledge-Intensive NLP Tasks](https://mlsanity.com/paper/2005.11401/) (2020)
- [ReAct: Synergizing Reasoning and Acting in Language Models](https://mlsanity.com/paper/2210.03629/) (2022)
- [Efficient Memory Management for Large Language Model Serving with PagedAttention](https://mlsanity.com/paper/2309.06180/) (2023)
- [PaLM: Scaling Language Modeling with Pathways](https://mlsanity.com/paper/2204.02311/) (2022)
- [Qwen Technical Report](https://mlsanity.com/paper/2309.16609/) (2023)
- [Mistral 7B](https://mlsanity.com/paper/2310.06825/) (2023)
- [Retrieval-Augmented Generation for Large Language Models: A Survey](https://mlsanity.com/paper/2312.10997/) (2023)
- [AutoGen: Enabling Next-Gen LLM Applications via Multi-Agent Conversation](https://mlsanity.com/paper/2308.08155/) (2023)
- [MetaGPT: Meta Programming for A Multi-Agent Collaborative Framework](https://mlsanity.com/paper/2308.00352/) (2023)
- [A Survey on Hallucination in Large Language Models: Principles, Taxonomy, Challenges, and Open Questions](https://mlsanity.com/paper/2311.05232/) (2023)
- [LlamaFactory: Unified Efficient Fine-Tuning of 100+ Language Models](https://mlsanity.com/paper/2403.13372/) (2024)
- [Improving language models by retrieving from trillions of tokens](https://mlsanity.com/paper/2112.04426/) (2021)
- [GLM: General Language Model Pretraining with Autoregressive Blank Infilling](https://mlsanity.com/paper/2103.10360/) (2021)
- [GLM-130B: An Open Bilingual Pre-trained Model](https://mlsanity.com/paper/2210.02414/) (2022)
- [Large Language Model based Multi-Agents: A Survey of Progress and Challenges](https://mlsanity.com/paper/2402.01680/) (2024)
- [PAL: Program-aided Language Models](https://mlsanity.com/paper/2211.10435/) (2022)
- [Augmented Language Models: a Survey](https://mlsanity.com/paper/2302.07842/) (2023)
- [OpenCodeInterpreter: Integrating Code Generation with Execution and Refinement](https://mlsanity.com/paper/2402.14658/) (2024)
- [Data Interpreter: An LLM Agent For Data Science](https://mlsanity.com/paper/2402.18679/) (2024)
- [Tool Learning with Large Language Models: A Survey](https://mlsanity.com/paper/2405.17935/) (2024)
- [Agent Hospital: A Simulacrum of Hospital with Evolvable Medical Agents](https://mlsanity.com/paper/2405.02957/) (2024)
- [An LLM Compiler for Parallel Function Calling](https://mlsanity.com/paper/2312.04511/) (2023)
- [Solving Challenging Math Word Problems Using GPT-4 Code Interpreter with Code-based Self-Verification](https://mlsanity.com/paper/2308.07921/) (2023)
- [Agents: An Open-source Framework for Autonomous Language Agents](https://mlsanity.com/paper/2309.07870/) (2023)
- [TaskWeaver: A Code-First Agent Framework](https://mlsanity.com/paper/2311.17541/) (2023)
- [MLCopilot: Unleashing the Power of Large Language Models in Solving Machine Learning Tasks](https://mlsanity.com/paper/2304.14979/) (2023)
- [Experiential Co-Learning of Software-Developing Agents](https://mlsanity.com/paper/2312.17025/) (2023)

## Built on by (most cited first, 30 of 30)

- [DeepAnalyze: Agentic Large Language Models for Autonomous Data Science](https://mlsanity.com/paper/2510.16872/) (2025)
- [UserRL: Training Interactive User-Centric Agent via Reinforcement Learning](https://mlsanity.com/paper/2509.19736/) (2025)
- [SciSciGPT: Advancing Human-AI Collaboration in the Science of Science](https://mlsanity.com/paper/2504.05559/) (2025)
- [Kolb-Based Experiential Learning for Generalist Agents with Human-Level Kaggle Data Science Performance](https://mlsanity.com/paper/2411.03562/) (2024)
- [Large Language Model-based Data Science Agent: A Survey](https://mlsanity.com/paper/2508.02744/) (2025)
- [A Survey on Large Language Model-based Agents for Statistics and Data Science](https://mlsanity.com/paper/2412.14222/) (2024)
- [Divide-Then-Aggregate: An Efficient Tool Learning Method via Parallel Tool Invocation](https://mlsanity.com/paper/2501.12432/) (2025)
- [IMPROVE: Iterative Model Pipeline Refinement and Optimization Leveraging LLM Experts](https://mlsanity.com/paper/2502.18530/) (2025)
- [LongDA: Benchmarking LLM Agents for Long-Document Data Analysis](https://mlsanity.com/paper/2601.02598/) (2026)
- [DSAEval: Evaluating Data Science Agents on a Wide Range of Real-World Data Science Problems](https://mlsanity.com/paper/2601.13591/) (2026)
- [DataMaster: Data-Centric Autonomous AI Research](https://mlsanity.com/paper/2605.10906/) (2026)
- [A note on conditional PAC-efficient reasoning in large language model routing](https://mlsanity.com/paper/2512.03057/) (2025)
- [EvoDS: Self-Evolving Autonomous Data Science Agent with Skill Learning and Context Management](https://mlsanity.com/paper/2606.03841/) (2026)
- [Data Agents Under Attack: Vulnerabilities in LLM-Driven Analytical Systems](https://mlsanity.com/paper/2606.08661/) (2026)
- [Context-Adaptive Synthesis and Compression for Enhanced Retrieval-Augmented Generation in Complex Domains](https://mlsanity.com/paper/2508.19357/) (2025)
- [ORCA: ORchestrating Causal Agent](https://mlsanity.com/paper/2508.21304/) (2025)
- [DARE: Aligning LLM Agents with the R Statistical Ecosystem via Distribution-Aware Retrieval](https://mlsanity.com/paper/2603.04743/) (2026)
- [InfoAtlas: A Foundation Model for Zero-Shot Statistical Dependence Estimate](https://mlsanity.com/paper/2606.00241/) (2026)
- [Automated reproducibility assessments in the social and behavioral sciences using large language models](https://mlsanity.com/paper/2606.13670/) (2026)
- [AutoXRD: Autonomous LLM Agents and Comprehensive Evaluation for Powder Diffraction Analysis](https://mlsanity.com/paper/2609.00070/) (2026)
- [NS-Copilot: An LLM-Driven Agent System for Autonomous Neuroscience Analysis](https://mlsanity.com/paper/2609.01971/) (2026)
- [Semantic-Enhanced Cross-Modal Place Recognition for Robust Robot Localization](https://mlsanity.com/paper/2509.13474/) (2025)
- [VDSAgents: A PCS-Guided Multi-Agent System for Veridical Data Science Automation](https://mlsanity.com/paper/2510.24339/) (2025)
- [AI4BayesCode: From Natural Language Descriptions to Validated Modular Stateful Bayesian Samplers](https://mlsanity.com/paper/2605.18476/) (2026)
- [From Intent to AI Pipelines: A Controlled Agentic Framework for Non-AI Expert Scientists](https://mlsanity.com/paper/2605.18764/) (2026)
- [Verilog-Evolve: Feedback-Driven and Skill-Evolving Verilog Generation](https://mlsanity.com/paper/2605.26498/) (2026)
- [Grading the Grader: Lessons from Evaluating an Agentic Data Analysis System](https://mlsanity.com/paper/2606.24839/) (2026)
- [UrbanDS: A Graph-Guided LLM Multi-Agent System for Data-Intensive Urban Tasks](https://mlsanity.com/paper/2607.26724/) (2026)
- [Data Agents: Agentic Data Systems](https://mlsanity.com/paper/2609.24137/) (2026)
- [SleuthBench: Benchmarking Statistical LLM Evaluation Using Tabular Hidden Signals](https://mlsanity.com/paper/2609.34228/) (2026)

## Abstract (arXiv)

We introduce LArge Model Based Data Agent (LAMBDA), a novel open-source, code-free multi-agent data analysis system that leverages the power of large language models. LAMBDA is designed to address data analysis challenges in data-driven applications through innovatively designed data agents using natural language. At the core of LAMBDA are two key agent roles: the programmer and the inspector, which are engineered to work together seamlessly. Specifically, the programmer generates code based on the user's instructions and domain-specific knowledge, while the inspector debugs the code when necessary. To ensure robustness and handle adverse scenarios, LAMBDA features a user interface that allows direct user intervention. Moreover, LAMBDA can flexibly integrate external models and algorithms through our proposed Knowledge Integration Mechanism, catering to the needs of customized data analysis. LAMBDA has demonstrated strong performance on various data analysis tasks. It has the potential to enhance data analysis paradigms by seamlessly integrating human and artificial intelligence, making it more accessible, effective, and efficient for users from diverse backgrounds. The strong performance of LAMBDA in solving data analysis problems is demonstrated using real-world data examples. The code for LAMBDA is available at https://github.com/AMA-CMFAI/LAMBDA and videos of three case studies can be viewed at https://www.polyu.edu.hk/ama/cmfai/lambda.html.