Enterprise DNA
O Open Source Orchestration medium

TaskWeaver

by Community

The first "code-first" agent framework for seamlessly planning and executing data analytics tasks.

T

OSS

TaskWeaver

Added 1 June 2026

#agent #ai-agents #code-interpreter #copilot #data-analysis #llm #openai

Overview

TaskWeaver is a code-first agent framework from Microsoft for planning and executing data analytics tasks. It uses a planner to break user requests into subtasks and generates Python code to execute each step, enabling complex multi-step workflows.

Best for

Best for
Developers building custom data analytics agents with Python

Use cases

  • Automating multi-step data analysis pipelines with code generation
  • Building agents that query databases and generate visualizations
  • Creating custom analytics assistants that execute Python scripts

Notes

TaskWeaver is a code-first agent framework from Microsoft for planning and executing data analytics tasks. It uses a planner to break user requests into subtasks and generates Python code to execute each step, enabling complex multi-step workflows.

6,162 stars on GitHub. Last updated 2026-03-23. Licensed MIT.

Use cases

  • Automating multi-step data analysis pipelines with code generation
  • Building agents that query databases and generate visualizations
  • Creating custom analytics assistants that execute Python scripts

Pros

  • Code-first approach gives fine-grained control over execution
  • Strong planning capabilities for complex, multi-step tasks
  • Active open-source community with over 6,000 GitHub stars

Cons

  • Requires Python expertise to set up and extend
  • Limited to data analytics use cases, not general-purpose agent
  • Documentation and examples can be sparse for advanced scenarios

Indexed from awesome-langchain and enriched against its public facts.

Pros

  • Code-first approach gives fine-grained control over execution
  • Strong planning capabilities for complex, multi-step tasks
  • Active open-source community with over 6,000 GitHub stars

Cons

  • Requires Python expertise to set up and extend
  • Limited to data analytics use cases, not general-purpose agent
  • Documentation and examples can be sparse for advanced scenarios