Task Researcher
About
Researcher for AI Coding that analyzes task complexity and runs deep research (STORM) to decompose complex tasks into subtasks, as an MCP Server or CLI.
Details
- Author
- tejpalvirk
- GitHub stars
- 5
- Downloads
- 217
- Categories
- Productivity
Jump to
- Parse specifications to generate initial tasks.
- AI-powered task expansion into subtasks.
- STORM-based research workflow for complex tasks.
- Task update and dependency management.
- Complexity analysis with research hints.
- Standalone research report generation on any topic.
Install via clone and Poetry, configure a .env file with LLM and search API keys, then use the task-researcher CLI command or run task-researcher serve-mcp to start the MCP server over stdio transport. Clients like Claude Desktop connect by editing their claude_desktop_config.json.
Task Researcher
A Python task management system designed for AI-driven development, featuring integrated, in-depth research capabilities using the knowledge-storm library. Break down complex projects, generate tasks, and leverage automated research to inform implementation details.
This package provides both a command-line interface (CLI) and a Model Context Protocol (MCP) Server.
Core Features
Parse Inputs: Generate initial tasks from project specification files (functional_spec.md, technical_spec.md, plan.md, background.md).
Expand Tasks:
Break down tasks into subtasks using AI (claude, gemini, etc. via litellm).
STORM-Powered Research (--research flag): For complex tasks, automatically identify research questions, group them into topics, run the knowledge-storm engine for each topic, and use the aggregated research to generate highly informed subtasks.
Update Tasks: Modify pending tasks based on new prompts or requirement changes.
Analyze Complexity: Assess task complexity using AI, generating a report with recommendations and tailored expansion prompts. (--research-hint flag available).
Dependency Management: Validate and automatically fix dependency issues (missing refs, self-deps, simple cycles).
Generate Files: Create individual .txt files for each task and subtask.
Standalone Research (research-topic): Generate a detailed research report on any topic using knowledge-storm.
Requirements
Python 3.10+
An API key for at least one supported LLM provider (e.g., Anthropic, Google Gemini, OpenAI) set in a .env file (used for task generation, complexity analysis, etc.).
knowledge-storm library (pip install knowledge-storm).
API key for a search engine supported by knowledge-storm (e.g., Bing Search, You.com, Tavily) set in .env (Required for --research in expand and the research-topic command).
(Optional) mcp library (pip install mcp) if running as an MCP server.
Installation
1. Clone the repository:
git clone <repository-url>
cd task-researcher
2. Install dependencies (using Poetry recommended):
pip install poetry
poetry install
3. Configure Environment:
Copy
.env.example to .env.Fill in your primary LLM API key (e.g.,
ANTHROPIC_API_KEY).Set the
LLM_MODEL for primary tasks (e.g., "claude-3-5-sonnet-20240620").Set the
STORM_RETRIEVER (e.g., "bing") and its corresponding API key (BING_SEARCH_API_KEY).(Optional) Set
BIG_STORM_MODEL and SMALL_STORM_MODEL to use a different (e.g., faster/cheaper) models for STORM research.(Optional) Adjust other settings like
MAX_TOKENS, TEMPERATURE, file paths, etc.
Usage Command Line Interface (CLI)
Use the task-researcher command (if installed via Poetry scripts) or python -m task_researcher.
```bash
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