@tejpalvirk
Basic information
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.
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Task Researcher is a Python task management system designed for AI-driven development. It integrates in-depth research capabilities using the knowledge-storm library to break down complex projects, generate tasks, and leverage automated research to inform implementation details. It provides both a command-line interface (CLI) and a Model Context Protocol (MCP) server.
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.
`expand --research` triggers the full STORM-based workflow, performing live web searches to inform subtasks. `analyze-complexity --research-hint` modifies the prompt to ask the primary LLM to leverage its internal knowledge more deeply without any live search.
Python 3.10+, an API key for at least one supported LLM provider (e.g., Anthropic, Google Gemini, OpenAI), and the `knowledge-storm` library. A search engine API key is required for the `--research` feature and the `research-topic` command.
Task data is stored locally in a `tasks/tasks.json` file following Pydantic models. The file includes a meta section and a list of task objects with dependencies, phases, and subtasks.
All credentials are set in a `.env` file. Required variables include `LLM_MODEL`, the provider's API key (e.g., `ANTHROPIC_API_KEY`), `STORM_RETRIEVER`, and the corresponding search API key (e.g., `BING_SEARCH_API_KEY`).
The MCP server uses stdio transport by default. It exposes tools like `parse_inputs`, `expand_task`, and `research_topic`, as well as resources such as `tasks://current` and `report://complexity`.
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