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What is LLM?

LLM is a tool for building and managing AI-powered knowledge bases using multiple agents working in parallel. It lets you create wiki-style compilations of information sourced from various inputs, then query and extract that knowledge in structured formats. The tool is designed for users who need to organise large amounts of information for AI agents to work with, rather than relying on a single model's training data. It's useful when you want to give your AI systems access to specific, pick information or when you need to investigate topics from multiple angles simultaneously. The parallel multi-agent approach means different research threads can happen at once, which can speed up information gathering compared to single-threaded approaches.

Key Features

Multi-agent parallel research

run several research agents at the same time to investigate different aspects of a topic

Thesis-driven investigation

structure research around a central argument or question to keep results focused

Source ingestion

feed documents, URLs, or other data sources into your knowledge base

Wiki compilation

organise ingested information into a structured wiki format

Knowledge base querying

search and retrieve information from your compiled knowledge bases

Artifact generation

export findings, reports, or structured data in various formats

Pros & Cons

Advantages

  • Parallel processing means faster research and information gathering than sequential approaches
  • Freemium model lets you try the tool without upfront costs
  • Useful for researchers and AI developers who need custom knowledge bases rather than relying on general-purpose LLM training data
  • Wiki structure makes information easy to handle and reference

Limitations

  • Early stage software; v0.0.20 indicates ongoing development so features and stability may change
  • Limited information available about data privacy, storage limits, or how long knowledge bases persist on the free tier
  • Requires some technical understanding to set up multi-agent workflows effectively

Use Cases

Researchers compiling information on a specific topic from multiple sources to create a reference database

AI developers building domain-specific knowledge bases for fine-tuned models or agentic systems

Competitive analysis teams gathering and organising market research from various sources

Documentation teams creating internal wikis that AI systems can reference and query

Thesis or academic work requiring systematic investigation of a topic from multiple angles