Mole: A Deep Research Agent for Your Terminal

Lajosdeme released Mole, an open-source deep research agent that runs from the terminal, combining large language models with a retrieval system to answer questions with up-to-date web or database information. The tool outputs structured data in JSON or YAML, integrates with scripts and workflow engines like n8n, and offers on-prem deployment for privacy and lower latency.

Mole, a new open‑source project from lajosdeme on GitHub, debuted as a deep research agent . It runs from the terminal, letting users ask questions and receive answers that pull in‑date information from the web or other databases. The tool couples large language models with a retrieval system, then outputs structured data for scripts and automation. The release ships with a command‑line interface, a lightweight Python library, and example workflows. Demo videos show Mole answering complex API queries, generating code snippets, and summarizing lengthy technical documents. Terminal‑native integration – Mole runs in the shell, so it can be invoked from scripts, Makefiles, or n8n https://dev.to/go/n8n nodes without a UI. Adding AI research to pipelines becomes straightforward. Structured output – Mole returns JSON or YAML. Downstream services such as n8n or Airflow can parse the results and trigger tasks like ticket creation or documentation updates. Lower latency – Running locally or on a private server cuts out the round‑trip to external APIs. This helps production workflows that need deterministic performance. Privacy and compliance – Keeping the agent on‑prem or in a controlled environment keeps sensitive data inside the premises, meeting strict data‑handling policies. Plugin extensibility – The repository includes a plugin system. Developers can add custom knowledge sources—internal KBs, GitHub repos, proprietary APIs—turning Mole into a tailored research assistant. Teams that already use n8n or similar workflow engines can drop Mole into their stack, prototype AI‑driven research steps, and iterate quickly without building a custom AI layer from scratch. Q: Can Mole run on a server behind a corporate firewall? A: Yes. Mole is a self‑contained Python package installable on any machine with network access to the required knowledge sources. It works inside a Docker container or virtual environment, making it suitable for on‑prem or cloud‑native setups. Q: Does Mole support multi‑step reasoning or chaining of queries? A: The current version handles single queries. The architecture allows chaining by piping one Mole call’s output into another. Future releases will add explicit multi‑step reasoning support. Q: How does Mole handle rate limits if it queries external APIs? A: Mole respects rate‑limit headers and can be configured with retry logic. For production, cache frequent queries or host a local knowledge cache to avoid hitting external limits. Originally published on Automations Cookbook.