What Happened
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.
Why This Matters for Builders
Terminal‑native integration – Mole runs in the shell, so it can be invoked from scripts, Makefiles, or 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.
FAQ
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.
