Mole
Deep research agent for your terminal
Details
- External ID
- 49303046
- Source
- HN
- Company
- —
- Product
- Mole
- Website domain
- github.com
- Launched
- Aug. 14, 2026
- Cohort
- —
- Upvotes
- 100
- Upvotes percentile
- 0.928763440860215
- Tags
- —
- Fetched at
- Sept. 10, 2026, 5:32 a.m.
- Updated at
- Sept. 10, 2026, 5:32 a.m.
Description
Doing research with agents is fun until they blow way past budget, jumble the sources, and don't even give you the best possible answer, just sound confident.And if you want to run some research task on local data - you have no idea where your data ends up after the prompt consumes it.So I built this tool: a deep-research agent with an enforced budget, verified quotes, and a privacy boundary for local data.1. Never spend more than you budgeted (measured overshoot is 0%). 2. Every claim carries a source 3. Data stays local (give a CSV, it'll analyze it without the data ever leaving your machine)Works with most LLMs, including coding agents, subscriptions, local models, etc.It's free and open source, would appreciate all feedback!
Enrichment
- Theme
- AI agent frameworks and developer tools
- Vertical
- Horizontal
- Function
- Agent / copilot
- Audience
- Developer
- AI stance
- AI-native
- Project type
- Commercial product
- Normalized one-liner
- research agent for your terminal
- Manually corrected
- False
Could you build this?
Yes Mole is a CLI-based deep research agent that takes local queries/files, coordinates search/retrieval via LLM calls, and streams formatted output to the terminal, which can be readily built using standard agentic prompting frameworks.
Discussion
14 comments analyzed.
Competitors mentioned: DataMole, Mole for Mac (system maintenance app), GitHub tw93/Mole
Concerns raised: Naming conflict with existing products (DataMole, Mole for Mac), Unclear how research relevance filtering works for specific queries like flight tickets, Excessive code complexity for basic functionality, LLM cost unpredictability and budget enforcement mechanism unclear, Difference between deep research and normal research not clearly explained
Feature requests: Better budget handling and cost predictability for LLM requests, Clarify caching role in pricing model
Competitors
Other products that read as similar to this one — 141 launches clear the similarity bar, closest 8 shown.
Attention rank: #13 of 142 (itself plus its competitors, highest first — normalized so YC and Product Hunt are compared fairly).
Launched 284 days after the earliest competitor.
- Visual Workspace for Agents Based on Unix · hn · 2026-06-25 · 10 upvotes · similarity 0.44
- OpenScience · ph · 2026-09-27 · 74 upvotes · similarity 0.43
- Agents, run any coding agent on your subscription not API costs · hn · 2026-05-31 · 6 upvotes · similarity 0.42
- DocMason · hn · 2026-04-04 · 11 upvotes · similarity 0.41
- Oodle.ai · hn · 2026-07-14 · 31 upvotes · similarity 0.41
- Capy · hn · 2026-08-05 · 13 upvotes · similarity 0.41
- My Open Source Deep Research tools beats Google and I can Prove it · hn · 2026-02-01 · 17 upvotes · similarity 0.41
- Clor · hn · 2026-06-02 · 11 upvotes · similarity 0.40
Other launches for this product
Same idea, different domain
Nobody's really built a agent / copilot tool for Agriculture yet.