The Research Assistant That Reads, Remembers, and Reasons
Hermes Agent reads papers, tracks citations, organizes findings, and builds a persistent knowledge graph of your research domain — available every session without rebuilding.
Researchers deal with a specific kind of cognitive overhead: the constant context switching between reading, note-taking, citation tracking, and synthesis. You read a paper on Monday, take notes in one app, lose the file location by Wednesday, forget the key finding by the time you need to cite it in March. Hermes solves this by making the reading and remembering part of the same workflow. When Hermes reads a paper, it doesn't just give you a summary and move on — it stores the key findings, the methodology, the authors, and your annotations in persistent memory. Future research sessions can query that accumulated knowledge. 'What did we find about attention mechanisms in sparse models last year?' — Hermes knows, without you having to search your notes.
The literature review problem is well-documented: it takes months to do thoroughly and the results are ephemeral. You do a deep dive, build a mental map of the field, then don't touch that specific corner for six months and have to rebuild everything from scratch. Hermes's episodic memory means your literature review context persists across arbitrary time gaps. It remembers what you've already read, what the key debates are in your field, which papers contradict each other, and where the gaps are that you're targeting. When you resume a research thread months later, the agent resumes with full context.
Hermes is also a genuine research tool, not just a summarizer. It can browse the web, access APIs, run code on datasets, generate visualizations, and chain these into multi-step research pipelines. A single task can: pull recent preprints from arXiv, extract methodology comparisons, run a statistical comparison in the sandbox, generate a chart, and write up findings — with full episodic recall of where that research thread left off. And because Hermes is model-agnostic, you can use Gemini 1.5 Pro's 1M token context for long documents, Claude for nuanced analysis, or any specialized model for your domain.
Key Capabilities
Paper Memory That Compounds
Hermes reads papers and stores key findings, methodology, and your annotations in persistent memory. Future sessions query your accumulated knowledge graph — 'what did we find about X?' — without rebuilding context from scratch.
Multi-Model Research Pipeline
Use Gemini 1.5 Pro's 1M token window for long documents, Claude for nuanced writing, and Codestral for code analysis — all in the same research workflow, routed automatically by task type.
Browser Automation for Web Research
Schedule Hermes to check arXiv, Semantic Scholar, or any research database daily. Extract new papers, summarize findings, and notify you on Telegram or Discord — the research pipeline runs unattended.
Code + Analysis + Writing in One Flow
Run statistical analysis on a dataset, generate visualizations, and write up the findings in a single task. Hermes chains browser automation, code execution, and document writing without glue code.
What You Can Actually Do
Systematic Literature Review That Doesn't Expire
Start a literature review on transformer architectures. Hermes tracks every paper you read, stores key findings and your annotations, and maintains a living knowledge graph. Come back to it three months later — the context is still there, still searchable.
Real-Time arXiv Monitoring
Schedule a daily task: check arXiv for new preprints in your domain, extract abstracts, rank by relevance to your current work, and send a briefing to your Telegram. New research lands in your inbox every morning without you checking the site.
Cross-Paper Hypothesis Testing
Ask Hermes: 'Find all the papers we've read that address X, extract their methodologies and conflicting findings, and identify what gap remains.' It queries your accumulated reading history and synthesizes an answer — not a web search, your actual library.
Automated Research Notes
After each research session, ask Hermes to write a structured note: hypothesis addressed, key findings, methodology used, remaining gaps, next steps. These notes compound into a thesis-ready narrative over time.
Dataset Analysis and Visualization
Upload a dataset or point Hermes at a public data source. It writes Python to clean, analyze, and visualize the data in the sandbox — returning charts, statistics, and a written interpretation in one task.
What People Are Saying
“The memory system means it actually gets better at our specific workflows — it learns your research methodology and applies it consistently”
— Nous Research Discord community
“Being able to run a research pipeline that browses, analyzes, and writes in one flow is genuinely new — I haven't found this anywhere else”
— Developer / researcher on Reddit
“Hermes remembers what you were working on even after weeks away — that context gap is usually where research momentum dies”
— Power user feedback
Frequently Asked Questions
How does Hermes handle large documents like long papers or theses?
Gemini 1.5 Pro's 1M token context window handles papers of any length natively. For standard papers, any model works. Hermes can also extract and summarize specific sections on demand without loading the entire document into active context.
Can Hermes track citations and build a bibliography?
Hermes stores citation information in persistent memory when it reads papers. You can query your accumulated citation library at any time. For formal bibliography generation, it can output in BibTeX, APA, or any format you specify.
How does the research memory differ from just saving notes in Notion?
Notion saves what you write. Hermes remembers what you read and what you concluded — automatically, without manual note-taking. It can also reason across your accumulated reading history, surfacing connections and contradictions between papers you read months apart.
Does Hermes work with reference managers like Zotero?
Hermes can interact with Zotero via its API or by reading exported BibTeX files. It can also read directly from PDF papers, extract metadata, and organize findings into your existing reference management workflow.
The Research Assistant That Gets Smarter the More You Read
Paper memory, citation tracking, and automated literature monitoring — all self-hosted and private.
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