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📚 Build Deep Research Agent: From Question to Insight Complex research rarely fits in one search box. The Deep Research workflow built with Dify Iterates, reasons, and sources until the answer is complete. 💡 Key workflow nodes include: 1) Start: capture the research topic and the loop budget 2)...

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Dify

22,407 subscribers

22,470 views • 1 year ago •via X (Twitter)

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jem1 year ago

Is there a template for it? since the blog post doesn’t cover the prompts and all of the structured outputs

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Dify1 year ago

The template is now live on our Explore page:

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ksminnovation1 year ago

Can AI redefine scientific discovery? Dr. Tal Patalon explores OpenAI’s Deep Research in her latest Forbes article. 🎨 Future by Eduardo Kobra, provided by Eden Gallery. @TalPatalon @forbes @edengallery_

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It takes 5 minutes to create a world-class web research agent that runs 24/7 and returns structured search results. 1. You can limit the sources it's allowed to use 2. You can specify the goals it needs to complete 3. You can design the format structure of the results 4. You can configure how much computing power it will use I show you how to set up a Web Search Agent in the video. A few benefits here: 1. This is great for complex research tasks 2. It's really easy to add this like a LEGO piece to a more complex workflow 3. Create an agent once, and reuse it with different inputs 4. The agent adapts to your use case as you work with it By the way, Nimble published a whole library of templates and research agents you can reuse and modify. You can start sending queries to one of these existing agents, and it will begin to adapt to Go here to try it out: Thanks to the Nimble team for partnering with me on this post.

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26,611 views • 1 year ago

🔥 Our latest LangChain Academy course – Deep Research with LangGraph – is now live! 🔥 Deep research agents are taking off – from major AI labs to companies building their own. Research is inherently open-ended. You can't always predict whether a question needs broad exploration or deep analysis. Agents excel here because they adapt on the fly, using each finding to decide where to dig next. Building these systems ourselves and with customers, we've learned that structure matters. The best research agents scope problems with users first. Then, they coordinate multiple specialists instead of overwhelming one generalist. LangGraph is built for these types of long-running, multi-agent workflows. Its persistence layer tracks progress across agents. LangSmith gives you the observability and evaluation tools you need to track and improve performance. In this course, you'll build and evaluate: - A user scoping agent to define research parameters - A multi-agent research team with supervisor - And add tool integration via MCP Enroll for free ➡️
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61,205 views • 1 year ago

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GREG ISENBERG

436,249 views • 5 months ago

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104,833 views • 8 months ago

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Julian Goldie SEO

15,449 views • 7 months ago

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10,780 views • 9 months ago

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I just vibe-coded a TikTok research AI agent in Claude Code 🤯 A complete research-to-brief pipeline that scrapes TikTok, analyzes video hooks with AI, and generates creative briefs on demand. All inside Claude Code. Perfect for creative agencies and DTC brands who are still turning competitor research into briefs manually. If you're spending hours scrolling TikTok "for research," watching videos one by one, screenshotting hooks, and rewriting briefs from scratch every time, this agent eliminates the entire loop: → Search TikTok by keyword, date range, and video count → Pull engagement metrics, captions, and thumbnails → Gemini watches the video and analyzes the hook → AI scrapes comments for common questions and insights → Generates a full creative brief from your template + brand bible No watching videos manually. No copying notes into docs. No rewriting briefs from scratch. What you control: - Multiple client projects with separate brand bibles - Your own creative brief template - Which videos to analyze and brief - Full customization through Replit's AI agent Research → Analysis → Brief. One workflow, running as a custom mini-SaaS inside your company. Every e-comm brand and agency should have at least one person who can vibe-code tools like this. It's becoming non-negotiable. I recorded a full walkthrough showing exactly how I built this from scratch. Want the full tutorial? > Like this post > Comment "CLAUDE" And I'll send it over (must be following so I can DM)

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97,342 views • 5 months ago

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Julian Goldie SEO

31,292 views • 3 months ago

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11,574 views • 6 months ago

BREAKING 🚨: Google is working on multi-agent systems to help you refine ideas with tournament-like evaluation. Each run takes around 40 minutes and brings you 100 detailed ideas on a given research topic. 2 new multi-agents are being developed for Gemini Enterprise: - Idea Generation - "Create a multi-agent innovation session" - Co-Scientist - "Drive novel scientific discovery with Co-Scientist" Co-Scientist 3-step workflow 👀 - Tell Co-Scientist what you plan to research, point it to relevant data, and set your evaluation criteria. - A team of agents will generate ideas on your topic using their available data - The agents will evaluate the ideas against your criteria and rank them, tournament-style Google is not only automating research but also preparing a product that will enable others to do so. This is the next level 🤯
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🚨 AI News | TestingCatalog

569,702 views • 8 months ago

I just ran a full research project without doing any research. Lemma by Analemma did everything: 1. Explore I typed a question. It mapped the entire field. Concepts, references, key papers. Done. 2. Survey I asked for a literature review. It gave me 50+ pages with real citations. Not a summary. A publication-grade draft. 3. Code I described an experiment. It wrote the code, ran it, showed results. 4. FARS I gave it a research direction. It generated hypotheses, planned experiments, ran them, and wrote the full paper. Zero input from me after that. This is the first tool I've used that actually runs research instead of just helping you do it. FARS already ran live and produced 100 papers in 10 days. One every 2 hours. Watch it here: This is what "Vibe Research" looks like.
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I just ran a full research project without doing any research. Lemma by Analemma did everything: 1. Explore I typed a question. It mapped the entire field. Concepts, references, key papers. Done. 2. Survey I asked for a literature review. It gave me 50+ pages with real citations. Not a summary. A publication-grade draft. 3. Code I described an experiment. It wrote the code, ran it, showed results. 4. FARS I gave it a research direction. It generated hypotheses, planned experiments, ran them, and wrote the full paper. Zero input from me after that. This is the first tool I've used that actually runs research instead of just helping you do it. FARS already ran live and produced 100 papers in 10 days. One every 2 hours. Watch it here: This is what "Vibe Research" looks like.

Muhammad Ayan

40,052 views • 2 months ago

PhD Students – How to easily understand a complex research topic? Meet Ponder – a tool for understanding complex research. 𝐇𝐨𝐰 𝐏𝐨𝐧𝐝𝐞𝐫 𝐰𝐨𝐫𝐤𝐬? 1. Go to and log in 2. Enter your research topic or research question 3. Ponder will start building a knowledge map 4. This knowledge map breaks down complex ideas into structured cards 𝐖𝐡𝐚𝐭 𝐜𝐚𝐧 𝐲𝐨𝐮 𝐝𝐨 𝐰𝐢𝐭𝐡 𝐭𝐡𝐞𝐬𝐞 𝐜𝐚𝐫𝐝𝐬? → You can add your own thoughts, questions, and insights. → Ask follow-up questions and deepen your exploration. → You can color the cards for better understanding → You can drag & organize them freely across the infinite canvas. 𝐇𝐨𝐰 𝐭𝐨 𝐚𝐝𝐝 𝐫𝐞𝐬𝐞𝐚𝐫𝐜𝐡 𝐩𝐚𝐩𝐞𝐫𝐬 𝐭𝐨 𝐭𝐡𝐞 𝐜𝐚𝐫𝐝𝐬? — You can search for relevant papers with built-in discovery. — Ponder will identify all relevant papers — You can then add or upload research papers — You can also attach papers to specific cards. 𝐀𝐟𝐭𝐞𝐫 𝐲𝐨𝐮𝐫 𝐩𝐨𝐧𝐝𝐞𝐫𝐢𝐧𝐠 𝐢𝐬 𝐜𝐨𝐦𝐩𝐥𝐞𝐭𝐞𝐝: ➟ You can change the view to document, browser, or full screen. ➟ You can also download your knowledge map as a PDF ➟ You can ask further questions and refine with Ponder’s Agent. 𝐖𝐡𝐚𝐭 𝐮𝐧𝐝𝐞𝐫𝐬𝐭𝐚𝐧𝐝𝐢𝐧𝐠 𝐫𝐞𝐬𝐞𝐚𝐫𝐜𝐡 𝐭𝐡𝐢𝐬 𝐰𝐚𝐲 𝐢𝐬 𝟏𝟎𝐱 𝐛𝐞𝐭𝐭𝐞𝐫? ↳ It brings discovery and analysis of research into one workspace ↳ It makes ideas branch and evolve naturally, just like your brain ↳ It helps you to easily identify research gaps ↳ It connects knowledge from all sources such as papers and web ↳ It enables you to export knowledge as maps, reports, or data. ↳ Designed for PhD students & researchers, who think deeply. Try Ponder here: Anything you'd like to add?
2:30

PhD Students – How to easily understand a complex research topic? Meet Ponder – a tool for understanding complex research. 𝐇𝐨𝐰 𝐏𝐨𝐧𝐝𝐞𝐫 𝐰𝐨𝐫𝐤𝐬? 1. Go to and log in 2. Enter your research topic or research question 3. Ponder will start building a knowledge map 4. This knowledge map breaks down complex ideas into structured cards 𝐖𝐡𝐚𝐭 𝐜𝐚𝐧 𝐲𝐨𝐮 𝐝𝐨 𝐰𝐢𝐭𝐡 𝐭𝐡𝐞𝐬𝐞 𝐜𝐚𝐫𝐝𝐬? → You can add your own thoughts, questions, and insights. → Ask follow-up questions and deepen your exploration. → You can color the cards for better understanding → You can drag & organize them freely across the infinite canvas. 𝐇𝐨𝐰 𝐭𝐨 𝐚𝐝𝐝 𝐫𝐞𝐬𝐞𝐚𝐫𝐜𝐡 𝐩𝐚𝐩𝐞𝐫𝐬 𝐭𝐨 𝐭𝐡𝐞 𝐜𝐚𝐫𝐝𝐬? — You can search for relevant papers with built-in discovery. — Ponder will identify all relevant papers — You can then add or upload research papers — You can also attach papers to specific cards. 𝐀𝐟𝐭𝐞𝐫 𝐲𝐨𝐮𝐫 𝐩𝐨𝐧𝐝𝐞𝐫𝐢𝐧𝐠 𝐢𝐬 𝐜𝐨𝐦𝐩𝐥𝐞𝐭𝐞𝐝: ➟ You can change the view to document, browser, or full screen. ➟ You can also download your knowledge map as a PDF ➟ You can ask further questions and refine with Ponder’s Agent. 𝐖𝐡𝐚𝐭 𝐮𝐧𝐝𝐞𝐫𝐬𝐭𝐚𝐧𝐝𝐢𝐧𝐠 𝐫𝐞𝐬𝐞𝐚𝐫𝐜𝐡 𝐭𝐡𝐢𝐬 𝐰𝐚𝐲 𝐢𝐬 𝟏𝟎𝐱 𝐛𝐞𝐭𝐭𝐞𝐫? ↳ It brings discovery and analysis of research into one workspace ↳ It makes ideas branch and evolve naturally, just like your brain ↳ It helps you to easily identify research gaps ↳ It connects knowledge from all sources such as papers and web ↳ It enables you to export knowledge as maps, reports, or data. ↳ Designed for PhD students & researchers, who think deeply. Try Ponder here: Anything you'd like to add?

Faheem Ullah

12,175 views • 1 year ago

🚨 BREAKING: Someone built an AI that replaces 6 months of literature review with a single workspace. It's called Moara. You dump your research papers in. The AI organizes, analyzes, and synthesizes everything across hundreds of millions of academic sources. You come back and there's a structured literature review waiting. Not a chatbot answer. Not a vague summary. The actual research synthesis. A full research workspace that thinks like a scientist. Works for medicine, engineering, social sciences, law, business, humanities — every discipline. Here's what it does on its own: → Searches hundreds of millions of papers across PubMed, arXiv, Google Scholar, Semantic Scholar, ClinicalTrials .gov, Cochrane, and more in one shot → Screens and tags results automatically, pulling out methodologies, findings, and contributions from every paper → Lets you chat with your entire library at once, find themes, spot gaps, and compare findings across dozens of papers simultaneously → Integrates with Zotero, LibKey, RIS, and BibTeX so your existing research workflow doesn't break → Traces every AI insight back to its original source so nothing is hallucinated and everything is verifiable → Supports full team collaboration so entire research groups can screen and synthesize together Try it here:
1:07

🚨 BREAKING: Someone built an AI that replaces 6 months of literature review with a single workspace. It's called Moara. You dump your research papers in. The AI organizes, analyzes, and synthesizes everything across hundreds of millions of academic sources. You come back and there's a structured literature review waiting. Not a chatbot answer. Not a vague summary. The actual research synthesis. A full research workspace that thinks like a scientist. Works for medicine, engineering, social sciences, law, business, humanities — every discipline. Here's what it does on its own: → Searches hundreds of millions of papers across PubMed, arXiv, Google Scholar, Semantic Scholar, ClinicalTrials .gov, Cochrane, and more in one shot → Screens and tags results automatically, pulling out methodologies, findings, and contributions from every paper → Lets you chat with your entire library at once, find themes, spot gaps, and compare findings across dozens of papers simultaneously → Integrates with Zotero, LibKey, RIS, and BibTeX so your existing research workflow doesn't break → Traces every AI insight back to its original source so nothing is hallucinated and everything is verifiable → Supports full team collaboration so entire research groups can screen and synthesize together Try it here:

Rimsha Bhardwaj

49,743 views • 4 months ago

RLM is the most import foundation of my Pi Harness (other than Pi of course). It's seeded with late interaction retrieval results (thanks to @lightonai for pylate). The Agent initiates it with query then.. 𝐒𝐞𝐭𝐮𝐩 A python REPL is created and seeded with: 1. Late interaction search to pre-filter. Instead of doing top 3/5/10, it's top hundreds of documents. This is set into a `context` variable. 2. Python functions are loaded in to do more searches if `context` variable isn't enough. And to make llm calls with cheaper models in parallel batches. 𝐈𝐭𝐞𝐫𝐚𝐭𝐢𝐨𝐧 𝐋𝐨𝐨𝐩 From there, an LLM iterates in the REPL based on the query. It's just like exploring in a jupyter notebook. The LLM writes prose (like a markdown cell) and code to be run in the REPL each turn. This allows the LLM to sort, filter, and synthesize information. It can fan out and ask smaller models to summarize, combine, contrast, or do anything else to documents to help it understand the data. After several turns the LLM reponds with the final answer. Either because it found the answer, or hit the budget limit. Context as a Python variable, LLM as the programmer, REPL as the runtime. 𝐖𝐡𝐲 𝐃𝐨𝐞𝐬 𝐓𝐡𝐢𝐬 𝐖𝐨𝐫𝐤 1. Richer Shell. Agents (and subagents) work by intermixing code and prose/thinking. But they use static scripts or bash that run and exit and start over each tool call. That's not ideal for exploration and synthesis of data. For that, state is useful to continue building and exploring the data as you learn more. There's a reason jupyter notebooks have been popular with data scientists. 2. Keeps main agent context clean. The better context you have the better the agent will perform (duh!). This means three thing: better human input, less missing search results, and less incorrect search results. Letting the agent iterate allows it to synthesize just what is needed and nothing else. All bad paths or peeks at something that turns out to be irrelevant stays out of main agent context. 3. Stack the good ideas! People often compare late interaction search vs RLM. Or static vs dynamic languages. Or agentic search vs semantic search. But...You can just use them all together for what they're each good at. Use them all for the area they're really great for. Read the full post which has more detail about how and why.
3:43

RLM is the most import foundation of my Pi Harness (other than Pi of course). It's seeded with late interaction retrieval results (thanks to @lightonai for pylate). The Agent initiates it with query then.. 𝐒𝐞𝐭𝐮𝐩 A python REPL is created and seeded with: 1. Late interaction search to pre-filter. Instead of doing top 3/5/10, it's top hundreds of documents. This is set into a `context` variable. 2. Python functions are loaded in to do more searches if `context` variable isn't enough. And to make llm calls with cheaper models in parallel batches. 𝐈𝐭𝐞𝐫𝐚𝐭𝐢𝐨𝐧 𝐋𝐨𝐨𝐩 From there, an LLM iterates in the REPL based on the query. It's just like exploring in a jupyter notebook. The LLM writes prose (like a markdown cell) and code to be run in the REPL each turn. This allows the LLM to sort, filter, and synthesize information. It can fan out and ask smaller models to summarize, combine, contrast, or do anything else to documents to help it understand the data. After several turns the LLM reponds with the final answer. Either because it found the answer, or hit the budget limit. Context as a Python variable, LLM as the programmer, REPL as the runtime. 𝐖𝐡𝐲 𝐃𝐨𝐞𝐬 𝐓𝐡𝐢𝐬 𝐖𝐨𝐫𝐤 1. Richer Shell. Agents (and subagents) work by intermixing code and prose/thinking. But they use static scripts or bash that run and exit and start over each tool call. That's not ideal for exploration and synthesis of data. For that, state is useful to continue building and exploring the data as you learn more. There's a reason jupyter notebooks have been popular with data scientists. 2. Keeps main agent context clean. The better context you have the better the agent will perform (duh!). This means three thing: better human input, less missing search results, and less incorrect search results. Letting the agent iterate allows it to synthesize just what is needed and nothing else. All bad paths or peeks at something that turns out to be irrelevant stays out of main agent context. 3. Stack the good ideas! People often compare late interaction search vs RLM. Or static vs dynamic languages. Or agentic search vs semantic search. But...You can just use them all together for what they're each good at. Use them all for the area they're really great for. Read the full post which has more detail about how and why.

Isaac Flath

40,212 views • 3 months ago

You do not need to listen to most podcasts anymore. I pasted a 2-hour interview into HypeScribe and got: • a full transcript • key takeaways • action items • a clean summary • answers from AI chat in under 5 minutes. The wild part: it works with YouTube, TikTok, Instagram, Zoom calls, voice memos, basically anything with audio. My current workflow: podcast/video → HypeScribe → summary → content ideas Way faster than rewatching everything manually. I’ve been using it to turn long-form content into threads, notes, and research without wasting hours. Feels less like “taking notes” and more like having an AI research assistant for everything you watch or record. If you work with content, meetings, research, or long videos, this is ridiculously useful. Try it here 👉🏻
0:30

You do not need to listen to most podcasts anymore. I pasted a 2-hour interview into HypeScribe and got: • a full transcript • key takeaways • action items • a clean summary • answers from AI chat in under 5 minutes. The wild part: it works with YouTube, TikTok, Instagram, Zoom calls, voice memos, basically anything with audio. My current workflow: podcast/video → HypeScribe → summary → content ideas Way faster than rewatching everything manually. I’ve been using it to turn long-form content into threads, notes, and research without wasting hours. Feels less like “taking notes” and more like having an AI research assistant for everything you watch or record. If you work with content, meetings, research, or long videos, this is ridiculously useful. Try it here 👉🏻

Onil Coder

12,075 views • 2 months ago

How to compare 10 papers in 10 seconds? Meet 𝐒𝐜𝐢𝐒𝐩𝐚𝐜𝐞 – this tool compares papers for you. Here is how it works. 1. Go to 2. Click on + 𝑠𝑖𝑔𝑛 and upload the 10 papers. 3. After uploading papers, write your prompt. 𝐶𝑜𝑚𝑝𝑎𝑟𝑒 𝑡ℎ𝑒 𝑢𝑝𝑙𝑜𝑎𝑑𝑒𝑑 10 𝑟𝑒𝑠𝑒𝑎𝑟𝑐ℎ 𝑝𝑎𝑝𝑒𝑟𝑠 4. SciSpace will start comparing the papers. 5. You will see the comparison result on right side. 6. You will see various insights with paper numbers. 7. At the end, you will see summary of the comparison. 8. SciSpace compares the papers based on: ✓ Similarities in research themes ✓ Differences in approaches ✓ Relative strengths and weaknesses ✓ Gaps identified across papers ✓ Relationships and building upon each other 9. To trace to each paper, click on the 𝑝𝑎𝑝𝑒𝑟 𝑛𝑢𝑚𝑏𝑒𝑟𝑠 10. To trace to exact location, click on 𝑙𝑜𝑐𝑎𝑡𝑒 𝑃𝐷𝐹. Where can you use such comparison? You can use it to: ➝ Understand the related literature. ➝ Position the novelty of your research paper. ➝ Understand niche questions in a research area. ➝ Grasp key insights from a bunch of papers in one go. Try SciSpace today:
0:40

How to compare 10 papers in 10 seconds? Meet 𝐒𝐜𝐢𝐒𝐩𝐚𝐜𝐞 – this tool compares papers for you. Here is how it works. 1. Go to 2. Click on + 𝑠𝑖𝑔𝑛 and upload the 10 papers. 3. After uploading papers, write your prompt. 𝐶𝑜𝑚𝑝𝑎𝑟𝑒 𝑡ℎ𝑒 𝑢𝑝𝑙𝑜𝑎𝑑𝑒𝑑 10 𝑟𝑒𝑠𝑒𝑎𝑟𝑐ℎ 𝑝𝑎𝑝𝑒𝑟𝑠 4. SciSpace will start comparing the papers. 5. You will see the comparison result on right side. 6. You will see various insights with paper numbers. 7. At the end, you will see summary of the comparison. 8. SciSpace compares the papers based on: ✓ Similarities in research themes ✓ Differences in approaches ✓ Relative strengths and weaknesses ✓ Gaps identified across papers ✓ Relationships and building upon each other 9. To trace to each paper, click on the 𝑝𝑎𝑝𝑒𝑟 𝑛𝑢𝑚𝑏𝑒𝑟𝑠 10. To trace to exact location, click on 𝑙𝑜𝑐𝑎𝑡𝑒 𝑃𝐷𝐹. Where can you use such comparison? You can use it to: ➝ Understand the related literature. ➝ Position the novelty of your research paper. ➝ Understand niche questions in a research area. ➝ Grasp key insights from a bunch of papers in one go. Try SciSpace today:

Faheem Ullah

24,341 views • 5 months ago