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+---
+title: "Replicating Deep Research in Jan"
+description: "A simple guide to running replicating Deep Research results for free, with Jan."
+tags: AI, local models, Jan, GGUF, Deep Research, local AI
+categories: guides
+date: 2025-08-04
+ogImage: assets/
+twitter:
+  card: summary_large_image
+  site: "@jandotai"
+  title: "Replicating Deep Research with Jan"
+  description: "Learn how to replicate Deep Research results with Jan."
+  image: assets/
+---
+
+import { Callout } from 'nextra/components'
+import CTABlog from '@/components/Blog/CTA'
+
+# Replicating Deep Research in Jan
+
+Ever wondered how OpenAI’s Deep Research works? Since the release of Deep Research in February this year, it has been lauded as a tool that is able to “output thorough research”. 
+
+There are two core features of Deep Research: 
+
+- Exhaustive searching: all major model providers that offer deep research as a service (proceed to list) would search from a variety of sources. This can be broken down (largely) into two forms of search: **breadth-first search** and **depth-first search**. There will be more of this later!
+- Report generation: most major model providers will provide an exhaustive, lengthy report at the end of completing the deep research task. For instance, OpenAI gives users the ability to export this to a PDF, while Kimi would also provide an interactive HTML webpage in their UI for easier visualization.
+
+## Unpacking Deep Research
+While the outputs of deep research might be mind blowing at first glance, it is possible to reverse engineer the process through which such a comprehensive output is generated! OpenAI’s [Deep Research API cookbook](https://cookbook.openai.com/examples/deep_research_api/introduction_to_deep_research_api) gives us some insight into the flow that they adopt:
+![OpenAI's Deep Research UX Flow](./_assets/openai-deep-research-flow.png)
+
+Deep Research, at a high level, is essentially a pipeline / tool chain through which a user would go through. The inner workings of this pipeline might vary from provider to provider, but we’ve done up a table for you to compare the results below. 
+
+At a high level, the pipeline would look something like this: 
+
+```
+user enters query → (optional) clarifying question through a smaller model → (optional) original query is appended to follow up response → response parsed through larger `deep-research` model → output 
+```
+The crux of Deep Research lies in the pipeline’s ability to output a substantiated report which has cited information from a variety of sources. 
+
+<Callout type="info">
+**Different Flows for Different Providers**
+
+Note that not all Deep Research flows are the same as what's shown in the above diagram, only OpenAI and Kimi does this currently! Most providers would adopt a simpler approach, as shown below in the comparison table.
+</Callout>
+
+| Platform | Pipeline Flow | Planning Approach | Sources Used | Duration (mins)  | Export Options | Unique Features | Deep Research Usage |
+| --- | --- | --- | --- | --- | --- | --- | --- |
+| OpenAI | Original prompt → clarification (smaller model) → user answers → rewriter bundles prompts → specific model → output | Chain of thought (COT), no explicit planning | 10–30 | 10–15 | PDF, Markdown with inline references | Clarification questions, lightweight rewriter model | Paid |
+| Grok’s DeeperSearch  | Planning → research execution → output with survey notes | Planning phase before research | 70–100 | 5–10  | Not specified | Heavy Twitter/X source integration, "Survey Note" reasoning | Free |
+| Claude | Research plan → breadth-first search → depth-first search | Research plan generated, multiple subagents for complex queries | 100+ | 5–10  | Artifact format | Breadth-first then depth-first approach, metadata extraction | Paid |
+| Gemini | Original prompt → editable research plan → execution | Editable research plan (user can modify) | 50+ | 10–20  | Google Docs export | Editable planning phase, newer pipeline | Free |
+| Perplexity | Research plan → execution with speed options | Research plan crafted and followed | 50–100 | 3–5  | Not specified | "Answer now" option for quick results | Paid |
+| Kimi | Query → clarifying questions → planning → research → synthesis | Explicit planning with clear phase transitions | 50–100 | 30–60+ (slower inference)  | PDF, Interactive website | Distinction between "search" vs "browser use", interactive output | Free |
+
+### Understanding Search Strategies
+
+In general, when looking at the number of sources that are used, we can classify whether a search is *breadth first* or *depth first*. 
+
+*Breadth first* searches would result in a greater number of sources that are used, which might be better for a brief survey over a landscape for instance. 
+
+*Depth first* searches would entail diving deeper into a few particular sources (or have multiple search variations between a particular context), which might be better for extracting nuanced insight. 
+
+<Callout>
+In Claude’s Deep Research, a *classifier* is used to determine whether a user query is *breadth first* or *depth first*. This results in a customization of the pipeline that is used for conducting research. For instance, a complex *breadth first* query might result in *sub-agents* being spun up to research various parts of the research query in parallel. 
+</Callout>
+
+## Replicating Deep Research Results with Jan
+Given this overview, how would we replicate this in Jan? 
+
+We’ll make use of features that are available to us in order to customize the model output such that it gets as gets as close to a Deep Research output without having to spend a cent, and having full control over your data!
+
+<Callout>
+This is using the latest version of Jan (v0.6.6!) The features in this guide require at least 0.6.3 but for the best experience please use 0.6.6!
+</Callout>
+
+### Setting up a Purpose Built Model for Deep Research with Jan
+1. Under `Hub`, go to `Jan-Nano-128k-Gguf` and enable `Show variants`. Download the largest model that your computer can run! Jan-nano is a 4B model, so even with the largest quantization it should be able to run on your hardware comfortably. 
+![Navigate to Jan-Nano-128k](./_assets/jan-nano-hub.png)
+
+### Enabling Search through MCP
+2. Once the model has been downloaded, it should appear under the `llama.cpp` provider.
+3. Go to `Settings` > `General` and click on the toggle button next to `Experimental Features`. `MCP Servers` should then appear on the side bar. 
+![Enable MCP in Jan](./_assets/experimental-settings-jan.png)
+4. Upon clicking on `MCP Servers`, click on the toggle beside `Allow All Tool Permissions` as well as the toggle beside `Serper`. Feel free to look through [this guide in our documentation](https://jan.ai/docs/mcp-examples/search/exa) if you would like to play around with different providers.
+5. Click on the pencil (edit) button on `Serper` and navigate to [Serper login](https://serper.dev/login) to retrieve your Serper API key. Ensure that that is loaded under `Environment Variables` as shown below. 
+![Edit MCP Settings in Jan](./_assets/edit-mcp-settings.gif)
+6. Upon inputting that, the following should appear on the top right hand of the screen. 
+![Successful Serper Init](./_assets/successful-serper.png)
+7. Ensure that the downloaded model has access to tools by navigating to the following: `Settings` > `Model Providers` > Clicking on the `Pencil` icon and toggling `Tools` on!
+![Enable Tools gif](./_assets/enable-tools-local.gif)
+
+### Customizing Assistants
+8. Great! Now that you have search set up, let’s [configure assistants](https://jan.ai/docs/assistants) to ensure that you make the most out of the model. Here’s the prompt that we used for our assistant “Report Writer”:
+```
+Report Writing Instructions
+You are a research analyst. Follow this exact process:
+MANDATORY RESEARCH PHASE (Do this first)
+
+Conduct 5-10 searches maximum - then STOP searching and write the report
+Each search query must be unique - no repeating previous searches
+Search different angles: statistics, expert opinions, case studies, recent news, industry reports
+Use web_fetch to read full articles from search results
+
+WRITING PHASE (Do this after research is complete)
+Write a comprehensive report with:
+
+Executive summary with key findings
+Evidence-based analysis with citations for every claim
+Actionable recommendations with rationale
+
+CRITICAL: After 10 searches, immediately stop searching and write the report. Do not continue searching beyond 10 calls.
+```
+
+## Piecing it All Together
+The final result should look something like this! And violá, you would have replicated the Deep Research flow on Jan!
+![Research Result Local](./_assets/research-result-local.png)
+
+## Need help?
+
+<Callout type="info">
+[Join our Discord community](https://discord.gg/Exe46xPMbK) for support and tips on using Jan as your offline ChatGPT alternative.
+</Callout>
+
+<CTABlog />