DeepWideResearch
Open-source agentic RAG with deep & wide research controls
If you want fine-grained control over research breadth and depth plus open-source freedom, DeepWideResearch is a solid choice. The credit system on cloud plans may limit heavy automation, and MCP adds setup friction. For a plug-and-play alternative, consider OpenAI's Deep Research or Gemini; for a more managed open-source option, look at LangChain.
Verified 2d ago · liveness 55/100 · cite: rightaichoice.com/tools/deepwideresearch
- Researchers needing adjustable depth and breadth
- Developers building custom research agents with MCP
- Enterprises requiring self-hosted, private research infrastructure
- Teams aggregating results from multiple search engines
- Non-technical users seeking a one-click research tool
- Teams needing native mobile or desktop applications
- Users who prefer a fully guided, configuration-free experience
We scan live Reddit threads, YouTube comments, X posts, G2 reviews and other communities — and hand you an honest verdict in under a minute.
- Honest verdict, not marketing
- Real pros & cons from real users
- Attributed quotes with receipts
3 free scans · no card needed
Skip DeepWideResearch if you need a plug-and-play research tool without any setup — you'll find MCP configuration and model API management too technical. It's also not for you if you require native mobile apps or a fully guided interface.
Cloud plans have monthly credit limits (e.g., Plus gives 2,000 credits), and exceeding them means you'll need to upgrade or wait for the next cycle — no pay-as-you-go overage.
The free tier (100 credits/mo) and self-hosting let you start at zero cost, making it great for individuals and evaluation. Plus at $15/mo (2,000 credits) is competitive for light automation, while Pro at $100/mo (15k credits) suits heavier use. Compared to OpenAI's Deep Research (which can be pricey per query) or Gemini's bundled subscriptions, DeepWideResearch's credit system offers predictable monthly costs, especially if you self-host.
In short
DeepWideResearch — Open-source agentic RAG with deep & wide research controls. Best for Researchers needing adjustable depth and breadth, Developers building custom research agents with MCP, Enterprises requiring self-hosted, private research infrastructure. Free to start; paid plans from $15/mo.
What's new in DeepWideResearch
Checked 2 days agoAcross the latest 3 updates: 2 feature updates and 1 pricing change.
Introduced cloud-hosted plans
Launched Free, Plus, Pro, and Enterprise cloud plans with credit-based usage, complementing the existing open-source self-hosting option.
Added Anthropic Claude support
DeepWideResearch now supports Anthropic Claude as an AI model in addition to OpenAI and open-source models.
Released MCP integration
Integrated Model Context Protocol (MCP) to connect external data sources and tools like Notion, Exa, and Tavily.
What people actually say about DeepWideResearch — is it worth it?
We ran a structured research pass across product reviews, community discussions, and post-purchase forum threads to surface the patterns vendors won't publish themselves. Below: the recurring strengths, the hidden costs people mention most, and the cohort that consistently regrets adopting this tool.
- +Granular control over research depth and width via two simple parameters.
- +Model flexibility: use OpenAI, Claude, or open-source models without lock-in.
- +Open-source MIT license enables full customization and self-hosting.
- +Multi-source search engine support including Exa, Tavily, and Context 7 Dev.
- +MCP integration allows connecting custom data sources and tools.
- −Lack of community feedback raises uncertainty about reliability.
- −Open-source models often produce inconsistent or lower-quality results.
- −Self-hosting setup requires advanced technical skills and time investment.
- −Documentation is sparse for advanced features and troubleshooting.
- −Cloud credit system can lead to unexpectedly high costs for heavy use.
- • Cloud credit consumption can escalate with complex research tasks
- • Self-hosting incurs compute and storage infrastructure costs
- • API usage for premium models (OpenAI, Claude) may add additional fees
Viability Score
How well maintained and how widely used is DeepWideResearch? Built from what the vendor actually publishes (docs, changelog, tutorials, integrations, pricing), whether the site is live, and how much real users discuss it. How we calculate this
Last calculated: August 2026
How we score →Key Features
- Depth and width sliders for research scope control
- MCP integration for custom data sources and tools
- Multi-engine search (Exa, Tavily, Context 7 Dev)
- Model flexibility (OpenAI, Anthropic Claude, open-source)
- Self-hostable under MIT license
- Cloud-hosted plans with credit-based usage
- API and SDK access (Python, Node.js, cURL, bash)
- Local knowledge base support (self-hosted)
- Agentic RAG for Q&A and deep analysis
- Multi-turn research conversations
- Open-source codebase
- Custom integrations for Enterprise
- Priority and dedicated support options
- Enterprise SLA and compliance options
About DeepWideResearch
DeepWideResearch is an open-source agentic RAG platform that gives you granular control over research scope through two simple parameters: depth (how thoroughly each topic is investigated) and width (how many related topics are explored). It's built for researchers, developers, and enterprises who need transparent, customizable research workflows without vendor lock-in. The platform plugs into the Model Context Protocol (MCP) to connect custom data sources and tools, supports a flexible choice of AI models (OpenAI, Anthropic Claude, open-source), and aggregates results from multiple search engines (Exa, Tavily, Context 7 Dev). You can self-host it freely under the MIT license or subscribe to cloud-hosted plans with credit-based usage: Free (100 credits/mo), Plus ($15/mo, 2,000 credits), Pro ($100/mo, 15,000 credits), and Enterprise (custom, unlimited credits). All cloud plans include API and SDK access for Python, Node.js, cURL, and bash, enabling you to trigger research programmatically. Compared to closed tools like OpenAI's Deep Research or Gemini, DeepWideResearch emphasizes transparency, data privacy, and full control — but it's not a plug-and-play product; MCP setup and self-hosting require some technical comfort. It's best suited for teams that value customization and data sovereignty over convenience.
Behind the Verdict
DeepWideResearch stands out for its depth and width controls, letting you fine-tune how thoroughly each topic is investigated and how many related topics are explored. The open-source MIT license means you can self-host and keep data on your own infrastructure — a big pull for privacy-conscious teams. MCP integration lets you plug in diverse data sources like Notion, Exa, and Tavily, and you can switch between OpenAI, Anthropic Claude, and open-source models. The cloud plans add convenience but are credit-based, which could pinch heavy automation. Setup involves technical work: configuring MCP servers, potentially self-hosting, and managing model API keys. It's a tool for developers and researchers who value control and customization; it's not for those who want a one-click, guided experience. Compared to closed tools like OpenAI Deep Research or Gemini, you get more transparency but less out-of-the-box polish. If you need to aggregate multiple search engines and tap private knowledge bases with granular control, this fits well.
Researching DeepWideResearch? Get your full AI stack in 60 seconds.
Free, no signup — tell us your goal and get tools matched to your budget & existing stack.
Real-world workflow fit
Concrete scenarios for the personas DeepWideResearch actually fits — and what changes day-one when you adopt it.
Conduct a competitor analysis with depth=0.8 and width=0.6, comparing pricing and features across multiple sources.
Outcome: Get a comprehensive report aggregating Exa and Tavily results, saving hours of manual searching.
Use the API with Python SDK to trigger research on demand, feeding results into a custom dashboard.
Outcome: Automate research tasks programmatically, with full control over depth and width via parameters.
Self-host and connect to a private Notion database via MCP to answer internal queries.
Outcome: Maintain data privacy while leveraging AI research on proprietary knowledge.
Use Cases
- Perform comprehensive market research with adjustable breadth and depth.
- Build an agentic RAG pipeline for enterprise knowledge discovery from private databases.
- Aggregate search results from multiple engines (Exa, Tavily, etc.) in one interface.
- Integrate with MCP servers to pull data from Notion and private databases.
- Self-host a private research assistant for sensitive internal data.
- Experiment with different AI models and search backends for optimal results.
Models Under the Hood
as of 2026-08-19
Limitations
- Cloud plans restrict usage to monthly credits (Free: 100, Plus: 2,000, Pro: 15,000), which may be limiting for heavy research.
- Self-hosting requires technical setup and maintenance.
- The tool's effectiveness depends on the quality of configured MCP servers and underlying models.
as of 2026-08-21
Verification history
We have re-verified DeepWideResearch 6 times since . Each pass re-reads the vendor's own pages and re-checks every listed field against that evidence; passes where nothing had changed are marked as such.
- — re-checked, vendor evidence unchanged
- — re-verified summary, description, our verdict, our analysis, pricing model, pricing tiers, features, integrations, who it suits, who should skip it
- — re-checked, vendor evidence unchanged
- — re-verified summary, description, our verdict, our analysis, pricing model, pricing tiers, features, integrations, who it suits, who should skip it
- — re-verified summary, description, our verdict, our analysis, pricing model, pricing tiers, features, integrations, who it suits, who should skip it
- — re-verified summary, description, our verdict, our analysis, pricing model, pricing tiers, features, integrations, who it suits, who should skip it
Free to cite with attribution — this page re-verifies continuously.
12-month cost
Project the real annual outlay, including the implied monthly cost when only an annual tier is published.
Vendor list price only. Add-on usage, seat overages, and contract minimums are surfaced under Hidden costs & gotchas.
Plans compared
For each published DeepWideResearch tier: who it actually fits, and what it adds vs. the previous tier. Cross-reference the cost calculator above for projected annual outlay.
Free
$0/mo
Ideal for
Individuals or teams wanting to evaluate the tool with light usage, or those who prefer to self-host without any cost.
What this tier adds
Starting tier with 100 credits/mo and community support; no API access.
Plus
$15/mo
Ideal for
Developers and small teams needing API/SDK access and moderate research volume (2,000 credits/mo) for automation.
What this tier adds
Adds API & SDK access, priority support, and advanced features over Free.
Pro
$100/mo
Ideal for
Heavy users or teams with significant research needs (15,000 credits/mo) requiring dedicated support and custom integrations.
What this tier adds
Increases credits to 15,000/mo, adds dedicated support and custom integrations over Plus.
Enterprise
Custom
Ideal for
Large organizations with unlimited research needs, requiring SLA, compliance, and custom deployment options.
What this tier adds
Unlimited credits, 24/7 support, SLA & compliance, and custom deployment over Pro.
Where the pricing makes sense
The company stage and team size where DeepWideResearch's pricing actually pencils out — and where peers do it cheaper.
The free tier (100 credits/mo) and self-hosting let you start at zero cost, making it great for individuals and evaluation. Plus at $15/mo (2,000 credits) is competitive for light automation, while Pro at $100/mo (15k credits) suits heavier use. Compared to OpenAI's Deep Research (which can be pricey per query) or Gemini's bundled subscriptions, DeepWideResearch's credit system offers predictable monthly costs, especially if you self-host.
Setup time & first value
How long it actually takes to get something useful out of DeepWideResearch — broken out by persona, not the marketing-page minute.
For cloud users: ~5-10 minutes to sign up, get an API key, and run a first query. For self-hosting: 30-60 minutes to clone the repo, set up dependencies, and configure MCP servers. Developers integrating the SDK can be productive within an hour.
Switching to or from DeepWideResearch
How to bring data in from common predecessors and how to get it back out — written for the switcher, not the buyer.
- →From OpenAI Deep Research: You can replicate similar research with more control by adjusting deep/wide parameters and aggregating multiple search engines.
- →From Gemini: Set up DeepWideResearch to use the same model (if you have an API key) while adding MCP integrations for private data.
- ↗To LangChain: If you want a more modular framework, you can replicate similar agentic RAG pipelines using LangChain's components.
- ↗To a closed tool: If you need a managed solution with less setup, you can move to OpenAI Deep Research or Gemini, though you'll lose granular controls.
Integrations
Resources & Guides
Tutorials & Learning
Official links
Tools that pair well with DeepWideResearch
Common stack mates teams adopt alongside DeepWideResearch, with the specific reason each pairing earns its keep.
Featured Head-to-Head Comparisons
Deepwideresearch vs Presto Voice
Presto Voice and DeepWideResearch serve entirely different domains. Presto Voice is a specialized drive-thru AI for QSR chains aiming to boost revenue through automation and upselling, with a proven 95% non-intervention rate. DeepWideResearch is a flexible, open-source research agent for technical users who need granular control over depth and width of analysis. Choose Presto Voice if you run a QSR chain; pick DeepWideResearch for customizable, self-hosted research.
Deepwideresearch vs Truleo
If you run a law enforcement agency and need to unify siloed data (RMS, jail calls, body cameras) with automated leads and report writing, Truleo is purpose-built for you. For technical researchers or developers who want granular control over research scope, multi-engine search, and self-hosting, DeepWideResearch offers unmatched flexibility at a lower starting cost.
Deepwideresearch vs Praktika
If your goal is to improve spoken fluency through AI conversation practice, Praktika is the right choice. If you need an open-source, configurable research tool to analyze large amounts of data across multiple sources, DeepWideResearch is better. They serve entirely different needs.
Alternatives to DeepWideResearch
View allFrequently Asked Questions
Best-of guides
Used DeepWideResearch? Help shape our editorial sentiment research.