DeerFlow for Researchers: Multi-Agent AI Research and Reports
Compiling a multi-source research report means juggling separate search tabs, scripts, and draft documents by hand. DeerFlow is an open-source multi-agent framework, a AI Agents tool, that plans, researches, and writes reports from one prompt. Particularly strong at deep, multi-source research — not built for interactive terminal coding work.
- China 33%
- United States 26.6%
- Singapore 7.2%
- India 6.5%
- Other 26.7%
What DeerFlow Does
DeerFlow solves the coordination problem of multi-step knowledge work by running specialized agents that research, code, and write in one workflow.
DeerFlow breaks a broad request into a structured plan, then delegates subtasks to agents for search, code execution, and file handling. The framework runs code in a sandboxed Docker environment, retains context through short- and long-term memory, and stores files across the session, so a task does not lose its working context between steps.
Outputs include reports, websites, dashboards, and slide decks generated as artifacts, so results arrive as finished deliverables rather than raw notes requiring further assembly across multiple tools.
Main Features
SuperAgent
SuperAgent is DeerFlow’s open-source harness that researches, codes, and creates across a full task rather than one isolated step. It coordinates planning, search, and execution instead of running them as separate disconnected scripts. Because the harness handles each stage internally, DeerFlow can turn one written prompt into a finished report, application, or document without manual handoffs between tools or copy-pasted context.
Multi-agent orchestration
DeerFlow splits a complex request into subtasks and assigns each one to a specialized agent rather than relying on a single model to do everything. Agents work in parallel on research, coding, and writing, then their outputs are merged into one deliverable. This division of labor means long, multi-part jobs complete as structured workflows instead of a single long monologue.
Deep research
Deep research lets DeerFlow search, retrieve, cross-check, and synthesize information from multiple web sources into one coherent output. The agent pulls from several sources in parallel rather than querying one search engine sequentially, then reconciles conflicting details before drafting. The result is a sourced report rather than a single unverified search result pasted into a document.
Sandboxed code execution
DeerFlow writes and runs code inside an isolated Docker-based Agent Runtime Environment that can execute commands, manage files, and run long tasks. Because execution happens in a sandbox rather than the host machine, scripts can run data analysis or automation jobs without exposing the local system. This lets DeerFlow return analyzed data or generated files directly as part of a report.
Use Cases
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Forecast 2026 Agent Trends
Researchers can ask DeerFlow to build a webpage with a deep research report forecasting 2026 agent technology trends. The deep research feature cross-checks multiple sources before drafting. Researchers can reuse the report for further studies.
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Generate a Video
Content creators can have DeerFlow locate a scene from ‘Pride and Prejudice,’ then generate a video and reference image from it. Multi-agent orchestration coordinates the search and generation steps. Creators can fold the output directly into a larger project.
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Exploratory Data Analysis
Data analysts can point DeerFlow at the Titanic dataset to identify key factors behind survival rates. Sandboxed code execution runs the analysis and returns processed results. Analysts receive insights without managing a separate Python environment.
Best For / Not For
DeerFlow is built for technically inclined people comfortable running an open-source multi-agent framework rather than installing a fully managed enterprise application with vendor support and onboarding.
Researchers compiling multi-source reports, content creators producing video or image assets from source material, and data analysts running exploratory studies on structured datasets are the clearest fits for DeerFlow’s sandboxed execution and deep research agents.
DeerFlow does not provide a managed enterprise deployment or named compliance certifications, so teams without engineers available to configure Docker, models, and API keys should avoid self-hosting it entirely.
Pricing
DeerFlow is free to self-host under an MIT license, while its hosted web service separately offers a Free plan alongside one-time, monthly, and annual paid tiers.
| Plan | Price | Included |
|---|---|---|
| Free | Free | |
| One-time | $5 | |
| Monthly | $7.9 / user/month, billed monthly | |
| Annual | $79 / user/year, billed annually |
Hosted plans run on credits rather than unlimited access, with each generated report costing 2 credits regardless of tier. Self-hosters additionally pay separately for their own model API usage and infrastructure, costs this table does not include.
Pricing checked 2026-10-24.
Quick Comparison
OpenHands is DeerFlow’s main alternative for agent-driven coding and issue-to-fix workflows. OpenHands focuses on reading, editing, and testing a codebase end to end for a single repository task. DeerFlow instead runs multi-agent orchestration across research, coding, and file handling in one workflow for broader deliverables. Choose OpenHands if the job is fixing a specific codebase issue. Choose DeerFlow if the job spans research, data analysis, and report generation together.
Verdict
DeerFlow runs sandboxed code execution, deep research, and multi-agent orchestration for researchers and analysts, with hosted plans starting at $79 per year or free self-hosting under an MIT license. Technical teams fit best; teams wanting a fully managed interface do not.
