DeerFlow Architecture and Runtime

1. The workspace shape

The repo structure and docs strongly suggest these main areas:

Area	Why it matters
src/	Core application and orchestration logic
web/	Browser-facing UI
examples/	Reference workflows and patterns
docs and harness material	Operational and evaluation guidance

The design language in the docs points to a workflow framework, not just a chat shell.

2. The runtime mental model

DeerFlow is best understood as a graph-like research pipeline:

1. the system plans work,
2. it gathers evidence,
3. it refines or branches the workflow,
4. it can involve a human reviewer,
5. it synthesizes a final report.

That is a different operating model from a single-loop coding agent.

3. Why LangGraph-style orchestration matters here

The official docs describe DeerFlow as framework-oriented and emphasize structured flow control. That means the architecture is optimized for:

• multi-step tasks,
• branching and resumption,
• tool-rich execution,
• inspectable process steps.

This is why DeerFlow feels closer to a research engine than to a normal AI chat app.

4. MCP and human review are core design choices

MCP support and human-in-the-loop review are not small extras. They change the whole workflow shape:

• MCP widens what the system can access,
• human review raises trust and control for longer tasks.

Together they make DeerFlow much more suitable for high-context knowledge work.

5. What to read first in the codebase

Start with:

1. docs overview and design principles,
2. harness docs,
3. src/,
4. web/,
5. examples once the core graph flow is clear.