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DeerFlow vs LlamaFactory

DeerFlow and LlamaFactory are both Agents & Developer Tooling tools in our directory. This side-by-side comparison covers pricing, editorial scores, supported platforms, key features and open-source status so you can decide which one fits your needs.

Side-by-side comparison

AttributeDeerFlowLlamaFactory
Product statusStatus not recordedStatus not recorded
VendorByteDanceLlamaFactory
Rating★ 4.4★ 4.5
PricingFreeFree
PlatformsLibrary, WebLibrary, CLI, Web
Key featuresSubagent orchestration, Skills and MCP tools, Memory and sandbox-aware executionYAML training configuration, LoRA, freezing or full tuning, Dataset-format preparation
Headquarters——
Launched——
Open sourceYesYes

Key differences

  • LlamaFactory carries the higher editorial score (4.5★ vs 4.4★).
  • Both DeerFlow and LlamaFactory use a Free model.
  • Both DeerFlow and LlamaFactory are open source.
  • DeerFlow runs on Library, Web; LlamaFactory runs on Library, CLI, Web.

DeerFlow and LlamaFactory at a glance

DeerFlow

ByteDance

Self-hosted agent harness with subagents, skills, memory and configurable tool execution.

Rating
4.4★
Pricing
Free

Editorial verdict

Consider it when you can operate and supervise an extensible agent runtime.

Strengths

  • Extensible skills, tools and subagent workflows
  • MIT source available for adaptation

Key features

  • Subagent orchestration
  • Skills and MCP tools
  • Memory and sandbox-aware execution

Best for

  • Multi-source research and report workflows
  • Prototyping supervised multi-step agents
Read the full DeerFlow review →

LlamaFactory

LlamaFactory

Apache-2.0 toolkit for configurable language-model fine-tuning, with CLI/WebUI workflows; model weights and training data have separate licenses.

Rating
4.5★
Pricing
Free

Editorial verdict

Choose it for a team prepared to configure and evaluate training; verify the exact model and dataset licenses before use or distribution.

Strengths

  • Explicit training and dataset configuration
  • Apache-2.0 toolkit code

Key features

  • YAML training configuration
  • LoRA, freezing or full tuning
  • Dataset-format preparation
  • Model export and inference workflows

Best for

  • Domain-data SFT experiments
  • Adapter training with reviewed datasets
Read the full LlamaFactory review →

Feature overlap

  • The two tools have no listed features in common — see both feature lists above.
  • DeerFlow also lists: Subagent orchestration, Skills and MCP tools, Memory and sandbox-aware execution
  • LlamaFactory also lists: YAML training configuration, LoRA, freezing or full tuning, Dataset-format preparation, Model export and inference workflows

Frequently asked questions

Is DeerFlow or LlamaFactory better?

By editorial score, LlamaFactory scores higher (4.5★ vs 4.4★). Both are capable Agents & Developer Tooling tools, so review the feature comparison for your use case.

Which is cheaper, DeerFlow or LlamaFactory?

Both DeerFlow and LlamaFactory use a Free pricing model.

Are DeerFlow and LlamaFactory open source?

Yes — both DeerFlow and LlamaFactory are open source.

What platforms do DeerFlow and LlamaFactory support?

DeerFlow supports Library, Web; LlamaFactory supports Library, CLI, Web.

What are the main features of DeerFlow and LlamaFactory?

DeerFlow: Subagent orchestration, Skills and MCP tools, Memory and sandbox-aware execution. LlamaFactory: YAML training configuration, LoRA, freezing or full tuning, Dataset-format preparation, Model export and inference workflows.

Visit DeerFlow →Visit LlamaFactory →

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