The Dify framework can partially realize the functional effects of Manus, but there are technical gaps in areas such as complex task automation and multi-agent collaboration.
1. Comparison of core functions
1. Task dismantling and execution capabilities
- Dify：
It supports task decomposition through the Agent mode, and developers need to manually configure the tool call order and Prompt logic. For example, a financial analysis agent can be built, and a Python script can be called to generate visual charts, but the task flow and exception handling logic need to be designed by yourself. - Manus：
Built-in multi-agent collaboration system (planning agent, execution agent, verification agent), users only need to enter natural language instructions to automatically generate SOP (standard operating procedures). For example, in the real estate screening task, Manus can be automatically decomposed into sub-tasks such as "community safety analysis → school assessment → budget calculation" and called different tool chains to complete.
Conclusion: Dify requires developers to design the process by themselves, and Manus is more automated.
2. Technology achieves differences
1. Model scheduling and toolchain
- Dify：
Supports multi-model access (such as OpenAI, Claude), but requires manual configuration of model call priority. Toolchains rely on plug-in extensions, such as generating Python scripts through code interpreters, but require the developer to predefined tool interfaces. - Manus：
The dynamic model routing mechanism is adopted, and the optimal model is automatically selected according to the task type (such as GPT-4 for inference and Claude for code generation), and an industry-level tool chain is built-in (such as financial data API, real estate crawler).
Conclusion: Dify's flexibility complements Manus' automation toolchain.
2. Independent learning and memory ability
- Dify：
Static data memory (such as enhanced generation of document retrieval) is realized through the knowledge base, but the ability to learn dynamically is lacking. Users need to manually update the knowledge base to optimize results. - Manus：
Support long-term memory storage and preference learning, such as automatically recording users' preferences for "table output" and prioritize application in subsequent tasks.
Conclusion: Manus is better at learning user behavior.
3. Feasible path to Dify to achieve Manus effect
1. Alternative solutions for some scenarios
- Complex task split:
Through Dify's workflow orchestration function, a multi-step task chain is manually designed. For example, break down stock analysis into the "Data acquisition → Cleaning → Visualization → Report Generation" subprocess, and configure model calls for each link. - Tool calls:
Integrate custom tools (such as crawlers, Excel generators) to achieve automated operations through Function Calling mode, but developers need to write interface code.
2. Technical limitations
- Multi-agent collaboration is missing:
Dify only supports linear execution of a single agent, and cannot achieve task optimization through multi-agent division of labor and collaboration like Manus (such as verifying the agent's automatic review results). - Perform environmental isolation:
Manus' tasks run in independent virtual machines to avoid resource conflicts; Dify needs to rely on external containerization technologies (such as Docker) to achieve similar effects and increase deployment complexity.
4. Applicable scenario suggestions
| Requirement Type | Recommended platform | illustrate |
|---|---|---|
| Standardized task automation | Dify | For example, customer service robots and weekly report generation can be achieved through low-code configuration. |
| Complex tasks across fields | Manus | Such as financial analysis and real estate screening, relying on multi-agent collaboration and dynamic toolchains. |
| Privatized custom development | Dify | When independent and controllable models and toolchains are required, Dify's open source features have more advantages. |
5. Summary
Dify has the potential to achieve some of Manus's effects in basic Agent functions, but due to the limited single agent architecture and toolchain depth, it is difficult to fully reproduce Manus' end-to-end automation capabilities.
If you need to achieve approximate results at low cost, you can use Dify's workflow orchestration + custom tool development + external containerized deployment of combination solutions, but you need to invest high development resources.
Manus is still a better choice for scenarios that pursue extreme automation and multi-domain task coverage.
Manus' architectural innovation has led AI to evolve from a "thinker" to an "executor".
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Link：/farwish/p/18758181