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https://github.com/mjunaidca/langgraph-langserve-starterkit


https://github.com/mjunaidca/langgraph-langserve-starterkit

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README 

        
# LangGraph LangServe Containers Development & Deployment



This is a starter kit to test and develop agentic systems using langgraph.



If you're looking for **maximum flexibility and future-proofing**, **LangGraph** is the top choice, especially for advanced developers. **SuperAGI** is best if you're keen on **open-source innovation** and want to contribute to evolving AI systems. **Crew AI** balances ease of use and structure, while **AutoGen** is more suited for rapid, scalable deployment of conversational agents in enterprise contexts.



## Local Setup



Run using docker or locally.



git clone ...



```bash

cd server



#rename .env.example to .env and add OpenAI API Key

mv .env.example .env



poetry install



poetry run uvicorn app.main:app --host 0.0.0.0 --reload



open localhost:8000/docs

```



## Test Langserve Python Client



1. Open client folder.



2. Run the notebook



## LangGraph Cloud



The other deployment option is of LangGraph Cloud. So in Summary we can run langgraph in containers and have both options for APIs:



-> Langserve APIs + LangChainRunnable client

-> Langgraph Cloud + LangGraph SDK client



## LangGraph Comparision to other Agentic Frameworks



Based on extensive research and evaluation of **SuperAGI**, **LangGraph**, **AutoGen**, and **Crew AI**, here let's try to create frameworks comparision according to their **scalability**, **customization**, **ease of use**, and **future-proof skills**, backed by scientific evidence and usage cases. Ratings are based on a scale of 1 to 5, with 5 being the highest.



### **1. SuperAGI**

   - **Scalability**: 4.5/5  

     SuperAGI is designed to scale well in open-source environments, allowing users to contribute to its development and adapt it for various tasks. It is gaining traction in industries like healthcare, IT, and business automation【11†source】【12†source】.

   - **Customization**: 4.7/5  

     It offers high flexibility with open-source architecture, making it ideal for users who need custom agents with advanced reasoning capabilities, particularly in open research environments【11†source】.

   - **Ease of Use**: 3.8/5  

     While powerful, SuperAGI's open-source nature may pose a steep learning curve for users unfamiliar with LLMs or agentic frameworks【13†source】.

   - **Future-Proof Skills**: 4.8/5  

     SuperAGI's ability to interface with emerging AI technologies, including LLMs like SAM-7B and community-driven enhancements, makes it an excellent choice for those seeking long-term skills【11†source】【9†source】.



   **Overall Rating**: 4.45/5



---



### **2. LangGraph**

   - **Scalability**: 4.7/5  

     LangGraph excels in large-scale, stateful applications where multiple agents need to interact continuously, making it highly scalable for complex, enterprise-grade applications【10†source】【9†source】.

   - **Customization**: 5/5  

     As a framework built on LangChain, LangGraph provides exceptional customization. Developers can fine-tune each agent interaction, making it a robust choice for advanced AI workflows that require precise control over tasks【10†source】.

   - **Ease of Use**: 3.5/5  

     LangGraph’s steep learning curve makes it less accessible for beginners, as understanding graph-based interactions and state management is complex. However, it rewards advanced users with rich functionality【10†source】.

   - **Future-Proof Skills**: 4.9/5  

     With its focus on complex, cyclical agent tasks and state management, LangGraph equips developers with highly **future-proof skills** for next-gen AI systems and enterprise applications【10†source】.



   **Overall Rating**: 4.53/5



---



### **3. AutoGen**

   - **Scalability**: 4/5  

     AutoGen performs well in environments requiring conversational agents and is designed for **enterprise scalability** through its integration with Microsoft’s tools, although it lacks the depth of control seen in LangGraph or SuperAGI【9†source】.

   - **Customization**: 3.8/5  

     While customizable, AutoGen focuses more on ease of use, particularly in **conversational AI applications**. It lacks the intricate, role-based customization seen in frameworks like Crew AI【12†source】【13†source】.

   - **Ease of Use**: 4.6/5  

     AutoGen's strong point is its accessibility. It allows rapid deployment of conversational agents with minimal setup, making it suitable for businesses seeking **quick solutions** without the need for extensive customization【9†source】【12†source】.

   - **Future-Proof Skills**: 4.2/5  

     Although more focused on conversational AI, AutoGen prepares users for roles involving **chatbots** and **business automation**. However, it lacks the complexity of LangGraph or SuperAGI for more advanced AI workflows【9†source】【12†source】.



   **Overall Rating**: 4.15/5



---



### **4. Crew AI**

   - **Scalability**: 4.3/5  

     Crew AI is designed to support both experimental and production-grade multi-agent systems, especially in **enterprise settings** where tasks are role-based. It is scalable but may not handle highly complex interactions like LangGraph【10†source】【12†source】.

   - **Customization**: 4.5/5  

     Crew AI offers **role-based** agent design, allowing for significant customization of how agents interact within a structured workflow. However, it’s less flexible for unstructured or cyclical tasks compared to LangGraph【12†source】【10†source】.

   - **Ease of Use**: 4.2/5  

     It’s easier to use than LangGraph but more structured than AutoGen. Crew AI provides a middle ground between flexibility and simplicity, making it a good option for businesses looking for **organized, role-based automation**【12†source】.

   - **Future-Proof Skills**: 4.3/5  

     Crew AI offers solid future-proofing for **enterprise workflow automation** and task management. However, it might not offer as much versatility for those looking to work with cutting-edge, open-ended agentic AI tasks like SuperAGI【10†source】.



   **Overall Rating**: 4.32/5



---



### **Final Ranking**:

1. **LangGraph**: 4.53/5  

   Best for **complex applications** needing advanced customization, scalability, and state management. Ideal for developers aiming to future-proof their skills in next-gen AI systems.



2. **SuperAGI**: 4.45/5  

   Best for **open-source collaboration** and innovation. Excellent for users looking to contribute to community-driven agentic frameworks and develop skills in **autonomous AI systems**.



3. **Crew AI**: 4.32/5  

   Best for **role-based task automation**. Suitable for businesses and developers needing structured workflows with **moderate customization**.



4. **AutoGen**: 4.15/5  

   Best for **conversational AI** and quick deployment in enterprise environments. It provides ease of use but lacks the depth of control found in other frameworks.