{"id":16201177,"url":"https://github.com/mjunaidca/langgraph-langserve-starterkit","last_synced_at":"2025-04-07T18:28:21.868Z","repository":{"id":257364838,"uuid":"858036466","full_name":"mjunaidca/langgraph-langserve-starterkit","owner":"mjunaidca","description":null,"archived":false,"fork":false,"pushed_at":"2024-09-16T07:59:20.000Z","size":121,"stargazers_count":1,"open_issues_count":0,"forks_count":0,"subscribers_count":1,"default_branch":"main","last_synced_at":"2025-02-13T20:19:15.490Z","etag":null,"topics":[],"latest_commit_sha":null,"homepage":null,"language":"Python","has_issues":true,"has_wiki":null,"has_pages":null,"mirror_url":null,"source_name":null,"license":null,"status":null,"scm":"git","pull_requests_enabled":true,"icon_url":"https://github.com/mjunaidca.png","metadata":{"files":{"readme":"README.md","changelog":null,"contributing":null,"funding":null,"license":null,"code_of_conduct":null,"threat_model":null,"audit":null,"citation":null,"codeowners":null,"security":null,"support":null,"governance":null,"roadmap":null,"authors":null,"dei":null,"publiccode":null,"codemeta":null}},"created_at":"2024-09-16T07:32:40.000Z","updated_at":"2024-10-28T04:54:56.000Z","dependencies_parsed_at":"2024-09-16T09:26:24.243Z","dependency_job_id":"a3c9c2ed-ad80-4363-8f30-91ef69361e1e","html_url":"https://github.com/mjunaidca/langgraph-langserve-starterkit","commit_stats":null,"previous_names":["mjunaidca/langgraph-langserve-starterkit"],"tags_count":0,"template":false,"template_full_name":null,"repository_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/mjunaidca%2Flanggraph-langserve-starterkit","tags_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/mjunaidca%2Flanggraph-langserve-starterkit/tags","releases_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/mjunaidca%2Flanggraph-langserve-starterkit/releases","manifests_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/mjunaidca%2Flanggraph-langserve-starterkit/manifests","owner_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/owners/mjunaidca","download_url":"https://codeload.github.com/mjunaidca/langgraph-langserve-starterkit/tar.gz/refs/heads/main","host":{"name":"GitHub","url":"https://github.com","kind":"github","repositories_count":247706562,"owners_count":20982625,"icon_url":"https://github.com/github.png","version":null,"created_at":"2022-05-30T11:31:42.601Z","updated_at":"2022-07-04T15:15:14.044Z","host_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub","repositories_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories","repository_names_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repository_names","owners_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/owners"}},"keywords":[],"created_at":"2024-10-10T09:36:07.139Z","updated_at":"2025-04-07T18:28:21.851Z","avatar_url":"https://github.com/mjunaidca.png","language":"Python","funding_links":[],"categories":[],"sub_categories":[],"readme":"# LangGraph LangServe Containers Development \u0026 Deployment\n\nThis is a starter kit to test and develop agentic systems using langgraph.\n\nIf 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.\n\n## Local Setup\n\nRun using docker or locally.\n\ngit clone ...\n\n```bash\ncd server\n\n#rename .env.example to .env and add OpenAI API Key\nmv .env.example .env\n\npoetry install\n\npoetry run uvicorn app.main:app --host 0.0.0.0 --reload\n\nopen localhost:8000/docs\n```\n\n## Test Langserve Python Client\n\n1. Open client folder.\n\n2. Run the notebook\n\n## LangGraph Cloud\n\nThe other deployment option is of LangGraph Cloud. So in Summary we can run langgraph in containers and have both options for APIs:\n\n-\u003e Langserve APIs + LangChainRunnable client\n-\u003e Langgraph Cloud + LangGraph SDK client\n\n## LangGraph Comparision to other Agentic Frameworks\n\nBased 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.\n\n### **1. SuperAGI**\n   - **Scalability**: 4.5/5  \n     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】.\n   - **Customization**: 4.7/5  \n     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】.\n   - **Ease of Use**: 3.8/5  \n     While powerful, SuperAGI's open-source nature may pose a steep learning curve for users unfamiliar with LLMs or agentic frameworks【13†source】.\n   - **Future-Proof Skills**: 4.8/5  \n     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】.\n\n   **Overall Rating**: 4.45/5\n\n---\n\n### **2. LangGraph**\n   - **Scalability**: 4.7/5  \n     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】.\n   - **Customization**: 5/5  \n     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】.\n   - **Ease of Use**: 3.5/5  \n     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】.\n   - **Future-Proof Skills**: 4.9/5  \n     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】.\n\n   **Overall Rating**: 4.53/5\n\n---\n\n### **3. AutoGen**\n   - **Scalability**: 4/5  \n     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】.\n   - **Customization**: 3.8/5  \n     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】.\n   - **Ease of Use**: 4.6/5  \n     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】.\n   - **Future-Proof Skills**: 4.2/5  \n     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】.\n\n   **Overall Rating**: 4.15/5\n\n---\n\n### **4. Crew AI**\n   - **Scalability**: 4.3/5  \n     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】.\n   - **Customization**: 4.5/5  \n     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】.\n   - **Ease of Use**: 4.2/5  \n     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】.\n   - **Future-Proof Skills**: 4.3/5  \n     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】.\n\n   **Overall Rating**: 4.32/5\n\n---\n\n### **Final Ranking**:\n1. **LangGraph**: 4.53/5  \n   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.\n\n2. **SuperAGI**: 4.45/5  \n   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**.\n\n3. **Crew AI**: 4.32/5  \n   Best for **role-based task automation**. Suitable for businesses and developers needing structured workflows with **moderate customization**.\n\n4. **AutoGen**: 4.15/5  \n   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.\n","project_url":"https://awesome.ecosyste.ms/api/v1/projects/github.com%2Fmjunaidca%2Flanggraph-langserve-starterkit","html_url":"https://awesome.ecosyste.ms/projects/github.com%2Fmjunaidca%2Flanggraph-langserve-starterkit","lists_url":"https://awesome.ecosyste.ms/api/v1/projects/github.com%2Fmjunaidca%2Flanggraph-langserve-starterkit/lists"}