--- title: "langchain-serve vs agent-framework" type: "comparison" canonical_url: "https://www.graphcanon.com/compare/jina-ai-langchain-serve-vs-microsoft-agent-framework" tools: ["jina-ai-langchain-serve", "microsoft-agent-framework"] --- # langchain-serve vs agent-framework *GraphCanon updated Sep 20, 2026* ## Verdict Pick langchain-serve if langchain-serve is a tool for deploying Langchain applications in production using Jina and FastAPI, with support for Docker Compose or Kubernetes deployment and secrets management; pick agent-framework if the agent-framework provides tools for developers to build and deploy AI agents and multi-agent workflows in Python and .NET environments. [langchain-serve](https://cloud.jina.ai) reports 1.6k GitHub stars, 133 forks, and 15 open issues, last pushed Sep 20, 2023. [agent-framework](https://aka.ms/agent-framework) has 14k stars, 2.3k forks, and 605 open issues, last pushed Sep 18, 2026. Figures are from public GitHub metadata via [langchain-serve's repository](https://github.com/jina-ai/langchain-serve) and [agent-framework's repository](https://github.com/microsoft/agent-framework). | | [langchain-serve](/tools/jina-ai-langchain-serve.md) | [agent-framework](/tools/microsoft-agent-framework.md) | | --- | --- | --- | | Tagline | Self-host LLM Apps with Docker Compose or Kubernetes | Framework for building and deploying AI agents and multi-agent workflows | | Stars | 1,639 | 13,573 | | Forks | 133 | 2,335 | | Open issues | 15 | 605 | | Language | Python | Python | | Adopt for | Langchain-serve is a tool for deploying Langchain applications in production using Jina and FastAPI, with support for Docker Compose or Kubernetes deployment and secrets management. | The agent-framework provides tools for developers to build and deploy AI agents and multi-agent workflows in Python and .NET environments. | | Persona | - | - | | Runtime | - | - | | License | Apache-2.0 | MIT | | Categories | Developer Tools, Inference & Serving | AI Agents, Developer Tools | ## Trust and health _Sourced signals - not a safety guarantee. No winner column._ | | [langchain-serve](/tools/jina-ai-langchain-serve.md) | [agent-framework](/tools/microsoft-agent-framework.md) | | --- | --- | --- | | Maintenance | Archived (8%) | Very active (96%) | | Days since push | 1094d | 0d | | Archived on GitHub | Yes | No | | Open issues (now) | 15 | 605 | | Stars delta | -1 (30d) | +855 (30d) | | Open issues delta | 0 (30d) | -80 (30d) | | Full report | [trust report](/tools/jina-ai-langchain-serve/trust.md) | [trust report](/tools/microsoft-agent-framework/trust.md) | ## Shared compatibility - **Python**: [langchain-serve](/tools/jina-ai-langchain-serve.md) - Python runtime; [agent-framework](/tools/microsoft-agent-framework.md) - Python runtime ## Decision facts: langchain-serve - **Pricing:** freemium - Base credits ensure high availability by maintaining at least one instance running continuously, while serving credits are charged when the application is actively serving requests. Pricing is based, - **Requirements:** Min 1 GB RAM; Requires Docker; Requires Docker Compose or Kubernetes for deployment.; Secrets management is supported via `.env` files. - **Adopt for:** Langchain-serve is a tool for deploying Langchain applications in production using Jina and FastAPI, with support for Docker Compose or Kubernetes deployment and secrets management. ## Decision facts: agent-framework - **Requirements:** Python version 3.6 or newer is required for Python installations.; The .NET Core SDK must be installed for utilizing the .NET packages. - **Adopt for:** The agent-framework provides tools for developers to build and deploy AI agents and multi-agent workflows in Python and .NET environments. ## Choose when ### Choose langchain-serve if… - License: langchain-serve is Apache-2.0, agent-framework is MIT. - Pricing: Base credits ensure high availability by maintaining at least one instance running continuously, while serving credits are charged when the application is actively serving requests. Pricing is based, . - Requirements: Min 1 GB RAM; Requires Docker; Requires Docker Compose or Kubernetes for deployment.; Secrets management is supported via `.env` files.. - Tags unique to langchain-serve: autogpt, autonomous-agents, babyagi, chatbot. - Also covers Inference & Serving. - When you need to deploy Langchain applications with Jina and FastAPI on your own infrastructure. ### Choose agent-framework if… - License: agent-framework is MIT, langchain-serve is Apache-2.0. - Requirements: Python version 3.6 or newer is required for Python installations.; The .NET Core SDK must be installed for utilizing the .NET packages.. - Tags unique to agent-framework: agent-framework, agentic-ai, agents, multi-agent. - Also covers AI Agents. - Choose agent-framework if your project requires support for both Python and .NET, allowing you to develop across different ecosystems. ## When NOT to use langchain-serve - If you do not require or prefer not to use Jina and FastAPI for your Langchain applications. - When you do not need or want to manage secrets through a `.env` file during deployment. - If you are not interested in deploying your applications using Docker Compose or Kubernetes. - If you are looking for a fully managed cloud service without the need to manage your own infrastructure. ## When NOT to use agent-framework - Avoid using the agent-framework if your team does not have proficiency in either Python or.NET, as this may cause difficulties in leveraging its features effectively. - Do not opt for agent-framework if you only need lightweight support for AI agents without a comprehensive orchestration and deployment framework. ## Common questions ### What is the difference between langchain-serve and agent-framework? langchain-serve: Self-host LLM Apps with Docker Compose or Kubernetes. agent-framework: Framework for building and deploying AI agents and multi-agent workflows. See the comparison table for live GitHub stats and shared categories. ### When should I choose langchain-serve over agent-framework? Choose langchain-serve over agent-framework when License: langchain-serve is Apache-2.0, agent-framework is MIT; Pricing: Base credits ensure high availability by maintaining at least one instance running continuously, while serving credits are charged when the application is actively serving requests. Pricing is based, ; Requirements: Min 1 GB RAM; Requires Docker; Requires Docker Compose or Kubernetes for deployment.; Secrets management is supported via `.env` files.; Tags unique to langchain-serve: autogpt, autonomous-agents, babyagi, chatbot; Also covers Inference & Serving; When you need to deploy Langchain applications with Jina and FastAPI on your own infrastructure. ### When should I choose agent-framework over langchain-serve? Choose agent-framework over langchain-serve when License: agent-framework is MIT, langchain-serve is Apache-2.0; Requirements: Python version 3.6 or newer is required for Python installations.; The .NET Core SDK must be installed for utilizing the .NET packages.; Tags unique to agent-framework: agent-framework, agentic-ai, agents, multi-agent; Also covers AI Agents; Choose agent-framework if your project requires support for both Python and .NET, allowing you to develop across different ecosystems. ### When should I avoid langchain-serve? If you do not require or prefer not to use Jina and FastAPI for your Langchain applications. When you do not need or want to manage secrets through a `.env` file during deployment. If you are not interested in deploying your applications using Docker Compose or Kubernetes. If you are looking for a fully managed cloud service without the need to manage your own infrastructure. ### When should I avoid agent-framework? Avoid using the agent-framework if your team does not have proficiency in either Python or.NET, as this may cause difficulties in leveraging its features effectively. Do not opt for agent-framework if you only need lightweight support for AI agents without a comprehensive orchestration and deployment framework. ### Is langchain-serve or agent-framework more popular on GitHub? agent-framework has more GitHub stars (13,573 vs 1,639). Stars measure visibility, not whether either tool fits your constraints. ### Are langchain-serve and agent-framework open source? Yes - both are open-source projects on GitHub (langchain-serve: Apache-2.0, agent-framework: MIT). ### Where can I find alternatives to langchain-serve or agent-framework? GraphCanon lists graph-backed alternatives at [langchain-serve alternatives](/tools/jina-ai-langchain-serve/alternatives) and [agent-framework alternatives](/tools/microsoft-agent-framework/alternatives) ([langchain-serve markdown twin](/tools/jina-ai-langchain-serve/alternatives.md), [agent-framework markdown twin](/tools/microsoft-agent-framework/alternatives.md)), ranked by typed relationship edges rather than popularity votes. ### Is there a machine-readable version of this comparison? Yes. The markdown twin at [this comparison](/compare/jina-ai-langchain-serve-vs-microsoft-agent-framework.md) mirrors this page for agents and LLM crawlers, with the same stats table and FAQ answers. ### Which is better maintained, langchain-serve or agent-framework? langchain-serve: Archived. agent-framework: Very active. Compare maintenance labels, days since push, and release cadence in the trust section below - stars alone do not measure maintenance. ### Where are the full trust reports for langchain-serve and agent-framework? GraphCanon publishes per-repo trust reports with dated maintenance, provenance, and scan summaries: [langchain-serve trust report](/tools/jina-ai-langchain-serve/trust); [agent-framework trust report](/tools/microsoft-agent-framework/trust). --- **Machine-readable endpoints** - JSON: [`/api/graphcanon/graph?tool=jina-ai-langchain-serve`](/api/graphcanon/graph?tool=jina-ai-langchain-serve) - LLM index: [/llms.txt](/llms.txt) - Full corpus: [/llms-full.txt](/llms-full.txt) _GraphCanon - The knowledge graph for AI development. https://www.graphcanon.com/_