--- title: "hello-agents vs sdl-mcp" type: "comparison" canonical_url: "https://www.graphcanon.com/compare/datawhalechina-hello-agents-vs-glitterkill-sdl-mcp" tools: ["datawhalechina-hello-agents", "glitterkill-sdl-mcp"] --- # hello-agents vs sdl-mcp *GraphCanon updated Sep 20, 2026* ## Verdict Pick hello-agents if hello-Agents is a systematic learning tutorial for building AI-native intelligent agents, offering hands-on experience with core principles and practical implementation in Python; pick sdl-mcp if sDL-MCP is a policy-centered tool designed specifically to improve AI-driven coding tasks by managing contexts more efficiently through technologies such as semantic analysis and tree-sitter. [hello-agents](https://hello-agents.datawhale.cc) reports 80k GitHub stars, 9.9k forks, and 82 open issues, last pushed Sep 17, 2026. [sdl-mcp](https://github.com/GlitterKill/sdl-mcp) has 486 stars, 31 forks, and 0 open issues, last pushed Sep 16, 2026. Figures are from public GitHub metadata via [hello-agents's repository](https://github.com/datawhalechina/hello-agents) and [sdl-mcp's repository](https://github.com/GlitterKill/sdl-mcp). | | [hello-agents](/tools/datawhalechina-hello-agents.md) | [sdl-mcp](/tools/glitterkill-sdl-mcp.md) | | --- | --- | --- | | Tagline | A comprehensive guide to building intelligent agents from scratch | A policy-centered context budget layer for coding agents that enhances code analysis and workflow efficiency. | | Stars | 79,727 | 486 | | Forks | 9,907 | 31 | | Open issues | 82 | 0 | | Language | Python | TypeScript | | Adopt for | Hello-Agents is a systematic learning tutorial for building AI-native intelligent agents, offering hands-on experience with core principles and practical implementation in Python. | SDL-MCP is a policy-centered tool designed specifically to improve AI-driven coding tasks by managing contexts more efficiently through technologies such as semantic analysis and tree-sitter. | | Persona | - | - | | Runtime | - | - | | License | The license is not specified as open source, but the content is free to read and learn from. | Other | | Categories | AI Agents, Model Training | AI Agents, Evaluation & Observability, Model Training | ## Trust and health _Sourced signals - not a safety guarantee. No winner column._ | | [hello-agents](/tools/datawhalechina-hello-agents.md) | [sdl-mcp](/tools/glitterkill-sdl-mcp.md) | | --- | --- | --- | | Days since push | 0d | 3d | | Open issues (now) | 82 | 0 | | Stars delta | +6.6k (30d) | +35 (30d) | | Open issues delta | -73 (30d) | -1 (30d) | | Owner type | Organization | User | | Full report | [trust report](/tools/datawhalechina-hello-agents/trust.md) | [trust report](/tools/glitterkill-sdl-mcp/trust.md) | ## Decision facts: hello-agents - **Pricing:** freemium - The content is free to access and learn from, but there may be additional paid services or support options not detailed in the repository. - **Requirements:** Min 4 GB RAM; Python is required for practical implementation and hands-on experience with the HelloAgents framework. - **Adopt for:** Hello-Agents is a systematic learning tutorial for building AI-native intelligent agents, offering hands-on experience with core principles and practical implementation in Python. - **License detail:** The license is not specified as open source, but the content is free to read and learn from. ## Decision facts: sdl-mcp - **Adopt for:** SDL-MCP is a policy-centered tool designed specifically to improve AI-driven coding tasks by managing contexts more efficiently through technologies such as semantic analysis and tree-sitter. ## Choose when ### Choose hello-agents if… - hello-agents is primarily Python; sdl-mcp is TypeScript. - Pricing: The content is free to access and learn from, but there may be additional paid services or support options not detailed in the repository.. - Requirements: Min 4 GB RAM; Python is required for practical implementation and hands-on experience with the HelloAgents framework.. - Tags unique to hello-agents: agent, llm, rag, tutorial. - When you want to focus on building AI-native agents and understand their core principles and practical implementation. ### Choose sdl-mcp if… - sdl-mcp is primarily TypeScript; hello-agents is Python. - Tags unique to sdl-mcp: agent-context, agent-tools, agentic-coding, agentic-engineering. - Also covers Evaluation & Observability. - sdl-mcp ships an MCP server manifest. - When working with sprawling or complex codebases where maintaining context across multiple files is crucial. ## When NOT to use hello-agents - If you are looking for a tutorial that focuses on software engineering-based agents like Dify or Coze, which are more about process-driven software development. - If you prefer a tool that does not emphasize the hands-on construction of an agent framework from the ground up using OpenAI's native API. ## When NOT to use sdl-mcp - In environments where TypeScript is not a preferred or supported language. - For tasks that do not benefit from context management layers, such as small-scale projects with straightforward workflows. - If your project requires real-time response times for every operation since SDL-MCP's focus on semantic analysis and context budgeting can introduce slight delays. ## Common questions ### What is the difference between hello-agents and sdl-mcp? hello-agents: A comprehensive guide to building intelligent agents from scratch. sdl-mcp: A policy-centered context budget layer for coding agents that enhances code analysis and workflow efficiency.. See the comparison table for live GitHub stats and shared categories. ### When should I choose hello-agents over sdl-mcp? Choose hello-agents over sdl-mcp when hello-agents is primarily Python; sdl-mcp is TypeScript; Pricing: The content is free to access and learn from, but there may be additional paid services or support options not detailed in the repository.; Requirements: Min 4 GB RAM; Python is required for practical implementation and hands-on experience with the HelloAgents framework.; Tags unique to hello-agents: agent, llm, rag, tutorial; When you want to focus on building AI-native agents and understand their core principles and practical implementation. ### When should I choose sdl-mcp over hello-agents? Choose sdl-mcp over hello-agents when sdl-mcp is primarily TypeScript; hello-agents is Python; Tags unique to sdl-mcp: agent-context, agent-tools, agentic-coding, agentic-engineering; Also covers Evaluation & Observability; sdl-mcp ships an MCP server manifest; When working with sprawling or complex codebases where maintaining context across multiple files is crucial. ### When should I avoid hello-agents? If you are looking for a tutorial that focuses on software engineering-based agents like Dify or Coze, which are more about process-driven software development. If you prefer a tool that does not emphasize the hands-on construction of an agent framework from the ground up using OpenAI's native API. ### When should I avoid sdl-mcp? In environments where TypeScript is not a preferred or supported language. For tasks that do not benefit from context management layers, such as small-scale projects with straightforward workflows. If your project requires real-time response times for every operation since SDL-MCP's focus on semantic analysis and context budgeting can introduce slight delays. ### Is hello-agents or sdl-mcp more popular on GitHub? hello-agents has more GitHub stars (79,727 vs 486). Stars measure visibility, not whether either tool fits your constraints. ### Are hello-agents and sdl-mcp open source? Yes - both are open-source projects on GitHub (hello-agents: Other, sdl-mcp: Other). ### Where can I find alternatives to hello-agents or sdl-mcp? GraphCanon lists graph-backed alternatives at [hello-agents alternatives](/tools/datawhalechina-hello-agents/alternatives) and [sdl-mcp alternatives](/tools/glitterkill-sdl-mcp/alternatives) ([hello-agents markdown twin](/tools/datawhalechina-hello-agents/alternatives.md), [sdl-mcp markdown twin](/tools/glitterkill-sdl-mcp/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/datawhalechina-hello-agents-vs-glitterkill-sdl-mcp.md) mirrors this page for agents and LLM crawlers, with the same stats table and FAQ answers. ### Which is better maintained, hello-agents or sdl-mcp? hello-agents: Very active. sdl-mcp: 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 hello-agents and sdl-mcp? GraphCanon publishes per-repo trust reports with dated maintenance, provenance, and scan summaries: [hello-agents trust report](/tools/datawhalechina-hello-agents/trust); [sdl-mcp trust report](/tools/glitterkill-sdl-mcp/trust). --- **Machine-readable endpoints** - JSON: [`/api/graphcanon/graph?tool=datawhalechina-hello-agents`](/api/graphcanon/graph?tool=datawhalechina-hello-agents) - 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/_