--- title: "code-review-graph vs agents" type: "comparison" canonical_url: "https://www.graphcanon.com/compare/tirth8205-code-review-graph-vs-wshobson-agents" tools: ["tirth8205-code-review-graph", "wshobson-agents"] --- # code-review-graph vs agents *GraphCanon updated Sep 20, 2026* ## Verdict Pick code-review-graph if code-review-graph is a Python-based tool that creates a local-first code intelligence graph to optimize AI coding tool interactions, reducing context and improving performance in code reviews and large-repo workflows; pick agents if the agents tool is a marketplace for plugins that enhances multiple AI agents, offering integration and management capabilities across several platforms, including Claude Code, Codex. [code-review-graph](https://code-review-graph.com) reports 32k GitHub stars, 2.9k forks, and 135 open issues, last pushed Sep 17, 2026. [agents](https://sethhobson.com) has 40k stars, 4.2k forks, and 12 open issues, last pushed Sep 19, 2026. Figures are from public GitHub metadata via [code-review-graph's repository](https://github.com/tirth8205/code-review-graph) and [agents's repository](https://github.com/wshobson/agents). | | [code-review-graph](/tools/tirth8205-code-review-graph.md) | [agents](/tools/wshobson-agents.md) | | --- | --- | --- | | Tagline | Local-first code intelligence graph for AI coding tools | Multi-harness agentic plugin marketplace for various AI agents | | Stars | 31,569 | 39,823 | | Forks | 2,871 | 4,246 | | Open issues | 135 | 12 | | Language | Python | Python | | Adopt for | code-review-graph is a Python-based tool that creates a local-first code intelligence graph to optimize AI coding tool interactions, reducing context and improving performance in code reviews and large-repo workflows. | The agents tool is a marketplace for plugins that enhances multiple AI agents, offering integration and management capabilities across several platforms, including Claude Code, Codex CLI, Cursor, OpenCode, GitHub Copilot | | Persona | - | - | | Runtime | - | - | | License | MIT License | MIT | | Categories | Developer Tools, Model Training | AI Agents, Developer Tools | ## Trust and health _Sourced signals - not a safety guarantee. No winner column._ | | [code-review-graph](/tools/tirth8205-code-review-graph.md) | [agents](/tools/wshobson-agents.md) | | --- | --- | --- | | Open issues (now) | 135 | 12 | | Stars delta | +5.1k (30d) | +895 (30d) | | Open issues delta | +13 (30d) | +7 (30d) | | Full report | [trust report](/tools/tirth8205-code-review-graph/trust.md) | [trust report](/tools/wshobson-agents/trust.md) | ## Shared compatibility - **Cursor**: [code-review-graph](/tools/tirth8205-code-review-graph.md) - Works with Cursor; [agents](/tools/wshobson-agents.md) - Works with Cursor ## Decision facts: code-review-graph - **Requirements:** Requires Python 3.10+. - **Adopt for:** code-review-graph is a Python-based tool that creates a local-first code intelligence graph to optimize AI coding tool interactions, reducing context and improving performance in code reviews and large-repo workflows. - **License detail:** MIT License ## Decision facts: agents - **Adopt for:** The agents tool is a marketplace for plugins that enhances multiple AI agents, offering integration and management capabilities across several platforms, including Claude Code, Codex CLI, Cursor, OpenCode, GitHub Copilot ## Choose when ### Choose code-review-graph if… - Requirements: Requires Python 3.10+.. - Tags unique to code-review-graph: ai-coding, claude, code-review, graph. - Also covers Model Training. - When you need to optimize AI coding tool interactions in large codebases, as it builds a persistent map of your codebase to reduce context and improve performance. ### Choose agents if… - Tags unique to agents: agent-skills, agentic-ai, automation, prompt-engineering. - Also covers AI Agents. - You are working specifically within the ecosystems of Claude Code, Codex CLI, Cursor, OpenCode, GitHub Copilot, or Gemini CLI, as it provides tailored plugins for these environments ## When NOT to use code-review-graph - If your project does not require integration with multiple AI coding tools, as the tool is designed to work with a variety of platforms and may introduce unnecessary complexity. - When you are working with small codebases where the overhead of building and maintaining a code intelligence graph outweighs the benefits. - If you are using a language other than Python, as the tool is specifically designed for Python projects and may not support other languages as effectively. ## When NOT to use agents - You are working solely within a niche environment that isn't one of the supported platforms (like Claude Code, Codex CLI, etc.) because it may not offer compatible plugins or extensive support - Your project requirements do not include interoperability between multiple AI agents and you only need to leverage functionalities from a single AI agent with a robust in-built plugin ecosystem ## Common questions ### What is the difference between code-review-graph and agents? code-review-graph: Local-first code intelligence graph for AI coding tools. agents: Multi-harness agentic plugin marketplace for various AI agents. See the comparison table for live GitHub stats and shared categories. ### When should I choose code-review-graph over agents? Choose code-review-graph over agents when Requirements: Requires Python 3.10+.; Tags unique to code-review-graph: ai-coding, claude, code-review, graph; Also covers Model Training; When you need to optimize AI coding tool interactions in large codebases, as it builds a persistent map of your codebase to reduce context and improve performance. ### When should I choose agents over code-review-graph? Choose agents over code-review-graph when Tags unique to agents: agent-skills, agentic-ai, automation, prompt-engineering; Also covers AI Agents; You are working specifically within the ecosystems of Claude Code, Codex CLI, Cursor, OpenCode, GitHub Copilot, or Gemini CLI, as it provides tailored plugins for these environments. ### When should I avoid code-review-graph? If your project does not require integration with multiple AI coding tools, as the tool is designed to work with a variety of platforms and may introduce unnecessary complexity. When you are working with small codebases where the overhead of building and maintaining a code intelligence graph outweighs the benefits. If you are using a language other than Python, as the tool is specifically designed for Python projects and may not support other languages as effectively. ### When should I avoid agents? You are working solely within a niche environment that isn't one of the supported platforms (like Claude Code, Codex CLI, etc.) because it may not offer compatible plugins or extensive support Your project requirements do not include interoperability between multiple AI agents and you only need to leverage functionalities from a single AI agent with a robust in-built plugin ecosystem ### Is code-review-graph or agents more popular on GitHub? agents has more GitHub stars (39,823 vs 31,569). Stars measure visibility, not whether either tool fits your constraints. ### Are code-review-graph and agents open source? Yes - both are open-source projects on GitHub (code-review-graph: MIT, agents: MIT). ### Where can I find alternatives to code-review-graph or agents? GraphCanon lists graph-backed alternatives at [code-review-graph alternatives](/tools/tirth8205-code-review-graph/alternatives) and [agents alternatives](/tools/wshobson-agents/alternatives) ([code-review-graph markdown twin](/tools/tirth8205-code-review-graph/alternatives.md), [agents markdown twin](/tools/wshobson-agents/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/tirth8205-code-review-graph-vs-wshobson-agents.md) mirrors this page for agents and LLM crawlers, with the same stats table and FAQ answers. ### Which is better maintained, code-review-graph or agents? code-review-graph: Very active. agents: 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 code-review-graph and agents? GraphCanon publishes per-repo trust reports with dated maintenance, provenance, and scan summaries: [code-review-graph trust report](/tools/tirth8205-code-review-graph/trust); [agents trust report](/tools/wshobson-agents/trust). --- **Machine-readable endpoints** - JSON: [`/api/graphcanon/graph?tool=tirth8205-code-review-graph`](/api/graphcanon/graph?tool=tirth8205-code-review-graph) - 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/_