--- title: "Agent-Reach vs scalene" type: "comparison" canonical_url: "https://www.graphcanon.com/compare/panniantong-agent-reach-vs-plasma-umass-scalene" tools: ["panniantong-agent-reach", "plasma-umass-scalene"] --- # Agent-Reach vs scalene *GraphCanon updated Jul 11, 2026* ## Verdict Pick Agent-Reach when license: Agent-Reach is MIT, scalene is Apache-2.0; pick scalene when license: scalene is Apache-2.0, Agent-Reach is MIT. [Agent-Reach](https://github.com/Panniantong/Agent-Reach) reports 55k GitHub stars, 4.5k forks, and 144 open issues, last pushed Jul 10, 2026. [scalene](https://github.com/plasma-umass/scalene) has 13k stars, 435 forks, and 151 open issues, last pushed Jul 5, 2026. Figures are from public GitHub metadata via [Agent-Reach's repository](https://github.com/Panniantong/Agent-Reach) and [scalene's repository](https://github.com/plasma-umass/scalene). | | [Agent-Reach](/tools/panniantong-agent-reach.md) | [scalene](/tools/plasma-umass-scalene.md) | | --- | --- | --- | | Tagline | Give your AI agent eyes to see the entire internet. Read & search Twitter, Reddit, YouTube, GitHub, Bilibili, XiaoHongShu — one CLI, zero API fees. | Scalene: a high-performance, high-precision CPU, GPU, and memory profiler for Python with AI-powered optimization proposals | | Stars | 54,715 | 13,467 | | Forks | 4,509 | 435 | | Open issues | 144 | 151 | | Language | Python | Python | | Adopt for | - | - | | Persona | - | - | | Runtime | - | - | | License | MIT | Apache-2.0 | | Categories | AI Agents, Developer Tools, LLM Frameworks | Developer Tools | ## Trust and health _Sourced signals - not a safety guarantee. No winner column._ | | [Agent-Reach](/tools/panniantong-agent-reach.md) | [scalene](/tools/plasma-umass-scalene.md) | | --- | --- | --- | | Days since push | 0d | 6d | | Open issues (now) | 144 | 151 | | Owner type | User | Organization | | Security scan | No MCP manifest | 18 low (18 low) | | Full report | [trust report](/tools/panniantong-agent-reach/trust.md) | [trust report](/tools/plasma-umass-scalene/trust.md) | ## Choose when ### Choose Agent-Reach if… - License: Agent-Reach is MIT, scalene is Apache-2.0. - Tags unique to Agent-Reach: agent-infrastructure, ai-agent, ai-search, automation. - Also covers AI Agents, LLM Frameworks. ### Choose scalene if… - License: scalene is Apache-2.0, Agent-Reach is MIT. - Tags unique to scalene: cpu, cpu-profiling, gpu, gpu-programming. ## When NOT to use Agent-Reach - AI Agents: Don't use an agent loop when a deterministic workflow would do; agents add latency, cost, and non-determinism. - Developer Tools: A gateway is overkill when you're pinned to a single provider and model. - LLM Frameworks: Avoid a framework for a single prompt-and-retrieve call; the abstraction can cost more than it saves. ## When NOT to use scalene - Developer Tools: A gateway is overkill when you're pinned to a single provider and model. ## Common questions ### What is the difference between Agent-Reach and scalene? Agent-Reach: Give your AI agent eyes to see the entire internet. Read & search Twitter, Reddit, YouTube, GitHub, Bilibili, XiaoHongShu — one CLI, zero API fees.. scalene: Scalene: a high-performance, high-precision CPU, GPU, and memory profiler for Python with AI-powered optimization proposals. See the comparison table for live GitHub stats and shared categories. ### When should I choose Agent-Reach over scalene? Choose Agent-Reach over scalene when License: Agent-Reach is MIT, scalene is Apache-2.0; Tags unique to Agent-Reach: agent-infrastructure, ai-agent, ai-search, automation; Also covers AI Agents, LLM Frameworks. ### When should I choose scalene over Agent-Reach? Choose scalene over Agent-Reach when License: scalene is Apache-2.0, Agent-Reach is MIT; Tags unique to scalene: cpu, cpu-profiling, gpu, gpu-programming. ### When should I avoid Agent-Reach? AI Agents: Don't use an agent loop when a deterministic workflow would do; agents add latency, cost, and non-determinism. Developer Tools: A gateway is overkill when you're pinned to a single provider and model. LLM Frameworks: Avoid a framework for a single prompt-and-retrieve call; the abstraction can cost more than it saves. ### When should I avoid scalene? Developer Tools: A gateway is overkill when you're pinned to a single provider and model. ### Is Agent-Reach or scalene more popular on GitHub? Agent-Reach has more GitHub stars (54,715 vs 13,467). Stars measure visibility, not whether either tool fits your constraints. ### Are Agent-Reach and scalene open source? Yes - both are open-source projects on GitHub (Agent-Reach: MIT, scalene: Apache-2.0). ### Where can I find alternatives to Agent-Reach or scalene? GraphCanon lists graph-backed alternatives at [Agent-Reach alternatives](/tools/panniantong-agent-reach/alternatives) and [scalene alternatives](/tools/plasma-umass-scalene/alternatives) ([Agent-Reach markdown twin](/tools/panniantong-agent-reach/alternatives.md), [scalene markdown twin](/tools/plasma-umass-scalene/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/panniantong-agent-reach-vs-plasma-umass-scalene.md) mirrors this page for agents and LLM crawlers, with the same stats table and FAQ answers. ### Which is better maintained, Agent-Reach or scalene? Agent-Reach: Very active. scalene: 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 Agent-Reach and scalene? GraphCanon publishes per-repo trust reports with dated maintenance, provenance, and scan summaries: [Agent-Reach trust report](/tools/panniantong-agent-reach/trust); [scalene trust report](/tools/plasma-umass-scalene/trust). --- **Machine-readable endpoints** - JSON: [`/api/graphcanon/graph?tool=panniantong-agent-reach`](/api/graphcanon/graph?tool=panniantong-agent-reach) - 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/_