--- title: "TinyZero vs Agent-Reach" type: "comparison" canonical_url: "https://www.graphcanon.com/compare/jiayi-pan-tinyzero-vs-panniantong-agent-reach" tools: ["jiayi-pan-tinyzero", "panniantong-agent-reach"] --- # TinyZero vs Agent-Reach *GraphCanon updated Jul 12, 2026* ## Verdict Pick TinyZero when license: TinyZero is Apache-2.0, Agent-Reach is MIT; pick Agent-Reach when license: Agent-Reach is MIT, TinyZero is Apache-2.0. [TinyZero](https://github.com/Jiayi-Pan/TinyZero) reports 13k GitHub stars, 1.6k forks, and 82 open issues, last pushed Feb 27, 2026. [Agent-Reach](https://github.com/Panniantong/Agent-Reach) has 55k stars, 4.5k forks, and 144 open issues, last pushed Jul 10, 2026. Figures are from public GitHub metadata via [TinyZero's repository](https://github.com/Jiayi-Pan/TinyZero) and [Agent-Reach's repository](https://github.com/Panniantong/Agent-Reach). | | [TinyZero](/tools/jiayi-pan-tinyzero.md) | [Agent-Reach](/tools/panniantong-agent-reach.md) | | --- | --- | --- | | Tagline | Minimal reproduction of DeepSeek R1-Zero | Give your AI agent eyes to see the entire internet. Read & search Twitter, Reddit, YouTube, GitHub, Bilibili, XiaoHongShu — one CLI, zero API fees. | | Stars | 13,192 | 54,715 | | Forks | 1,582 | 4,509 | | Open issues | 82 | 144 | | Language | Python | Python | | Adopt for | TinyZero is a scaled-down version of the R1-Zero architecture from DeepSeek, focusing on minimal setup with essential components. | - | | Persona | - | - | | Runtime | - | - | | License | TinyZero is licensed under Apache-2.0, allowing for broad usage with attribution requirements. | MIT | | Categories | LLM Frameworks | LLM Frameworks, AI Agents, Developer Tools | ## Trust and health _Sourced signals - not a safety guarantee. No winner column._ | | [TinyZero](/tools/jiayi-pan-tinyzero.md) | [Agent-Reach](/tools/panniantong-agent-reach.md) | | --- | --- | --- | | Maintenance | Slowing (36%) | Very active (96%) | | Days since push | 134d | 0d | | Open issues (now) | 82 | 144 | | Security scan | No criticals | No MCP manifest | | Full report | [trust report](/tools/jiayi-pan-tinyzero/trust.md) | [trust report](/tools/panniantong-agent-reach/trust.md) | ## Decision facts: TinyZero - **Pricing:** freemium - The framework itself is free and can be used without charge; - **Requirements:** Min 4 GB RAM; Specific Python environment setup (Python 3.9) and dependency installation steps are outlined in the README. - **Adopt for:** TinyZero is a scaled-down version of the R1-Zero architecture from DeepSeek, focusing on minimal setup with essential components. - **License detail:** TinyZero is licensed under Apache-2.0, allowing for broad usage with attribution requirements. ## Choose when ### Choose TinyZero if… - License: TinyZero is Apache-2.0, Agent-Reach is MIT. - Pricing: The framework itself is free and can be used without charge;. - Requirements: Min 4 GB RAM; Specific Python environment setup (Python 3.9) and dependency installation steps are outlined in the README.. - Tags unique to TinyZero: ray, deepseek, vllm, r1-zero. - When you need a streamlined implementation of the R1-Zero architecture without unnecessary complexity. ### Choose Agent-Reach if… - License: Agent-Reach is MIT, TinyZero is Apache-2.0. - Tags unique to Agent-Reach: agent-infrastructure, ai-search, bilibili, claude-code. - Also covers AI Agents, Developer Tools. ## When NOT to use TinyZero - If your project demands extensive customization options not available in this minimal version. - When working with environments where specific versions of PyTorch older than 2.4.0 are required, as TinyZero mandates the use of PyTorch 2.4.0 or allows vLLM to manage its installation. ## When NOT to use Agent-Reach - LLM Frameworks: Avoid a framework for a single prompt-and-retrieve call; the abstraction can cost more than it saves. - 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. ## Common questions ### What is the difference between TinyZero and Agent-Reach? TinyZero: Minimal reproduction of DeepSeek R1-Zero. 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.. See the comparison table for live GitHub stats and shared categories. ### When should I choose TinyZero over Agent-Reach? Choose TinyZero over Agent-Reach when License: TinyZero is Apache-2.0, Agent-Reach is MIT; Pricing: The framework itself is free and can be used without charge;; Requirements: Min 4 GB RAM; Specific Python environment setup (Python 3.9) and dependency installation steps are outlined in the README.; Tags unique to TinyZero: ray, deepseek, vllm, r1-zero; When you need a streamlined implementation of the R1-Zero architecture without unnecessary complexity. ### When should I choose Agent-Reach over TinyZero? Choose Agent-Reach over TinyZero when License: Agent-Reach is MIT, TinyZero is Apache-2.0; Tags unique to Agent-Reach: agent-infrastructure, ai-search, bilibili, claude-code; Also covers AI Agents, Developer Tools. ### When should I avoid TinyZero? If your project demands extensive customization options not available in this minimal version. When working with environments where specific versions of PyTorch older than 2.4.0 are required, as TinyZero mandates the use of PyTorch 2.4.0 or allows vLLM to manage its installation. ### When should I avoid Agent-Reach? LLM Frameworks: Avoid a framework for a single prompt-and-retrieve call; the abstraction can cost more than it saves. 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. ### Is TinyZero or Agent-Reach more popular on GitHub? Agent-Reach has more GitHub stars (54,715 vs 13,192). Stars measure visibility, not whether either tool fits your constraints. ### Are TinyZero and Agent-Reach open source? Yes - both are open-source projects on GitHub (TinyZero: Apache-2.0, Agent-Reach: MIT). ### Where can I find alternatives to TinyZero or Agent-Reach? GraphCanon lists graph-backed alternatives at [TinyZero alternatives](/tools/jiayi-pan-tinyzero/alternatives) and [Agent-Reach alternatives](/tools/panniantong-agent-reach/alternatives) ([TinyZero markdown twin](/tools/jiayi-pan-tinyzero/alternatives.md), [Agent-Reach markdown twin](/tools/panniantong-agent-reach/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/jiayi-pan-tinyzero-vs-panniantong-agent-reach.md) mirrors this page for agents and LLM crawlers, with the same stats table and FAQ answers. ### Which is better maintained, TinyZero or Agent-Reach? TinyZero: Slowing. Agent-Reach: 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 TinyZero and Agent-Reach? GraphCanon publishes per-repo trust reports with dated maintenance, provenance, and scan summaries: [TinyZero trust report](/tools/jiayi-pan-tinyzero/trust); [Agent-Reach trust report](/tools/panniantong-agent-reach/trust). --- **Machine-readable endpoints** - JSON: [`/api/graphcanon/graph?tool=jiayi-pan-tinyzero`](/api/graphcanon/graph?tool=jiayi-pan-tinyzero) - 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/_