--- title: "Agent-Reach vs DS-1000" type: "comparison" canonical_url: "https://www.graphcanon.com/compare/panniantong-agent-reach-vs-xlang-ai-ds-1000" tools: ["panniantong-agent-reach", "xlang-ai-ds-1000"] --- # Agent-Reach vs DS-1000 *GraphCanon updated Jul 11, 2026* ## Verdict Pick Agent-Reach when license: Agent-Reach is MIT, DS-1000 is CC-BY-SA-4.0; pick DS-1000 when license: DS-1000 is CC-BY-SA-4.0, 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. [DS-1000](https://ds1000-code-gen.github.io) has 273 stars, 31 forks, and 2 open issues, last pushed Oct 30, 2024. Figures are from public GitHub metadata via [Agent-Reach's repository](https://github.com/Panniantong/Agent-Reach) and [DS-1000's repository](https://github.com/xlang-ai/DS-1000). | | [Agent-Reach](/tools/panniantong-agent-reach.md) | [DS-1000](/tools/xlang-ai-ds-1000.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. | [ICML 2023] Data and code release for the paper "DS-1000: A Natural and Reliable Benchmark for Data Science Code Generation". | | Stars | 54,715 | 273 | | Forks | 4,509 | 31 | | Open issues | 144 | 2 | | Language | Python | Python | | Adopt for | - | - | | Persona | - | - | | Runtime | - | - | | License | MIT | CC-BY-SA-4.0 | | Categories | AI Agents, Developer Tools, LLM Frameworks | Evaluation & Observability, LLM Frameworks, Model Training | ## Trust and health _Sourced signals - not a safety guarantee. No winner column._ | | [Agent-Reach](/tools/panniantong-agent-reach.md) | [DS-1000](/tools/xlang-ai-ds-1000.md) | | --- | --- | --- | | Maintenance | Very active (96%) | Dormant (18%) | | Days since push | 0d | 619d | | Open issues (now) | 144 | 2 | | Owner type | User | Organization | | Security scan | No MCP manifest | No lockfile | | Full report | [trust report](/tools/panniantong-agent-reach/trust.md) | [trust report](/tools/xlang-ai-ds-1000/trust.md) | ## Choose when ### Choose Agent-Reach if… - License: Agent-Reach is MIT, DS-1000 is CC-BY-SA-4.0. - Tags unique to Agent-Reach: agent-infrastructure, ai-agent, ai-search, automation. - Also covers AI Agents, Developer Tools. ### Choose DS-1000 if… - License: DS-1000 is CC-BY-SA-4.0, Agent-Reach is MIT. - Tags unique to DS-1000: benchmark, code-generation, data-science, large language models. - Also covers Evaluation & Observability, Model Training. ## 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 DS-1000 - Last GitHub push was 619 days ago (dormant maintenance, Oct 30, 2024). Validate activity before betting a new project on DS-1000. - Evaluation & Observability: Defer heavyweight eval infra only until you have real traffic - never skip it once users depend on answers. - LLM Frameworks: Avoid a framework for a single prompt-and-retrieve call; the abstraction can cost more than it saves. - Model Training: Try prompting and RAG first; fine-tuning is the answer to style/format, not missing knowledge. ## Common questions ### What is the difference between Agent-Reach and DS-1000? 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.. DS-1000: [ICML 2023] Data and code release for the paper "DS-1000: A Natural and Reliable Benchmark for Data Science Code Generation".. See the comparison table for live GitHub stats and shared categories. ### When should I choose Agent-Reach over DS-1000? Choose Agent-Reach over DS-1000 when License: Agent-Reach is MIT, DS-1000 is CC-BY-SA-4.0; Tags unique to Agent-Reach: agent-infrastructure, ai-agent, ai-search, automation; Also covers AI Agents, Developer Tools. ### When should I choose DS-1000 over Agent-Reach? Choose DS-1000 over Agent-Reach when License: DS-1000 is CC-BY-SA-4.0, Agent-Reach is MIT; Tags unique to DS-1000: benchmark, code-generation, data-science, large language models; Also covers Evaluation & Observability, Model Training. ### 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 DS-1000? Last GitHub push was 619 days ago (dormant maintenance, Oct 30, 2024). Validate activity before betting a new project on DS-1000. Evaluation & Observability: Defer heavyweight eval infra only until you have real traffic - never skip it once users depend on answers. LLM Frameworks: Avoid a framework for a single prompt-and-retrieve call; the abstraction can cost more than it saves. Model Training: Try prompting and RAG first; fine-tuning is the answer to style/format, not missing knowledge. ### Is Agent-Reach or DS-1000 more popular on GitHub? Agent-Reach has more GitHub stars (54,715 vs 273). Stars measure visibility, not whether either tool fits your constraints. ### Are Agent-Reach and DS-1000 open source? Yes - both are open-source projects on GitHub (Agent-Reach: MIT, DS-1000: CC-BY-SA-4.0). ### Where can I find alternatives to Agent-Reach or DS-1000? GraphCanon lists graph-backed alternatives at [Agent-Reach alternatives](/tools/panniantong-agent-reach/alternatives) and [DS-1000 alternatives](/tools/xlang-ai-ds-1000/alternatives) ([Agent-Reach markdown twin](/tools/panniantong-agent-reach/alternatives.md), [DS-1000 markdown twin](/tools/xlang-ai-ds-1000/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-xlang-ai-ds-1000.md) mirrors this page for agents and LLM crawlers, with the same stats table and FAQ answers. ### Which is better maintained, Agent-Reach or DS-1000? Agent-Reach: Very active. DS-1000: Dormant. 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 DS-1000? GraphCanon publishes per-repo trust reports with dated maintenance, provenance, and scan summaries: [Agent-Reach trust report](/tools/panniantong-agent-reach/trust); [DS-1000 trust report](/tools/xlang-ai-ds-1000/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/_