--- title: "FEDOT vs Agent-Reach" type: "comparison" canonical_url: "https://www.graphcanon.com/compare/aimclub-fedot-vs-panniantong-agent-reach" tools: ["aimclub-fedot", "panniantong-agent-reach"] --- # FEDOT vs Agent-Reach *GraphCanon updated Jul 11, 2026* ## Verdict Pick FEDOT when license: FEDOT is BSD-3-Clause, Agent-Reach is MIT; pick Agent-Reach when license: Agent-Reach is MIT, FEDOT is BSD-3-Clause. [FEDOT](https://fedot.readthedocs.io) reports 709 GitHub stars, 92 forks, and 83 open issues, last pushed Jul 8, 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 [FEDOT's repository](https://github.com/aimclub/FEDOT) and [Agent-Reach's repository](https://github.com/Panniantong/Agent-Reach). | | [FEDOT](/tools/aimclub-fedot.md) | [Agent-Reach](/tools/panniantong-agent-reach.md) | | --- | --- | --- | | Tagline | Automated modeling and machine learning framework FEDOT | Give your AI agent eyes to see the entire internet. Read & search Twitter, Reddit, YouTube, GitHub, Bilibili, XiaoHongShu — one CLI, zero API fees. | | Stars | 709 | 54,715 | | Forks | 92 | 4,509 | | Open issues | 83 | 144 | | Language | Python | Python | | Adopt for | - | - | | Persona | - | - | | Runtime | - | - | | License | BSD-3-Clause | MIT | | Categories | Data & Retrieval, LLM Frameworks, Computer Vision | LLM Frameworks, AI Agents, Developer Tools | ## Trust and health _Sourced signals - not a safety guarantee. No winner column._ | | [FEDOT](/tools/aimclub-fedot.md) | [Agent-Reach](/tools/panniantong-agent-reach.md) | | --- | --- | --- | | Days since push | 3d | 0d | | Open issues (now) | 83 | 144 | | Owner type | Organization | User | | Security scan | 27 low (27 low) | No MCP manifest | | Full report | [trust report](/tools/aimclub-fedot/trust.md) | [trust report](/tools/panniantong-agent-reach/trust.md) | ## Choose when ### Choose FEDOT if… - License: FEDOT is BSD-3-Clause, Agent-Reach is MIT. - Tags unique to FEDOT: automl, evolutionary-algorithms, genetic-programming, machine-learning. - Also covers Data & Retrieval, Computer Vision. ### Choose Agent-Reach if… - License: Agent-Reach is MIT, FEDOT is BSD-3-Clause. - Tags unique to Agent-Reach: agent-infrastructure, ai-search, bilibili, claude-code. - Also covers AI Agents, Developer Tools. ## When NOT to use FEDOT - Data & Retrieval: Skip a heavy ingestion framework when your corpus is small and static; a script plus the embedding API is enough. - LLM Frameworks: Avoid a framework for a single prompt-and-retrieve call; the abstraction can cost more than it saves. ## 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 FEDOT and Agent-Reach? FEDOT: Automated modeling and machine learning framework FEDOT. 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 FEDOT over Agent-Reach? Choose FEDOT over Agent-Reach when License: FEDOT is BSD-3-Clause, Agent-Reach is MIT; Tags unique to FEDOT: automl, evolutionary-algorithms, genetic-programming, machine-learning; Also covers Data & Retrieval, Computer Vision. ### When should I choose Agent-Reach over FEDOT? Choose Agent-Reach over FEDOT when License: Agent-Reach is MIT, FEDOT is BSD-3-Clause; Tags unique to Agent-Reach: agent-infrastructure, ai-search, bilibili, claude-code; Also covers AI Agents, Developer Tools. ### When should I avoid FEDOT? Data & Retrieval: Skip a heavy ingestion framework when your corpus is small and static; a script plus the embedding API is enough. LLM Frameworks: Avoid a framework for a single prompt-and-retrieve call; the abstraction can cost more than it saves. ### 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 FEDOT or Agent-Reach more popular on GitHub? Agent-Reach has more GitHub stars (54,715 vs 709). Stars measure visibility, not whether either tool fits your constraints. ### Are FEDOT and Agent-Reach open source? Yes - both are open-source projects on GitHub (FEDOT: BSD-3-Clause, Agent-Reach: MIT). ### Where can I find alternatives to FEDOT or Agent-Reach? GraphCanon lists graph-backed alternatives at [FEDOT alternatives](/tools/aimclub-fedot/alternatives) and [Agent-Reach alternatives](/tools/panniantong-agent-reach/alternatives) ([FEDOT markdown twin](/tools/aimclub-fedot/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/aimclub-fedot-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, FEDOT or Agent-Reach? FEDOT: Very active. 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 FEDOT and Agent-Reach? GraphCanon publishes per-repo trust reports with dated maintenance, provenance, and scan summaries: [FEDOT trust report](/tools/aimclub-fedot/trust); [Agent-Reach trust report](/tools/panniantong-agent-reach/trust). --- **Machine-readable endpoints** - JSON: [`/api/graphcanon/graph?tool=aimclub-fedot`](/api/graphcanon/graph?tool=aimclub-fedot) - 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/_