--- title: "ECC vs Devon" type: "comparison" canonical_url: "https://www.graphcanon.com/compare/affaan-m-ecc-vs-entropy-research-devon" tools: ["affaan-m-ecc", "entropy-research-devon"] --- # ECC vs Devon *GraphCanon updated Jul 15, 2026* ## Verdict Pick ECC when eCC is primarily JavaScript; Devon is Python; pick Devon when devon is primarily Python; ECC is JavaScript. [ECC](https://ecc.tools) reports 228k GitHub stars, 35k forks, and 93 open issues, last pushed Jul 9, 2026. [Devon](https://github.com/entropy-research/Devon) has 3.4k stars, 282 forks, and 26 open issues, last pushed May 26, 2025. Figures are from public GitHub metadata via [ECC's repository](https://github.com/affaan-m/ECC) and [Devon's repository](https://github.com/entropy-research/Devon). | | [ECC](/tools/affaan-m-ecc.md) | [Devon](/tools/entropy-research-devon.md) | | --- | --- | --- | | Tagline | The agent harness performance optimization system for AI agents | Devon: An open-source pair programmer | | Stars | 228,395 | 3,449 | | Forks | 35,037 | 282 | | Open issues | 93 | 26 | | Language | JavaScript | Python | | Adopt for | ECC is a performance optimization system for AI agents built to enhance skills, instincts, memory, security, and development processes. | - | | Persona | - | - | | Runtime | - | - | | License | MIT | AGPL-3.0 | | Categories | AI Agents, Developer Tools | AI Agents, Developer Tools, Inference & Serving | ## Trust and health _Sourced signals - not a safety guarantee. No winner column._ | | [ECC](/tools/affaan-m-ecc.md) | [Devon](/tools/entropy-research-devon.md) | | --- | --- | --- | | Maintenance | Very active (96%) | Dormant (18%) | | Days since push | 2d | 415d | | Open issues (now) | 93 | 26 | | Owner type | User | Organization | | Full report | [trust report](/tools/affaan-m-ecc/trust.md) | [trust report](/tools/entropy-research-devon/trust.md) | ## Decision facts: ECC - **Hosting:** unknown - ECC requires JavaScript environment and is open-source, licensed under MIT. Specific setup details are not provided within repository data. - **Pricing:** freemium - Being open source with an MIT license, ECC itself is free to use, but additional features or support might incur costs outside of the core project. - **Adopt for:** ECC is a performance optimization system for AI agents built to enhance skills, instincts, memory, security, and development processes. ## Choose when ### Choose ECC if… - ECC is primarily JavaScript; Devon is Python. - License: ECC is MIT, Devon is AGPL-3.0. - ECC requires JavaScript environment and is open-source, licensed under MIT. Specific setup details are not provided within repository data. - Pricing: Being open source with an MIT license, ECC itself is free to use, but additional features or support might incur costs outside of the core project.. - Tags unique to ECC: ai-agents, anthropic, claude, claude code. - When you are specifically working with AI agents like Claude Code and Codex that require advanced performance tuning across multiple dimensions such as skills and memory management. ### Choose Devon if… - Devon is primarily Python; ECC is JavaScript. - License: Devon is AGPL-3.0, ECC is MIT. - Tags unique to Devon: agent, agent-based-framework, agent-based-model, ai. - Also covers Inference & Serving. ## When NOT to use ECC - For projects focusing solely on traditional software development workflows without AI components, ECC's specialized tools are not necessary. - In scenarios where you're working with closed-source or proprietary AI systems that do not allow for the same levels of customization as open platforms like those optimized by ECC. ## When NOT to use Devon - Last GitHub push was 415 days ago (dormant maintenance, May 26, 2025). Validate activity before betting a new project on Devon. - 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. - Inference & Serving: Self-hosting rarely beats a hosted API on cost until you have steady, high-volume traffic. ## Common questions ### What is the difference between ECC and Devon? ECC: The agent harness performance optimization system for AI agents. Devon: Devon: An open-source pair programmer. See the comparison table for live GitHub stats and shared categories. ### When should I choose ECC over Devon? Choose ECC over Devon when ECC is primarily JavaScript; Devon is Python; License: ECC is MIT, Devon is AGPL-3.0; ECC requires JavaScript environment and is open-source, licensed under MIT. Specific setup details are not provided within repository data; Pricing: Being open source with an MIT license, ECC itself is free to use, but additional features or support might incur costs outside of the core project.; Tags unique to ECC: ai-agents, anthropic, claude, claude code; When you are specifically working with AI agents like Claude Code and Codex that require advanced performance tuning across multiple dimensions such as skills and memory management. ### When should I choose Devon over ECC? Choose Devon over ECC when Devon is primarily Python; ECC is JavaScript; License: Devon is AGPL-3.0, ECC is MIT; Tags unique to Devon: agent, agent-based-framework, agent-based-model, ai; Also covers Inference & Serving. ### When should I avoid ECC? For projects focusing solely on traditional software development workflows without AI components, ECC's specialized tools are not necessary. In scenarios where you're working with closed-source or proprietary AI systems that do not allow for the same levels of customization as open platforms like those optimized by ECC. ### When should I avoid Devon? Last GitHub push was 415 days ago (dormant maintenance, May 26, 2025). Validate activity before betting a new project on Devon. 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. Inference & Serving: Self-hosting rarely beats a hosted API on cost until you have steady, high-volume traffic. ### Is ECC or Devon more popular on GitHub? ECC has more GitHub stars (228,395 vs 3,449). Stars measure visibility, not whether either tool fits your constraints. ### Are ECC and Devon open source? Yes - both are open-source projects on GitHub (ECC: MIT, Devon: AGPL-3.0). ### Where can I find alternatives to ECC or Devon? GraphCanon lists graph-backed alternatives at [ECC alternatives](/tools/affaan-m-ecc/alternatives) and [Devon alternatives](/tools/entropy-research-devon/alternatives) ([ECC markdown twin](/tools/affaan-m-ecc/alternatives.md), [Devon markdown twin](/tools/entropy-research-devon/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/affaan-m-ecc-vs-entropy-research-devon.md) mirrors this page for agents and LLM crawlers, with the same stats table and FAQ answers. ### Which is better maintained, ECC or Devon? ECC: Very active. Devon: 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 ECC and Devon? GraphCanon publishes per-repo trust reports with dated maintenance, provenance, and scan summaries: [ECC trust report](/tools/affaan-m-ecc/trust); [Devon trust report](/tools/entropy-research-devon/trust). --- **Machine-readable endpoints** - JSON: [`/api/graphcanon/graph?tool=affaan-m-ecc`](/api/graphcanon/graph?tool=affaan-m-ecc) - 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/_