--- title: "ECC vs tma1" type: "comparison" canonical_url: "https://www.graphcanon.com/compare/affaan-m-ecc-vs-tma1-ai-tma1" tools: ["affaan-m-ecc", "tma1-ai-tma1"] --- # ECC vs tma1 *GraphCanon updated Jul 15, 2026* ## Verdict Pick ECC when eCC is primarily JavaScript; tma1 is Go; pick tma1 when tma1 is primarily Go; ECC is JavaScript. [ECC](https://ecc.tools) reports 228k GitHub stars, 35k forks, and 93 open issues, last pushed Jul 9, 2026. [tma1](https://tma1.ai/) has 109 stars, 12 forks, and 5 open issues, last pushed Jul 11, 2026. Figures are from public GitHub metadata via [ECC's repository](https://github.com/affaan-m/ECC) and [tma1's repository](https://github.com/tma1-ai/tma1). | | [ECC](/tools/affaan-m-ecc.md) | [tma1](/tools/tma1-ai-tma1.md) | | --- | --- | --- | | Tagline | The agent harness performance optimization system for AI agents | Local-first observability your agent reads back. TMA1 records every LLM call, then routes what it sees into the agent's next turn via hooks and MCP. | | Stars | 228,395 | 109 | | Forks | 35,037 | 12 | | Open issues | 93 | 5 | | Language | JavaScript | Go | | 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 | Apache-2.0 | | Categories | AI Agents, Developer Tools | AI Agents, Developer Tools, LLM Frameworks | ## Trust and health _Sourced signals - not a safety guarantee. No winner column._ | | [ECC](/tools/affaan-m-ecc.md) | [tma1](/tools/tma1-ai-tma1.md) | | --- | --- | --- | | Days since push | 2d | 3d | | Open issues (now) | 93 | 5 | | Owner type | User | Organization | | Full report | [trust report](/tools/affaan-m-ecc/trust.md) | [trust report](/tools/tma1-ai-tma1/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; tma1 is Go. - License: ECC is MIT, tma1 is Apache-2.0. - 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: anthropic, claude, llm, productivity. - 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 tma1 if… - tma1 is primarily Go; ECC is JavaScript. - License: tma1 is Apache-2.0, ECC is MIT. - Tags unique to tma1: agent-loop, agent-observability, codex, copilot-cli. - Also covers LLM Frameworks. ## 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 tma1 - 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. ## Common questions ### What is the difference between ECC and tma1? ECC: The agent harness performance optimization system for AI agents. tma1: Local-first observability your agent reads back. TMA1 records every LLM call, then routes what it sees into the agent's next turn via hooks and MCP.. See the comparison table for live GitHub stats and shared categories. ### When should I choose ECC over tma1? Choose ECC over tma1 when ECC is primarily JavaScript; tma1 is Go; License: ECC is MIT, tma1 is Apache-2.0; 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: anthropic, claude, llm, productivity; 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 tma1 over ECC? Choose tma1 over ECC when tma1 is primarily Go; ECC is JavaScript; License: tma1 is Apache-2.0, ECC is MIT; Tags unique to tma1: agent-loop, agent-observability, codex, copilot-cli; Also covers LLM Frameworks. ### 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 tma1? 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. ### Is ECC or tma1 more popular on GitHub? ECC has more GitHub stars (228,395 vs 109). Stars measure visibility, not whether either tool fits your constraints. ### Are ECC and tma1 open source? Yes - both are open-source projects on GitHub (ECC: MIT, tma1: Apache-2.0). ### Where can I find alternatives to ECC or tma1? GraphCanon lists graph-backed alternatives at [ECC alternatives](/tools/affaan-m-ecc/alternatives) and [tma1 alternatives](/tools/tma1-ai-tma1/alternatives) ([ECC markdown twin](/tools/affaan-m-ecc/alternatives.md), [tma1 markdown twin](/tools/tma1-ai-tma1/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-tma1-ai-tma1.md) mirrors this page for agents and LLM crawlers, with the same stats table and FAQ answers. ### Which is better maintained, ECC or tma1? ECC: Very active. tma1: 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 ECC and tma1? GraphCanon publishes per-repo trust reports with dated maintenance, provenance, and scan summaries: [ECC trust report](/tools/affaan-m-ecc/trust); [tma1 trust report](/tools/tma1-ai-tma1/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/_