--- title: "CoDA-Bench vs AutoGPT" type: "comparison" canonical_url: "https://www.graphcanon.com/compare/ruc-datalab-coda-bench-vs-significant-gravitas-autogpt" tools: ["ruc-datalab-coda-bench", "significant-gravitas-autogpt"] --- # CoDA-Bench vs AutoGPT *GraphCanon updated Jul 15, 2026* ## Verdict Pick CoDA-Bench when license: CoDA-Bench is MIT, AutoGPT is Other; pick AutoGPT when license: AutoGPT is Other, CoDA-Bench is MIT. [CoDA-Bench](https://coda-bench.github.io/) reports 39 GitHub stars, 0 forks, and 0 open issues, last pushed Jun 17, 2026. [AutoGPT](https://agpt.co) has 185k stars, 46k forks, and 494 open issues, last pushed Jul 11, 2026. Figures are from public GitHub metadata via [CoDA-Bench's repository](https://github.com/ruc-datalab/CoDA-Bench) and [AutoGPT's repository](https://github.com/Significant-Gravitas/AutoGPT). | | [CoDA-Bench](/tools/ruc-datalab-coda-bench.md) | [AutoGPT](/tools/significant-gravitas-autogpt.md) | | --- | --- | --- | | Tagline | CoDA-Bench is a benchmark for code agents on data-intensive tasks. 🎈代码智能体能搞定数据密集型任务吗? | AutoGPT is the vision of accessible AI for everyone, to use and to build on. | | Stars | 39 | 185,464 | | Forks | 0 | 46,111 | | Open issues | 0 | 494 | | Language | Python | Python | | Adopt for | - | AutoGPT is a Python-based tool for creating accessible autonomous AI agents that can leverage various LLM APIs including OpenAI's GPT and Anthropic's Claude. | | Persona | - | - | | Runtime | - | - | | License | MIT | Other | | Categories | AI Agents, LLM Frameworks, Vector Databases | AI Agents, LLM Frameworks | ## Trust and health _Sourced signals - not a safety guarantee. No winner column._ | | [CoDA-Bench](/tools/ruc-datalab-coda-bench.md) | [AutoGPT](/tools/significant-gravitas-autogpt.md) | | --- | --- | --- | | Maintenance | Active (82%) | Very active (96%) | | Days since push | 28d | 0d | | Open issues (now) | 0 | 494 | | Full report | [trust report](/tools/ruc-datalab-coda-bench/trust.md) | [trust report](/tools/significant-gravitas-autogpt/trust.md) | ## Decision facts: AutoGPT - **Adopt for:** AutoGPT is a Python-based tool for creating accessible autonomous AI agents that can leverage various LLM APIs including OpenAI's GPT and Anthropic's Claude. ## Choose when ### Choose CoDA-Bench if… - License: CoDA-Bench is MIT, AutoGPT is Other. - Tags unique to CoDA-Bench: agent, agentic, benchmark, code-agent. - Also covers Vector Databases. ### Choose AutoGPT if… - License: AutoGPT is Other, CoDA-Bench is MIT. - Tags unique to AutoGPT: agents, artificial-intelligence, autonomous-agents, claude. - When you need to rapidly prototype or deploy an autonomous agent using existing language models without deep AI expertise. ## When NOT to use CoDA-Bench - AI Agents: Don't use an agent loop when a deterministic workflow would do; agents add latency, cost, and non-determinism. - LLM Frameworks: Avoid a framework for a single prompt-and-retrieve call; the abstraction can cost more than it saves. - Vector Databases: Don't reach for a dedicated vector DB under ~100k vectors; pgvector on your existing Postgres is simpler to operate. ## When NOT to use AutoGPT - Avoid if you require absolute control over the underlying AI infrastructure and APIs used by your autonomous agents, as AutoGPT imposes its own framework. - If your project demands proprietary or specialized models that aren't supported by AutoGPT's API ecosystem (e.g., custom TensorFlow or PyTorch models), consider other tools. ## Common questions ### What is the difference between CoDA-Bench and AutoGPT? CoDA-Bench: CoDA-Bench is a benchmark for code agents on data-intensive tasks. 🎈代码智能体能搞定数据密集型任务吗?. AutoGPT: AutoGPT is the vision of accessible AI for everyone, to use and to build on.. See the comparison table for live GitHub stats and shared categories. ### When should I choose CoDA-Bench over AutoGPT? Choose CoDA-Bench over AutoGPT when License: CoDA-Bench is MIT, AutoGPT is Other; Tags unique to CoDA-Bench: agent, agentic, benchmark, code-agent; Also covers Vector Databases. ### When should I choose AutoGPT over CoDA-Bench? Choose AutoGPT over CoDA-Bench when License: AutoGPT is Other, CoDA-Bench is MIT; Tags unique to AutoGPT: agents, artificial-intelligence, autonomous-agents, claude; When you need to rapidly prototype or deploy an autonomous agent using existing language models without deep AI expertise. ### When should I avoid CoDA-Bench? AI Agents: Don't use an agent loop when a deterministic workflow would do; agents add latency, cost, and non-determinism. LLM Frameworks: Avoid a framework for a single prompt-and-retrieve call; the abstraction can cost more than it saves. Vector Databases: Don't reach for a dedicated vector DB under ~100k vectors; pgvector on your existing Postgres is simpler to operate. ### When should I avoid AutoGPT? Avoid if you require absolute control over the underlying AI infrastructure and APIs used by your autonomous agents, as AutoGPT imposes its own framework. If your project demands proprietary or specialized models that aren't supported by AutoGPT's API ecosystem (e.g., custom TensorFlow or PyTorch models), consider other tools. ### Is CoDA-Bench or AutoGPT more popular on GitHub? AutoGPT has more GitHub stars (185,464 vs 39). Stars measure visibility, not whether either tool fits your constraints. ### Are CoDA-Bench and AutoGPT open source? Yes - both are open-source projects on GitHub (CoDA-Bench: MIT, AutoGPT: Other). ### Where can I find alternatives to CoDA-Bench or AutoGPT? GraphCanon lists graph-backed alternatives at [CoDA-Bench alternatives](/tools/ruc-datalab-coda-bench/alternatives) and [AutoGPT alternatives](/tools/significant-gravitas-autogpt/alternatives) ([CoDA-Bench markdown twin](/tools/ruc-datalab-coda-bench/alternatives.md), [AutoGPT markdown twin](/tools/significant-gravitas-autogpt/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/ruc-datalab-coda-bench-vs-significant-gravitas-autogpt.md) mirrors this page for agents and LLM crawlers, with the same stats table and FAQ answers. ### Which is better maintained, CoDA-Bench or AutoGPT? CoDA-Bench: Active. AutoGPT: 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 CoDA-Bench and AutoGPT? GraphCanon publishes per-repo trust reports with dated maintenance, provenance, and scan summaries: [CoDA-Bench trust report](/tools/ruc-datalab-coda-bench/trust); [AutoGPT trust report](/tools/significant-gravitas-autogpt/trust). --- **Machine-readable endpoints** - JSON: [`/api/graphcanon/graph?tool=ruc-datalab-coda-bench`](/api/graphcanon/graph?tool=ruc-datalab-coda-bench) - 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/_