--- title: "bigcode-evaluation-harness vs EAGLE" type: "comparison" canonical_url: "https://www.graphcanon.com/compare/bigcode-project-bigcode-evaluation-harness-vs-safeailab-eagle" tools: ["bigcode-project-bigcode-evaluation-harness", "safeailab-eagle"] --- # bigcode-evaluation-harness vs EAGLE *GraphCanon updated Aug 24, 2026* ## Verdict Pick bigcode-evaluation-harness if bigcode-evaluation-harness is tailored towards evaluating autoregressive code generation models via Python and Docker containers for secure and reproducible execution environments; pick EAGLE if eAGLE offers official implementations for its series of large language models with emphasis on inference and speculative decoding. [bigcode-evaluation-harness](https://github.com/bigcode-project/bigcode-evaluation-harness) reports 1.1k GitHub stars, 261 forks, and 96 open issues, last pushed Jul 22, 2025. [EAGLE](https://arxiv.org/pdf/2503.01840) has 2.5k stars, 297 forks, and 101 open issues, last pushed Feb 20, 2026. Figures are from public GitHub metadata via [bigcode-evaluation-harness's repository](https://github.com/bigcode-project/bigcode-evaluation-harness) and [EAGLE's repository](https://github.com/SafeAILab/EAGLE). | | [bigcode-evaluation-harness](/tools/bigcode-project-bigcode-evaluation-harness.md) | [EAGLE](/tools/safeailab-eagle.md) | | --- | --- | --- | | Tagline | A framework for evaluating autoregressive code generation language models. | Official Implementation of EAGLE Series Models | | Stars | 1,055 | 2,510 | | Forks | 261 | 297 | | Open issues | 96 | 101 | | Language | Python | Python | | Adopt for | bigcode-evaluation-harness is tailored towards evaluating autoregressive code generation models via Python and Docker containers for secure and reproducible execution environments. | EAGLE offers official implementations for its series of large language models with emphasis on inference and speculative decoding. | | Persona | - | - | | Runtime | - | - | | License | bigcode-evaluation-harness is distributed under the Apache-2.0 license. | Other | | Categories | Evaluation & Observability | Inference & Serving, LLM Frameworks | ## Trust and health _Sourced signals - not a safety guarantee. No winner column._ | | [bigcode-evaluation-harness](/tools/bigcode-project-bigcode-evaluation-harness.md) | [EAGLE](/tools/safeailab-eagle.md) | | --- | --- | --- | | Maintenance | Dormant (18%) | Slowing (36%) | | Days since push | 378d | 155d | | Open issues (now) | 96 | 101 | | Full report | [trust report](/tools/bigcode-project-bigcode-evaluation-harness/trust.md) | [trust report](/tools/safeailab-eagle/trust.md) | ## Decision facts: bigcode-evaluation-harness - **Requirements:** Users must have Docker installed to leverage the isolated execution environments for model output evaluation. - **Adopt for:** bigcode-evaluation-harness is tailored towards evaluating autoregressive code generation models via Python and Docker containers for secure and reproducible execution environments. - **License detail:** bigcode-evaluation-harness is distributed under the Apache-2.0 license. ## Decision facts: EAGLE - **Adopt for:** EAGLE offers official implementations for its series of large language models with emphasis on inference and speculative decoding. ## Choose when ### Choose bigcode-evaluation-harness if… - License: bigcode-evaluation-harness is Apache-2.0, EAGLE is Other. - Requirements: Users must have Docker installed to leverage the isolated execution environments for model output evaluation.. - Tags unique to bigcode-evaluation-harness: autoregressive models, code generation, docker, python. - Also covers Evaluation & Observability. - bigcode-evaluation-harness ships Docker support for self-hosted deployment. - When you need to isolate the evaluation environment from your local development setup, ensuring that no external variables affect the outcomes of model performance assessments. ### Choose EAGLE if… - License: EAGLE is Other, bigcode-evaluation-harness is Apache-2.0. - Tags unique to EAGLE: large language models, llm-inference, speculative-decoding. - Also covers Inference & Serving, LLM Frameworks. - If your project requires the latest advancements in model capabilities from ICML'24, EMNLP'24, and NeurIPS'25 as provided by EAGLE-1, EAGLE-2, or EAGLE-3. ## When NOT to use bigcode-evaluation-harness - When you require real-time evaluation without the overhead of generating outputs locally and then evaluating them within isolated environments via Docker. - If your model's evaluation process does not necessitate autoregressive setup or the security features provided by Docker, using bigcode-evaluation-harness might introduce unnecessary complexity. ## When NOT to use EAGLE - If the specific advancements and techniques implemented in ICML'24 papers are not relevant to your project. - In cases where speculative decoding does not align with the goals or methods of your application, opting for EAGLE may not be beneficial. ## Common questions ### What is the difference between bigcode-evaluation-harness and EAGLE? bigcode-evaluation-harness: A framework for evaluating autoregressive code generation language models.. EAGLE: Official Implementation of EAGLE Series Models. See the comparison table for live GitHub stats and shared categories. ### When should I choose bigcode-evaluation-harness over EAGLE? Choose bigcode-evaluation-harness over EAGLE when License: bigcode-evaluation-harness is Apache-2.0, EAGLE is Other; Requirements: Users must have Docker installed to leverage the isolated execution environments for model output evaluation.; Tags unique to bigcode-evaluation-harness: autoregressive models, code generation, docker, python; Also covers Evaluation & Observability; bigcode-evaluation-harness ships Docker support for self-hosted deployment; When you need to isolate the evaluation environment from your local development setup, ensuring that no external variables affect the outcomes of model performance assessments. ### When should I choose EAGLE over bigcode-evaluation-harness? Choose EAGLE over bigcode-evaluation-harness when License: EAGLE is Other, bigcode-evaluation-harness is Apache-2.0; Tags unique to EAGLE: large language models, llm-inference, speculative-decoding; Also covers Inference & Serving, LLM Frameworks; If your project requires the latest advancements in model capabilities from ICML'24, EMNLP'24, and NeurIPS'25 as provided by EAGLE-1, EAGLE-2, or EAGLE-3. ### When should I avoid bigcode-evaluation-harness? When you require real-time evaluation without the overhead of generating outputs locally and then evaluating them within isolated environments via Docker. If your model's evaluation process does not necessitate autoregressive setup or the security features provided by Docker, using bigcode-evaluation-harness might introduce unnecessary complexity. ### When should I avoid EAGLE? If the specific advancements and techniques implemented in ICML'24 papers are not relevant to your project. In cases where speculative decoding does not align with the goals or methods of your application, opting for EAGLE may not be beneficial. ### Is bigcode-evaluation-harness or EAGLE more popular on GitHub? EAGLE has more GitHub stars (2,510 vs 1,055). Stars measure visibility, not whether either tool fits your constraints. ### Are bigcode-evaluation-harness and EAGLE open source? Yes - both are open-source projects on GitHub (bigcode-evaluation-harness: Apache-2.0, EAGLE: Other). ### Where can I find alternatives to bigcode-evaluation-harness or EAGLE? GraphCanon lists graph-backed alternatives at [bigcode-evaluation-harness alternatives](/tools/bigcode-project-bigcode-evaluation-harness/alternatives) and [EAGLE alternatives](/tools/safeailab-eagle/alternatives) ([bigcode-evaluation-harness markdown twin](/tools/bigcode-project-bigcode-evaluation-harness/alternatives.md), [EAGLE markdown twin](/tools/safeailab-eagle/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/bigcode-project-bigcode-evaluation-harness-vs-safeailab-eagle.md) mirrors this page for agents and LLM crawlers, with the same stats table and FAQ answers. ### Which is better maintained, bigcode-evaluation-harness or EAGLE? bigcode-evaluation-harness: Dormant. EAGLE: Slowing. 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 bigcode-evaluation-harness and EAGLE? GraphCanon publishes per-repo trust reports with dated maintenance, provenance, and scan summaries: [bigcode-evaluation-harness trust report](/tools/bigcode-project-bigcode-evaluation-harness/trust); [EAGLE trust report](/tools/safeailab-eagle/trust). --- **Machine-readable endpoints** - JSON: [`/api/graphcanon/graph?tool=bigcode-project-bigcode-evaluation-harness`](/api/graphcanon/graph?tool=bigcode-project-bigcode-evaluation-harness) - 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/_