Comparison
CodeGen vs CodeRL
Verdict
Pick CodeGen if codeGen is a family of open-source models for program synthesis developed by Salesforce AI Research, available in various sizes from 350M to 16B parameters. It is trained on TPU-v4 and competes with OpenAI Codex; pick CodeRL if codeRL is an advanced tool that uses pretrained models and deep reinforcement learning to generate code.
Markdown twin · CodeGen alternatives · CodeRL alternatives
GraphCanon updated Oct 7, 2026
12views this month
Trust & integrity
| Signal | CodeGen | CodeRL |
|---|---|---|
| Maintenance | Slowing (107d since push) As of Sep 18, 2026 · github_public_v1 | Slowing (126d since push) As of Oct 7, 2026 · github_public_v1 |
| Provenance | Not a fork · Organization account As of Sep 18, 2026 · github_public_v1 | Not a fork · Organization account As of Oct 7, 2026 · github_public_v1 |
| OSV dependency advisories | No lockfile (source not queried) As of Sep 18, 2026 · osv@v1 | Published findings As of Jul 11, 2026 · osv@v1 |
| deps.dev advisories | Not queried deps.dev@v1 | Not queried deps.dev@v1 |
| OpenSSF Scorecard | Not queried openssf-scorecard@v1 | Not queried openssf-scorecard@v1 |
Tagline
- CodeGen
- A family of open-source models for program synthesis
- CodeRL
- CodeRL: Combines pretrained models and reinforcement learning for code generation.
Stars
- CodeGen
- 5.2k
- CodeRL
- 576
Forks
- CodeGen
- 420
- CodeRL
- 69
Open issues
- CodeGen
- 49
- CodeRL
- 42
Language
- CodeGen
- Python
- CodeRL
- Python
Adopt for
- CodeGen
- CodeGen is a family of open-source models for program synthesis developed by Salesforce AI Research, available in various sizes from 350M to 16B parameters. It is trained on TPU-v4 and competes with OpenAI Codex.
- CodeRL
- CodeRL is an advanced tool that uses pretrained models and deep reinforcement learning to generate code.
Persona
- CodeGen
- -
- CodeRL
- -
Runtime
- CodeGen
- -
- CodeRL
- -
License
- CodeGen
- Apache-2.0
- CodeRL
- BSD-3-Clause
Last pushed
- CodeGen
- Jun 2, 2026
- CodeRL
- Jun 2, 2026
Categories
- CodeGen
- LLM Frameworks, Model Training
- CodeRL
- Developer Tools, Model Training
Trust and health
Days since push
- CodeGen
- 107d
- CodeRL
- 126d
Open issues (now)
- CodeGen
- 49
- CodeRL
- 42
Open issues delta
- CodeGen
- +1 (30d)
- CodeRL
- 0 (30d)
OSV dependency advisories
- CodeGen
- No lockfile (source not queried)
- CodeRL
- Published findings
Full report
- CodeGen
- Trust report
- CodeRL
- Trust report
Shared compatibility
- Python · CodeGen: Python runtime · CodeRL: Python runtime
Choose CodeGen if…
- License: CodeGen is Apache-2.0, CodeRL is BSD-3-Clause.
- Tags unique to CodeGen: codex, generativemodel, llm, tpu-acceleration.
- Also covers LLM Frameworks.
- When you need an open-source alternative to proprietary models like OpenAI Codex for program synthesis.
When NOT to use CodeGen
- If your project strictly requires proprietary models with specific features not available in open-source alternatives.
- When the hardware requirements for TPU-v4 trained models are not met or are not preferred in your environment.
- If the latest version of CodeGen does not meet the specific performance or feature requirements of your project.
- For projects that require models with a different licensing model, as CodeGen is licensed under Apache-2.0.
Choose CodeRL if…
- License: CodeRL is BSD-3-Clause, CodeGen is Apache-2.0.
- Tags unique to CodeRL: ai, codegeneration, machinelearning, reinforcementlearning.
- Also covers Developer Tools.
- When you need to generate complex and context-aware code snippets utilizing the latest in reinforcement learning techniques.
When NOT to use CodeRL
- Avoid if your project requires only simple, quick code generation without deep reinforcement learning support.
- Do not use if compatibility with versions of the Hugging Face transformers library other than 4.16.1 is critical to avoid potential issues.
Explore
Sources
Every stat on this page traces to a dated GitHub sync, license file, enrichment field, or trust scan.
- GitHub stars (salesforce/CodeGen) · observed Sep 20, 2026
- GitHub forks (salesforce/CodeGen) · observed Sep 20, 2026
- Last push (salesforce/CodeGen) · observed Jun 2, 2026
- License file (Apache-2.0) · observed Sep 20, 2026
- Decision facts (enrichment) · observed Sep 18, 2026
- Trust scan (lockfile / OSV) · observed Sep 18, 2026
- GitHub stars (salesforce/CodeRL) · observed Oct 7, 2026
- GitHub forks (salesforce/CodeRL) · observed Oct 7, 2026
- Last push (salesforce/CodeRL) · observed Jun 2, 2026
- License file (BSD-3-Clause) · observed Oct 7, 2026
- Decision facts (enrichment) · observed Jul 17, 2026
- Trust scan (lockfile / OSV) · observed Jul 11, 2026
GitHub stars on cards: CodeGen 5.2k · CodeRL 576 (synced Sep 20, 2026).
Common questions
- What is the difference between CodeGen and CodeRL?
- CodeGen: A family of open-source models for program synthesis. CodeRL: CodeRL: Combines pretrained models and reinforcement learning for code generation.. See the comparison table for live GitHub stats and shared categories.
- When should I choose CodeGen over CodeRL?
- Choose CodeGen over CodeRL when License: CodeGen is Apache-2.0, CodeRL is BSD-3-Clause; Tags unique to CodeGen: codex, generativemodel, llm, tpu-acceleration; Also covers LLM Frameworks; When you need an open-source alternative to proprietary models like OpenAI Codex for program synthesis.
- When should I choose CodeRL over CodeGen?
- Choose CodeRL over CodeGen when License: CodeRL is BSD-3-Clause, CodeGen is Apache-2.0; Tags unique to CodeRL: ai, codegeneration, machinelearning, reinforcementlearning; Also covers Developer Tools; When you need to generate complex and context-aware code snippets utilizing the latest in reinforcement learning techniques.
- When should I avoid CodeGen?
- If your project strictly requires proprietary models with specific features not available in open-source alternatives. When the hardware requirements for TPU-v4 trained models are not met or are not preferred in your environment. If the latest version of CodeGen does not meet the specific performance or feature requirements of your project. For projects that require models with a different licensing model, as CodeGen is licensed under Apache-2.0.
- When should I avoid CodeRL?
- Avoid if your project requires only simple, quick code generation without deep reinforcement learning support. Do not use if compatibility with versions of the Hugging Face transformers library other than 4.16.1 is critical to avoid potential issues.
- Is CodeGen or CodeRL more popular on GitHub?
- CodeGen has more GitHub stars (5,180 vs 576). Stars measure visibility, not whether either tool fits your constraints.
- Are CodeGen and CodeRL open source?
- Yes - both are open-source projects on GitHub (CodeGen: Apache-2.0, CodeRL: BSD-3-Clause).
- Where can I find alternatives to CodeGen or CodeRL?
- GraphCanon lists graph-backed alternatives at CodeGen alternatives and CodeRL alternatives (CodeGen markdown twin, CodeRL markdown twin), 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 mirrors this page for agents and LLM crawlers, with the same stats table and FAQ answers.
- Which is better maintained, CodeGen or CodeRL?
- CodeGen: Slowing. CodeRL: 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 CodeGen and CodeRL?
- GraphCanon publishes per-repo trust reports with dated maintenance, provenance, and scan summaries: CodeGen trust report; CodeRL trust report.