Comparison
LLMDebugger vs self-repair
Verdict
Pick LLMDebugger if lLMDebugger offers step-by-step verification of runtime execution for large language models; pick self-repair if self-repair provides tools to replicate research experiments on code generation systems self-reparation techniques.
Markdown twin · LLMDebugger alternatives · self-repair alternatives
GraphCanon updated Oct 7, 2026
vs
Trust & integrity
| Signal | LLMDebugger | self-repair |
|---|---|---|
| Maintenance | Dormant (756d since push) As of Oct 7, 2026 · github_public_v1 | Archived (887d since push) As of Oct 7, 2026 · github_public_v1 |
| Provenance | Not a fork · Personal account As of Oct 7, 2026 · github_public_v1 | Not a fork · Personal account As of Oct 7, 2026 · github_public_v1 |
| OSV dependency advisories | No published findings from this source as of 2026-07-11 As of Jul 11, 2026 · osv@v1 | No published findings from this source as of 2026-07-11 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
- LLMDebugger
- A Large Language Model Debugger verifying runtime execution step by step
- self-repair
- Supports research on self-repair mechanisms for code generation
Stars
- LLMDebugger
- 585
- self-repair
- 15
Forks
- LLMDebugger
- 57
- self-repair
- 3
Open issues
- LLMDebugger
- 5
- self-repair
- 1
Language
- LLMDebugger
- Python
- self-repair
- Python
Adopt for
- LLMDebugger
- LLMDebugger offers step-by-step verification of runtime execution for large language models.
- self-repair
- Self-repair provides tools to replicate research experiments on code generation systems self-reparation techniques.
Persona
- LLMDebugger
- -
- self-repair
- -
Runtime
- LLMDebugger
- -
- self-repair
- -
License
- LLMDebugger
- The LLMDebugger is distributed under the Apache-2.0 license.
- self-repair
- -
Last pushed
- LLMDebugger
- Sep 10, 2024
- self-repair
- May 2, 2024
Categories
- LLMDebugger
- Developer Tools, Evaluation & Observability
- self-repair
- Developer Tools
Trust and health
Maintenance
- LLMDebugger
- Dormant (18%)
- self-repair
- Archived (8%)
Days since push
- LLMDebugger
- 756d
- self-repair
- 887d
Archived on GitHub
- LLMDebugger
- No
- self-repair
- Yes
Open issues (now)
- LLMDebugger
- 5
- self-repair
- 1
Stars delta
- LLMDebugger
- +1 (30d)
- self-repair
- 0 (30d)
Full report
- LLMDebugger
- Trust report
- self-repair
- Trust report
Shared compatibility
- Python · LLMDebugger: Python runtime · self-repair: Python runtime
Choose LLMDebugger if…
- Pricing: Free for use, based on its open-source nature with an Apache-2.0 license..
- Tags unique to LLMDebugger: acl'24, llm debugging, python debugger for ai, runtime verification.
- Also covers Evaluation & Observability.
- When detailed step-by-step inspection of the runtime behavior of large language models is required, LLMDebugger can provide precise insights into each execution phase.
When NOT to use LLMDebugger
- Avoid using if you are only interested in higher-level performance metrics rather than the intricate details of runtime behavior, as LLMDebugger emphasizes step-by-step execution.
- Not recommended for teams lacking experience with Python or specific to this tool's installation and usage workflow that involves setting up a Conda environment.
Choose self-repair if…
- Tags unique to self-repair: code-repair, experiments replication, python, research-tools.
- When aiming to replicate specific experimental data from the ICLR 2024 paper on self-repair in code generation
- Leaner open-issue backlog (1).
When NOT to use self-repair
- For creating entirely new datasets or conducting large-scale human studies, as this repository focuses on replicating existing experiments
- When the internal dependencies or incomplete code functions such as exec_sample in APPS are critical for your application without modification
Explore
Sources
Every stat on this page traces to a dated GitHub sync, license file, enrichment field, or trust scan.
- GitHub stars (FloridSleeves/LLMDebugger) · observed Oct 7, 2026
- GitHub forks (FloridSleeves/LLMDebugger) · observed Oct 7, 2026
- Last push (FloridSleeves/LLMDebugger) · observed Sep 10, 2024
- License file (Apache-2.0) · observed Oct 7, 2026
- Decision facts (enrichment) · observed Jul 17, 2026
- Trust scan (lockfile / OSV) · observed Jul 11, 2026
- GitHub stars (theoxo/self-repair) · observed Oct 7, 2026
- GitHub forks (theoxo/self-repair) · observed Oct 7, 2026
- Last push (theoxo/self-repair) · observed May 2, 2024
- License file (unknown) · observed Oct 7, 2026
- Decision facts (enrichment) · observed Jul 16, 2026
- Trust scan (lockfile / OSV) · observed Jul 11, 2026
GitHub stars on cards: LLMDebugger 585 · self-repair 15 (synced Oct 7, 2026).
Common questions
- What is the difference between LLMDebugger and self-repair?
- LLMDebugger: A Large Language Model Debugger verifying runtime execution step by step. self-repair: Supports research on self-repair mechanisms for code generation. See the comparison table for live GitHub stats and shared categories.
- When should I choose LLMDebugger over self-repair?
- Choose LLMDebugger over self-repair when Pricing: Free for use, based on its open-source nature with an Apache-2.0 license.; Tags unique to LLMDebugger: acl'24, llm debugging, python debugger for ai, runtime verification; Also covers Evaluation & Observability; When detailed step-by-step inspection of the runtime behavior of large language models is required, LLMDebugger can provide precise insights into each execution phase.
- When should I choose self-repair over LLMDebugger?
- Choose self-repair over LLMDebugger when Tags unique to self-repair: code-repair, experiments replication, python, research-tools; When aiming to replicate specific experimental data from the ICLR 2024 paper on self-repair in code generation; Leaner open-issue backlog (1).
- When should I avoid LLMDebugger?
- Avoid using if you are only interested in higher-level performance metrics rather than the intricate details of runtime behavior, as LLMDebugger emphasizes step-by-step execution. Not recommended for teams lacking experience with Python or specific to this tool's installation and usage workflow that involves setting up a Conda environment.
- When should I avoid self-repair?
- For creating entirely new datasets or conducting large-scale human studies, as this repository focuses on replicating existing experiments When the internal dependencies or incomplete code functions such as exec_sample in APPS are critical for your application without modification
- Is LLMDebugger or self-repair more popular on GitHub?
- LLMDebugger has more GitHub stars (585 vs 15). Stars measure visibility, not whether either tool fits your constraints.
- Are LLMDebugger and self-repair open source?
- Yes - both are open-source projects on GitHub.
- Where can I find alternatives to LLMDebugger or self-repair?
- GraphCanon lists graph-backed alternatives at LLMDebugger alternatives and self-repair alternatives (LLMDebugger markdown twin, self-repair 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, LLMDebugger or self-repair?
- LLMDebugger: Dormant. self-repair: Archived. 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 LLMDebugger and self-repair?
- GraphCanon publishes per-repo trust reports with dated maintenance, provenance, and scan summaries: LLMDebugger trust report; self-repair trust report.