Introducing Chronos-1
Introducing
Chronos-1
A language model engineered for autonomous bug localization, causal trace analysis,
and test-driven patch generation at repository scale.
A language model engineered for autonomous bug localization, causal trace analysis, and test-driven patch generation at repository scale.
90.2% HumanEval pass rate
Benchmarks*
87.1% success with Adaptive Graph Retrieval (AGR)
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Benchmarks*
71.2% success rate on cross-file bugs
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Benchmarks*
90.2% HumanEval pass rate
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Benchmarks*
90.2% HumanEval pass rate
Benchmarks*
87.1% success with Adaptive Graph Retrieval (AGR)
-
Benchmarks*
71.2% success rate on cross-file bugs
-
Benchmarks*
90.2% HumanEval pass rate
-
Benchmarks*
LLMs Don’t Debug
Static tokens don’t scale with dynamic bugs.
Static tokens don’t scale with dynamic bugs.
Sliding windows miss cross-file dependencies.
Sliding windows miss cross-file dependencies.
High token count ≠ high fix quality.
High token count ≠ high fix quality.
Prediction isn’t understanding.
Prediction isn’t understanding.
Prediction isn’t understanding.
A New Class of Language Model
Chronos-1 is built to debug code at scale.
It moves beyond token prediction to perform structured reasoning across entire codebases.
By tracing logic paths, identifying failure causes, and generating test-passing fixes, Chronos-1 redefines how developers interact with bugs.
Private research is ongoing. Launching in Q4 2025 with Kodezi OS.
[ LLM SYSTEM DESIGN ]
LLM Autonomic Debugging Stack
Chronos operates as a self-healing substrate across source, test, and infrastructure layers, enabling token-efficient, memory-driven software repair at scale.
Memory-Guided Code Retrieval
Chronos traverses a graph-indexed memory of code, tests, logs, and history to extract bug-relevant context.
Memory-Guided Code Retrieval
Chronos traverses a graph-indexed memory of code, tests, logs, and history to extract bug-relevant context.
Memory-Guided Code Retrieval
Chronos traverses a graph-indexed memory of code, tests, logs, and history to extract bug-relevant context.
Multi-Hop Contextual Retrieval
Scales context depth to bug complexity with token-efficient precision.
Multi-Hop Contextual Retrieval
Scales context depth to bug complexity with token-efficient precision.
Multi-Hop Contextual Retrieval
Scales context depth to bug complexity with token-efficient precision.
AGR-Guided Fix Planning
Reconstructs logic state to guide patch synthesis.
AGR-Guided Fix Planning
Reconstructs logic state to guide patch synthesis.
AGR-Guided Fix Planning
Reconstructs logic state to guide patch synthesis.
Test-Validated Fix Generation
Fixes are synthesized and accepted only upon passing full-suite validation.
Test-Validated Fix Generation
Fixes are synthesized and accepted only upon passing full-suite validation.
Test-Validated Fix Generation
Fixes are synthesized and accepted only upon passing full-suite validation.
Autonomous Debugging Feedback Loop
Validated repairs reshape retrieval and context flow.
Autonomous Debugging Feedback Loop
Validated repairs reshape retrieval and context flow.
Autonomous Debugging Feedback Loop
Validated repairs reshape retrieval and context flow.
Purpose-Built for Debugging at Scale
TOKEN-EFFICIENT AUTONOMY
Memory-guided fixes with minimal tokens
TOKEN-EFFICIENT AUTONOMY
Memory-guided fixes with minimal tokens
TOKEN-EFFICIENT AUTONOMY
Memory-guided fixes with minimal tokens
CONTEXTUAL INTEGRATION
Retrieves across files, tests, and logs
CONTEXTUAL INTEGRATION
Retrieves across files, tests, and logs
CONTEXTUAL INTEGRATION
Retrieves across files, tests, and logs
DEBUGGING ENGINEERED FOR YOU
LLM-native patching from learned bugs
DEBUGGING ENGINEERED FOR YOU
LLM-native patching from learned bugs
DEBUGGING ENGINEERED FOR YOU
LLM-native patching from learned bugs
CONTINUOUS LEARNING
Retrains memory after every resolution
CONTINUOUS LEARNING
Retrains memory after every resolution
CONTINUOUS LEARNING
Retrains memory after every resolution
Chronos fits into your existing workflows with zero disruption and full production awareness.
Chronos fits into your existing workflows with zero disruption and full production awareness.
[ RESULTS]
Benchmarks
Comparative Performance and Capabilities of Code Intelligence Tools
This table compares Chronos with other code tools across context, memory, debugging support, and success rate. Chronos is the only system with persistent memory, full CI/CD integration, and a validated debugging loop. It achieves a 65.3 percent success rate, significantly higher than all others.
Model Comparison:
Average Debugging Cycles
Chronos significantly reduces the number of cycles needed to fix bugs compared to leading language models. While GPT-4, Claude, and CodeT5+ require 5 to 7 iterations on average, Chronos resolves issues in just 2.2 cycles, showcasing its efficiency in debugging workflows.
Debugging Accuracy
by Bug Type
This chart compares success rates across bug types for Chronos, GPT-4, Claude-3, and Gemini-1.5. Chronos consistently outperforms all others across syntax, logic, memory, concurrency, and integration bugs, achieving over 85 percent in each category while others remain below 25 percent.
Scalability of Debugging
Performance by Codebase Size
This chart compares how models perform as repository size increases. Chronos maintains high accuracy across all scales, from small to million-line codebases, while other models drop sharply beyond 100K lines of code.
Comparative Performance and Capabilities of Code Intelligence Tools
This table compares Chronos with other code tools across context, memory, debugging support, and success rate. Chronos is the only system with persistent memory, full CI/CD integration, and a validated debugging loop. It achieves a 65.3 percent success rate, significantly higher than all others.
Model Comparison:
Average Debugging CyclesChronos significantly reduces the number of cycles needed to fix bugs compared to leading language models. While GPT-4, Claude, and CodeT5+ require 5 to 7 iterations on average, Chronos resolves issues in just 2.2 cycles, showcasing its efficiency in debugging workflows.
Debugging Accuracy
by Bug TypeThis chart compares success rates across bug types for Chronos, GPT-4, Claude-3, and Gemini-1.5. Chronos consistently outperforms all others across syntax, logic, memory, concurrency, and integration bugs, achieving over 85 percent in each category while others remain below 25 percent.
Scalability of Debugging
Performance by Codebase SizeThis chart compares how models perform as repository size increases. Chronos maintains high accuracy across all scales, from small to million-line codebases, while other models drop sharply beyond 100K lines of code.
Comparative Performance and Capabilities of Code Intelligence Tools
This table compares Chronos with other code tools across context, memory, debugging support, and success rate. Chronos is the only system with persistent memory, full CI/CD integration, and a validated debugging loop. It achieves a 65.3 percent success rate, significantly higher than all others.
Model Comparison:
Average Debugging CyclesChronos significantly reduces the number of cycles needed to fix bugs compared to leading language models. While GPT-4, Claude, and CodeT5+ require 5 to 7 iterations on average, Chronos resolves issues in just 2.2 cycles, showcasing its efficiency in debugging workflows.
Debugging Accuracy
by Bug TypeThis chart compares success rates across bug types for Chronos, GPT-4, Claude-3, and Gemini-1.5. Chronos consistently outperforms all others across syntax, logic, memory, concurrency, and integration bugs, achieving over 85 percent in each category while others remain below 25 percent.
Scalability of Debugging
Performance by Codebase SizeThis chart compares how models perform as repository size increases. Chronos maintains high accuracy across all scales, from small to million-line codebases, while other models drop sharply beyond 100K lines of code.
Comparative Performance and Capabilities of Code Intelligence Tools
This table compares Chronos with other code tools across context, memory, debugging support, and success rate. Chronos is the only system with persistent memory, full CI/CD integration, and a validated debugging loop. It achieves a 65.3 percent success rate, significantly higher than all others.
Model Comparison:
Average Debugging CyclesChronos significantly reduces the number of cycles needed to fix bugs compared to leading language models. While GPT-4, Claude, and CodeT5+ require 5 to 7 iterations on average, Chronos resolves issues in just 2.2 cycles, showcasing its efficiency in debugging workflows.
Debugging Accuracy
by Bug TypeThis chart compares success rates across bug types for Chronos, GPT-4, Claude-3, and Gemini-1.5. Chronos consistently outperforms all others across syntax, logic, memory, concurrency, and integration bugs, achieving over 85 percent in each category while others remain below 25 percent.
Scalability of Debugging
Performance by Codebase SizeThis chart compares how models perform as repository size increases. Chronos maintains high accuracy across all scales, from small to million-line codebases, while other models drop sharply beyond 100K lines of code.
[ RESULTS]
Benchmarks
Comparative Performance and Capabilities of Code Intelligence Tools
This table compares Chronos with other code tools across context, memory, debugging support, and success rate. Chronos is the only system with persistent memory, full CI/CD integration, and a validated debugging loop. It achieves a 65.3 percent success rate, significantly higher than all others.
Model Comparison: Average Debugging Cycles
Chronos significantly reduces the number of cycles needed to fix bugs compared to leading language models. While GPT-4, Claude, and CodeT5+ require 5 to 7 iterations on average, Chronos resolves issues in just 2.2 cycles, showcasing its efficiency in debugging workflows.
Debugging Accuracy
by Bug TypeThis chart compares success rates across bug types for Chronos, GPT-4, Claude-3, and Gemini-1.5. Chronos consistently outperforms all others across syntax, logic, memory, concurrency, and integration bugs, achieving over 85 percent in each category while others remain below 25 percent.
Scalability of Debugging
Performance by Codebase SizeThis chart compares how models perform as repository size increases. Chronos maintains high accuracy across all scales, from small to million-line codebases, while other models drop sharply beyond 100K lines of code.
Comparative Performance and Capabilities of Code Intelligence Tools
This table compares Chronos with other code tools across context, memory, debugging support, and success rate. Chronos is the only system with persistent memory, full CI/CD integration, and a validated debugging loop. It achieves a 65.3 percent success rate, significantly higher than all others.
Model Comparison: Average Debugging Cycles
Chronos significantly reduces the number of cycles needed to fix bugs compared to leading language models. While GPT-4, Claude, and CodeT5+ require 5 to 7 iterations on average, Chronos resolves issues in just 2.2 cycles, showcasing its efficiency in debugging workflows.
Debugging Accuracy
by Bug TypeThis chart compares success rates across bug types for Chronos, GPT-4, Claude-3, and Gemini-1.5. Chronos consistently outperforms all others across syntax, logic, memory, concurrency, and integration bugs, achieving over 85 percent in each category while others remain below 25 percent.
Scalability of Debugging
Performance by Codebase SizeThis chart compares how models perform as repository size increases. Chronos maintains high accuracy across all scales, from small to million-line codebases, while other models drop sharply beyond 100K lines of code.
Comparative Performance and Capabilities of Code Intelligence Tools
This table compares Chronos with other code tools across context, memory, debugging support, and success rate. Chronos is the only system with persistent memory, full CI/CD integration, and a validated debugging loop. It achieves a 65.3 percent success rate, significantly higher than all others.
Model Comparison: Average Debugging Cycles
Chronos significantly reduces the number of cycles needed to fix bugs compared to leading language models. While GPT-4, Claude, and CodeT5+ require 5 to 7 iterations on average, Chronos resolves issues in just 2.2 cycles, showcasing its efficiency in debugging workflows.
Debugging Accuracy
by Bug TypeThis chart compares success rates across bug types for Chronos, GPT-4, Claude-3, and Gemini-1.5. Chronos consistently outperforms all others across syntax, logic, memory, concurrency, and integration bugs, achieving over 85 percent in each category while others remain below 25 percent.
Scalability of Debugging
Performance by Codebase SizeThis chart compares how models perform as repository size increases. Chronos maintains high accuracy across all scales, from small to million-line codebases, while other models drop sharply beyond 100K lines of code.
Comparative Performance and Capabilities of Code Intelligence Tools
This table compares Chronos with other code tools across context, memory, debugging support, and success rate. Chronos is the only system with persistent memory, full CI/CD integration, and a validated debugging loop. It achieves a 65.3 percent success rate, significantly higher than all others.
Model Comparison: Average Debugging Cycles
Chronos significantly reduces the number of cycles needed to fix bugs compared to leading language models. While GPT-4, Claude, and CodeT5+ require 5 to 7 iterations on average, Chronos resolves issues in just 2.2 cycles, showcasing its efficiency in debugging workflows.
Debugging Accuracy
by Bug TypeThis chart compares success rates across bug types for Chronos, GPT-4, Claude-3, and Gemini-1.5. Chronos consistently outperforms all others across syntax, logic, memory, concurrency, and integration bugs, achieving over 85 percent in each category while others remain below 25 percent.
Scalability of Debugging
Performance by Codebase SizeThis chart compares how models perform as repository size increases. Chronos maintains high accuracy across all scales, from small to million-line codebases, while other models drop sharply beyond 100K lines of code.
[ RESULTS]
Benchmarks
Comparative Performance and Capabilities of Code Intelligence Tools
This table compares Chronos with other code tools across context, memory, debugging support, and success rate. Chronos is the only system with persistent memory, full CI/CD integration, and a validated debugging loop. It achieves a 65.3 percent success rate, significantly higher than all others.
Model Comparison: Average Debugging Cycles
Chronos significantly reduces the number of cycles needed to fix bugs compared to leading language models. While GPT-4, Claude, and CodeT5+ require 5 to 7 iterations on average, Chronos resolves issues in just 2.2 cycles, showcasing its efficiency in debugging workflows.
Debugging Accuracy
by Bug TypeThis chart compares success rates across bug types for Chronos, GPT-4, Claude-3, and Gemini-1.5. Chronos consistently outperforms all others across syntax, logic, memory, concurrency, and integration bugs, achieving over 85 percent in each category while others remain below 25 percent.
Scalability of Debugging
Performance by Codebase SizeThis chart compares how models perform as repository size increases. Chronos maintains high accuracy across all scales, from small to million-line codebases, while other models drop sharply beyond 100K lines of code.
Comparative Performance and Capabilities of Code Intelligence Tools
This table compares Chronos with other code tools across context, memory, debugging support, and success rate. Chronos is the only system with persistent memory, full CI/CD integration, and a validated debugging loop. It achieves a 65.3 percent success rate, significantly higher than all others.
Model Comparison: Average Debugging Cycles
Chronos significantly reduces the number of cycles needed to fix bugs compared to leading language models. While GPT-4, Claude, and CodeT5+ require 5 to 7 iterations on average, Chronos resolves issues in just 2.2 cycles, showcasing its efficiency in debugging workflows.
Debugging Accuracy
by Bug TypeThis chart compares success rates across bug types for Chronos, GPT-4, Claude-3, and Gemini-1.5. Chronos consistently outperforms all others across syntax, logic, memory, concurrency, and integration bugs, achieving over 85 percent in each category while others remain below 25 percent.
Scalability of Debugging
Performance by Codebase SizeThis chart compares how models perform as repository size increases. Chronos maintains high accuracy across all scales, from small to million-line codebases, while other models drop sharply beyond 100K lines of code.
Comparative Performance and Capabilities of Code Intelligence Tools
This table compares Chronos with other code tools across context, memory, debugging support, and success rate. Chronos is the only system with persistent memory, full CI/CD integration, and a validated debugging loop. It achieves a 65.3 percent success rate, significantly higher than all others.
Model Comparison: Average Debugging Cycles
Chronos significantly reduces the number of cycles needed to fix bugs compared to leading language models. While GPT-4, Claude, and CodeT5+ require 5 to 7 iterations on average, Chronos resolves issues in just 2.2 cycles, showcasing its efficiency in debugging workflows.
Debugging Accuracy
by Bug TypeThis chart compares success rates across bug types for Chronos, GPT-4, Claude-3, and Gemini-1.5. Chronos consistently outperforms all others across syntax, logic, memory, concurrency, and integration bugs, achieving over 85 percent in each category while others remain below 25 percent.
Scalability of Debugging
Performance by Codebase SizeThis chart compares how models perform as repository size increases. Chronos maintains high accuracy across all scales, from small to million-line codebases, while other models drop sharply beyond 100K lines of code.
Comparative Performance and Capabilities of Code Intelligence Tools
This table compares Chronos with other code tools across context, memory, debugging support, and success rate. Chronos is the only system with persistent memory, full CI/CD integration, and a validated debugging loop. It achieves a 65.3 percent success rate, significantly higher than all others.
Model Comparison: Average Debugging Cycles
Chronos significantly reduces the number of cycles needed to fix bugs compared to leading language models. While GPT-4, Claude, and CodeT5+ require 5 to 7 iterations on average, Chronos resolves issues in just 2.2 cycles, showcasing its efficiency in debugging workflows.
Debugging Accuracy
by Bug TypeThis chart compares success rates across bug types for Chronos, GPT-4, Claude-3, and Gemini-1.5. Chronos consistently outperforms all others across syntax, logic, memory, concurrency, and integration bugs, achieving over 85 percent in each category while others remain below 25 percent.
Scalability of Debugging
Performance by Codebase SizeThis chart compares how models perform as repository size increases. Chronos maintains high accuracy across all scales, from small to million-line codebases, while other models drop sharply beyond 100K lines of code.
Get Access to Chronos-1
Chronos-1 will be released in Q4 2025 as part of the Kodezi OS.
Join the waitlist for early access.
Get Access to Chronos-1
Chronos-1 will be released in Q4 2025 as part of the Kodezi OS.
Join the waitlist for early access.
Get Access to Chronos-1
Chronos-1 will be released in Q4 2025 as part of the Kodezi OS.
Join the waitlist for early access.
[CHRONICLE]
Journal
[
Research
]
Why We Spent 4 Years on Debugging
What we got wrong about LLMs, what we learned from failure, and why Chronos became necessary
[
Research
]
Why We Spent 4 Years on Debugging
What we got wrong about LLMs, what we learned from failure, and why Chronos became necessary
[
Research
]
Why We Spent 4 Years on Debugging
What we got wrong about LLMs, what we learned from failure, and why Chronos became necessary
Kodezi Team
Jul 18, 2025
[
Research
]
How Real Bugs Taught Chronos More Than Any Dataset
What we thought we were teaching the model, and what it ended up learning from us instead.
[
Research
]
How Real Bugs Taught Chronos More Than Any Dataset
What we thought we were teaching the model, and what it ended up learning from us instead.
[
Research
]
How Real Bugs Taught Chronos More Than Any Dataset
What we thought we were teaching the model, and what it ended up learning from us instead.
Kodezi Team
Jul 20, 2025