Kimi-K2: LLM for Automated Docker Environments

• Kimi-K2 is an open-source large language model for environment automation that demonstrates competitive performance on generating reproducible Dockerfiles.
• It uses multi-agent frameworks like SWE-Builder to enhance test script robustness, achieving fail-to-pass rates up to 37.7% in benchmark evaluations.
• The evaluation on Multi-Docker-Eval reveals key challenges in environment construction and highlights the need for advanced dependency solvers.

Kimi-K2 is an open-source LLM, evaluated in the context of environment automation for software engineering. In the Multi-Docker-Eval benchmark, Kimi-K2 demonstrates competitive effectiveness relative to both open and closed proprietary models. Environment-building—the process of generating correct, reproducible Dockerfiles and achieving testable, runnable software states—serves as the central bottleneck for fully automated software engineering pipelines. This article systematically details the construction of Multi-Docker-Eval, the metrics underpinning its evaluation of models like Kimi-K2, and the model’s performance and implications compared to other state-of-the-art systems.

1. Multi-Docker-Eval Benchmark: Overview and Design

Multi-Docker-Eval is a large-scale benchmark designed to evaluate automated environment configuration, specifically the ability of LLM agents to build Dockerized environments for running and testing real-world software repositories (Fu et al., 7 Dec 2025). The benchmark draws 334 tasks from 40 GitHub repositories across 9 languages—Python, JavaScript, Java, C, C++, Go, Rust, Ruby, PHP—selected for moderate popularity (1,000–1,500 stars), sufficient community activity (≥20 forks, ≥10 contributors), and manageable repository sizes (≤100 MB). Each benchmark instance includes:

• $R$: a frozen repository snapshot
• $D$: a natural-language bug or feature request
• $P^*$: the ground-truth human patch making tests pass

The evaluation requires the LLM agent to:

1. Generate a Dockerfile and necessary installation/testing commands forming a runnable environment.
2. Provide a test function $T(\cdot)$ such that $T(R) = \text{False}$ and $T(R \oplus P^*) = \text{True}$ (fail-to-pass transition).

Execution proceeds under strict resource bounds: 1,800 s for Docker build, 2,700 s for test execution, and up to 16 concurrent workers (CPU-only, 32-core, 128 GB RAM per VM). Each test is run three times to average out flakiness.

2. Metrics and Evaluation Protocol

Two principal metrics underpin model evaluation on Multi-Docker-Eval (Fu et al., 7 Dec 2025):

• Commit Rate (CR): Proportion of instances where the agent produces a non-empty answer: $CR = |S| / N$, where $S$ is the set of committed answers and $N$ the total instances.
• Fail-to-Pass Rate (F2P): Fraction of instances where agent output moves the tests from failed to passed: $F2P = |F| / N$, with $$F = \{i \in S \mid T(R_i) = \text{False} \wedge T(R_i \oplus P_i^*) = \text{True}\}$$.

An $F_2$-score formalism applies if one regards F2P as an $F$-measure over “does the test go from fail to pass,” i.e.,

$F2P = 2 \cdot P \cdot R / (P+R)$

with $P = |F| / |S|$ and $R = |F| / N$.

Additional efficiency metrics are collected: wall-clock build and test times, CPU time, peak resident memory, Docker image size, and token consumption per run.

3. Model Families, Agent Frameworks, and Their Evaluation

Kimi-K2 is directly reported in two variants ("Kimi-K2-0905" and "Kimi-K2-thinking") within the open-source model cohort. Other open models include DeepSeek-v3.1, DeepSeek-R1, Qwen3-235B-A22B, GPT-OSS-20B/120B, while closed competitors are Claude-Sonnet-4, GPT-5-Mini, Gemini-2.5-Flash (Fu et al., 7 Dec 2025). Evaluation examines both single-agent (“RepoLaunch”) and multi-agent (“SWE-Builder”) agent frameworks:

• SWE-Builder: Multi-agent, memory-augmented, with explicit error-repair loops, yielding much higher F2P.
• RepoLaunch: Single-agent, sequential bash-command interaction, with lower overall performance.

Under SWE-Builder, F2P scores range 17%–37.7%. The highest performers are:

Model	F2P (%)	CR (%)
DeepSeek-v3.1	37.72	not stated
Kimi-K2-0905	37.62	not stated
Claude-Sonnet-4	35.53	not stated
GPT-5-Mini	34.13	not stated

Wall clock per run is ~100,000 s (≈28 h), average RAM ~7.5 GB, image size ~1 GB. Open models (DeepSeek-v3.1, Kimi-K2-0905) match or surpass closed alternatives while exhibiting lower token usage and similar resource usage distributions.

4. Analysis of Model and System Factors

The Multi-Docker-Eval benchmark provides a rigorous statistical assessment of influencing factors. Empirical findings include:

• Model Size and Token Usage: Little to no correlation with F2P (Pearson $r \leq 0.1$, $p > 0.05$).
• Prompt/Reasoning Length: Chain-of-thought variants ("-thinking") reduce script generation failures but do not alleviate Docker build errors.
• Agent Framework Impact: SWE-Builder achieves ≈30.6% ± 6.5% F2P vs. RepoLaunch’s 8.9% ± 3.1% ($p<0.01$, Wilcoxon signed-rank).
• Programming Language: Success rates vary considerably—Go 54.5%, Python ~48%, JavaScript ~46%, while C/C++/Rust/Java/PHP trail due to complex build/test systems.

A plausible implication is that Kimi-K2’s chain-of-thought variants improve the reliability of test script generation, but system-level dependency inference remains a fundamental challenge for all models.

5. Environment Construction and Error Bottlenecks

Environment construction—the synthesis of Dockerfiles and associated package/toolchain solving—constitutes the dominant bottleneck, accounting for ~36% of overall failures. Even the highest-ranked models achieve F2P below 40%; thus, model selection alone provides marginal benefit absent stronger reasoning about OS-level dependencies (Fu et al., 7 Dec 2025).

Key efficiency metrics are stable across models: input/output tokens and wall time exhibit wide variance, but RAM (~7.4–7.7 GB) and image size (~1 GB) do not. Commit rate (model self-confidence) weakly predicts actual task success, necessitating additional verification layers.

6. Positioning of Kimi-K2: Comparative Effectiveness

Kimi-K2-0905, as measured on Multi-Docker-Eval, is statistically indistinguishable in total F2P from DeepSeek-v3.1 (leader by 0.1 percentage points)—far exceeding GPT-OSS-20B (17%) and matching closed alternatives (Claude-Sonnet-4, GPT-5-Mini). This suggests that open-source architectures of the Kimi-K2 class have reached parity with proprietary models under current resource and prompt regimes, at least for environment-building automation.

Chain-of-thought Kimi-K2 variants enhance test script robustness but do not meaningfully decrease environment build errors. A plausible implication is that task decomposition and explicit reasoning about dependency resolution are needed to further increase F2P.

7. Recommendations for Automated SWE Pipelines

Multi-Docker-Eval analysis yields several actionable guidelines for deploying Kimi-K2 and similar models in pipeline construction:

1. Feedback-driven, Multi-agent Workflows: Use agents specialized for error diagnosis and repair; this increases F2P 3× over single-agent systems.
2. Environment-Configuration Memory Pools: Caching and reusing validated Dockerfiles lower redundant reasoning and resource churn.
3. Emphasize System-level Dependency Solvers: Future agent architectures must deeply integrate OS package and toolchain resolution.
4. Leverage Declarative Ecosystems Early: Focus on languages/platforms with standardized module/test systems to maximize initial effectiveness.
5. Monitor Wall-Time and Token Budgets: These are more variable—plan accordingly.
6. Supplement Self-Assessment with Verification: Commit rate does not reliably reflect true success; additional automated verifiers are required.

These principles define a design space for robust SWE automation: multi-agent, memory-augmented, language-aware systems, in which models such as Kimi-K2 can exploit their strengths but remain subject to environment construction limits (Fu et al., 7 Dec 2025).