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web/tailwind.config.js create mode 100644 web/tsconfig.app.json create mode 100644 web/tsconfig.json create mode 100644 web/tsconfig.node.json create mode 100644 web/vite.config.ts diff --git a/web/.gitignore b/web/.gitignore new file mode 100644 index 0000000..a547bf3 --- /dev/null +++ b/web/.gitignore @@ -0,0 +1,24 @@ +# Logs +logs +*.log +npm-debug.log* +yarn-debug.log* +yarn-error.log* +pnpm-debug.log* +lerna-debug.log* + +node_modules +dist +dist-ssr +*.local + +# Editor directories and files +.vscode/* +!.vscode/extensions.json +.idea +.DS_Store +*.suo +*.ntvs* +*.njsproj +*.sln +*.sw? diff --git a/web/README.md b/web/README.md new file mode 100644 index 0000000..8cf3381 --- /dev/null +++ b/web/README.md @@ -0,0 +1,95 @@ +each commit in the `main` on this repo triggers a benchmark execution. each benchmark execution runs few `${agent}:${provider?}/${model}` combinations. + +the current combinations: +``` +opencode:opencode/gpt-5-codex +opencode:opencode/claude-sonnet-4-5 +opencode:opencode/big-pickle +opencode:opencode/glm-4.6 +claude-code:claude-sonnet-4-5 +codex:gpt-5-codex +``` + +the list is dynamic and new combinations can be added to it. + +each of the combinations is measured across a handful of evals, currently 3, short-term goal is 25 and the long-term goal is 100 afaik. each eval consists of a mainstream github repo, a `from` commit that the agent starts with, a `to` commit that it should replicate but it does not know about, and few scores that measure how well did the agent replicate `to` commit. + +for example, the `api-signature` measures the combination's performmance in replicating the same architectural patterns that is apparent across the eval and specifically across the `to` commit diff. each score has 3 judges (claude sonnet, gpt-5-codex and kimi k2) that each rate by the binary decision 0 (FAIL) or 1 (PASS) and by aggregating all rates together, we produce a number fluid between 0 to 1. each judge produces a rationale as well. we also have an overall summary of an analysis over the rationales. +each score has its own description that should be shown to the user seeing it. + +the `weight` captures the score's importance in the final score. the list of scores is dynamic as well. each eval might have a different list of scores compared to another eval. + +```yaml +- repo: DataDog/datadog-lambda-python + from: 93d4a07fa61a4d4d2feec08e722505a9e0cc8657 + to: d7763789f262b2da228f8210509e302e6e510d0a + prompts: prompts/datadog-lambda-python.yaml + issues: [] + scores: + api-signature: + weight: 0.2 + logic-equivalence: + weight: 0.3 + integration-points: + weight: 0.2 + test-coverage: + weight: 0.2 + checks: + weight: 0.1 + args: + setup: + - python3 -m venv .venv + - ./.venv/bin/python -m pip install ".[dev]" + commands: + - ./.venv/bin/pytest -vv + - ./.venv/bin/flake8 datadog_lambda/ +``` + +any commit between `from` (exclusive) and `to` (inclusive) has a specific prompt (task) that the agent should act on. these prompts might change on a month to month basis by a maintainer so we avoid making the benchmarks too deterministic. the prompts change slightly so this does not make old benchmarks incomparable to the new benchmarks even though that's not the goal of OpenCode-bench because with this benchmark we're trying to compare agents & models to each other. + +```yaml +generated_at: 2025-11-04T01:45:24.286Z +prompts: + - commit: d7763789f262b2da228f8210509e302e6e510d0a + prompt: "Add a metric to track Lambda batch item failures. When Lambda functions return a response containing batch item failures (the batchItemFailures field), emit a count of how many items failed as an enhanced metric. This should only happen when enhanced metrics are enabled and the response structure is valid (response is a dict and batchItemFailures is a list). The metric should be submitted asynchronously with enhanced metrics tags. Integrate this into the wrapper's after-execution hook so it automatically captures the response from any wrapped Lambda handler. Follow existing codebase patterns for function signatures and integration points. Include comprehensive test coverage for various scenarios: responses with multiple failures, empty failure lists, missing batchItemFailures field, None responses, invalid field types, and disabled enhanced metrics. Also add integration tests in the wrapper test suite to verify the metric submission is called with the correct response object." +``` + +that's a general flow of how the benchmarking system works under the hood. + +--- + +now let's jump into what the UI might need to show to the user. + +since each commit has a benchmark execution with it, we let the user navigate between commit (we might want to show the date of each of those commits, but the commit hash looks cooler) and see the associated run. each of those runs has enough information about each agent and model across each eval. + +by default, the last run is shown on the home page as the main information. but that can change through navigating the commit history to show the benchmarks of a 1 month old run for instance. + +each run shows a per agent and a per model comparison/chart is formed by aggregating the scores of each combination per eval. so the user is able to see more specific information that is specific to a single eval. + +we store a per agent:model analysis summary as well that talks about how that agent behaved in a specific run. there's also a difference analysis summary that is per eval, which talks about how different agents/models behvaed in that eval. + +[scatter charts](https://recharts.github.io/en-US/examples/SimpleScatterChart/) are often used to demonstrate the performace of AI models compared to each other. + +we can use [radar charts](https://recharts.github.io/en-US/examples/SimpleRadarChart/) to show the scores (e.g. `api-signature`) of a specific agent:model combination. + +we can draw a difficulty line based on the 70th percentile to differentiate between top and average agents & models. + +across the history of benchmark runs, the difficulty line (vertical) can be a chart as well to demonstrate the volatility of the difficulty run. the more time passes, difficulty decreases since models get better over time, but by adding and adjusting evals, we can keep the difficulty high. that's kinda the point of having a difficulty chart. + +### error-analysis + +https://www.youtube.com/watch?v=e2i6JbU2R-s +https://hamel.dev/blog/posts/field-guide/index.html + +the community should help us carry out error analysis to improve the task generation capability (e.g. how much do those generated tasks resemble real-world user prompting) and the accuracy of the judges (e.g. catch the strong inconsistency between the judges). they should be able to see each task that led the agent to reproduce a specific diff, compare it to the ideal original diff (extracted from the evals the community gifts us) and see the judges response to that. + +this way (hopefully through the ui) they can suggest us improvments, or whether they think a judge output was too strict or lean or even prompt changes to the way judges behave or the way our planner generates tasks. + +basically, wherever we have an LLM as judge or a part of our workflow that relies on an LLM, we need a detailed error analysis 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"rsc": false, + "tsx": true, + "tailwind": { + "config": "tailwind.config.js", + "css": "src/index.css", + "baseColor": "neutral", + "cssVariables": true, + "prefix": "" + }, + "iconLibrary": "lucide", + "aliases": { + "components": "@/components", + "utils": "@/lib/utils", + "ui": "@/components/ui", + "lib": "@/lib", + "hooks": "@/hooks" + }, + "registries": {} +} diff --git a/web/data/README.md b/web/data/README.md new file mode 100644 index 0000000..d33f80d --- /dev/null +++ b/web/data/README.md @@ -0,0 +1,50 @@ +# Benchmark Data + +This directory contains artifacts fetched from GitHub Actions workflows. + +## Current Data + +**Commit**: `ea446df3c3284cf6be379486a9807d0c48ef7d78` +**Workflow Run**: `19057352801` - "Publish and Benchmark Preview Packages" +**Fetched**: See `metadata.json` for details + +## Artifacts Found + +### Benchmark Artifacts (12) +- `benchmark-opencode-opencode-claude-sonnet-4-5-prismicio-community-course-fizzi-next` +- `benchmark-opencode-opencode-big-pickle-prismicio-community-course-fizzi-next` +- `benchmark-claude-code-claude-sonnet-4-5-prismicio-community-course-fizzi-next` +- `benchmark-opencode-opencode-claude-sonnet-4-5-AlaminPu1007-algorithm-visualizer` +- `benchmark-opencode-opencode-claude-sonnet-4-5-DataDog-datadog-lambda-python` +- `benchmark-claude-code-claude-sonnet-4-5-DataDog-datadog-lambda-python` +- `benchmark-claude-code-claude-sonnet-4-5-AlaminPu1007-algorithm-visualizer` +- `benchmark-codex-gpt-5-codex-prismicio-community-course-fizzi-next` +- `benchmark-opencode-opencode-big-pickle-DataDog-datadog-lambda-python` +- `benchmark-codex-gpt-5-codex-AlaminPu1007-algorithm-visualizer` +- `benchmark-codex-gpt-5-codex-DataDog-datadog-lambda-python` +- `benchmark-opencode-opencode-big-pickle-AlaminPu1007-algorithm-visualizer` + +### Analysis Artifacts (3) +- `analysis-AlaminPu1007-algorithm-visualizer` +- `analysis-prismicio-community-course-fizzi-next` +- `analysis-DataDog-datadog-lambda-python` + +## Downloading Artifacts + +GitHub Actions artifacts require authentication. To download the artifacts, run: + +```bash +GITHUB_TOKEN=your_token_here bun scripts/fetch-artifacts.ts +``` + +You can create a GitHub Personal Access Token with `actions:read` permission at: +https://github.com/settings/tokens + +## Data Structure + +Each benchmark artifact contains: +- `benchmark.json` - Full evaluation run export with scores, episodes, and usage data + +Each analysis artifact contains: +- `analysis.txt` - Judge analysis text +- `analysis-info.json` - Metadata with eval info and job URL diff --git a/web/data/analysis-AlaminPu1007-algorithm-visualizer.zip b/web/data/analysis-AlaminPu1007-algorithm-visualizer.zip new file mode 100644 index 0000000000000000000000000000000000000000..dd03b4d1b68a48f95424834d45f0d3e970308325 GIT binary patch literal 3194 zcmZ{mS5On|5{4ta3IY;}gis;`q&GE)K0kfwD0AP{;-M3gQi zROwx$MT+zmYJ$MgITyckX3o2_vwN}cH{b5ngVLv@;sDV7j%wjY)_}jn^t-opvvu|H zbn=w+^7e|c)c54Az4T?z+^c)Z0xMWcgzqLxLMM}z%G*B0Lfp&UC~nC`NH<^Ofk&z= zCGx-hDmDEeUS3*C&nrHvCtp;#uE`vaBs|&aEh&k-8>+NuI%884A|C#HAPg8Kf+Q?U zP*-uP7>T}g9E4(!Ttd{<7SrQYX}d=hPv|A!V3LU21}IY9r!y>PW6)5agBlm#CooPm zRL#1sN+qiO1G0fA4NhWd!?||GVbGT~OvZ+`Y~g6_MS}n70;yz-MD7H>|4}7>ag!T2 zLL}{_5J~Hv*Jr>ZlRR!Nc!D%lQ%F#`AfdcC!Y+_z5mp#yt36&WpVSdYvy~|5fNJTw z#QHGi61HD!#EQaoieNP6q36}i#hl`Ys}#Q6m%I@mq3Q2|Y8+VEiZ0Z#6@xEuJ&w; zYmxJtqpgpb3aeKH?+DX27=Ox=SPiWp*T3v}>K-k}{5{-0tPjK^FPV*Q4bCQTMMvWD zM-05O$15heGeezK30-LecFp5J;GD8J?$7tUxNCNJ5=D`YGDDG)@cI{;RCVS^x4tpg{q9uieHGZv|!N1Z%&B*_$ z9O-B18SQm(hKirD8=!~MW|~>FCxC|wrr<|kr}ccjJ}&DpYvmA}n{Bp5uSM5YJ|CQ^ z{MZQTRb_JJv6E@;53e64&n(TaPrQ#>57oiUo$Vrz-`bdLv9sQIxt}-2MgN(@B2tLI zalMDSnDchm+{r5p^vJ1Q)2KntURRB?MO{0>-bHmaN=Hp}EgLiXpD^O!`{r>M-TiLUhU z`GA=!g-Vi^)t0MX4w0Gd6S5TTL@Co_@OUFc3OXmhBA*FJj%!8XXkj4WuOseQ0-wfq zS!$M%mZ>v6Ur)Zs#CMIIC-3o0Ui@-&n`gNbpga^glv9O$Z8AmfZTtb9F0FcR9@%7v zS3jl-{>7LhW|_`aEXB<`RYP^aY6EU0>~OGiU$bfL{AS8jPtHq~S{^emn@$;ly?dr# zvNCMXORys0(V6T4jrRv?CCc7?IB81oiyIzP17u5lm#ofYhi8ehMfEAoXZ*?;zODEU z5;4+RewDVv>7zm&99E)%<1YEw>hOU#SKU!2_ccF=ab*=8Ns9WA-~1S70ETdDl32SQ z3is2=#@PN;ZVMONVGU*Y%Vo^dI<8a zVb?da67pTO5YiMMG73LtHSt0?)C=~8b zN^3kwGJ>C3;$idS8Zr>PzBF1Rx28L7$VAHXQP_xA>;%#lKEBN+c`Yj+tmP5XC%myB zA?0Zvt0tDIGjBa&71^jD+@6NBX+LUqI51_|rq~>JH$v@Bm)>q#Zl_nYs z#qpB-#Qcm@RWH2Ocuvb;JIUyy!}8Q` zB;Z;a*~9{yVS0U2T^&gQ+hy+C2>*cx4k5@Pq=$Ix_5={QWaE^@YdpJ(&vOIa3+%12 z>!x0)9V93rY^Ua~Tl)xwjU*h&4!v*X$YR`Ux ztA=v7NMj~y6jX4ptUa*rWJg1GY4js~7<{IbCcABXVYyEC7lVnx(3cDyQ8!POkb8-3=Mmq%+?9={>c2_to`fB5hPat>s7xB>j;5p+$`U6>~{J{zN zB9^_N_|L~n7TF1#4j~3jQxC{Dg*<`rAdp+m@HBqXgNrVt&rj#Nbvt%iPj+TQ@?-Xc zR&67dSMkZPe!T6%fmh8XRrOZ`!jB;Z%RW+SCygvmZ{;N_Yd44cp9X-oJ^y)hJX9Rk z$-8=%N_wFHd2yX>yUp|Dj`Lf?lpr4K7XLR4GWL0eF_^@vWk1ir(NjysQ^ZEuFX9Wt zy9;|!aT;fx+Z>f=N#nHIt~#I#!)uhLrT&rEto1pWM#-#iAd||G#U#Ylygr`&*w2q% z!fv@wla&uuczm_@8W7Kb0==xaRHm9&BYdF6qc5V8?;?f-v#)0;&UNf4$G&iyMZOUt zj>upn@9d~}1I@>CKyyL}9vGl{*3mHeAg*SFrx%qpIqAu&qRYZMh;RqxOXM&`S4JggjC}IJpxX{xg+Pw=h!akx&wh8J& z1$vQ*_dY;Yz8e?I@V?0{*siB)Uq4C4=+=!YnYA^{R|Q4DtLSWZ4S#>N;L1CuaNQD z2dm@Br!ypJw*%Kxkik>(AZDecbHx?=h*sDAL9J1YOOXZc8+E7pZY87c zF3TaeUV5m2k!LE(Bl(MxQHSOYbze=@B!Fa%B5CrNEpnbr_auxiUF*=a&cOGIhDbQ* znNt8Z+XDZ?g}8IhGVjG?zr&2+HZ7YjEc_pZx61isSqlF~jVhT>WaZ zXun&Knlpw<7MC0zQhE=ZEMrg4uG% zv=Yic|Klouu}UCQP+jtp+Pj)7Hz#B7Hs3M;pL}tt<#?#P^>(9}rrq+Oc=pdS)?A^- zF~7gs{yS0n7bqks|7HEZ7yZNf{}mXJ8~msHoBaRR`S+`T4)I_8aq=<#>-_gDD1B<0 RKQk1+!}oVx`#l8!{0IFD_3r=x literal 0 HcmV?d00001 diff --git a/web/data/analysis-AlaminPu1007-algorithm-visualizer/analysis-info.json b/web/data/analysis-AlaminPu1007-algorithm-visualizer/analysis-info.json new file mode 100644 index 0000000..04bc688 --- /dev/null +++ b/web/data/analysis-AlaminPu1007-algorithm-visualizer/analysis-info.json @@ -0,0 +1,5 @@ +{ + "eval": "AlaminPu1007/algorithm-visualizer", + "safe": "AlaminPu1007-algorithm-visualizer", + "url": "https://github.com/sst/opencode-bench/actions/runs/19057352801/job/54432051186#step:7:0" +} diff --git a/web/data/analysis-AlaminPu1007-algorithm-visualizer/analysis.txt b/web/data/analysis-AlaminPu1007-algorithm-visualizer/analysis.txt new file mode 100644 index 0000000..097b69d --- /dev/null +++ b/web/data/analysis-AlaminPu1007-algorithm-visualizer/analysis.txt @@ -0,0 +1,119 @@ +# Cross-Agent Benchmark Analysis + +## Overall Performance Pattern + +The benchmark reveals remarkably **consistent performance** across the top three agents, with scores clustering tightly between 0.804-0.827 (final) and 0.813-0.870 (base). This narrow range suggests the task has well-defined success criteria that multiple approaches can satisfy effectively. + +### Performance Ranking +1. **claude-code (claude-sonnet-4-5)**: 0.827 final — Highest base score (0.870), lowest penalty +2. **opencode (big-pickle)**: 0.827 final — Tied for first, slightly higher penalty (0.043) +3. **opencode (claude-sonnet-4-5)**: 0.804 final — Higher penalty (0.054) dragged down final score +4. **codex (gpt-5-codex)**: 0.770 final — Technical failure prevented summary generation + +## Key Insights + +### 1. **Model Consistency vs. Scope Management** + +**Claude-code's advantage**: Achieved the highest base score (0.870) with minimal penalty (0.043) by maintaining **strict consistency** across episodes. The agent followed an identical approach in all three runs, with only Episode 1 showing minor exploratory variation (schema file search). + +**OpenCode's trade-off**: Both OpenCode variants achieved identical base scores to claude-code (0.870 for big-pickle, 0.858 for claude-sonnet-4-5) but incurred higher penalties. The big-pickle variant matched claude-code's penalty, while the claude-sonnet-4-5 variant showed **25% higher penalty** (0.054 vs 0.043), suggesting: +- Episode 2 was "more focused" (documentation only) +- Episodes 1 & 3 included "comprehensive UI refinements" +- **Inconsistent scope across episodes** likely triggered the penalty increase + +### 2. **Systematic Approach Patterns** + +All successful agents followed a similar workflow: +``` +Explore → Identify files → Update version → Document changes → Verify +``` + +**Differentiation emerged in execution details:** + +- **Claude-code**: Used `TodoWrite` for explicit task tracking, demonstrating structured project management +- **OpenCode (big-pickle)**: Emphasized verification with "linting and build verification commands" after changes +- **OpenCode (claude-sonnet-4-5)**: Focused on tool efficiency, using "Edit operations extensively" with occasional Read operations + +### 3. **The Episode Consistency Problem** + +The **most significant performance differentiator** was episode-to-episode consistency: + +- **Claude-code**: "All three episodes achieved identical outcomes" +- **OpenCode (claude-sonnet-4-5)**: "Episode 2 was more focused... Episodes 1 and 3 included comprehensive UI refinements" + +This suggests the evaluation system **penalizes scope variation** across episodes, even when individual episodes are successful. The 0.054 penalty for opencode/claude-sonnet-4-5 (vs 0.043 for others) directly correlates with its documented inconsistency. + +### 4. **Technical Failure Analysis** + +**Codex (gpt-5-codex)** encountered a critical infrastructure issue: "Body has already been used. It can only be read once." This is a **stream consumption error**, not an agent logic failure. Despite this, it achieved: +- Base score: 0.813 (only 7% below the leader) +- Same penalty structure: 0.043 + +This suggests the agent **completed the task successfully** but failed during post-processing/summary generation, indicating a **tooling issue** rather than capability gap. + +## Performance Gaps Analysis + +### Largest Delta: 0.057 (claude-code vs codex) +- **Primary factor**: Codex's base score (0.813 vs 0.870) — a 6.5% gap +- **Secondary factor**: Summary generation failure suggests incomplete observability +- **Implication**: The gap may be smaller than it appears if the technical issue masked successful work + +### Smallest Delta: 0.000 (claude-code vs opencode/big-pickle) +- Both achieved identical final scores through different paths +- Big-pickle emphasized **verification** (linting, builds) +- Claude-code emphasized **planning** (todo tracking) +- **Implication**: Multiple valid strategies exist for this task type + +## Agent Behavioral Tendencies + +### Safety vs. Completeness Trade-offs + +1. **Claude-code**: Prioritizes **reproducibility** — identical outcomes across episodes suggest conservative, proven approach +2. **OpenCode (big-pickle)**: Prioritizes **validation** — explicit mention of quality checks indicates defensive programming +3. **OpenCode (claude-sonnet-4-5)**: Prioritizes **efficiency** — variable scope suggests optimization for individual episode requirements + +### Tool Usage Patterns + +- **TodoWrite** (claude-code only): Explicit task management overhead, but may improve consistency +- **Verification commands** (big-pickle only): Quality assurance overhead, but catches errors early +- **Read-then-Edit** (claude-sonnet-4-5): More cautious file modification approach + +## Recommendations + +### 1. **For Evaluation System Improvement** +- **Clarify penalty criteria**: Document whether episode consistency is required or if adaptive scope is acceptable +- **Fix infrastructure issues**: Resolve stream consumption errors affecting summary generation +- **Add consistency metrics**: Explicitly measure and report episode-to-episode variance + +### 2. **For Agent Development** + +**To match claude-code's performance:** +- Implement explicit task tracking (TodoWrite or equivalent) +- Standardize episode workflows to minimize variation +- Front-load exploration to avoid mid-episode scope changes + +**To improve on current leaders:** +- Combine claude-code's consistency with big-pickle's verification +- Add automated testing to catch regressions +- Implement episode planning phase to determine optimal scope upfront + +### 3. **For Future Experiments** + +**Test hypothesis**: Does episode consistency matter? +- Run controlled experiment with intentionally variable vs. fixed scope +- Measure penalty impact independently + +**Investigate codex failure:** +- Reproduce stream consumption error +- Determine if it's model-specific or infrastructure-wide +- Assess true capability gap once technical issues resolved + +**Benchmark verification strategies:** +- Compare outcomes with/without explicit linting steps +- Measure correlation between verification overhead and final scores + +## Conclusion + +This benchmark reveals a **mature evaluation environment** where multiple agents achieve near-identical results through different optimization strategies. The 2.3% spread between top performers suggests diminishing returns on further optimization without changing the task complexity. + +The most actionable insight: **Episode consistency appears more valuable than per-episode optimization**. Claude-code's success stems from reproducible execution, not superior individual episode performance. 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Recommendations + +### For Evaluation System Improvements +1. **Investigate penalty calibration**: Why does higher base performance correlate with higher penalties? Consider whether this discourages comprehensive solutions. + +2. **Episode isolation verification**: The "already complete" issue in big-pickle runs suggests episodes may not be properly isolated. Verify each episode starts from clean state. + +3. **Clarify scoring criteria**: Document whether creating new test files vs. extending existing ones affects scores, and why. + +4. **Tool availability standardization**: Ensure all agents have access to expected tools (e.g., `rg`) or document fallback strategies. + +### For Agent Development +1. **Pattern recognition training**: GPT-5-codex showed "strong pattern recognition" but still scored lowest—investigate whether this capability translates to correct implementation decisions. + +2. **Exploration efficiency**: Implement guardrails to prevent analysis paralysis (e.g., GPT-5-codex's 183-action Episode 2). Consider action budgets or progress metrics. + +3. **Codebase structure understanding**: Train agents to extend existing test files rather than creating new ones, following project conventions. + +4. **Integration point precision**: Top performers specified exact line numbers and integration rationale—this should be a training target. + +### For Future Benchmarks +1. **Add complexity tiers**: This task may be too straightforward for Claude Sonnet 4-5, causing ceiling effects. Consider more challenging scenarios. + +2. **Measure efficiency**: Track actions-per-episode and correlate with quality to identify optimal exploration/execution ratios. + +3. **Test edge case handling**: The summaries mention validation logic but don't detail how agents handle malformed responses—add specific edge case scenarios to evaluation. + +4. **Cross-episode learning**: Evaluate whether agents improve from Episode 1→3 or simply repeat the same approach (as most did here). + +--- + +## Conclusion + +This benchmark reveals a **model-dominated landscape** where Claude Sonnet 4-5's reasoning capabilities drive 70-80% of performance variance. However, the **penalty structure anomaly** and **episode isolation concerns** suggest evaluation system refinements could provide clearer signals. 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The performance gap between the highest scorer (codex at 0.268) and lowest (opencode/big-pickle at 0.157) reveals fundamental differences in agent behavior, with codex's investigative approach paradoxically earning higher scores despite incomplete implementation. + +## Systematic Patterns + +### 1. **Investigation vs. Execution Trade-off** +The most striking pattern is the inverse relationship between exploration depth and task completion: + +- **Codex (0.268)**: Spent 60-80+ actions investigating authentication mechanisms and migration APIs but **never created the replacement script**. Achieved highest score despite incomplete work. +- **Other agents (0.157-0.190)**: All successfully completed the full task (deleted folders, created scripts, updated documentation) but scored lower. + +This suggests the evaluation system may be **rewarding process over outcomes**, or that codex's extensive exploration generated more valuable intermediate artifacts that scored well in the base metric. + +### 2. **Tool Selection Consistency** +Clear agent personality differences emerged: + +- **Codex**: Heavy bash command usage (`ls`, `cat`, `grep`, `sed`), Python scripts for file operations +- **OpenCode/Claude-Sonnet-4-5 (0.190)**: Specialized file operation tools (Read, Edit, Write, Remove) - explicitly noted as "best practices" +- **Claude-Code (0.172)**: Mixed approach with Bash for deletion, structured tools (Glob, TodoWrite) for tracking +- **OpenCode/Big-Pickle (0.157)**: Bash + EditFile combination + +The highest-scoring agent (codex) used the most "manual" approach, while the agent explicitly following "best practices" (opencode/claude-sonnet-4-5) scored in the middle. + +### 3. **Completion Rates** +- **Codex**: 0/3 episodes completed the script replacement +- **All others**: 3/3 episodes fully completed + +Despite 100% failure rate on the primary objective, codex scored 41% higher than the lowest performer. + +## Performance Gaps & Explanations + +### The Codex Paradox +Codex's 0.268 score with incomplete work suggests several possibilities: + +1. **Base metric rewards exploration**: The 0.344 base score (highest by far) indicates the evaluation heavily weights investigative actions +2. **Penalty calculation**: Codex's 0.077 penalty is the highest, but insufficient to offset the base advantage +3. **Quality over completion**: Deep API understanding may generate higher-quality intermediate outputs than rushed implementations + +### Penalty Analysis +Penalties don't correlate with completion: +- **Codex (incomplete)**: 0.077 penalty +- **OpenCode/Claude-Sonnet-4-5 (complete)**: 0.054 penalty +- **Claude-Code (complete)**: 0.040 penalty +- **OpenCode/Big-Pickle (complete)**: 0.032 penalty + +The incomplete run received the **highest penalty**, yet still won overall. This suggests penalties are measuring something other than task completion (possibly action efficiency or error rates). + +## Agent Behavioral Tendencies + +### Codex: The Over-Researcher +- **Strength**: Thorough understanding of authentication flows, migration APIs, TypeScript definitions +- **Weakness**: Analysis paralysis—consumed action budget before implementation +- **Pattern**: Prioritizes comprehension over execution +- **Optimization**: "Understanding over implementation" approach + +### OpenCode/Claude-Sonnet-4-5: The Methodical Completer +- **Strength**: Consistent 4-step workflow, production-ready implementations, comprehensive documentation +- **Weakness**: May lack the deep exploration that scores well in base metrics +- **Pattern**: Process-oriented, follows best practices explicitly +- **Optimization**: Completeness and user experience + +### Claude-Code: The Balanced Executor +- **Strength**: Structured tracking (TodoWrite), streamlined workflows in Episodes 1 & 3 +- **Weakness**: Episode 2 bloat (126 actions vs. 67/99) suggests inconsistent efficiency +- **Pattern**: Middle-ground between exploration and execution +- **Optimization**: Task completion with progress tracking + +### OpenCode/Big-Pickle: The Pragmatic Implementer +- **Strength**: Consistent completion, clear documentation updates +- **Weakness**: Lowest scores suggest minimal exploration or lower-quality artifacts +- **Pattern**: Direct path to working solution +- **Optimization**: Functional outcomes with minimal overhead + +## Notable Insights + +### 1. **The Completion Penalty** +Agents that finished the task scored **lower** than the agent that didn't. This is either: +- A feature: Rewarding thorough research over hasty implementation +- A bug: Misaligned incentives in the evaluation system + +### 2. **Documentation Correlation** +All completing agents updated README.md with onboarding instructions, but this didn't differentiate scores significantly (0.157-0.190 range). Documentation quality appears less weighted than exploration depth. + +### 3. **File Naming Variations** +Minor inconsistencies appeared even within single agents: +- Claude-Code: `content-setup.ts` vs `setup-content.ts` +- OpenCode/Big-Pickle: Similar variations + +These variations had no apparent score impact, suggesting the evaluation focuses on semantic correctness over naming conventions. + +### 4. **Episode Consistency** +All agents showed high consistency across episodes (3/3 similar approaches), indicating stable behavioral patterns rather than random variation. + +## Recommendations + +### For Evaluation System Improvement: +1. **Rebalance base vs. penalty metrics**: If task completion is the goal, incomplete runs shouldn't score highest. Consider adding explicit completion bonuses. +2. **Clarify optimization target**: Is the system rewarding research quality, implementation quality, or both? Make this explicit in scoring. +3. **Add completion gates**: Consider requiring minimum task completion thresholds before awarding high base scores. +4. **Investigate base metric composition**: What specifically drove codex's 0.344 base score? If it's action count or exploration breadth, this may incentivize inefficiency. + +### For Agent Development: +1. **Codex**: Implement action budgeting—reserve 30-40% of actions for implementation after exploration phase +2. **OpenCode/Claude-Sonnet-4-5**: Add exploratory phase before implementation to potentially boost base scores +3. **Claude-Code**: Investigate Episode 2 bloat—identify what caused 2x action count and eliminate inefficiencies +4. **OpenCode/Big-Pickle**: Consider adding intermediate exploration steps to generate richer artifacts + +### For Future Experiments: +1. **Test exploration caps**: Run codex with forced implementation deadlines (e.g., "must begin writing script by action 40") +2. **Hybrid approaches**: Test agents that combine codex's exploration depth with other agents' completion rates +3. **Metric ablation**: Run evaluations with base-only and penalty-only scoring to understand component contributions +4. **Task decomposition**: Split evaluation into "research phase" and "implementation phase" to reward both behaviors appropriately + +## Conclusion +This benchmark reveals a fundamental tension between **depth of understanding** and **speed of execution**. The current evaluation system appears to favor the former, creating a counterintuitive scenario where incomplete work scores highest. Whether this is intentional (rewarding thorough analysis) or a misalignment (incomplete tasks shouldn't win) depends on the evaluation's intended purpose. 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"size": 4148, + "createdAt": "2025-11-04T04:38:01Z", + "expired": false + } + ], + "fetchedAt": "2025-11-05T17:57:20.022Z" +} \ No newline at end of file diff --git a/web/eslint.config.js b/web/eslint.config.js new file mode 100644 index 0000000..b19330b --- /dev/null +++ b/web/eslint.config.js @@ -0,0 +1,23 @@ +import js from '@eslint/js' +import globals from 'globals' +import reactHooks from 'eslint-plugin-react-hooks' +import reactRefresh from 'eslint-plugin-react-refresh' +import tseslint from 'typescript-eslint' +import { defineConfig, globalIgnores } from 'eslint/config' + +export default defineConfig([ + globalIgnores(['dist']), + { + files: ['**/*.{ts,tsx}'], + extends: [ + js.configs.recommended, + tseslint.configs.recommended, + reactHooks.configs['recommended-latest'], + reactRefresh.configs.vite, + ], + languageOptions: { + ecmaVersion: 2020, + globals: globals.browser, + }, + }, +]) diff --git a/web/index.html b/web/index.html new file mode 100644 index 0000000..af88f03 --- /dev/null +++ b/web/index.html @@ -0,0 +1,13 @@ + + + + + + + web + + +

+ + + diff --git a/web/package.json b/web/package.json new file mode 100644 index 0000000..e6f38b7 --- /dev/null +++ b/web/package.json @@ -0,0 +1,40 @@ +{ + "name": "web", + "private": true, + "version": "0.0.0", + "type": "module", + "scripts": { + "dev": "vite", + "build": "tsc -b && vite build", + "lint": "eslint .", + "preview": "vite preview" + }, + "dependencies": { + "class-variance-authority": "^0.7.1", + "clsx": "^2.1.1", + "lucide-react": "^0.552.0", + "react": "^19.1.1", + "react-dom": "^19.1.1", + "recharts": "2.15.4", + "tailwind-merge": "^3.3.1", + "tailwindcss-animate": "^1.0.7" + }, + "devDependencies": { + "@eslint/js": "^9.36.0", + "@tailwindcss/postcss": "^4.1.16", + "@types/node": "^24.6.0", + "@types/react": "^19.1.16", + "@types/react-dom": "^19.1.9", + "@vitejs/plugin-react": "^5.0.4", + "autoprefixer": "^10.4.21", + "eslint": "^9.36.0", + "eslint-plugin-react-hooks": "^5.2.0", + "eslint-plugin-react-refresh": "^0.4.22", + "globals": "^16.4.0", + "postcss": "^8.5.6", + "tailwindcss": "^4.1.16", + "typescript": "~5.9.3", + "typescript-eslint": "^8.45.0", + "vite": "^7.1.7" + } +} diff --git a/web/postcss.config.js b/web/postcss.config.js new file mode 100644 index 0000000..c621ea1 --- /dev/null +++ b/web/postcss.config.js @@ -0,0 +1,9 @@ +import tailwindcss from '@tailwindcss/postcss'; +import autoprefixer from 'autoprefixer'; + +export default { + plugins: [ + tailwindcss, + autoprefixer, + ], +} diff --git a/web/public/data/README.md b/web/public/data/README.md new file mode 100644 index 0000000..d33f80d --- /dev/null +++ b/web/public/data/README.md @@ -0,0 +1,50 @@ +# Benchmark Data + +This directory contains artifacts fetched from GitHub Actions workflows. + +## Current Data + +**Commit**: `ea446df3c3284cf6be379486a9807d0c48ef7d78` +**Workflow Run**: `19057352801` - "Publish and Benchmark Preview Packages" +**Fetched**: See `metadata.json` for details + +## Artifacts Found + +### Benchmark Artifacts (12) +- `benchmark-opencode-opencode-claude-sonnet-4-5-prismicio-community-course-fizzi-next` +- `benchmark-opencode-opencode-big-pickle-prismicio-community-course-fizzi-next` +- `benchmark-claude-code-claude-sonnet-4-5-prismicio-community-course-fizzi-next` +- `benchmark-opencode-opencode-claude-sonnet-4-5-AlaminPu1007-algorithm-visualizer` +- `benchmark-opencode-opencode-claude-sonnet-4-5-DataDog-datadog-lambda-python` +- `benchmark-claude-code-claude-sonnet-4-5-DataDog-datadog-lambda-python` +- `benchmark-claude-code-claude-sonnet-4-5-AlaminPu1007-algorithm-visualizer` +- `benchmark-codex-gpt-5-codex-prismicio-community-course-fizzi-next` +- `benchmark-opencode-opencode-big-pickle-DataDog-datadog-lambda-python` +- `benchmark-codex-gpt-5-codex-AlaminPu1007-algorithm-visualizer` +- `benchmark-codex-gpt-5-codex-DataDog-datadog-lambda-python` +- `benchmark-opencode-opencode-big-pickle-AlaminPu1007-algorithm-visualizer` + +### Analysis Artifacts (3) +- `analysis-AlaminPu1007-algorithm-visualizer` +- `analysis-prismicio-community-course-fizzi-next` +- `analysis-DataDog-datadog-lambda-python` + +## Downloading Artifacts + +GitHub Actions artifacts require authentication. To download the artifacts, run: + +```bash +GITHUB_TOKEN=your_token_here bun scripts/fetch-artifacts.ts +``` + +You can create a GitHub Personal Access Token with `actions:read` permission at: +https://github.com/settings/tokens + +## Data Structure + +Each benchmark artifact contains: +- `benchmark.json` - Full evaluation run export with scores, episodes, and usage data + +Each analysis artifact contains: +- `analysis.txt` - Judge analysis text +- `analysis-info.json` - Metadata with eval info and job URL diff --git a/web/public/data/analysis-AlaminPu1007-algorithm-visualizer.zip b/web/public/data/analysis-AlaminPu1007-algorithm-visualizer.zip new file mode 100644 index 0000000000000000000000000000000000000000..dd03b4d1b68a48f95424834d45f0d3e970308325 GIT binary patch literal 3194 zcmZ{mS5On|5{4ta3IY;}gis;`q&GE)K0kfwD0AP{;-M3gQi zROwx$MT+zmYJ$MgITyckX3o2_vwN}cH{b5ngVLv@;sDV7j%wjY)_}jn^t-opvvu|H zbn=w+^7e|c)c54Az4T?z+^c)Z0xMWcgzqLxLMM}z%G*B0Lfp&UC~nC`NH<^Ofk&z= zCGx-hDmDEeUS3*C&nrHvCtp;#uE`vaBs|&aEh&k-8>+NuI%884A|C#HAPg8Kf+Q?U zP*-uP7>T}g9E4(!Ttd{<7SrQYX}d=hPv|A!V3LU21}IY9r!y>PW6)5agBlm#CooPm zRL#1sN+qiO1G0fA4NhWd!?||GVbGT~OvZ+`Y~g6_MS}n70;yz-MD7H>|4}7>ag!T2 zLL}{_5J~Hv*Jr>ZlRR!Nc!D%lQ%F#`AfdcC!Y+_z5mp#yt36&WpVSdYvy~|5fNJTw z#QHGi61HD!#EQaoieNP6q36}i#hl`Ys}#Q6m%I@mq3Q2|Y8+VEiZ0Z#6@xEuJ&w; zYmxJtqpgpb3aeKH?+DX27=Ox=SPiWp*T3v}>K-k}{5{-0tPjK^FPV*Q4bCQTMMvWD zM-05O$15heGeezK30-LecFp5J;GD8J?$7tUxNCNJ5=D`YGDDG)@cI{;RCVS^x4tpg{q9uieHGZv|!N1Z%&B*_$ z9O-B18SQm(hKirD8=!~MW|~>FCxC|wrr<|kr}ccjJ}&DpYvmA}n{Bp5uSM5YJ|CQ^ z{MZQTRb_JJv6E@;53e64&n(TaPrQ#>57oiUo$Vrz-`bdLv9sQIxt}-2MgN(@B2tLI zalMDSnDchm+{r5p^vJ1Q)2KntURRB?MO{0>-bHmaN=Hp}EgLiXpD^O!`{r>M-TiLUhU z`GA=!g-Vi^)t0MX4w0Gd6S5TTL@Co_@OUFc3OXmhBA*FJj%!8XXkj4WuOseQ0-wfq zS!$M%mZ>v6Ur)Zs#CMIIC-3o0Ui@-&n`gNbpga^glv9O$Z8AmfZTtb9F0FcR9@%7v zS3jl-{>7LhW|_`aEXB<`RYP^aY6EU0>~OGiU$bfL{AS8jPtHq~S{^emn@$;ly?dr# zvNCMXORys0(V6T4jrRv?CCc7?IB81oiyIzP17u5lm#ofYhi8ehMfEAoXZ*?;zODEU z5;4+RewDVv>7zm&99E)%<1YEw>hOU#SKU!2_ccF=ab*=8Ns9WA-~1S70ETdDl32SQ z3is2=#@PN;ZVMONVGU*Y%Vo^dI<8a zVb?da67pTO5YiMMG73LtHSt0?)C=~8b zN^3kwGJ>C3;$idS8Zr>PzBF1Rx28L7$VAHXQP_xA>;%#lKEBN+c`Yj+tmP5XC%myB zA?0Zvt0tDIGjBa&71^jD+@6NBX+LUqI51_|rq~>JH$v@Bm)>q#Zl_nYs z#qpB-#Qcm@RWH2Ocuvb;JIUyy!}8Q` zB;Z;a*~9{yVS0U2T^&gQ+hy+C2>*cx4k5@Pq=$Ix_5={QWaE^@YdpJ(&vOIa3+%12 z>!x0)9V93rY^Ua~Tl)xwjU*h&4!v*X$YR`Ux ztA=v7NMj~y6jX4ptUa*rWJg1GY4js~7<{IbCcABXVYyEC7lVnx(3cDyQ8!POkb8-3=Mmq%+?9={>c2_to`fB5hPat>s7xB>j;5p+$`U6>~{J{zN zB9^_N_|L~n7TF1#4j~3jQxC{Dg*<`rAdp+m@HBqXgNrVt&rj#Nbvt%iPj+TQ@?-Xc zR&67dSMkZPe!T6%fmh8XRrOZ`!jB;Z%RW+SCygvmZ{;N_Yd44cp9X-oJ^y)hJX9Rk z$-8=%N_wFHd2yX>yUp|Dj`Lf?lpr4K7XLR4GWL0eF_^@vWk1ir(NjysQ^ZEuFX9Wt zy9;|!aT;fx+Z>f=N#nHIt~#I#!)uhLrT&rEto1pWM#-#iAd||G#U#Ylygr`&*w2q% z!fv@wla&uuczm_@8W7Kb0==xaRHm9&BYdF6qc5V8?;?f-v#)0;&UNf4$G&iyMZOUt zj>upn@9d~}1I@>CKyyL}9vGl{*3mHeAg*SFrx%qpIqAu&qRYZMh;RqxOXM&`S4JggjC}IJpxX{xg+Pw=h!akx&wh8J& z1$vQ*_dY;Yz8e?I@V?0{*siB)Uq4C4=+=!YnYA^{R|Q4DtLSWZ4S#>N;L1CuaNQD z2dm@Br!ypJw*%Kxkik>(AZDecbHx?=h*sDAL9J1YOOXZc8+E7pZY87c zF3TaeUV5m2k!LE(Bl(MxQHSOYbze=@B!Fa%B5CrNEpnbr_auxiUF*=a&cOGIhDbQ* znNt8Z+XDZ?g}8IhGVjG?zr&2+HZ7YjEc_pZx61isSqlF~jVhT>WaZ zXun&Knlpw<7MC0zQhE=ZEMrg4uG% zv=Yic|Klouu}UCQP+jtp+Pj)7Hz#B7Hs3M;pL}tt<#?#P^>(9}rrq+Oc=pdS)?A^- zF~7gs{yS0n7bqks|7HEZ7yZNf{}mXJ8~msHoBaRR`S+`T4)I_8aq=<#>-_gDD1B<0 RKQk1+!}oVx`#l8!{0IFD_3r=x literal 0 HcmV?d00001 diff --git a/web/public/data/analysis-AlaminPu1007-algorithm-visualizer/analysis-info.json b/web/public/data/analysis-AlaminPu1007-algorithm-visualizer/analysis-info.json new file mode 100644 index 0000000..04bc688 --- /dev/null +++ b/web/public/data/analysis-AlaminPu1007-algorithm-visualizer/analysis-info.json @@ -0,0 +1,5 @@ +{ + "eval": "AlaminPu1007/algorithm-visualizer", + "safe": "AlaminPu1007-algorithm-visualizer", + "url": "https://github.com/sst/opencode-bench/actions/runs/19057352801/job/54432051186#step:7:0" +} diff --git a/web/public/data/analysis-AlaminPu1007-algorithm-visualizer/analysis.txt b/web/public/data/analysis-AlaminPu1007-algorithm-visualizer/analysis.txt new file mode 100644 index 0000000..097b69d --- /dev/null +++ b/web/public/data/analysis-AlaminPu1007-algorithm-visualizer/analysis.txt @@ -0,0 +1,119 @@ +# Cross-Agent Benchmark Analysis + +## Overall Performance Pattern + +The benchmark reveals remarkably **consistent performance** across the top three agents, with scores clustering tightly between 0.804-0.827 (final) and 0.813-0.870 (base). This narrow range suggests the task has well-defined success criteria that multiple approaches can satisfy effectively. + +### Performance Ranking +1. **claude-code (claude-sonnet-4-5)**: 0.827 final — Highest base score (0.870), lowest penalty +2. **opencode (big-pickle)**: 0.827 final — Tied for first, slightly higher penalty (0.043) +3. **opencode (claude-sonnet-4-5)**: 0.804 final — Higher penalty (0.054) dragged down final score +4. **codex (gpt-5-codex)**: 0.770 final — Technical failure prevented summary generation + +## Key Insights + +### 1. **Model Consistency vs. Scope Management** + +**Claude-code's advantage**: Achieved the highest base score (0.870) with minimal penalty (0.043) by maintaining **strict consistency** across episodes. The agent followed an identical approach in all three runs, with only Episode 1 showing minor exploratory variation (schema file search). + +**OpenCode's trade-off**: Both OpenCode variants achieved identical base scores to claude-code (0.870 for big-pickle, 0.858 for claude-sonnet-4-5) but incurred higher penalties. The big-pickle variant matched claude-code's penalty, while the claude-sonnet-4-5 variant showed **25% higher penalty** (0.054 vs 0.043), suggesting: +- Episode 2 was "more focused" (documentation only) +- Episodes 1 & 3 included "comprehensive UI refinements" +- **Inconsistent scope across episodes** likely triggered the penalty increase + +### 2. **Systematic Approach Patterns** + +All successful agents followed a similar workflow: +``` +Explore → Identify files → Update version → Document changes → Verify +``` + +**Differentiation emerged in execution details:** + +- **Claude-code**: Used `TodoWrite` for explicit task tracking, demonstrating structured project management +- **OpenCode (big-pickle)**: Emphasized verification with "linting and build verification commands" after changes +- **OpenCode (claude-sonnet-4-5)**: Focused on tool efficiency, using "Edit operations extensively" with occasional Read operations + +### 3. **The Episode Consistency Problem** + +The **most significant performance differentiator** was episode-to-episode consistency: + +- **Claude-code**: "All three episodes achieved identical outcomes" +- **OpenCode (claude-sonnet-4-5)**: "Episode 2 was more focused... Episodes 1 and 3 included comprehensive UI refinements" + +This suggests the evaluation system **penalizes scope variation** across episodes, even when individual episodes are successful. The 0.054 penalty for opencode/claude-sonnet-4-5 (vs 0.043 for others) directly correlates with its documented inconsistency. + +### 4. **Technical Failure Analysis** + +**Codex (gpt-5-codex)** encountered a critical infrastructure issue: "Body has already been used. It can only be read once." This is a **stream consumption error**, not an agent logic failure. Despite this, it achieved: +- Base score: 0.813 (only 7% below the leader) +- Same penalty structure: 0.043 + +This suggests the agent **completed the task successfully** but failed during post-processing/summary generation, indicating a **tooling issue** rather than capability gap. + +## Performance Gaps Analysis + +### Largest Delta: 0.057 (claude-code vs codex) +- **Primary factor**: Codex's base score (0.813 vs 0.870) — a 6.5% gap +- **Secondary factor**: Summary generation failure suggests incomplete observability +- **Implication**: The gap may be smaller than it appears if the technical issue masked successful work + +### Smallest Delta: 0.000 (claude-code vs opencode/big-pickle) +- Both achieved identical final scores through different paths +- Big-pickle emphasized **verification** (linting, builds) +- Claude-code emphasized **planning** (todo tracking) +- **Implication**: Multiple valid strategies exist for this task type + +## Agent Behavioral Tendencies + +### Safety vs. Completeness Trade-offs + +1. **Claude-code**: Prioritizes **reproducibility** — identical outcomes across episodes suggest conservative, proven approach +2. **OpenCode (big-pickle)**: Prioritizes **validation** — explicit mention of quality checks indicates defensive programming +3. **OpenCode (claude-sonnet-4-5)**: Prioritizes **efficiency** — variable scope suggests optimization for individual episode requirements + +### Tool Usage Patterns + +- **TodoWrite** (claude-code only): Explicit task management overhead, but may improve consistency +- **Verification commands** (big-pickle only): Quality assurance overhead, but catches errors early +- **Read-then-Edit** (claude-sonnet-4-5): More cautious file modification approach + +## Recommendations + +### 1. **For Evaluation System Improvement** +- **Clarify penalty criteria**: Document whether episode consistency is required or if adaptive scope is acceptable +- **Fix infrastructure issues**: Resolve stream consumption errors affecting summary generation +- **Add consistency metrics**: Explicitly measure and report episode-to-episode variance + +### 2. **For Agent Development** + +**To match claude-code's performance:** +- Implement explicit task tracking (TodoWrite or equivalent) +- Standardize episode workflows to minimize variation +- Front-load exploration to avoid mid-episode scope changes + +**To improve on current leaders:** +- Combine claude-code's consistency with big-pickle's verification +- Add automated testing to catch regressions +- Implement episode planning phase to determine optimal scope upfront + +### 3. **For Future Experiments** + +**Test hypothesis**: Does episode consistency matter? +- Run controlled experiment with intentionally variable vs. fixed scope +- Measure penalty impact independently + +**Investigate codex failure:** +- Reproduce stream consumption error +- Determine if it's model-specific or infrastructure-wide +- Assess true capability gap once technical issues resolved + +**Benchmark verification strategies:** +- Compare outcomes with/without explicit linting steps +- Measure correlation between verification overhead and final scores + +## Conclusion + +This benchmark reveals a **mature evaluation environment** where multiple agents achieve near-identical results through different optimization strategies. The 2.3% spread between top performers suggests diminishing returns on further optimization without changing the task complexity. + +The most actionable insight: **Episode consistency appears more valuable than per-episode optimization**. Claude-code's success stems from reproducible execution, not superior individual episode performance. 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Recommendations + +### For Evaluation System Improvements +1. **Investigate penalty calibration**: Why does higher base performance correlate with higher penalties? Consider whether this discourages comprehensive solutions. + +2. **Episode isolation verification**: The "already complete" issue in big-pickle runs suggests episodes may not be properly isolated. Verify each episode starts from clean state. + +3. **Clarify scoring criteria**: Document whether creating new test files vs. extending existing ones affects scores, and why. + +4. **Tool availability standardization**: Ensure all agents have access to expected tools (e.g., `rg`) or document fallback strategies. + +### For Agent Development +1. **Pattern recognition training**: GPT-5-codex showed "strong pattern recognition" but still scored lowest—investigate whether this capability translates to correct implementation decisions. + +2. **Exploration efficiency**: Implement guardrails to prevent analysis paralysis (e.g., GPT-5-codex's 183-action Episode 2). Consider action budgets or progress metrics. + +3. **Codebase structure understanding**: Train agents to extend existing test files rather than creating new ones, following project conventions. + +4. **Integration point precision**: Top performers specified exact line numbers and integration rationale—this should be a training target. + +### For Future Benchmarks +1. **Add complexity tiers**: This task may be too straightforward for Claude Sonnet 4-5, causing ceiling effects. Consider more challenging scenarios. + +2. **Measure efficiency**: Track actions-per-episode and correlate with quality to identify optimal exploration/execution ratios. + +3. **Test edge case handling**: The summaries mention validation logic but don't detail how agents handle malformed responses—add specific edge case scenarios to evaluation. + +4. **Cross-episode learning**: Evaluate whether agents improve from Episode 1→3 or simply repeat the same approach (as most did here). + +--- + +## Conclusion + +This benchmark reveals a **model-dominated landscape** where Claude Sonnet 4-5's reasoning capabilities drive 70-80% of performance variance. However, the **penalty structure anomaly** and **episode isolation concerns** suggest evaluation system refinements could provide clearer signals. 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The performance gap between the highest scorer (codex at 0.268) and lowest (opencode/big-pickle at 0.157) reveals fundamental differences in agent behavior, with codex's investigative approach paradoxically earning higher scores despite incomplete implementation. + +## Systematic Patterns + +### 1. **Investigation vs. Execution Trade-off** +The most striking pattern is the inverse relationship between exploration depth and task completion: + +- **Codex (0.268)**: Spent 60-80+ actions investigating authentication mechanisms and migration APIs but **never created the replacement script**. Achieved highest score despite incomplete work. +- **Other agents (0.157-0.190)**: All successfully completed the full task (deleted folders, created scripts, updated documentation) but scored lower. + +This suggests the evaluation system may be **rewarding process over outcomes**, or that codex's extensive exploration generated more valuable intermediate artifacts that scored well in the base metric. + +### 2. **Tool Selection Consistency** +Clear agent personality differences emerged: + +- **Codex**: Heavy bash command usage (`ls`, `cat`, `grep`, `sed`), Python scripts for file operations +- **OpenCode/Claude-Sonnet-4-5 (0.190)**: Specialized file operation tools (Read, Edit, Write, Remove) - explicitly noted as "best practices" +- **Claude-Code (0.172)**: Mixed approach with Bash for deletion, structured tools (Glob, TodoWrite) for tracking +- **OpenCode/Big-Pickle (0.157)**: Bash + EditFile combination + +The highest-scoring agent (codex) used the most "manual" approach, while the agent explicitly following "best practices" (opencode/claude-sonnet-4-5) scored in the middle. + +### 3. **Completion Rates** +- **Codex**: 0/3 episodes completed the script replacement +- **All others**: 3/3 episodes fully completed + +Despite 100% failure rate on the primary objective, codex scored 41% higher than the lowest performer. + +## Performance Gaps & Explanations + +### The Codex Paradox +Codex's 0.268 score with incomplete work suggests several possibilities: + +1. **Base metric rewards exploration**: The 0.344 base score (highest by far) indicates the evaluation heavily weights investigative actions +2. **Penalty calculation**: Codex's 0.077 penalty is the highest, but insufficient to offset the base advantage +3. **Quality over completion**: Deep API understanding may generate higher-quality intermediate outputs than rushed implementations + +### Penalty Analysis +Penalties don't correlate with completion: +- **Codex (incomplete)**: 0.077 penalty +- **OpenCode/Claude-Sonnet-4-5 (complete)**: 0.054 penalty +- **Claude-Code (complete)**: 0.040 penalty +- **OpenCode/Big-Pickle (complete)**: 0.032 penalty + +The incomplete run received the **highest penalty**, yet still won overall. This suggests penalties are measuring something other than task completion (possibly action efficiency or error rates). + +## Agent Behavioral Tendencies + +### Codex: The Over-Researcher +- **Strength**: Thorough understanding of authentication flows, migration APIs, TypeScript definitions +- **Weakness**: Analysis paralysis—consumed action budget before implementation +- **Pattern**: Prioritizes comprehension over execution +- **Optimization**: "Understanding over implementation" approach + +### OpenCode/Claude-Sonnet-4-5: The Methodical Completer +- **Strength**: Consistent 4-step workflow, production-ready implementations, comprehensive documentation +- **Weakness**: May lack the deep exploration that scores well in base metrics +- **Pattern**: Process-oriented, follows best practices explicitly +- **Optimization**: Completeness and user experience + +### Claude-Code: The Balanced Executor +- **Strength**: Structured tracking (TodoWrite), streamlined workflows in Episodes 1 & 3 +- **Weakness**: Episode 2 bloat (126 actions vs. 67/99) suggests inconsistent efficiency +- **Pattern**: Middle-ground between exploration and execution +- **Optimization**: Task completion with progress tracking + +### OpenCode/Big-Pickle: The Pragmatic Implementer +- **Strength**: Consistent completion, clear documentation updates +- **Weakness**: Lowest scores suggest minimal exploration or lower-quality artifacts +- **Pattern**: Direct path to working solution +- **Optimization**: Functional outcomes with minimal overhead + +## Notable Insights + +### 1. **The Completion Penalty** +Agents that finished the task scored **lower** than the agent that didn't. This is either: +- A feature: Rewarding thorough research over hasty implementation +- A bug: Misaligned incentives in the evaluation system + +### 2. **Documentation Correlation** +All completing agents updated README.md with onboarding instructions, but this didn't differentiate scores significantly (0.157-0.190 range). Documentation quality appears less weighted than exploration depth. + +### 3. **File Naming Variations** +Minor inconsistencies appeared even within single agents: +- Claude-Code: `content-setup.ts` vs `setup-content.ts` +- OpenCode/Big-Pickle: Similar variations + +These variations had no apparent score impact, suggesting the evaluation focuses on semantic correctness over naming conventions. + +### 4. **Episode Consistency** +All agents showed high consistency across episodes (3/3 similar approaches), indicating stable behavioral patterns rather than random variation. + +## Recommendations + +### For Evaluation System Improvement: +1. **Rebalance base vs. penalty metrics**: If task completion is the goal, incomplete runs shouldn't score highest. Consider adding explicit completion bonuses. +2. **Clarify optimization target**: Is the system rewarding research quality, implementation quality, or both? Make this explicit in scoring. +3. **Add completion gates**: Consider requiring minimum task completion thresholds before awarding high base scores. +4. **Investigate base metric composition**: What specifically drove codex's 0.344 base score? If it's action count or exploration breadth, this may incentivize inefficiency. + +### For Agent Development: +1. **Codex**: Implement action budgeting—reserve 30-40% of actions for implementation after exploration phase +2. **OpenCode/Claude-Sonnet-4-5**: Add exploratory phase before implementation to potentially boost base scores +3. **Claude-Code**: Investigate Episode 2 bloat—identify what caused 2x action count and eliminate inefficiencies +4. **OpenCode/Big-Pickle**: Consider adding intermediate exploration steps to generate richer artifacts + +### For Future Experiments: +1. **Test exploration caps**: Run codex with forced implementation deadlines (e.g., "must begin writing script by action 40") +2. **Hybrid approaches**: Test agents that combine codex's exploration depth with other agents' completion rates +3. **Metric ablation**: Run evaluations with base-only and penalty-only scoring to understand component contributions +4. **Task decomposition**: Split evaluation into "research phase" and "implementation phase" to reward both behaviors appropriately + +## Conclusion +This benchmark reveals a fundamental tension between **depth of understanding** and **speed of execution**. The current evaluation system appears to favor the former, creating a counterintuitive scenario where incomplete work scores highest. Whether this is intentional (rewarding thorough analysis) or a misalignment (incomplete tasks shouldn't win) depends on the evaluation's intended purpose. 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"node:fs"; +import { join } from "node:path"; +import { execSync } from "node:child_process"; + +const REPO = "sst/opencode-bench"; +const COMMIT_SHA = "ea446df3c3284cf6be379486a9807d0c48ef7d78"; +const DATA_DIR = join(import.meta.dir, "..", "data"); + +// GitHub API base URL +const GITHUB_API = "https://api.github.com"; +const GITHUB_TOKEN = process.env.GITHUB_TOKEN; + +interface GitHubArtifact { + id: number; + node_id: string; + name: string; + size_in_bytes: number; + url: string; + archive_download_url: string; + expired: boolean; + created_at: string; + updated_at: string; +} + +interface GitHubWorkflowRun { + id: number; + name: string; + head_sha: string; + conclusion: string; + status: string; + created_at: string; +} + +async function fetchJson(url: string): Promise { + const response = await fetch(url, { + headers: { + Accept: "application/vnd.github+json", + "User-Agent": "opencode-bench-web", + }, + }); + + if (!response.ok) { + throw new Error(`Failed to fetch ${url}: ${response.status} ${response.statusText}`); + } + + return response.json() as Promise; +} + +async function downloadArtifact(artifact: GitHubArtifact): Promise { + console.log(`Downloading artifact: ${artifact.name} (${(artifact.size_in_bytes / 1024 / 1024).toFixed(2)} MB)`); + + const headers: Record = { + Accept: "application/vnd.github+json", + "User-Agent": "opencode-bench-web", + }; + + if (GITHUB_TOKEN) { + headers.Authorization = `token ${GITHUB_TOKEN}`; + } + + const response = await fetch(artifact.archive_download_url, { headers }); + + if (!response.ok) { + throw new Error(`Failed to download artifact ${artifact.name}: ${response.status} ${response.statusText}`); + } + + const arrayBuffer = await response.arrayBuffer(); + const zipPath = join(DATA_DIR, `${artifact.name}.zip`); + writeFileSync(zipPath, Buffer.from(arrayBuffer)); + console.log(`Saved ZIP to: ${zipPath}`); + + // Extract the zip file + const extractPath = join(DATA_DIR, artifact.name); + mkdirSync(extractPath, { recursive: true }); + + try { + execSync(`unzip -q -o "${zipPath}" -d "${extractPath}"`, { stdio: "inherit" }); + console.log(`Extracted to: ${extractPath}`); + } catch (error) { + console.error(`Failed to extract ${artifact.name}:`, error); + throw error; + } +} + +async function main() { + console.log(`Fetching artifacts for commit ${COMMIT_SHA} from ${REPO}...`); + + if (!GITHUB_TOKEN) { + console.warn("⚠️ Warning: GITHUB_TOKEN not set. Some artifacts may require authentication."); + console.warn(" Set GITHUB_TOKEN environment variable if downloads fail."); + } + + // Create data directory if it doesn't exist + mkdirSync(DATA_DIR, { recursive: true }); + + // Get workflow runs for this commit + const runsUrl = `${GITHUB_API}/repos/${REPO}/actions/runs?head_sha=${COMMIT_SHA}&per_page=100`; + console.log(`Fetching workflow runs: ${runsUrl}`); + + const runsData = await fetchJson<{ workflow_runs: GitHubWorkflowRun[] }>(runsUrl); + + if (runsData.workflow_runs.length === 0) { + console.error("No workflow runs found for this commit"); + process.exit(1); + } + + console.log(`Found ${runsData.workflow_runs.length} workflow run(s)`); + + // Log all runs for debugging + for (const run of runsData.workflow_runs) { + console.log(` - ${run.id}: ${run.name} (${run.status}/${run.conclusion})`); + } + + // Try to find the "Publish and Benchmark Preview Packages" workflow first + let run = runsData.workflow_runs.find(r => + r.status === "completed" && r.name.includes("Publish and Benchmark") + ); + + // Fallback to any completed run + if (!run) { + run = runsData.workflow_runs.find(r => r.status === "completed"); + } + + // Fallback to most recent run + if (!run) { + run = runsData.workflow_runs[0]; + } + + if (!run) { + console.error("No workflow run found"); + process.exit(1); + } + + console.log(`\nUsing workflow run: ${run.id} (${run.name}) - Status: ${run.status}, Conclusion: ${run.conclusion}`); + + // Get artifacts for this run + const artifactsUrl = `${GITHUB_API}/repos/${REPO}/actions/runs/${run.id}/artifacts`; + console.log(`Fetching artifacts: ${artifactsUrl}`); + + const artifactsData = await fetchJson<{ artifacts: GitHubArtifact[] }>(artifactsUrl); + + if (artifactsData.artifacts.length === 0) { + console.error("No artifacts found for this workflow run"); + process.exit(1); + } + + console.log(`Found ${artifactsData.artifacts.length} artifact(s)`); + + // Download all artifacts + for (const artifact of artifactsData.artifacts) { + if (artifact.expired) { + console.log(`Skipping expired artifact: ${artifact.name}`); + continue; + } + + try { + await downloadArtifact(artifact); + } catch (error) { + console.error(`Error downloading ${artifact.name}:`, error); + } + } + + // Save metadata + const metadata = { + commit: COMMIT_SHA, + workflowRun: { + id: run.id, + name: run.name, + status: run.status, + conclusion: run.conclusion, + createdAt: run.created_at, + }, + artifacts: artifactsData.artifacts.map(a => ({ + name: a.name, + size: a.size_in_bytes, + createdAt: a.created_at, + expired: a.expired, + })), + fetchedAt: new Date().toISOString(), + }; + + writeFileSync( + join(DATA_DIR, "metadata.json"), + JSON.stringify(metadata, null, 2) + ); + + console.log("\n✅ Done! Artifacts downloaded to:", DATA_DIR); + console.log(`📊 Metadata saved to: ${join(DATA_DIR, "metadata.json")}`); +} + +main().catch((error) => { + console.error("Error:", error); + process.exit(1); +}); diff --git a/web/src/App.css b/web/src/App.css new file mode 100644 index 0000000..b9d355d --- /dev/null +++ b/web/src/App.css @@ -0,0 +1,42 @@ +#root { + max-width: 1280px; + margin: 0 auto; + padding: 2rem; + text-align: center; +} + +.logo { + height: 6em; + padding: 1.5em; + will-change: filter; + transition: filter 300ms; +} +.logo:hover { + filter: drop-shadow(0 0 2em #646cffaa); +} +.logo.react:hover { + filter: drop-shadow(0 0 2em #61dafbaa); +} + +@keyframes logo-spin { + from { + transform: rotate(0deg); + } + to { + transform: rotate(360deg); + } +} + +@media (prefers-reduced-motion: no-preference) { + a:nth-of-type(2) .logo { + animation: logo-spin infinite 20s linear; + } +} + +.card { + padding: 2em; +} + +.read-the-docs { + color: #888; +} diff --git a/web/src/App.tsx b/web/src/App.tsx new file mode 100644 index 0000000..9cefcd4 --- /dev/null +++ b/web/src/App.tsx @@ -0,0 +1,196 @@ +import { useState, useEffect } from "react"; +import type { BenchmarkData } from "./types/benchmark"; +import { loadBenchmarkData } from "./utils/loadData"; +import { fetchLatestCommit } from "./utils/github"; +import { OverviewDashboard } from "./components/OverviewDashboard"; +import { ComparisonMatrix } from "./components/ComparisonMatrix"; +import { DetailedScoreView } from "./components/DetailedScoreView"; +import { CommitSelector } from "./components/CommitSelector"; +import "./App.css"; + +type View = "overview" | "comparison" | "detailed"; + +function App() { + const [data, setData] = useState(null); + const [loading, setLoading] = useState(true); + const [error, setError] = useState(null); + const [view, setView] = useState("overview"); + const [selectedCommit, setSelectedCommit] = useState(null); + + // Fetch latest commit on mount + useEffect(() => { + async function init() { + try { + const latestCommit = await fetchLatestCommit(); + if (latestCommit) { + setSelectedCommit(latestCommit.sha); + } else { + // Fallback to hardcoded commit if API fails + setSelectedCommit("ea446df3c3284cf6be379486a9807d0c48ef7d78"); + } + } catch (err) { + console.error("Failed to fetch latest commit:", err); + setSelectedCommit("ea446df3c3284cf6be379486a9807d0c48ef7d78"); + } + } + init(); + }, []); + + // Load data when commit is selected + useEffect(() => { + if (!selectedCommit) return; + + setLoading(true); + setError(null); + + loadBenchmarkData(selectedCommit) + .then((loadedData) => { + setData(loadedData); + setLoading(false); + }) + .catch((err) => { + setError(err.message); + setLoading(false); + }); + }, [selectedCommit]); + + if (loading) { + return ( +

+ ); + } + + if (error) { + return ( +
+
+

Error loading data

+

{error}

+
+
+ ); + } + + if (!data || data.runs.length === 0) { + return ( +
+
+

No benchmark data available

+
+
+ ); + } + + return ( +
+ {/* Navigation */} + + + {/* Main Content */} +
+ {data.commitData && ( + + )} + + {view === "overview" && } + {view === "comparison" && } + {view === "detailed" && } + + {/* Debug info */} + {import.meta.env.DEV && ( +
+

Loaded {data.runs.length} runs

+ {data.runs.length > 0 && ( +

First run: {data.runs[0].agent} / {data.runs[0].model}

+ )} + {data.commitData && ( +

Commit: {data.commitData.commitSha}

+ )} +
+ )} +
+
+ ); +} + +export default App; diff --git a/web/src/assets/react.svg b/web/src/assets/react.svg new file mode 100644 index 0000000..6c87de9 --- /dev/null +++ b/web/src/assets/react.svg @@ -0,0 +1 @@ + \ No newline at end of file diff --git a/web/src/components/AgentComparisonChart.tsx b/web/src/components/AgentComparisonChart.tsx new file mode 100644 index 0000000..faec5d7 --- /dev/null +++ b/web/src/components/AgentComparisonChart.tsx @@ -0,0 +1,83 @@ +import { useMemo } from "react"; +import { BarChart, Bar, XAxis, YAxis, CartesianGrid } from "recharts"; +import { ChartContainer, ChartTooltip, ChartTooltipContent, type ChartConfig } from "@/components/ui/chart"; +import type { EvaluationRunExport } from "../types/benchmark"; +import { theme } from "../theme"; + +interface AgentComparisonChartProps { + runs: EvaluationRunExport[]; +} + +const chartConfig = { + avgScore: { + label: "Average Score", + color: theme.fillColor, + }, +} satisfies ChartConfig; + +export function AgentComparisonChart({ runs }: AgentComparisonChartProps) { + const data = useMemo(() => { + const agentMap = new Map(); + + runs.forEach((run) => { + const agent = run.agent; + const current = agentMap.get(agent) || { total: 0, count: 0 }; + current.total += run.finalScore; + current.count += 1; + agentMap.set(agent, current); + }); + + return Array.from(agentMap.entries()) + .map(([agent, { total, count }]) => ({ + agent, + avgScore: total / count, + })) + .sort((a, b) => b.avgScore - a.avgScore); + }, [runs]); + + if (data.length === 0) { + return ( +
+

+ Average Score by Agent +

+

No data available

+
+ ); + } + + return ( +
+
+

Average Score by Agent

+

Comparison of performance across different agents

+
+ + + + + + } + formatter={(value: number) => `${(value * 100).toFixed(2)}%`} + /> + + + +
+ ); +} diff --git a/web/src/components/CommitSelector.tsx b/web/src/components/CommitSelector.tsx new file mode 100644 index 0000000..141cb6d --- /dev/null +++ b/web/src/components/CommitSelector.tsx @@ -0,0 +1,130 @@ +import { useState, useEffect } from "react"; +import { fetchRecentCommits, fetchLatestCommit, type GitHubCommit } from "../utils/github"; + +interface CommitSelectorProps { + selectedCommit: string; + onCommitChange: (commitSha: string) => void; +} + +export function CommitSelector({ selectedCommit, onCommitChange }: CommitSelectorProps) { + const [commits, setCommits] = useState([]); + const [loading, setLoading] = useState(true); + const [isOpen, setIsOpen] = useState(false); + + useEffect(() => { + async function loadCommits() { + try { + const latestCommit = await fetchLatestCommit(); + const recentCommits = await fetchRecentCommits(30); + + // Ensure latest commit is first + const allCommits = latestCommit + ? [latestCommit, ...recentCommits.filter(c => c.sha !== latestCommit.sha)] + : recentCommits; + + setCommits(allCommits); + } catch (error) { + console.error("Failed to load commits:", error); + } finally { + setLoading(false); + } + } + + loadCommits(); + }, []); + + const selectedCommitData = commits.find(c => c.sha === selectedCommit); + const shortSha = selectedCommit.slice(0, 7); + const commitMessage = selectedCommitData?.commit.message.split("\n")[0] || ""; + + return ( +
+ + + {isOpen && ( + <> +
setIsOpen(false)} + /> +
+
+
+ Recent Commits +
+
+ {commits.map((commit) => { + const isSelected = commit.sha === selectedCommit; + const shortMessage = commit.commit.message.split("\n")[0]; + const date = new Date(commit.commit.author.date).toLocaleDateString(); + + return ( + + ); + })} +
+
+
+ + )} +
+ ); +} diff --git a/web/src/components/ComparisonMatrix.tsx b/web/src/components/ComparisonMatrix.tsx new file mode 100644 index 0000000..27c35bc --- /dev/null +++ b/web/src/components/ComparisonMatrix.tsx @@ -0,0 +1,128 @@ +import { useMemo, useState } from "react"; +import type { EvaluationRunExport } from "../types/benchmark"; +import { getUniqueAgents, getUniqueModels, getUniqueEvaluations } from "../utils/loadData"; + +interface ComparisonMatrixProps { + runs: EvaluationRunExport[]; +} + +export function ComparisonMatrix({ runs }: ComparisonMatrixProps) { + const agents = useMemo(() => getUniqueAgents(runs), [runs]); + const models = useMemo(() => getUniqueModels(runs), [runs]); + const evaluations = useMemo(() => getUniqueEvaluations(runs), [runs]); + + // Create a map for quick lookup: agent+model+evaluation -> score + const scoreMap = useMemo(() => { + const map = new Map(); + for (const run of runs) { + const key = `${run.agent}|${run.model}|${run.evaluation.repo}`; + map.set(key, run); + } + return map; + }, [runs]); + + const formatScore = (score: number) => (score * 100).toFixed(1); + + const getScoreColor = (score: number) => { + if (score >= 0.8) return "bg-gradient-to-br from-green-500 to-emerald-600"; + if (score >= 0.6) return "bg-gradient-to-br from-green-400 to-green-500"; + if (score >= 0.4) return "bg-gradient-to-br from-yellow-400 to-orange-400"; + if (score >= 0.2) return "bg-gradient-to-br from-orange-400 to-orange-500"; + return "bg-gradient-to-br from-red-400 to-red-500"; + }; + + // Group by evaluation for tabs + const [selectedEval, setSelectedEval] = useState( + evaluations[0] || "" + ); + + return ( +
+
+

+ Comparison Matrix +

+

Side-by-side comparison of agents and models

+
+ + {/* Evaluation Selector */} +
+ + +
+ + {/* Matrix Table */} +
+ + + + + {models.map((model) => ( + + ))} + + + + {agents.map((agent) => ( + + + {models.map((model) => { + const key = `${agent}|${model}|${selectedEval}`; + const run = scoreMap.get(key); + const score = run?.finalScore ?? null; + + return ( + + ); + })} + + ))} + +
Agent + {model.split("/").pop()} +
+ + {agent} + + + {score !== null ? ( +
+
+ {formatScore(score)}% +
+ {run && ( + + View Job + + )} +
+ ) : ( + — + )} +
+
+
+ ); +} diff --git a/web/src/components/DetailedScoreView.tsx b/web/src/components/DetailedScoreView.tsx new file mode 100644 index 0000000..ad6f42b --- /dev/null +++ b/web/src/components/DetailedScoreView.tsx @@ -0,0 +1,218 @@ +import { useState } from "react"; +import type { EvaluationRunExport } from "../types/benchmark"; +import { RadarScoreChart } from "./RadarScoreChart"; +import { EpisodeScoreChart } from "./EpisodeScoreChart"; + +interface DetailedScoreViewProps { + runs: EvaluationRunExport[]; +} + +export function DetailedScoreView({ runs }: DetailedScoreViewProps) { + const [selectedRun, setSelectedRun] = useState( + runs[0] || null + ); + + const formatScore = (score: number) => (score * 100).toFixed(2); + + if (!selectedRun) { + return
No benchmark data available
; + } + + return ( +
+

Detailed Score Breakdown

+ + {/* Run Selector */} +
+ + +
+ + {/* Run Summary */} +
+
+
+

Final Score

+

{formatScore(selectedRun.finalScore)}%

+
+
+

Base Score

+

+ {formatScore(selectedRun.baseScore)}% +

+
+
+

Variance Penalty

+

+ -{formatScore(selectedRun.variancePenalty)}% +

+
+
+

Episodes

+

{selectedRun.episodes.length}

+
+
+ +
+

Usage

+
+ Input: {selectedRun.usage.input.toLocaleString()} tokens + Output: {selectedRun.usage.output.toLocaleString()} tokens + + Total: {(selectedRun.usage.input + selectedRun.usage.output).toLocaleString()} tokens + +
+
+ + +
+ + {/* Charts */} +
+ + +
+ + {/* Score Breakdown by Assignment */} +
+

Score Breakdown by Assignment

+
+ {selectedRun.scores.map((scoreResult, index) => ( + + ))} +
+
+ + {/* Summary */} + {selectedRun.summary && ( +
+

Summary

+
+
+ 
{selectedRun.summary}
+
+
+
+ )} +
+ ); +} + +function ScoreAssignmentCard({ + scoreResult, + formatScore, +}: { + scoreResult: EvaluationRunExport["scores"][0]; + formatScore: (score: number) => string; +}) { + const [expanded, setExpanded] = useState(false); + + return ( +
+
+
+

{scoreResult.assignment.name}

+
+ + Avg Score:{" "} + + {formatScore(scoreResult.averageScore)}% + + + + Weight:{" "} + + {formatScore(scoreResult.normalizedWeight)}% + + + + Variance:{" "} + + {formatScore(scoreResult.variance)} + + +
+
+ +
+ + {expanded && ( +
+ {scoreResult.judges.map((judge, index) => ( + + ))} +
+ )} +
+ ); +} + +function JudgeCard({ + judge, +}: { + judge: EvaluationRunExport["scores"][0]["judges"][0]; +}) { + const [expanded, setExpanded] = useState(false); + + return ( +
+
+
+ {judge.name} + ({judge.model}) + + {judge.score === 1 ? "✓ Pass" : "✗ Fail"} + +
+ +
+ + {expanded && ( +
+ 
{judge.rationale}
+
+ )} +
+ ); +} diff --git a/web/src/components/EpisodeScoreChart.tsx b/web/src/components/EpisodeScoreChart.tsx new file mode 100644 index 0000000..b9f5f4b --- /dev/null +++ b/web/src/components/EpisodeScoreChart.tsx @@ -0,0 +1,68 @@ +import { LineChart, Line, XAxis, YAxis, CartesianGrid } from "recharts"; +import { ChartContainer, ChartTooltip, ChartTooltipContent, type ChartConfig } from "@/components/ui/chart"; +import type { EvaluationRunExport } from "../types/benchmark"; +import { theme } from "../theme"; + +interface EpisodeScoreChartProps { + run: EvaluationRunExport; +} + +const chartConfig = { + finalScore: { + label: "Final Score", + color: theme.strokeColor, + }, + baseScore: { + label: "Base Score", + color: theme.textSecondary, + }, +} satisfies ChartConfig; + +export function EpisodeScoreChart({ run }: EpisodeScoreChartProps) { + const data = run.episodes.map((episode, index) => ({ + episode: `Episode ${index + 1}`, + finalScore: episode.finalScore, + baseScore: episode.baseScore, + })); + + return ( +
+
+

Episode Scores Over Time

+

Performance trends across multiple episodes

+
+ + + + + + } + formatter={(value: number) => `${(value * 100).toFixed(2)}%`} + /> + + + + +
+ ); +} diff --git a/web/src/components/ModelComparisonChart.tsx b/web/src/components/ModelComparisonChart.tsx new file mode 100644 index 0000000..82f35fc --- /dev/null +++ b/web/src/components/ModelComparisonChart.tsx @@ -0,0 +1,84 @@ +import { useMemo } from "react"; +import { BarChart, Bar, XAxis, YAxis, CartesianGrid } from "recharts"; +import { ChartContainer, ChartTooltip, ChartTooltipContent, type ChartConfig } from "@/components/ui/chart"; +import type { EvaluationRunExport } from "../types/benchmark"; +import { theme } from "../theme"; + +interface ModelComparisonChartProps { + runs: EvaluationRunExport[]; +} + +const chartConfig = { + avgScore: { + label: "Average Score", + color: theme.fillColor, + }, +} satisfies ChartConfig; + +export function ModelComparisonChart({ runs }: ModelComparisonChartProps) { + const data = useMemo(() => { + if (runs.length === 0) return []; + const modelMap = new Map(); + + runs.forEach((run) => { + const model = run.model; + const current = modelMap.get(model) || { total: 0, count: 0 }; + current.total += run.finalScore; + current.count += 1; + modelMap.set(model, current); + }); + + return Array.from(modelMap.entries()) + .map(([model, { total, count }]) => ({ + model: model.replaceAll("/", ":"), // Keep provider prefix, replace all / with : for display + avgScore: total / count, + })) + .sort((a, b) => b.avgScore - a.avgScore); + }, [runs]); + + if (data.length === 0) { + return ( +
+

+ Average Score by Model +

+

No data available

+
+ ); + } + + return ( +
+
+

Average Score by Model

+

Comparison of performance across different models

+
+ + + + + + } + formatter={(value: number) => `${(value * 100).toFixed(2)}%`} + /> + + + +
+ ); +} diff --git a/web/src/components/OverviewDashboard.tsx b/web/src/components/OverviewDashboard.tsx new file mode 100644 index 0000000..b9ae4d7 --- /dev/null +++ b/web/src/components/OverviewDashboard.tsx @@ -0,0 +1,213 @@ +import { useMemo } from "react"; +import type { EvaluationRunExport } from "../types/benchmark"; +import { calculateStats, getTopPerformers } from "../utils/loadData"; +import { AgentComparisonChart } from "./AgentComparisonChart"; +import { ModelComparisonChart } from "./ModelComparisonChart"; + +interface OverviewDashboardProps { + runs: EvaluationRunExport[]; +} + +export function OverviewDashboard({ runs }: OverviewDashboardProps) { + const stats = useMemo(() => calculateStats(runs), [runs]); + const topPerformers = useMemo(() => getTopPerformers(runs, 10), [runs]); + + const formatScore = (score: number) => (score * 100).toFixed(2); + const formatTokens = (tokens: number) => { + if (tokens >= 1_000_000) return `${(tokens / 1_000_000).toFixed(2)}M`; + if (tokens >= 1_000) return `${(tokens / 1_000).toFixed(2)}K`; + return tokens.toLocaleString(); + }; + + return ( +
+
+

+ Benchmark Overview +

+

Performance metrics and comparisons across all benchmark runs

+
+ + {/* Summary Cards */} +
+ + + + +
+ + {/* Top Performers Leaderboard */} +
+
+
+ 🏆 +
+
+

Top Performers

+

Best performing agent/model combinations

+
+
+
+ + + + + + + + + + + + + + {topPerformers.map((performer, index) => ( + + + + + + + + + + ))} + +
RankAgentModelEvaluationFinal ScoreBase ScoreVariance
+
+ {index < 3 && ( + + {index === 0 ? "🥇" : index === 1 ? "🥈" : "🥉"} + + )} + #{index + 1} +
+ 		+ + {performer.agent} + + + + {performer.model.replace("/", ":")} + + + + {performer.evaluation} + + + + {formatScore(performer.score)}% + + + {formatScore(performer.baseScore)}% + + + -{formatScore(performer.variancePenalty)}% + +
+
+
+ + {/* Agent and Model Comparison Charts */} +
+ + +
+ + {/* Additional Stats */} +
+ + + +
+
+ ); +} + +function SummaryCard({ + title, + value, + icon, + gradient, +}: { + title: string; + value: string | number; + icon: string; + gradient: string; +}) { + return ( +
+
+
+

{title}

+

{value}

+
+
+ {icon} +
+
+
+ ); +} + +function StatCard({ + title, + value, + description, + color = "blue", +}: { + title: string; + value: string; + description: string; + color?: "blue" | "purple" | "red" | "green"; +}) { + const colorClasses = { + blue: "from-blue-50 to-blue-100 border-blue-200", + purple: "from-purple-50 to-purple-100 border-purple-200", + red: "from-red-50 to-red-100 border-red-200", + green: "from-green-50 to-green-100 border-green-200", + }; + + return ( +
+

{title}

+

{value}

+

{description}

+
+ ); +} diff --git a/web/src/components/RadarScoreChart.tsx b/web/src/components/RadarScoreChart.tsx new file mode 100644 index 0000000..1ae0991 --- /dev/null +++ b/web/src/components/RadarScoreChart.tsx @@ -0,0 +1,54 @@ +import { RadarChart, PolarGrid, PolarAngleAxis, PolarRadiusAxis, Radar } from "recharts"; +import { ChartContainer, ChartTooltip, ChartTooltipContent, type ChartConfig } from "@/components/ui/chart"; +import type { EvaluationRunExport } from "../types/benchmark"; +import { theme } from "../theme"; + +interface RadarScoreChartProps { + run: EvaluationRunExport; +} + +const chartConfig = { + score: { + label: "Score", + color: theme.radarBorderColor, + }, +} satisfies ChartConfig; + +export function RadarScoreChart({ run }: RadarScoreChartProps) { + const data = run.scores.map((score) => ({ + assignment: score.assignment.name, + score: score.averageScore, + })); + + return ( +
+
+

Score Breakdown by Assignment

+

Radar chart showing performance across different metrics

+
+ + + + + + } /> + + + +
+ ); +} diff --git a/web/src/components/ScoreDistributionChart.tsx b/web/src/components/ScoreDistributionChart.tsx new file mode 100644 index 0000000..1b607cc --- /dev/null +++ b/web/src/components/ScoreDistributionChart.tsx @@ -0,0 +1,82 @@ +import { useMemo } from "react"; +import { BarChart, Bar, XAxis, YAxis, CartesianGrid } from "recharts"; +import { ChartContainer, ChartTooltip, ChartTooltipContent, type ChartConfig } from "@/components/ui/chart"; +import type { EvaluationRunExport } from "../types/benchmark"; +import { theme } from "../theme"; + +interface ScoreDistributionChartProps { + runs: EvaluationRunExport[]; +} + +const chartConfig = { + count: { + label: "Count", + color: theme.fillColor, + }, +} satisfies ChartConfig; + +export function ScoreDistributionChart({ runs }: ScoreDistributionChartProps) { + const data = useMemo(() => { + // Create buckets for score distribution + const buckets = [ + { range: "0-0.2", min: 0, max: 0.2, count: 0 }, + { range: "0.2-0.4", min: 0.2, max: 0.4, count: 0 }, + { range: "0.4-0.6", min: 0.4, max: 0.6, count: 0 }, + { range: "0.6-0.8", min: 0.6, max: 0.8, count: 0 }, + { range: "0.8-1.0", min: 0.8, max: 1.0, count: 0 }, + ]; + + runs.forEach((run) => { + const score = run.finalScore; + for (const bucket of buckets) { + if (score >= bucket.min && score < bucket.max) { + bucket.count++; + break; + } + } + }); + + return buckets.map((bucket) => ({ + range: bucket.range, + count: bucket.count, + })); + }, [runs]); + + if (data.length === 0) { + return ( +
+

+ Score Distribution +

+

No data available

+
+ ); + } + + return ( +
+

+ Score Distribution +

+ + + + + + } /> + + + +
+ ); +} diff --git a/web/src/components/ui/card.tsx b/web/src/components/ui/card.tsx new file mode 100644 index 0000000..681ad98 --- /dev/null +++ b/web/src/components/ui/card.tsx @@ -0,0 +1,92 @@ +import * as React from "react" + +import { cn } from "@/lib/utils" + +function Card({ className, ...props }: React.ComponentProps<"div">) { + return ( +
+ ) +} + +function CardHeader({ className, ...props }: React.ComponentProps<"div">) { + return ( +
+ ) +} + +function CardTitle({ className, ...props }: React.ComponentProps<"div">) { + return ( +
+ ) +} + +function CardDescription({ className, ...props }: React.ComponentProps<"div">) { + return ( +
+ ) +} + +function CardAction({ className, ...props }: React.ComponentProps<"div">) { + return ( +
+ ) +} + +function CardContent({ className, ...props }: React.ComponentProps<"div">) { + return ( +
+ ) +} + +function CardFooter({ className, ...props }: React.ComponentProps<"div">) { + return ( +
+ ) +} + +export { + Card, + CardHeader, + CardFooter, + CardTitle, + CardAction, + CardDescription, + CardContent, +} diff --git a/web/src/components/ui/chart.tsx b/web/src/components/ui/chart.tsx new file mode 100644 index 0000000..23dc1c1 --- /dev/null +++ b/web/src/components/ui/chart.tsx @@ -0,0 +1,367 @@ +import * as React from "react" +import * as RechartsPrimitive from "recharts" + +import { cn } from "@/lib/utils" + +// Format: { THEME_NAME: CSS_SELECTOR } +const THEMES = { light: "", dark: ".dark" } as const + +export type ChartConfig = { + [k in string]: { + label?: React.ReactNode + icon?: React.ComponentType + } & ( + | { color?: string; theme?: never } + | { color?: never; theme: Record } + ) +} + +type ChartContextProps = { + config: ChartConfig +} + +const ChartContext = React.createContext(null) + +function useChart() { + const context = React.useContext(ChartContext) + + if (!context) { + throw new Error("useChart must be used within a ") + } + + return context +} + +const ChartContainer = React.forwardRef< + HTMLDivElement, + React.ComponentProps<"div"> & { + config: ChartConfig + children: React.ComponentProps< + typeof RechartsPrimitive.ResponsiveContainer + >["children"] + } +>(({ id, className, children, config, ...props }, ref) => { + const uniqueId = React.useId() + const chartId = `chart-${id || uniqueId.replace(/:/g, "")}` + + return ( + +
+ + + {children} + +
+ + ) +}) +ChartContainer.displayName = "Chart" + +const ChartStyle = ({ id, config }: { id: string; config: ChartConfig }) => { + const colorConfig = Object.entries(config).filter( + ([, config]) => config.theme || config.color + ) + + if (!colorConfig.length) { + return null + } + + return ( +

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