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Benchmarks and Methodology
Dominic edited this page Jul 19, 2026
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Atlas publishes benchmark results for accuracy, token use, latency, language compatibility, and agent workflows:
Open the Atlas benchmark explorer
Confirm all of the following before comparing tools:
- Atlas and baseline tool versions
- repository URL and pinned commit
- language and query set
- hardware and operating system
- cold-cache or warm-cache state
- repeat count and aggregation method
- accuracy oracle or reviewer
- token measurement method
- failure and timeout handling
Do not combine these categories into one unsupported headline:
- Compatibility: whether a format indexes successfully
- Correctness: precision, recall, or F1 against an oracle
- Latency: elapsed index or query time under stated cache conditions
- Token use: measured model/tool usage or a documented estimate
- Agent workflow: end-to-end behavior for a pinned assistant and task set
- Fixture results do not prove production-repository accuracy.
- One repository does not establish performance across a language.
- Different assistants may consume the same MCP result differently.
- Estimated tokens are not equivalent to provider-reported token usage.
- Warm-cache query latency is not cold-start installation or indexing time.
- Language support and result quality can differ by syntax and framework.
The public repository includes a self-contained agent benchmark guide:
python3 agent-bench/agent_token_bench.py \
--setup \
--agents auto \
--qa-set agent-bench/QA_SET_logrus.json \
--workdir agentbench-workRead agent-bench/README.md for prerequisites, expected external cost, pinned inputs, and interpretation.
Publish:
- exact commands
- raw machine-readable output
- all failed and timed-out cases
- environment metadata
- evidence limitations
- the Atlas release tag and checksum
Benchmark results are dated evidence under stated conditions, not a guarantee for every repository, machine, language, or assistant.
Atlas | Releases | Benchmarks | License