Agent Retrieval Bench

Agent Retrieval Bench (ARB) evaluates the context-acquisition layer of coding agents: given a real workflow signal and a repository at a frozen base commit, can a retriever find the files an agent needs to read next, or correctly decide that the repository contains no useful local context?

ARB differs from generic semantic code search in its relevance definition. Relevant files are selected by the next need of the coding workflow, not by query-file text similarity alone. A failure trace may expose a test while the needed file is its root-cause implementation; a review comment may name one file while the missing constraint lives elsewhere.

Benchmark

ARB contains 427 samples across 25 repositories:

Subset	Samples	Query signal	Expected output
code2test	106	PR intent or implementation-change signal	Related tests
comment2context	80	Review comment plus an already-given file	Additional context files
trace2code	101	Reproduced failure output	Root-cause source files
edit2ripple	58	Intent plus one anchored file change	Other affected files
Natural no-gold	50	Issue resolved outside the local repository	Abstain
Counterfactual no-gold	32	Plausible query paired with the wrong repository	Abstain
Total	427	Four positive workflows plus selective retrieval	Files or abstention

The four positive tasks contain 345 samples. The no-gold samples are evaluated only in the selective-retrieval track and are not mixed into positive-only MRR, Recall, or BCY.

The combined release corpus contains 308 reusable repository/base-commit snapshots, approximately 392K files, and 7.9M chunks. The evaluated rows use 271 of those snapshots.

Repository coverage

The 25 sample-bearing repositories span six primary languages: 13 Python, three Go, three Rust, three TypeScript, two Java, and one JavaScript repository. The table reports all four positive tasks and both no-gold strata. Counterfactual rows are counted under the repository whose corpus is searched.

Repository	Language	C2T	C2C	T2C	E2R	Natural	Counterfactual	All
HypothesisWorks/hypothesis	Python	0	1	0	0	0	5	6
astral-sh/ruff	Rust	0	6	0	0	1	3	10
caddyserver/caddy	Go	0	0	7	6	3	0	16
clap-rs/clap	Rust	4	0	1	5	0	1	11
eslint/eslint	JavaScript	0	1	0	0	1	4	6
etcd-io/etcd	Go	15	16	4	6	0	0	41
fastapi/fastapi	Python	3	0	0	0	0	0	3
gin-gonic/gin	Go	15	0	56	17	0	0	88
huggingface/diffusers	Python	6	9	0	1	0	2	18
huggingface/transformers	Python	15	13	0	8	0	0	36
ipython/ipython	Python	0	2	0	0	0	2	4
microsoft/playwright	TypeScript	0	4	0	0	3	1	8
mockito/mockito	Java	1	0	0	0	0	0	1
numpy/numpy	Python	0	1	0	0	5	1	7
pallets/click	Python	0	0	26	0	0	0	26
pydantic/pydantic	Python	0	1	0	0	0	1	2
pypa/pip	Python	0	5	0	0	4	2	11
pytest-dev/pytest	Python	3	2	1	1	14	5	26
python/mypy	Python	0	1	0	0	0	0	1
scrapy/scrapy	Python	0	3	0	0	3	0	6
spring-projects/spring-boot	Java	5	0	0	3	0	0	8
tokio-rs/tokio	Rust	15	11	6	6	2	0	40
tox-dev/tox	Python	0	2	0	0	0	3	5
vitejs/vite	TypeScript	19	1	0	5	14	2	41
vuejs/core	TypeScript	5	1	0	0	0	0	6
Total		106	80	101	58	50	32	427

C2T, C2C, T2C, and E2R denote code2test, comment2context, trace2code, and edit2ripple, respectively. The repository distribution is not sample-balanced: Gin contributes 88 of 345 positive samples, and the four largest positive repositories contribute 203 (58.8%).

Design principles

• Agentic relevance: gold files represent context needed next in a coding workflow, including indirect structural, causal, and project-conventional relationships.
• Base-commit corpora: positive samples are evaluated before the resolving change, preventing fixed-code leakage.
• All-file retrieval: the official candidate set is all_files; task-aware filters are diagnostic ablations.
• Context budgets: canonical Budgeted Context Yield (BCY) uses token-based ranked-file packing and is reported over 4k/8k/16k/32k budgets.
• Selective retrieval: natural no-gold cases are analyzed separately from easier wrong-repository counterfactual controls.
• Explicit scope: the primary benchmark is file-level retrieval. Span diagnostics are available for code2test, comment2context, and trace2code, while edit2ripple does not yet have span gold.

Results at a Glance

Positive retrieval results over all 345 samples:

Method	Recall@20	MRR	BCY@8k
Qwen3-Embedding-4B	0.6306	0.2379	0.3409
Qwen3-Embedding-8B	0.7029	0.2336	0.3732
pplx-embed-v1-4b*	0.6072	0.2267	0.3549
RepoMap	0.6333	0.2158	0.3788
nomic-embed-code	0.5244	0.1986	0.2781
jina-code-embeddings-0.5b	0.4823	0.1914	0.2783
Lexical	0.4940	0.1574	0.2650
BM25	0.4452	0.1520	0.2051

* The recorded pplx run is provisional because of a tokenizer warning.

No retrieval family dominates all workflow signals:

• Qwen3-Embedding-4B leads code2test MRR.
• Jina leads comment2context MRR.
• RepoMap leads both MRR and Recall@20 on trace2code.
• On edit2ripple, Qwen3-4B has the best Recall@20, while the verified Qwen ordering reverses between fully packed 4k and 8k context budgets.

Selective retrieval exposes a negative result. Under repo-grouped cross-validation on the 50 natural no-gold cases, top-score thresholding with Lexical, BM25, or Jina does not improve selective success over always returning a ranking. Raw retrieval confidence is not yet a reliable repository-independent abstention signal.

Installation

ARB requires Python 3.10 or newer.

python3 -m venv .venv
. .venv/bin/activate
pip install -e .

Install embedding dependencies only when evaluating embedding models:

pip install -e ".[embedding]"

The dataset repository is public; authentication is not required.

Download

The benchmark is distributed as five self-contained subset releases. Each bundle contains its benchmark JSONL, referenced corpus snapshots, checksums, and available evaluation/report artifacts.

arb releases
arb download-benchmark --all --local-dir data --force

To download only one subset, pass its release ID explicitly:

arb download-benchmark \
  --version v2_edit2ripple \
  --local-dir data \
  --force

download-benchmark requires either --version or --all; it does not silently select a legacy unversioned bundle.

Each release extracts to:

data/benchmark/<release-id>/
data/corpus/<release-id>/
data/eval/<release-id>/
data/reports/<release-id>/

Verify one subset after extraction:

arb validate data/benchmark/v2_edit2ripple/samples.jsonl

Selective-retrieval inputs

The exact mixed sample lists are distributed separately:

arb download-benchmark \
  --version v2_selective_retrieval_balanced \
  --local-dir data \
  --force

arb download-benchmark \
  --version v2_selective_retrieval_natural \
  --local-dir data \
  --force

Selective evaluation shares the five subset corpora. Build a deduplicated manifest after downloading the base releases:

arb merge-corpus-manifests --local-dir data

The five self-contained release corpora merge to 271 unique snapshots used by the released samples. The larger 308-snapshot corpus inventory reported in the paper also includes reusable snapshots not referenced by these 427 samples.

Evaluate a Retriever

The following examples use edit2ripple; replace the release ID to evaluate a different positive subset.

Lexical and BM25:

arb eval-baseline \
  --derived data/benchmark/v2_edit2ripple \
  --corpus data/corpus/v2_edit2ripple \
  --ranker lexical \
  --candidate-filter all_files \
  --no-keep-list \
  --out data/eval/v2_edit2ripple/lexical_summary.json \
  --details data/eval/v2_edit2ripple/lexical_details.jsonl

arb eval-baseline \
  --derived data/benchmark/v2_edit2ripple \
  --corpus data/corpus/v2_edit2ripple \
  --ranker bm25 \
  --candidate-filter all_files \
  --no-keep-list \
  --out data/eval/v2_edit2ripple/bm25_summary.json \
  --details data/eval/v2_edit2ripple/bm25_details.jsonl

RepoMap:

arb eval-repomap \
  --derived data/benchmark/v2_edit2ripple \
  --corpus data/corpus/v2_edit2ripple \
  --candidate-filter all_files \
  --no-keep-list \
  --out data/eval/v2_edit2ripple/repomap_summary.json \
  --details data/eval/v2_edit2ripple/repomap_details.jsonl

A SentenceTransformers-compatible embedding model:

arb eval-embedding \
  --version v2_edit2ripple \
  --model /path/to/Qwen3-Embedding-4B \
  --device cuda \
  --batch-size 8 \
  --query-batch-size 8 \
  --resume-details

Run a selective lexical baseline:

arb eval-selective-baseline \
  --derived data/benchmark/v2_selective_retrieval_natural \
  --corpus data/corpus/v2_selective_mixed \
  --ranker lexical \
  --candidate-filter all_files \
  --no-keep-list \
  --out data/eval/v2_selective/lexical_summary.json \
  --details data/eval/v2_selective/lexical_details.jsonl

Available evaluator families include Lexical, BM25, grep-style exact search, RepoMap, embedding retrieval, selective abstention, logged trajectories, and closed-tool context acquisition. Run arb --help or arb <command> --help for the complete CLI.

Metrics

• MRR: reciprocal rank of the first gold file.
• Recall@k: fraction of gold files recovered in the first k ranked files.
• Precision@k / F0.5@k: context-selection precision diagnostics.
• BCY@B: fraction of gold files exposed after token-based ranked-file packing under budget B.
• Selective success@20: a no-gold abstention is correct; an accepted positive is correct only when a gold file appears in the top 20.
• File/line context metrics: used for logged and closed-tool trajectories, not mixed into the static retrieval leaderboard.

Legacy JSON fields named gold_coverage@8k and context_efficiency@8k use an older character-counted packing implementation. They are not canonical BCY.

Repository Layout

src/agent_retrieval_bench/   evaluator and reporting code
tests/                       unit and integration tests
scripts/                     reproducibility entry points
docs/                        construction and analysis documentation
data/                        lightweight reports plus downloaded releases

Run the test suite with:

PYTHONPATH=src pytest -q

Scope

ARB isolates retrieval and context acquisition. It does not claim that a file-level hit identifies the correct function or span, or that better retrieval necessarily produces a test-passing patch. The current seed intervention evaluates context-selection behavior, not end-to-end repair success.

Corpus source files remain under their upstream repository licenses. Evaluator code, benchmark metadata, and documentation are MIT licensed; see DATA_LICENSE.md.

Citation

@misc{qin2026agentretrievalbench,
  title  = {Agent Retrieval Bench: Evaluating Repository Context Retrieval for Coding Agents},
  author = {Bowen Qin and Yi Xie},
  year   = {2026},
  url    = {https://github.com/eyuansu62/agent-retrieval-bench}
}

A machine-readable citation record is available in CITATION.cff.