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README.md

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+---
+license: apache-2.0
+task_categories:
+  - question-answering
+  - text-generation
+language:
+  - en
+  - code
+tags:
+  - code
+  - call-graph
+  - reasoning
+  - benchmark
+  - software-engineering
+  - agentic
+  - python
+  - typescript
+size_categories:
+  - n<1K
+---
+
+# GraphCode-Bench-500-v0
+
+**GraphCode-Bench** is a benchmark for evaluating LLMs on *call-graph reasoning* — given a function in a real-world repository, can a model identify which functions call it (upstream) or which functions it calls (downstream), across 1 and 2 hops?
+
+Models are evaluated **agentically**: they receive read-only filesystem tools (`list_directory`, `read_file`, `search_in_file`) and up to 10 turns to explore the codebase before producing an answer.
+
+## Dataset summary
+
+| Split | Records | Repos | Languages |
+|-------|---------|-------|-----------|
+| `train` (bench500) | 483 | 22 | Python, TypeScript |
+
+**Stratification**: 5 repos × 2 question types (upstream/downstream) × 2 hop depths (1-hop/2-hop).
+
+## Task definition
+
+Each record contains:
+
+- **anchor**: a named function in a real open-source repository
+- **question_type**: `upstream` (who calls this?) or `downstream` (what does this call?)
+- **hop_depth**: `1` (direct callers/callees) or `2` (one level further)
+- **gold**: the ground-truth set of function names at each hop level (extracted via LSP)
+
+Models must enumerate the correct function names. Scoring uses **set F1** against the gold answer.
+
+## Record schema
+
+```json
+{
+  "sample_id": "psf__requests__send__upstream__1hop_abc123",
+  "repo": "psf/requests",
+  "question_type": "upstream",
+  "hop_depth": 1,
+  "gold": {
+    "hop_1": ["mount", "request"],
+    "hop_1_files": ["requests/sessions.py"]
+  },
+  "metadata": {
+    "anchor": "send",
+    "anchor_file": "requests/adapters.py",
+    "anchor_source": "def send(self, request, ...):",
+    "result_size": 4,
+    "created_at": "2026-03-20T16:58:18.104721+00:00",
+    "file_content": "..."
+  }
+}
+```
+
+## Repositories included
+
+**Python** (250 samples): `psf/requests`, `pallets/flask`, `pallets/click`, `scrapy/scrapy`, `celery/celery`, `encode/httpx`, `pytest-dev/pytest`, `psf/black`, `PyCQA/flake8`, `rq/rq`, `paramiko/paramiko`
+
+**TypeScript** (233 samples): `sindresorhus/got`, `colinhacks/zod`, `trpc/trpc`, `immerjs/immer`, `node-fetch/node-fetch`
+
+## Pipeline
+
+Ground truth is extracted by:
+1. Running [basedpyright](https://github.com/DetachHead/basedpyright) / typescript-language-server over each repo via LSP
+2. Walking call edges from the anchor to the requested depth
+3. Applying 15 quality filters (no builtins, no generics, minimum result size, etc.)
+
+See the companion paper for full pipeline details.
+
+## Evaluation results (v0)
+
+| Model | F1 | EM | Pass@0.5 | Avg Turns |
+|-------|----|----|----------|-----------|
+| GPT-5.4-nano (API)† | 0.364 | 0.170 | 0.400 | 6.19 |
+| Qwen3-Coder-30B-A3B | 0.351 | 0.126 | 0.369 | 7.29 |
+| GPT-OSS-20B | 0.313 | 0.116 | 0.362 | 7.72 |
+| Mistral-Small-24B | 0.199 | 0.066 | 0.211 | 5.05 |
+
+† Closed model, shown for reference. Open-weight models evaluated via vLLM on HPC cluster.
+
+**Key finding**: 2-hop questions are 3–4× harder than 1-hop (Qwen3: F1=0.546 at 1-hop vs 0.151 at 2-hop).
+
+## Citation
+
+```bibtex
+@misc{graphcodebench2026,
+  title   = {GraphCode-Bench: Evaluating LLMs on Agentic Call-Graph Reasoning},
+  author  = {Rossi, Vittorio},
+  year    = {2026},
+  url     = {https://huggingface.co/datasets/VittorioRossi/GraphCode-Bench-500-v0}
+}
+```
+
+## License
+
+Apache 2.0. The source code snippets included in `anchor_source` and `file_content` fields are derived from their respective open-source repositories under their original licenses.