Building Supervised Fine-Tuning Data from NVIDIA Open-SWE-Traces: Trajectory Parsing, Patch Analysis, Token Budgets, and Tool-Use Metrics

In this tutorial, we explore the Open-SWE-Traces dataset as a practical resource for studying and preparing agentic software-engineering trajectories for fine-tuning. We stream the dataset directly from Hugging Face, so we can work with a large dataset efficiently in Google Colab without downloading everything locally. We inspect individual records, normalize multi-turn agent conversations, parse final code patches, extract useful metadata, and build an analysis DataFrame to understand trajectory length, tool usage, patch size, language distribution, and resolution outcomes. We then use these insights to create a curated supervised fine-tuning subset that keeps only high-quality trajectories based on success labels, token limits, language filters, and patch availability.

Installing Dependencies and Configuration

Copy CodeCopiedUse a different Browser

import subprocess, sys def _pip(*pkgs): subprocess.run([sys.executable, "-m", "pip", "install", "-q", *pkgs], check=False) _pip("-U", "datasets", "huggingface_hub") _pip("tiktoken", "pandas", "matplotlib") import json import re import textwrap from itertools import islice from collections import Counter import pandas as pd import matplotlib.pyplot as plt from datasets import load_dataset pd.set_option("display.max_columns", 50) pd.set_option("display.width", 160) plt.rcParams.update({ "figure.figsize": (9, 4.6), "figure.dpi": 110, "axes.grid": True, "grid.alpha": 0.25, "axes.spines.top": False, "axes.spines.right": False, "font.size": 11, "axes.titlesize": 13, "axes.titleweight": "bold", }) BLUE, ORANGE, GREEN, RED = "#4C72B0", "#DD8452", "#55A868", "#C44E52" def banner(title): line = "=" * 78 print(f"\n{line}\n {title}\n{line}") DATASET = "nvidia/Open-SWE-Traces" AGENTS = ["openhands", "sweagent"] MODELS = ["minimax_m25", "qwen35_122b"] SAMPLE_ALL = True PER_COMBO = 400 N_SINGLE = 1500 MAX_SFT_TOKENS = 32000 SFT_REQUIRE_RESOLVED = True SFT_LANGUAGES = None

We start by installing and importing the core libraries needed for streaming, parsing, analysis, and visualization. We configure pandas and matplotlib to ensure our tables and plots remain readable in Google Colab. We also define the dataset name, agent/model combinations, sampling size, and SFT filtering settings that control the rest of the tutorial.

Defining Trajectory Parsing Helpers

Copy CodeCopiedUse a different Browser

def message_text(msg): if not isinstance(msg, dict): return "" content = msg.get("content", "") if content is None: return "" if isinstance(content, str): return content if isinstance(content, list): parts = [] for block in content: if isinstance(block, dict): parts.append(block.get("text") or block.get("content") or "") elif isinstance(block, str): parts.append(block) return "\n".join(p for p in parts if p) return str(content) def normalize_trajectory(traj): if traj is None: return [] if isinstance(traj, str): try: traj = json.loads(traj) except Exception: return [] norm = [] for msg in traj: if isinstance(msg, str): try: msg = json.loads(msg) except Exception: msg = {"role": "unknown", "content": msg} if isinstance(msg, dict): norm.append(msg) return norm def normalize_metadata(meta): if isinstance(meta, str): try: return json.loads(meta) except Exception: return {} return meta if isinstance(meta, dict) else {} def role_counts(trajectory): c = Counter() for msg in trajectory or []: if isinstance(msg, dict): c[msg.get("role", "unknown")] += 1 return c _FUNC_XML = re.compile(r"", re.IGNORECASE) _BASH_FENCE = re.compile(r"```(?:bash|sh|shell)\b", re.IGNORECASE) def extract_tool_names(trajectory): names = Counter() for msg in trajectory or []: if not isinstance(msg, dict): continue for call in msg.get("tool_calls") or []: fn = (call or {}).get("function", {}) if isinstance(call, dict) else {} if fn.get("name"): names[fn["name"]] += 1 if msg.get("role") == "tool" and msg.get("name"): names[msg["name"]] += 1 if msg.get("role") == "assistant": text = message_text(msg) for m in _FUNC_XML.findall(text): names[m.lower()] += 1 for m in _EXEC_TAG.findall(text): names[m.lower()] += 1 if _BASH_FENCE.search(text): names["bash_block"] += 1 return names def parse_patch(diff_text): if not diff_text or not isinstance(diff_text, str): return 0, 0, 0, [], Counter() files, exts = [], Counter() additions = deletions = 0 for line in diff_text.splitlines(): if line.startswith("diff --git"): parts = line.split() if len(parts) >= 3: path = parts[2][2:] if parts[2].startswith("a/") else parts[2] files.append(path) base = path.split("/")[-1] if "." in base: exts[base.rsplit(".", 1)[-1].lower()] += 1 elif line.startswith("+") and not line.startswith("+++"): additions += 1 elif line.startswith("-") and not line.startswith("---"): deletions += 1 return len(files), additions, deletions, files, exts def make_token_counter(): try: import tiktoken enc = tiktoken.get_encoding("cl100k_base") return lambda s: len(enc.encode(s, disallowed_special=())) except Exception: return lambda s: max(1, len(s) // 4) count_tokens = make_token_counter()

We define helper functions that make the dataset easier to process, even when fields appear in different formats. We normalize trajectories, extract message text, count roles, detect tool usage, parse code patches, and estimate token lengths. We build these utilities defensively so that our analysis remains stable across schema variations in large streamed datasets.

Streaming and Inspecting Trajectories

Copy CodeCopiedUse a different Browser

def stream_take(agent, model, n): ds = load_dataset(DATASET, agent, split=model, streaming=True) rows = [] for ex in islice(ds, n): ex = dict(ex) ex["_agent"], ex["_model"] = agent, model rows.append(ex) return rows banner("STEP 1 — Streaming trajectories from the Hub") raw_rows = [] if SAMPLE_ALL: combos = [(a, m) for a in AGENTS for m in MODELS] for agent, model in combos: try: part = stream_take(agent, model, PER_COMBO) raw_rows.extend(part) print(f" ✓ {agent: {len(part):>4} rows") except Exception as e: print(f" ✗ {agent}/{model} failed: {type(e).name}: {e}") else: raw_rows = stream_take(AGENTS[0], MODELS[0], N_SINGLE) print(f" ✓ {AGENTS[0]} / {MODELS[0]} -> {len(raw_rows)} rows") print(f"\n Total rows pulled into memory: {len(raw_rows)}") assert raw_rows, "No rows streamed — check your internet connection and retry." banner("STEP 2 — Anatomy of a single record") sample = raw_rows[0] print("Top-level fields :", list(sample.keys())) print("instance_id :", sample.get("instance_id")) print("repo / language :", sample.get("repo"), "/", sample.get("language")) print("license :", sample.get("license")) print("resolved (1/0/-1):", sample.get("resolved")) print("metadata :", normalize_metadata(sample.get("metadata"))) traj0 = normalize_trajectory(sample.get("trajectory")) print(f"\nTrajectory has {len(traj0)} messages. Role histogram: {dict(role_counts(traj0))}") print("\n--- Trajectory walkthrough (each message truncated to 240 chars) ---") for i, msg in enumerate(traj0[:8]): role = msg.get("role", "unknown").upper() body = " ".join(message_text(msg).split()) print(f"\n[{i}] {role}") print(textwrap.fill(body[:240] + ("…" if len(body) > 240 else ""), width=92, subsequent_indent=" ")) if len(traj0) > 8: print(f"\n… (+{len(traj0) - 8} more messages)") print("\n--- Final patch (model_patch), first 25 lines ---") print("\n".join((sample.get("model_patch") or "").splitlines()[:25]) or "(empty)")

We stream a small sample of Open-SWE-Traces directly from Hugging Face instead of downloading the full dataset. We collect examples across agent and model combinations, then inspect the structure of a single record in detail. We walk through the first few trajectory messages and preview the final patch to understand what each training example contains.

Building the Analysis DataFrame

Copy CodeCopiedUse a different Browser

banner("STEP 3 — Building the analysis DataFrame") def process_example(ex): traj = normalize_trajectory(ex.get("trajectory")) rc = role_counts(traj) nf, add, dele, _files, _exts = parse_patch(ex.get("model_patch")) meta = normalize_metadata(ex.get("metadata")) full_text = "\n".join(message_text(m) for m in traj) return { "instance_id": ex.get("instance_id"), "repo": ex.get("repo"), "language": (ex.get("language") or "unknown").lower(), "license": ex.get("license"), "resolved": ex.get("resolved"), "agent": ex.get("_agent"), "model": ex.get("_model"), "n_messages": len(traj), "n_system": rc.get("system", 0), "n_user": rc.get("user", 0), "n_assistant": rc.get("assistant", 0), "n_tool": rc.get("tool", 0), "patch_files": nf, "patch_add": add, "patch_del": dele, "patch_churn": add + dele, "traj_tokens": count_tokens(full_text), "category": meta.get("category"), "meta_files": meta.get("num_modified_files"), "meta_lines": meta.get("num_modified_lines"), "_tools": extract_tool_names(traj), } records = [process_example(ex) for ex in raw_rows] df = pd.DataFrame(records) df["is_resolved"] = (df["resolved"] == 1) df["known_label"] = df["resolved"].isin([0, 1]) print(f"DataFrame: {df.shape[0]} rows x {df.shape[1]} cols") print("\nNumeric summary:") print(df[["n_messages", "n_assistant", "n_tool", "patch_files", "patch_churn", "traj_tokens"]].describe().round(1))

We transform the raw streamed records into a structured pandas DataFrame for analysis. We extract trajectory-level features such as message counts, role counts, patch churn, token estimates, metadata fields, and tool-use counters. We also create resolution flags to compare successful and unsuccessful software-engineering trajectories.

Visualizing Trajectory Distributions

Copy CodeCopiedUse a different Browser

banner("STEP 4 — Distributions & visualizations") lang_counts = df["language"].value_counts() print("Trajectories per language:\n", lang_counts.to_string()) ax = lang_counts.plot(kind="bar", color=BLUE) ax.set_title("Trajectories per language (sample)") ax.set_xlabel(""); ax.set_ylabel("count") plt.tight_layout(); plt.show() known = df[df["known_label"]] by_lang = (known.groupby("language")["is_resolved"] .agg(rate="mean", n="size") .query("n >= 25") .sort_values("rate", ascending=False)) print("\nResolution rate by language (n>=25):\n", by_lang.round(3).to_string()) if not by_lang.empty: ax = by_lang["rate"].plot(kind="bar", color=GREEN) ax.set_title("Resolution rate by language") ax.set_xlabel(""); ax.set_ylabel("fraction resolved"); ax.set_ylim(0, 1) plt.tight_layout(); plt.show() if known["agent"].nunique() > 1 or known["model"].nunique() > 1: pivot = (known.groupby(["agent", "model"])["is_resolved"].mean().unstack()) print("\nResolution rate by scaffold x model:\n", pivot.round(3).to_string()) ax = pivot.plot(kind="bar", color=[BLUE, ORANGE]) ax.set_title("Resolution rate: scaffold x model") ax.set_xlabel("agent"); ax.set_ylabel("fraction resolved"); ax.set_ylim(0, 1) ax.legend(title="model"); plt.tight_layout(); plt.show() ax = df["n_messages"].plot(kind="hist", bins=40, color=BLUE, alpha=0.85) ax.set_title("Messages per trajectory") ax.set_xlabel("number of messages"); ax.set_ylabel("trajectories") plt.tight_layout(); plt.show() churn = df["patch_churn"].clip(upper=df["patch_churn"].quantile(0.97)) ax = churn.plot(kind="hist", bins=40, color=ORANGE, alpha=0.85) ax.set_title("Patch size — lines changed (clipped at p97)") ax.set_xlabel("added + deleted lines"); ax.set_ylabel("trajectories") plt.tight_layout(); plt.show() if known["is_resolved"].nunique() > 1: fig, ax = plt.subplots() for flag, color, lab in [(True, GREEN, "resolved"), (False, RED, "unresolved")]: sub = known[known["is_resolved"] == flag] ax.scatter(sub["n_messages"], sub["traj_tokens"], s=10, alpha=0.4, color=color, label=lab) ax.set_title("Trajectory length vs. token size, by outcome") ax.set_xlabel("messages"); ax.set_ylabel("estimated tokens") ax.legend(); plt.tight_layout(); plt.show()

Analyzing Token Budget Requirements

Copy CodeCopiedUse a different Browser

banner("STEP 5 — Token budget (what context window do you need?)") tok =

[truncated for AI cost control]