Datasets:

RohitManglik
/

Coding-Trajectories-Sample

Error code:   StreamingRowsError
Exception:    CastError
Message:      Couldn't cast
episode_id: large_string
event_id: large_string
seq: int64
turn: int64
on_abandoned_branch: bool
tool: large_string
kinds: large_string
model: large_string
effort: large_string
input: large_string
result: large_string
labels: large_string
evidence: large_string
reasoning: large_string
scrubbed: large_string
-- schema metadata --
pandas: '{"index_columns": [], "column_indexes": [], "columns": [{"name":' + 1746
to
{'episode_id': Value('large_string'), 'role': Value('large_string'), 'parent_episode_id': Value('large_string'), 'agent': Value('large_string'), 'client_surface': Value('large_string'), 'models': Value('large_string'), 'model_calls': Value('large_string'), 'capture_sources': Value('large_string'), 'contributor_ref': Value('large_string'), 'num_turns': Value('int64'), 'num_events': Value('int64'), 'session_summary': Value('large_string'), 'session_outcome': Value('large_string'), 'resolution_score': Value('float64'), 'views': Value('large_string'), 'label_counts': Value('large_string'), 'corrections': Value('large_string')}
because column names don't match
Traceback:    Traceback (most recent call last):
                File "/src/services/worker/src/worker/utils.py", line 149, in get_rows_or_raise
                  return get_rows(
                      dataset=dataset,
                  ...<4 lines>...
                      column_names=column_names,
                  )
                File "/src/libs/libcommon/src/libcommon/utils.py", line 272, in decorator
                  return func(*args, **kwargs)
                File "/src/services/worker/src/worker/utils.py", line 129, in get_rows
                  rows_plus_one = list(itertools.islice(safe_iter(ds, dataset=dataset), rows_max_number + 1))
                File "/src/services/worker/src/worker/utils.py", line 544, in safe_iter
                  yield from ds.decode(False) if ds.features else ds
                File "/usr/local/lib/python3.14/site-packages/datasets/iterable_dataset.py", line 3052, in __iter__
                  for key, example in ex_iterable:
                                      ^^^^^^^^^^^
                File "/usr/local/lib/python3.14/site-packages/datasets/iterable_dataset.py", line 2559, in __iter__
                  for key, pa_table in self._iter_arrow():
                                       ~~~~~~~~~~~~~~~~^^
                File "/usr/local/lib/python3.14/site-packages/datasets/iterable_dataset.py", line 2584, in _iter_arrow
                  for key, pa_table in self.ex_iterable._iter_arrow():
                                       ~~~~~~~~~~~~~~~~~~~~~~~~~~~~^^
                File "/usr/local/lib/python3.14/site-packages/datasets/iterable_dataset.py", line 558, in _iter_arrow
                  for key, pa_table in iterator:
                                       ^^^^^^^^
                File "/usr/local/lib/python3.14/site-packages/datasets/iterable_dataset.py", line 440, in _iter_arrow
                  for key, pa_table in self.generate_tables_fn(**gen_kwags):
                                       ~~~~~~~~~~~~~~~~~~~~~~~^^^^^^^^^^^^^
                File "/usr/local/lib/python3.14/site-packages/datasets/packaged_modules/parquet/parquet.py", line 220, in _generate_tables
                  yield Key(file_idx, batch_idx), self._cast_table(pa_table)
                                                  ~~~~~~~~~~~~~~~~^^^^^^^^^^
                File "/usr/local/lib/python3.14/site-packages/datasets/packaged_modules/parquet/parquet.py", line 156, in _cast_table
                  pa_table = table_cast(pa_table, self.info.features.arrow_schema)
                File "/usr/local/lib/python3.14/site-packages/datasets/table.py", line 2381, in table_cast
                  return cast_table_to_schema(table, schema)
                File "/usr/local/lib/python3.14/site-packages/datasets/table.py", line 2309, in cast_table_to_schema
                  raise CastError(
                  ...<3 lines>...
                  )
              datasets.table.CastError: Couldn't cast
              episode_id: large_string
              event_id: large_string
              seq: int64
              turn: int64
              on_abandoned_branch: bool
              tool: large_string
              kinds: large_string
              model: large_string
              effort: large_string
              input: large_string
              result: large_string
              labels: large_string
              evidence: large_string
              reasoning: large_string
              scrubbed: large_string
              -- schema metadata --
              pandas: '{"index_columns": [], "column_indexes": [], "columns": [{"name":' + 1746
              to
              {'episode_id': Value('large_string'), 'role': Value('large_string'), 'parent_episode_id': Value('large_string'), 'agent': Value('large_string'), 'client_surface': Value('large_string'), 'models': Value('large_string'), 'model_calls': Value('large_string'), 'capture_sources': Value('large_string'), 'contributor_ref': Value('large_string'), 'num_turns': Value('int64'), 'num_events': Value('int64'), 'session_summary': Value('large_string'), 'session_outcome': Value('large_string'), 'resolution_score': Value('float64'), 'views': Value('large_string'), 'label_counts': Value('large_string'), 'corrections': Value('large_string')}
              because column names don't match

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Coding Trajectory Dataset Samples

Overview

The Coding Trajectory Dataset Samples collection provides real-world agentic coding sessions in which an AI coding agent works with a developer inside a live software environment. Each session is captured as a structured trajectory covering developer requests, agent reasoning, tool calls, command results, file edits, and final outcomes.

Trajectories are organized at the session, turn, and event level, and are enriched with outcome labels (such as accepted, committed, overwritten, reverted, test passed, and test failed), human-correction signals, and session-level resolution scores. Curated views make the data ready for supervised fine-tuning, failure-recovery learning, verified-outcome training, and human-correction and preference research.

These resources can support research and development in coding agents, code-focused Generative AI, agent evaluation, reinforcement learning from human feedback, and software engineering automation.

Dataset Highlights

Dataset Name: Coding Trajectory Dataset Samples
Asset Type: Agentic coding session trajectories
File Format: Parquet (four linked tables)
Granularity: Session, turn, and tool-call event level
Agent Surfaces: Terminal and desktop application coding agents, with main-agent and sub-agent sessions
Model Coverage: Multiple model tiers (Opus, Sonnet, Haiku, and specialized tiers)
Content Includes: Developer prompts, agent reasoning, tool inputs and results, file edits, test outcomes, commits, and human corrections
Primary AI Use Cases: Coding agent SFT, failure-recovery training, verified-outcome training, human-correction and preference learning, and agent evaluation

Dataset Features

Complete multi-turn coding sessions rather than isolated prompt-response pairs.
Tool-call level events covering shell commands, file reads, edits, writes, searches, sub-agent calls, and external tool integrations.
Outcome labels attached to individual events, such as accepted, committed, overwritten, reverted, test passed, and test failed.
Human-correction records with the rejected action, the preferred action, the correction reason, and a quality score.
Session-level summaries, outcome status, and resolution scores.
Agent reasoning attached to turns, with the reasoning source recorded.
Tracking of abandoned branches, so recovery behavior can be studied.
Structured judge labels for developer intent, such as confirmation, clarification, redirection, and correction.
Personal and environment-specific identifiers scrubbed from the data.

Data Structure

The samples are organized into four linked Parquet tables, joined by episode_id.

1. Episodes (`episodes.parquet`)

One row per session. Includes agent and client surface, session role (main or sub-agent), models used, capture source, session summary, session outcome, resolution score, curated view flags, label counts, and human corrections.

2. Turns (`turns.parquet`)

One row per developer-agent turn. Includes the user message and its kind (human, slash command, interrupt, queued message), model and effort level, agent reasoning, turn labels, judge labels, turn metadata (files touched, lines added and removed, duration), and the ordered steps taken.

3. Events (`events.parquet`)

One row per tool call. Includes tool name, event kind (command, edit), model, effort level, tool input, tool result, event labels, supporting evidence, reasoning, and a record of what was scrubbed.

4. Views (`views.parquet`)

Curated, task-ready records. Each row belongs to one of four views:

sft_ready: Sessions suitable for supervised fine-tuning.
failure_recovery: Sessions where the agent hits a failure and recovers.
verified_outcomes: Sessions with verified results, such as passing tests or commits.
human_corrections: Cases where the developer corrected the agent, with rejected and preferred actions.

Label Taxonomy

Event and turn labels include:

Outcome signals: accepted, committed, overwritten, reverted
Test signals: test passed, test failed
Human signals: human correction, human rejected, user confirm, user clarify, user nudge, user redirect
Agent behavior: agent asked, agent revised prior work, agent self-fixed
Turn type: no edit, outcome accepted, outcome overwritten, tests passed, tests failed

Session Environment Labels

Sessions are classified by environment and quality-assurance label:

human_like: Sessions resembling real developer workflows.
no_cwd: Sessions without a working directory.
bare-container: Sessions run in minimal container environments.
testbed: Sessions run in test or benchmark environments.

Sessions with conflicting labels are held out as a separate quality-assurance pool.

AI Tasks Supported

The dataset may be useful for research and development involving:

Coding Agent Supervised Fine-Tuning (SFT)
Reinforcement Learning from Human Feedback (RLHF)
Preference and Human-Correction Learning
Failure Recovery and Self-Correction Training
Verified-Outcome and Reward Modeling
Tool-Use and Function-Calling Training
Agent Evaluation and Benchmarking
Long-Horizon Planning in Software Tasks
Code Generation and Code Editing
Developer Intent Understanding

Potential Applications

Training and fine-tuning AI coding assistants and autonomous coding agents.
Building reward models from test results, commits, and accepted or overwritten edits.
Studying how agents recover from failed commands and failing tests.
Learning from developer corrections to align agents with real developer expectations.
Evaluating agent behavior across models, effort levels, and environments.
Analyzing real developer-agent collaboration patterns.
Building debugging, refactoring, and code review automation.
Researching tool-use behavior in agentic workflows.

Industries and Research Areas

Potential areas of application include:

Artificial Intelligence and Machine Learning
Generative AI and Code LLMs
Agentic AI
Developer Tools and DevOps
Software Engineering Automation
Reinforcement Learning
AI Safety and Evaluation
Enterprise Software
Education Technology (EdTech)

Intended Audience

This dataset may be useful for:

AI and machine learning researchers
Generative AI and code LLM developers
Coding agent and developer tool builders
Reinforcement learning and alignment teams
AI evaluation and benchmarking teams
Enterprise AI teams
Software engineering researchers
EdTech companies
Academic institutions

Privacy and Data Handling

Trajectories are processed to remove personal and environment-specific identifiers. Scrubbed categories include local usernames, person names, GitHub handles, social profile IDs, personal sites, commit hashes, session and agent IDs, local proxy and temporary directory details, public IP addresses, and third-party names. Images are omitted from the samples.

Advantages

Real developer-agent sessions covering the full workflow from request to outcome.
Rich outcome signals that support reward modeling and verification-based training.
Human-correction records suited to preference learning.
Failure and recovery behavior preserved, not filtered out.
Multiple models and effort levels for comparative research.
Task-ready curated views alongside full session detail.
Personal identifiers scrubbed for safer research use.

Summary

The Coding Trajectory Dataset Samples collection represents real agentic coding sessions captured at session, turn, and tool-call level, with outcome labels, human-correction signals, and curated views for SFT, failure recovery, verified outcomes, and human-correction learning. It can support research and development in coding agents, agent evaluation, RLHF, tool-use training, and software engineering automation.

Data Creation

Procured through formal agreements and generated in the ordinary course of business.

Considerations

This dataset is provided for research and educational purposes only. It contains only sample data. For access to the full dataset and enterprise licensing options, please visit our website InfoBay.AI or contact us directly:

Phone: +91 9236398619
Email: datareq@infobay.ai

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