Datasets:

gss1147
/

AgentAngel_1k_Demo

Error code:   FeaturesError
Exception:    ArrowInvalid
Message:      Schema at index 1 was different: 
swebench_original: string
swebench_repo: string
swebench_verified_openai: string
codex_intro: string
mcp_spec: string
mcp_tools: string
owasp_top10_llm: string
owasp_llm01: string
ncsc_prompt_injection_news: string
vs
id: string
format: string
messages: list<item: struct<role: string, content: string>>
rights_wrongs: struct<label: string, rationale: string>
evidence_sources: list<item: string>
created: timestamp[s]
brand: string
Traceback:    Traceback (most recent call last):
                File "/src/services/worker/src/worker/job_runners/split/first_rows.py", line 243, in compute_first_rows_from_streaming_response
                  iterable_dataset = iterable_dataset._resolve_features()
                                     ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
                File "/usr/local/lib/python3.12/site-packages/datasets/iterable_dataset.py", line 3496, in _resolve_features
                  features = _infer_features_from_batch(self.with_format(None)._head())
                                                        ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
                File "/usr/local/lib/python3.12/site-packages/datasets/iterable_dataset.py", line 2257, in _head
                  return next(iter(self.iter(batch_size=n)))
                         ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
                File "/usr/local/lib/python3.12/site-packages/datasets/iterable_dataset.py", line 2461, in iter
                  for key, example in iterator:
                                      ^^^^^^^^
                File "/usr/local/lib/python3.12/site-packages/datasets/iterable_dataset.py", line 1952, in __iter__
                  for key, pa_table in self._iter_arrow():
                                       ^^^^^^^^^^^^^^^^^^
                File "/usr/local/lib/python3.12/site-packages/datasets/iterable_dataset.py", line 1974, in _iter_arrow
                  yield from self.ex_iterable._iter_arrow()
                File "/usr/local/lib/python3.12/site-packages/datasets/iterable_dataset.py", line 547, in _iter_arrow
                  yield new_key, pa.Table.from_batches(chunks_buffer)
                                 ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
                File "pyarrow/table.pxi", line 5039, in pyarrow.lib.Table.from_batches
                File "pyarrow/error.pxi", line 155, in pyarrow.lib.pyarrow_internal_check_status
                File "pyarrow/error.pxi", line 92, in pyarrow.lib.check_status
              pyarrow.lib.ArrowInvalid: Schema at index 1 was different: 
              swebench_original: string
              swebench_repo: string
              swebench_verified_openai: string
              codex_intro: string
              mcp_spec: string
              mcp_tools: string
              owasp_top10_llm: string
              owasp_llm01: string
              ncsc_prompt_injection_news: string
              vs
              id: string
              format: string
              messages: list<item: struct<role: string, content: string>>
              rights_wrongs: struct<label: string, rationale: string>
              evidence_sources: list<item: string>
              created: timestamp[s]
              brand: string

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Within Us AI — AgentAngel_1k_Demo (Agentic Coding 2026)

AgentAngel is a verification-first dataset family for training and evaluating agentic coding models that plan, patch, run checks, and iterate.

This demo pack contains 1,000 examples per split (5,000 JSONL rows total):

Q&A: fact-grounded with rights/wrongs
Instruct: chat messages
Thinking: concise rationales
Reasoning: constraints + verification checks
Chat: multi-turn

Evidence discipline

Each row includes evidence_sources URLs to primary references (benchmark pages, specs, official docs, and security guidance). Prescriptive guidance is written as recommendation (not as a factual claim).

Files

splits/agentangel_1k.qa.jsonl
splits/agentangel_1k.instruct.jsonl
splits/agentangel_1k.thinking.jsonl
splits/agentangel_1k.reasoning.jsonl
splits/agentangel_1k.chat.jsonl
sources.json

Community results

If you train with AgentAngel, please post results in Discussions (model, method, eval, deltas).

— Within Us AI

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