OpenThoughts-Agent is an open-source effort to curate the best datasets for training agents. Our first release includes datasets, models and our research codebase.
OpenThinker-Agent-v1 is a model trained for agentic tasks such as Terminal-Bench 2.0 and SWE-Bench.
We built OpenThinker-Agent-v1 in two stages: supervised fine-tuning, followed by reinforcement learning.
Each stage required its own data pipeline – RL tasks (instructions, environments, and verifiers) and SFT traces from strong teacher agents completing tasks.
OpenThoughts-Agent-v1-SFT is an SFT trace dataset containing approximately 15,200 traces drawn from two different data sources we curate:
nl2bash: Simple synthetically generated tasks where the agent has to format shell commands effectively
InferredBugs: A set of bugs in C# and Java collected by Microsoft that we turned into tasks
OpenThoughts-Agent-v1-RL is an RL dataset containing ~720 tasks drawn from the nl2bash verified dataset.
To stabilize training, we built a three-stage filtration pipeline that prunes tasks before they ever hit the learner:
Bad verifiers filter: drop tasks with flaky or excessively slow verifiers.
Environment stability: remove tasks whose containers take too long to build or tear down.
Optional difficulty filter: discard tasks that even a strong model (GPT-5 Codex) cannot solve in a single pass.
