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OpenSeeker
OpenSeeker is a research search-agent project with model checkpoints, web-search tools and code for generating and evaluating answers to question datasets.
The publisher documents v2 alongside retained v1 materials. A model checkpoint, a training dataset and a configured evaluation run are different parts of the project. Use this as a first read, not a recommendation. Open the original project before trusting details like terms, limits, privacy, cost, setup, or safety.
What it is
A checkpoint plus a search workflow
The project combines a served model with search and page-visit tools. Its v2 implementation also includes E2B sandbox tools, rather than treating the checkpoint alone as the whole agent.
Why it stands out
Generation and scoring are separate
The README shows one script generating answers into a results file and a separate evaluator scoring that file. Producing an answer and evaluating it are separate stages.
Availability
Versioned models and research code
The README announces v2 code on 12 May 2026 and links its 30B SFT checkpoint. It also retains v1 model and training-data references.
Why it matters
What makes it useful
For a research comparison of answers that require finding and visiting web sources, the README supplies both the agent workflow and an evaluation path. Its v2 layout includes search, page visits and remote sandbox tools alongside the model server. This makes the configured tools part of what is being compared; downloading the checkpoint alone does not reproduce the documented workflow.
What to know
Where it fits
For someone moving an existing v1 setup to v2, the README names two changes: MODEL_PATH points to the v2 checkpoint, and answer generation uses eval/generate_answer_v2.py instead of eval/generate_answer.py. It says the installation, server script, environment configuration and eval/eval.py evaluator are shared. The model version and generation entrypoint therefore belong together when identifying the run.
Notable points
What stands out
The linked training-data card is OpenSeeker-v1-Data: it names openseeker_v1_data.jsonl and describes 11.7K examples used with Qwen3-30B-A3B-Thinking-2507. The separately linked OpenSeeker-v2-30B-SFT card describes the v2 checkpoint. For someone studying the training examples, those version labels matter: the v1 dataset link is not a v2 dataset reference.
Before using
What to review
The publisher's setup_env.sh separates the OpenSeeker model endpoint from search and page-visit services. It documents Serper as the default search provider, optional Tavily, Jina and a summary-model endpoint for visits, and E2B for remote sandbox tools.
The same file gives eval/eval.py its own scorer model, URLs and key. A configured answer-generation endpoint does not by itself configure scoring; hosting the checkpoint locally does not make the documented external tool services local.
Reader fit
Who may find it relevant
For someone supplying their own question file, the v2 generator reads JSONL entries with a query field. Its --use_box option is enabled by default and appends a boxed-final-answer formatting instruction; the documented --no-use_box option disables that instruction. This is an answer-format choice for the run, rather than a property of the original question file.
Editorial note
Why LifeHubber lists it
Returning to an output directory can resume existing answers rather than generate every question again. In the publisher's v2 generator, get_queries_without_answer reads result_tool{tool_count_max}.jsonl and skips a matching query when its final_response is nonempty after stripping whitespace. That test checks whether an answer is present, not whether it is correct. For someone comparing runs, the saved results therefore determine which questions are still generated; the separate evaluator remains the scoring step.
Source links
Source materials
Reader note
Before relying on this entry
LifeHubber lists entries to help readers inspect AI projects, not to endorse them or prove they are safe, suitable, accurate, maintained, or right for a specific use. We do not verify every entry in depth. Before relying on anything listed, review the original materials, terms, privacy practices, limits, and risks that matter for your situation.
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