Score breakdown
Popularity is tracked separately. Support, ads, sponsorships, and tips never affect these signals.
Why it matters
Useful for AI researchers, deep-research agent developers, AI-curious readers, and any developer benchmarking deep-research agents on public benchmarks -- and who can pair ApodexAI/AgentHarness with `uv` for the Python install surface, SGLang for the model serving surface, the apodex/Apodex-1.0-35B-A3B model weights from Hugging Face for the model surface, the apodex/Deep-Research-Benchmarks datas
Who should use it
Who should skip it
Skip ApodexAI/AgentHarness if the source repository or demo is inactive, unmaintained, or no longer matches the description shown here.
About this signal
ApodexAI/AgentHarness is tracked by RepoRadar as a public eval harness for the apod in the Deep-Research Eval Harness (ReAct, 9 Benchmarks) section. It was first seen on 2026-07-07 and last updated on 2026-07-07. The current verdict is 'try now' with a Silver tier and moderate setup difficulty. The standout signals for ApodexAI/AgentHarness are workflow potential (8.9) and open-source/build quality (8.4), while maturity (5.7) trails — that balance shapes where it fits best. This page summarizes the evidence RepoRadar has captured from captured source metadata. The score, tier, risk label, and verdict on this page are never influenced by sponsorship, ads, or tips — they reflect only the usefulness, popularity, novelty, momentum, maturity, and evidence signals described in the RepoRadar methodology.
How this item is evaluated
RepoRadar assigned ApodexAI/AgentHarness a composite score of 7.8 out of 10, placing it in the Silver tier. This score combines weighted sub-signals: usefulness (35%), novelty (18%), momentum (14%), maturity (10%), open-source/build quality (7%), evidence quality (6%), workflow potential (6%), and setup ease (4%). Popularity is tracked separately at 1.0 and never affects the composite score or tier. The risk label of 'low' reflects inherent user-impacting hazards, not generic novelty. Items with no risk flag may still require normal code review before production use.
Putting this into practice? Read How to vet an AI agent or MCP server before you wire it in for the checklist behind this score.
Risk explanation
The 340* / 38-fork repo is at active maintenance but the eval pipeline requires significant GPU infrastructure -- treat the first evaluation cycle as a smoke test (`uv sync --python 3.12` + serve the model via SGLang + fill in `.env` + download benchmarks + run smoke test on `browsecomp` with `--limit 1`) before relying on the harness in production; the 35B-A3B model serving requires 8-way tensor parallelism (--tp 8) -- the consumer SHOULD verify the GPU infrastructure before deploying; the SERPER_API_KEY + JINA_API_KEY + E2B_API_KEY are required for web search / fetch / sandbox -- the consumer SHOULD budget for the third-party API costs before running a full benchmark; the HLE benchmark is not included (its license forbids redistributing the answers) -- the consumer SHOULD accept the license on `cais/hle` and place the JSONL at `benchmarks/datasets/HLE-text/standardized_data.jsonl` to run `hle_text`.