From 4516b71c331c9a54b29ae8854df72f4d7b0ea4d7 Mon Sep 17 00:00:00 2001 From: minixalpha Date: Sun, 6 Sep 2026 18:45:31 +0800 Subject: [PATCH 1/3] docs(benchmarks): record the 20-task terminal-bench pilot baseline --- .../tb21-flash0731-pilot20-20260906.json | 2468 +++++++++++++++++ docs/dev_notes/en/0.8.x.md | 256 ++ docs/dev_notes/zh-CN/0.8.x.md | 208 ++ 3 files changed, 2932 insertions(+) create mode 100644 benchmarks/harbor/results/tb21-flash0731-pilot20-20260906.json diff --git a/benchmarks/harbor/results/tb21-flash0731-pilot20-20260906.json b/benchmarks/harbor/results/tb21-flash0731-pilot20-20260906.json new file mode 100644 index 0000000..6d462f8 --- /dev/null +++ b/benchmarks/harbor/results/tb21-flash0731-pilot20-20260906.json @@ -0,0 +1,2468 @@ +{ + "schema_version": 1, + "job_name": "tb21-flash0731-pilot20-20260906", + "job_id": "f81da209-ce59-4970-a5f4-32c8fee2bfaa", + "experiment": { + "agent_ref": "2b8309794cb9e00cb4d3b08baf1e4733153105e0", + "harbor_version": "0.21.0", + "dataset": "terminal-bench/terminal-bench-2-1", + "dataset_ref": "sha256:7d7bdc1cbedad549fc1140404bd4dc45e5fd0ea7c4186773687d177ad3a0699a", + "dataset_size": 89, + "model": "openrouter/deepseek/deepseek-v4-flash-0731", + "endpoint": "https://openrouter.ai/api", + "provider_routing": "No request-level override; 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354313, + "total_completion_tokens": 19527, + "total_cached_tokens": 312576 + }, + "task_limits_and_environment": { + "agent": { + "timeout_sec": 900.0 + }, + "verifier": { + "timeout_sec": 900.0 + }, + "environment": { + "build_timeout_sec": 600.0, + "docker_image": "alexgshaw/pytorch-model-recovery:20260430", + "cpus": 1, + "memory_mb": 2048, + "storage_mb": 10240, + "gpus": 0, + "allow_internet": true, + "mcp_servers": [] + } + } + }, + { + "task": "terminal-bench/circuit-fibsqrt", + "task_ref": "sha256:9bcffe1054bb33249aa578a9a2a74f3c8cca66b0cb7aa1328233f1d31822aae3", + "trial_name": "circuit-fibsqrt__758KQCh", + "status": "finished", + "reward": 0.0, + "agent_version": "0.8.1.dev3+g2b8309794", + "exception_type": null, + "started_at": "2026-09-06T10:24:56.914074Z", + "finished_at": "2026-09-06T10:29:58.529059Z", + "duration_sec": 301.615, + "environment_setup_sec": 16.123, + "agent_setup_sec": 46.034, + "agent_execution_sec": 205.079, + "verifier_sec": 23.154, + 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"6d9360cab76c50a8d3a4f8168509af72b151ae2723615140f9138d1c1aa5a123", + "trajectory": "jobs/tb21-flash0731-pilot20-20260906/circuit-fibsqrt__758KQCh/agent/trajectory.json", + "trajectory_sha256": "8bc143ba6f59ba6ef2218cb890d1000ca4227ecce556a82252a0ddff9b9270be", + "native_trajectory_present": true + }, + "post_run_resolved_generations": [], + "post_run_unresolved_generations": [], + "post_run_known_cost_usd": 0.001051326, + "post_run_cost_usd": 0.001051326, + "atif_final_metrics": { + "total_steps": 3, + "total_prompt_tokens": 7590, + "total_completion_tokens": 8808, + "total_cached_tokens": 2304, + "total_cost_usd": 0.001051326 + }, + "task_limits_and_environment": { + "agent": { + "timeout_sec": 3600.0 + }, + "verifier": { + "timeout_sec": 3600.0 + }, + "environment": { + "build_timeout_sec": 600.0, + "docker_image": "alexgshaw/circuit-fibsqrt:20251031", + "cpus": 1, + "memory_mb": 2048, + "storage_mb": 10240, + "gpus": 0, + "allow_internet": true, + "mcp_servers": [] + } + } + }, + { + "task": "terminal-bench/merge-diff-arc-agi-task", + "task_ref": "sha256:6aab6511a5344ce87698293bb1ce4cc51d9a45f1ad9f0c075d2a83197b36727d", + "trial_name": "merge-diff-arc-agi-task__w5sZoRS", + "status": "finished", + "reward": 1.0, + "agent_version": "0.8.1.dev3+g2b8309794", + "exception_type": null, + "started_at": "2026-09-06T10:28:53.861023Z", + "finished_at": "2026-09-06T10:32:51.291945Z", + "duration_sec": 237.431, + "environment_setup_sec": 14.434, + "agent_setup_sec": 66.768, + "agent_execution_sec": 123.169, + "verifier_sec": 21.67, + "agent_execution_started": true, + "n_input_tokens": 130371, + "n_cache_tokens": 121856, + "n_output_tokens": 8768, + "cost_usd": null, + "known_cost_usd": 0.002175354, + "cost_is_partial": true, + "usage_complete": true, + "trajectory_status": "partial", + "trajectory_valid": true, + "model_metrics_source": "native trajectory", + "trajectory_arrived_after_missing_diagnostic": false, + "trajectory_validation_errors": [], + "metrics_match": { + "n_input_tokens": true, + "n_cache_tokens": true, + "n_output_tokens": true, + "cost_usd": true + }, + "steps": 15, + "model_steps": 14, + "model_stop_reasons": { + "tool_use": 13, + "end_turn": 1 + }, + "last_model_stop_reason": "end_turn", + "tool_calls": 15, + "terminal_status": "completed", + "terminal_outcome": "completed", + "terminal_error_type": null, + "missing_generation_ids": [ + "gen-1788690615-MZxqjQeopSlBndazGddU" + ], + "artifacts": { + "result": "jobs/tb21-flash0731-pilot20-20260906/merge-diff-arc-agi-task__w5sZoRS/result.json", + "result_sha256": "afcd91b2f225a6f4882cafe499b8c396488e3a717201ebd9c3287b6772ca25e2", + "trajectory": "jobs/tb21-flash0731-pilot20-20260906/merge-diff-arc-agi-task__w5sZoRS/agent/trajectory.json", + "trajectory_sha256": "880b1bcdce351f4879dfefaa8d92ad666bc11c0cd7ba4465460328c318473b17", + "native_trajectory_present": true + }, + "post_run_resolved_generations": [ + "gen-1788690615-MZxqjQeopSlBndazGddU" + ], + "post_run_unresolved_generations": [], + "post_run_known_cost_usd": 0.002268999, + "post_run_cost_usd": 0.002268999, + "atif_final_metrics": { + "total_steps": 15, + "total_prompt_tokens": 130371, + "total_completion_tokens": 8768, + "total_cached_tokens": 121856 + }, + "task_limits_and_environment": { + "agent": { + "timeout_sec": 900.0 + }, + "verifier": { + "timeout_sec": 900.0 + }, + "environment": { + "build_timeout_sec": 600.0, + "docker_image": "alexgshaw/merge-diff-arc-agi-task:20251031", + "cpus": 1, + "memory_mb": 4096, + "storage_mb": 10240, + "gpus": 0, + "allow_internet": true, + "mcp_servers": [] + } + } + } + ], + "follow_up_plan": [ + "Handle max_tokens explicitly and evaluate the per-response reasoning/output budget.", + "Make missing tool arguments recoverable without crashing stdout event projection.", + "Stop the container agent before verification on timeout and collect the final trajectory reliably.", + "Support separate delayed cost reconciliation without replacing incomplete values with zero.", + "After fixes, rerun these same 20 task hashes under a new agent revision and job name; use that pilot to budget the full 89-task run." + ], + "raw_artifact_archive": { + "path": "jobs/tb21-flash0731-pilot20-20260906-artifacts.tar.gz", + "sha256": "680a59abb6d80b8f2d12b5c164219475eee6acf8665ee84f96d9211785fd2dc2", + "size_bytes": 861842, + "availability": "Local workspace archive under git-ignored jobs/; not uploaded or committed. This JSON summary and supplemental billing receipts are versioned.", + "contents": [ + "Harbor job with credential redactions listed below", + "Task manifest", + "Full derived summary", + "Generation cost ledger", + "Supplemental receipts", + "Environment metadata", + "Filtered timeout timeline", + "Independent trajectory backup", + "One-off run and analysis scripts" + ], + "redactions": [ + { + "path": "jobs/tb21-flash0731-pilot20-20260906/torch-pipeline-parallelism__9DcJ62N/agent/nanopycodeagent.txt", + "original_sha256": "c2cd573b34a1a76f5452463037301c2bfc302c9e42eae181efd60a2ef4b577c9", + "archived_sha256": "b43413be32f7c43f2793237d02d2be726b8586efd3b890d696bd1be46bbc7560", + "reason": "Runtime credential appeared in captured agent output; replaced only in archive copies. Local originals have mode 0600." + }, + { + "path": "jobs/tb21-flash0731-pilot20-20260906/torch-pipeline-parallelism__9DcJ62N/agent/trajectory.json", + "original_sha256": "caffe9c44221a892c76896f923aaf70527d6c7bf299e444c717f5b47d65c2218", + "archived_sha256": "8e776cf8055026812c76402ab44e8037ea84f92a84952ec74eac2ce86f15e0c2", + "reason": "Runtime credential appeared in captured agent output; replaced only in archive copies. Local originals have mode 0600." + } + ] + } +} diff --git a/docs/dev_notes/en/0.8.x.md b/docs/dev_notes/en/0.8.x.md index 025b0c0..c79baa1 100644 --- a/docs/dev_notes/en/0.8.x.md +++ b/docs/dev_notes/en/0.8.x.md @@ -293,3 +293,259 @@ uv run pytest - Trajectory: 11 steps, including 10 model steps. - Tokens: 49,878 input, 42,240 cache, and 5,845 output. - Cost: USD 0.00222441; all 10 reconciliation lookups succeeded on their first attempt. + +### Running Terminal-Bench in batches and establishing a baseline + +The single-task validation has established that the task container, agent, +trajectory, cost reconciliation, and official verifier work together. The next +step is a 20-task pilot to observe failure modes and actual usage across different +tasks, then decide the budget for a baseline over the full 89-task set. The +20-task pass rate is a pilot result, not a full Terminal-Bench 2.1 score. + +#### Fixed experiment configuration + +This run was prepared on 2026-09-06 with the following conditions. Future harness +comparisons should reuse the tasks and execution limits while recording each new +agent revision separately. + +| Item | Configuration for this run | +| --- | --- | +| Harbor | 0.21.0, using `benchmarks/harbor/uv.lock` | +| Agent commit | `2b8309794cb9e00cb4d3b08baf1e4733153105e0` | +| Dataset | `terminal-bench/terminal-bench-2-1`, 89 tasks | +| Dataset content hash | `sha256:7d7bdc1cbedad549fc1140404bd4dc45e5fd0ea7c4186773687d177ad3a0699a` | +| Model | `openrouter/deepseek/deepseek-v4-flash-0731` | +| API endpoint | `https://openrouter.ai/api`, using the Anthropic Messages API | +| Provider routing | No request-level provider override; account routing settings are also an experiment condition | +| Pilot size | First 20 tasks in the dataset list, one attempt each | +| Environment | Local Docker, concurrency 2 | +| Agent limit | At most 50 model replies per task | +| Per-response limit | `MAX_TOKENS = 8192`; requests do not explicitly set reasoning effort or temperature | +| Harbor retries | 0; this does not disable request retries inside the Anthropic SDK | +| Timeouts and resources | Preserve the task defaults | + +In Harbor 0.21.0, `--n-tasks 20` takes the first 20 entries after filtering; it is +not random sampling. Pinning the dataset hash fixes task versions, but the actual +selected task list should still be saved. Repeated `--include-task-name` options +can select those exact tasks later. Provider routing and server behavior on +OpenRouter may still change, so a fixed model slug does not make the experiment +fully deterministic. + +Host Harbor dependencies use the lockfile. Inside containers, `uv tool install` +pins the agent source revision but does not lock dependency resolution. This run +also records the Docker version and task images' local IDs/RepoDigests to help +compare environments in later experiments. + +#### How to run + +First confirm that `docker info` succeeds, then run the following from the +repository root. Credentials are supplied through environment variables, not +command arguments or experiment records. `ANTHROPIC_MODEL` overrides the model +name the adapter derives from `--model`, so this run explicitly removes it. + +```bash +export ANTHROPIC_API_KEY="${OPENROUTER_API_KEY:?Set OPENROUTER_API_KEY first}" +export ANTHROPIC_BASE_URL="https://openrouter.ai/api" +unset ANTHROPIC_MODEL + +BENCH_AGENT_REF="2b8309794cb9e00cb4d3b08baf1e4733153105e0" +BENCH_DATASET="terminal-bench/terminal-bench-2-1@sha256:7d7bdc1cbedad549fc1140404bd4dc45e5fd0ea7c4186773687d177ad3a0699a" + +uv run --locked --project benchmarks/harbor harbor run \ + --dataset "$BENCH_DATASET" \ + --n-tasks 20 \ + --agent harbor_adapter:NanoPyCodeAgent \ + --agent-kwarg "git_ref=$BENCH_AGENT_REF" \ + --agent-kwarg max_turns=50 \ + --model openrouter/deepseek/deepseek-v4-flash-0731 \ + --env docker \ + --n-concurrent 2 \ + --n-attempts 1 \ + --max-retries 0 \ + --job-name tb21-flash0731-pilot20-20260906 +``` + +If credentials are already stored in `~/.nanoPyCodeAgent/settings.json` on the +host, the Python process that launches Harbor can call +`nanopycodeagent.settings.load_settings_env()`, remove `ANTHROPIC_MODEL`, and +start the same Harbor command. Having only the CLI inside the task container +read its configuration is insufficient: the host configuration file is not +automatically mounted into the container, so Harbor must first receive the +connection environment variables on the host. This run uses that approach in a +new right split in the current Herdr tab, preserving focus in the original pane. + +Use a new `--job-name` for a rerun to keep experiments distinct. For the full +89-task run, remove `--n-tasks 20` and choose a separate baseline job name while +keeping the other conditions unchanged. This reruns the entire set, with pilot +costs counted separately. The full set and additional attempts are deferred +until the pilot results have been analyzed. + +#### Budget and recording plan + +The earlier certificate task cost 0.00222441 USD, but a simple task cannot +represent average spending across the full set. According to the +[OpenRouter model prices](https://openrouter.ai/deepseek/deepseek-v4-flash-0731) +and [provider quotes](https://openrouter.ai/api/v1/models/deepseek/deepseek-v4-flash-0731/endpoints) +checked on 2026-09-06, Baidu's uncached input, cache read, and output rates were +0.04998, 0.009996, and 0.09996 USD per million tokens; DeepSeek's base rates were +0.22, 0.007, and 0.66 USD. Routing, discounts, and time of day affect actual rates. + +Assuming cumulative usage per task of one million input tokens and 30,000 output +tokens with an 80% input cache hit rate, 20 tasks would cost approximately +0.4–1.4 USD at those two price levels. With three million input tokens and +150,000 output tokens per task, the estimate becomes 1.4–5 USD. These are budget +scenarios, not measured averages or spending caps. The 50-turn limit is not a +hard dollar limit either. These estimates cover model API charges only, excluding +cloud containers, networking, and local operating costs. + +Record and review this run in the following order: + +1. Save `jobs//config.json`, `lock.json`, task content hashes, and the + list of 20 tasks. +2. Save each task's `result.json`, `agent/trajectory.json`, agent text log, and + verifier log. +3. Count tasks with reward 1, tasks with reward 0, tasks without a score, and + infrastructure exceptions separately. Report passes over the planned task + count without silently reducing the denominator. +4. Check every trajectory's ATIF validation, usage and cost completeness, and + Harbor metric population. Missing or partial costs remain unknown; a known + subtotal is not a complete total. +5. Record total input, cache, and output tokens, actual USD cost, job wall time, + and per-task durations. Input already includes cached tokens, so do not add + the two together. +6. Distinguish incorrect solutions, exhausted turn budgets, execution timeouts, + API errors, and installation, image, or verifier environment failures. Preserve + first-attempt results and record any subsequent rerun separately. +7. Use measured pilot usage to adjust the full 89-task budget, then establish a + single-attempt baseline. To assess variability later, run separate repeated + experiments and retain every result instead of replacing the first attempt + with the best rerun. + +`/jobs/` is ignored by Git. Archive raw results separately, and keep reviewable +aggregate metrics, per-task results, and artifact locations in the development +notes. A local directory name alone does not put a baseline under version control. + +#### Actual results of the 20-task pilot + +The job `tb21-flash0731-pilot20-20260906` has finished all 20 tasks. The +[machine-readable result](../../../benchmarks/harbor/results/tb21-flash0731-pilot20-20260906.json) preserves per-task versions, metrics, +termination reasons, and separate cost lookup receipts. + +| Metric | Measured result | +| --- | --- | +| Job start / finish | `2026-09-06T16:41:41.721662` / `2026-09-06T18:32:51.295051` (Harbor host local time, Asia/Shanghai) | +| Job wall time | 111 minutes 10 seconds | +| Passed / failed / unscored | 8 / 12 / 0 | +| Pass rate over 20 planned tasks | 8 / 20 = 40% | +| Passes without a Harbor exception | 7 / 20 = 35% | +| Harbor exceptions / automatic retries | 1 / 0 | +| Model replies / tool calls | 267 / 329 | +| Original Harbor input / cache / output tokens | 5,853,395 / 5,050,368 / 311,514 | +| Input / cache / output tokens recomputed from final ATIF files | 6,207,708 / 5,362,944 / 331,041 | +| ATIF file validation | 20 / 20 | +| Agreement with original Harbor metrics | 19 / 20 | +| Trajectories with incomplete cost before supplemental lookups | 13 / 20 | +| Original Harbor job cost aggregate | 0.045695378 USD | +| Known cost subtotal across all trajectories | 0.11053603 USD | +| Cost after separate lookups | 0.119200332 USD (complete) | + +The table uses model metrics from the final ATIF files, with costs from +the supplemental lookups. Original `result.json` files and trajectories have not +been rewritten. + +| Task | Reward | Model replies | Last stop reason | Cost (USD) | +| --- | ---: | ---: | --- | ---: | +| `write-compressor` | 0.0 | 2 | `max_tokens` | 0.00102374 | +| `torch-tensor-parallelism` | 0.0 | 1 | `max_tokens` | 0.00081648 | +| `schemelike-metacircular-eval` | 0.0 | 14 | `max_tokens` | 0.005474196 | +| `kv-store-grpc` | 1.0 | 11 | `end_turn` | 0.001279134 | +| `pypi-server` | 1.0 | 13 | `end_turn` | 0.001163556 | +| `dna-assembly` | 0.0 | 3 | `max_tokens` | 0.001096542 | +| `torch-pipeline-parallelism` | 0.0 | 5 | `max_tokens` | 0.002161215 | +| `qemu-alpine-ssh` | 1.0 | 32 | `end_turn` | 0.011533989 | +| `openssl-selfsigned-cert` | 1.0 | 14 | `end_turn` | 0.001429425 | +| `regex-chess` | 0.0 | 3 | `max_tokens` | 0.000980712 | +| `log-summary-date-ranges` | 1.0 | 6 | `end_turn` | 0.000608526 | +| `model-extraction-relu-logits` | 0.0 | 2 | `max_tokens` | 0.000850059 | +| `path-tracing` | 0.0 | 39 | `max_tokens` | 0.029570788 | +| `regex-log` | 0.0 | 1 | `max_tokens` | 0.00081279 | +| `caffe-cifar-10` | 0.0 | 6 | `max_tokens` | 0.001660104 | +| `mteb-leaderboard` | 0.0 | 50 | `tool_use` | 0.01964673 | +| `llm-inference-batching-scheduler` | 1.0 | 29 | `end_turn` | 0.029323242 | +| `pytorch-model-recovery` | 1.0 | 20 | `tool_use` | 0.006448779 | +| `circuit-fibsqrt` | 0.0 | 2 | `max_tokens` | 0.001051326 | +| `merge-diff-arc-agi-task` | 1.0 | 14 | `end_turn` | 0.002268999 | + +`pytorch-model-recovery` has both reward=1 and `AgentTimeoutError`. Preserve the +original Harbor pass rate of **8/20 (40%)** and separately report **7/20 (35%) +passing without a Harbor exception**, both over the 20 planned tasks. This trial +cannot be treated as a normally completed success. + +#### Problems exposed by this run + +1. **The final reply in 11 failed tasks has `stop_reason=max_tokens`.** Each reply + currently has an 8192-token limit. Some replies spend most of that budget on + thinking and end before delivering a complete solution. The model loop in + [`agent.py`](../../../src/nanopycodeagent/agent.py) treats every stop reason + other than `tool_use` as completion, so these trajectories still end with + `completed`. Truncation needs explicit handling, and the reasoning/output + budget needs evaluation. Pass rate and cost with a higher limit require a + separate experiment. +2. **`mteb-leaderboard` exhausts 50 model replies.** Its terminal outcome is + `max_turns_exhausted`, its final reply still requests tool use, and the + `/app/result.txt` required by the verifier has not been created. This is a + different budget limit from truncation within one reply. +3. **The agent continues after timeout and later fails on missing tool input.** + `pytorch-model-recovery` reaches its 900-second limit at 10:20:32 UTC on + 2026-09-06; verification starts at 10:20:33 UTC. The internal journal records + two new model calls starting at 10:21:00 and 10:21:11 UTC. The last `edit` has + only `old_text` and `new_text`, with no `path`. The stdout event subscriber + directly reads `arguments['path']` and raises `KeyError`; `run.failed` is + persisted at 10:23:15 UTC. That error occurs after the timeout and cannot + explain the earlier timeout. Agent execution overlaps verification, which + limits how this trial's reward can be interpreted. +4. **The original Harbor aggregate misses the timed-out trial's metrics, and + billing data arrives late.** The trajectory is marked missing when metrics + are collected at timeout, but a valid ATIF file later appears in the final + directory and matches the independent backup. Harbor token fields remain + null, so original metrics agree with final ATIF files in only 19/20 trials. + Original costs are partial in 13 trials, with 18 generations unresolved by + native reconciliation. Separate lookups against the same OpenRouter + generation endpoint resolve all 18 receipts: **all 267 recorded model replies + now have costs**. The original job value of 0.045695378 USD is incomplete; + the complete measured total is **0.119200332 USD**. It includes the two calls + made after timeout. + +Total input is 6,207,708 tokens, including 5,362,944 cached tokens; output is +331,041 tokens. Costs use generation `total_cost` values, without deriving them +from advertised prices or comparing the entire OpenRouter account balance. +All 20 tasks have rewards and valid ATIF files. Harbor records no other trial +exceptions besides the timeout above. + +The original job is at `jobs/tb21-flash0731-pilot20-20260906/`. The local archive +`jobs/tb21-flash0731-pilot20-20260906-artifacts.tar.gz` contains the task manifest, +logs, trajectories, generation cost ledger, supplemental receipts, Docker image +identifiers, and one-off run/analysis scripts. It has not been uploaded; its +SHA-256 and file checksums are in the machine-readable result. Two original +`torch-pipeline-parallelism` logs contain the runtime API credential. Their local +permissions are now 0600, and archive copies are redacted; the result JSON records +checksums for both originals and redacted copies. The summary and supplemental +receipts are versioned, without credentials or full conversation logs. + +#### Next run and cost assessment + +First fix truncation handling, crashes on missing tool arguments, timeout +termination, and trajectory collection, then improve delayed cost reconciliation. +Those fixes belong to a new revision; the agent source was fixed throughout this +run. Rerun the same 20 task hashes under a new job name and compare passes without +exceptions, termination reasons, tokens, and costs. Preserve this run as the +pilot baseline before those fixes. Once execution and accounting are reliable, +run all 89 tasks and record a separate baseline for the full dataset. + +This pilot costs about 0.12 USD. Multiplying by 89/20 gives about 0.53 USD, but +11 tasks truncate early and the sample consists of the first 20 list entries, so +that extrapolation cannot budget the full dataset after fixes. Under the earlier +scenario of 3 million input tokens, 150,000 output tokens, and 80% input cache hits +per task, 89 tasks cost roughly 6–22 USD. Adjust that estimate using provider +prices at the time and the next pilot's measured consumption. Only the 20-task +pilot was executed; the full 89-task run remains pending. diff --git a/docs/dev_notes/zh-CN/0.8.x.md b/docs/dev_notes/zh-CN/0.8.x.md index 131d1a2..41d30f3 100644 --- a/docs/dev_notes/zh-CN/0.8.x.md +++ b/docs/dev_notes/zh-CN/0.8.x.md @@ -293,3 +293,211 @@ uv run pytest - Trajectory：11 个 steps，其中 10 个模型 steps。 - Tokens：49,878 input、42,240 cache、5,845 output。 - Cost：0.00222441 USD，10 次成本对账均首次成功。 + +### 批量运行 Terminal-Bench 并建立 baseline + +单题验收已经证明任务容器、agent、trajectory、费用对账与官方 verifier 的链路可用。 +下一步先扩大到 20 题，观察不同任务上的失败方式和实际消耗，再决定完整 89 题的 +baseline 运行预算。20 题是 pilot，不把它的通过率当作完整 Terminal-Bench 2.1 分数。 + +#### 固定实验配置 + +本轮于 2026-09-06 准备，固定如下条件；后续比较 harness 改进时，应复用题目和运行 +限制，并分别记录新的 agent revision。 + +| 项目 | 本轮配置 | +| --- | --- | +| Harbor | 0.21.0，使用 `benchmarks/harbor/uv.lock` | +| Agent commit | `2b8309794cb9e00cb4d3b08baf1e4733153105e0` | +| Dataset | `terminal-bench/terminal-bench-2-1`，共 89 题 | +| Dataset content hash | `sha256:7d7bdc1cbedad549fc1140404bd4dc45e5fd0ea7c4186773687d177ad3a0699a` | +| Model | `openrouter/deepseek/deepseek-v4-flash-0731` | +| API endpoint | `https://openrouter.ai/api`，使用 Anthropic Messages 接口 | +| Provider routing | 请求不额外指定供应商；账户路由设置也属于实验条件 | +| Pilot size | 数据集列表前 20 题，每题 1 次 | +| Environment | 本地 Docker，并发 2 | +| Agent limit | 每题最多 50 次模型回复 | +| Per-response limit | `MAX_TOKENS = 8192`；请求未显式设置 reasoning effort 或 temperature | +| Harbor retries | 0；该设置不关闭 Anthropic SDK 内部的请求重试 | +| Timeouts and resources | 保留题目默认配置 | + +Harbor 0.21.0 的 `--n-tasks 20` 在过滤后截取列表前 20 项，不是随机抽样。固定数据集 +hash 可以固定任务版本，但仍应保存实际选中的任务清单，后续可用重复的 +`--include-task-name` 精确选题。模型在 OpenRouter 上的供应商路由和服务端行为仍可能 +变化，固定 model slug 不等于完全确定性的实验。 + +宿主机 Harbor 使用 lockfile；容器内 `uv tool install` 只固定 agent 源码 revision， +没有锁定依赖解析。本轮另外保存了 Docker 版本及任务镜像的本地 ID／RepoDigests， +用于核对后续实验环境。 + +#### 运行方法 + +先确认 `docker info` 成功，再从仓库根目录执行。凭据通过环境变量提供，不写入 +命令参数或实验记录。`ANTHROPIC_MODEL` 会覆盖 adapter 从 `--model` 推导的模型名， +因此本轮显式移除它。 + +```bash +export ANTHROPIC_API_KEY="${OPENROUTER_API_KEY:?Set OPENROUTER_API_KEY first}" +export ANTHROPIC_BASE_URL="https://openrouter.ai/api" +unset ANTHROPIC_MODEL + +BENCH_AGENT_REF="2b8309794cb9e00cb4d3b08baf1e4733153105e0" +BENCH_DATASET="terminal-bench/terminal-bench-2-1@sha256:7d7bdc1cbedad549fc1140404bd4dc45e5fd0ea7c4186773687d177ad3a0699a" + +uv run --locked --project benchmarks/harbor harbor run \ + --dataset "$BENCH_DATASET" \ + --n-tasks 20 \ + --agent harbor_adapter:NanoPyCodeAgent \ + --agent-kwarg "git_ref=$BENCH_AGENT_REF" \ + --agent-kwarg max_turns=50 \ + --model openrouter/deepseek/deepseek-v4-flash-0731 \ + --env docker \ + --n-concurrent 2 \ + --n-attempts 1 \ + --max-retries 0 \ + --job-name tb21-flash0731-pilot20-20260906 +``` + +如果凭据已经保存在本机 `~/.nanoPyCodeAgent/settings.json`，可在启动 Harbor 的 Python +进程中调用 `nanopycodeagent.settings.load_settings_env()`，随后移除 +`ANTHROPIC_MODEL`，再启动同一条 Harbor 命令。仅由 task 容器内的 CLI 读取配置不够： +宿主机配置文件不会自动挂载到容器，必须先让宿主机 Harbor 获得连接环境变量。本次 +实跑采用这一方式，通过 Herdr 在当前 tab 新建右侧 split 执行，保持原 pane 的焦点。 + +复跑时使用新的 `--job-name`，避免与既有实验目录混淆。完整 89 题运行时删除 +`--n-tasks 20`，并另取 baseline job 名，其余条件保持一致；这会重新跑完整集合， +pilot 费用另计。暂不执行完整集合或增加每题 attempts，先分析本轮结果。 + +#### 预算与记录计划 + +之前的证书任务实际消耗为 0.00222441 USD，但简单任务不能代表整套集合的平均费用。 +按 2026-09-06 查到的 [OpenRouter 模型价格](https://openrouter.ai/deepseek/deepseek-v4-flash-0731) +和[供应商报价](https://openrouter.ai/api/v1/models/deepseek/deepseek-v4-flash-0731/endpoints)， +Baidu 当时的未缓存输入／缓存读取／输出价格分别为每百万 tokens +0.04998／0.009996／0.09996 USD，DeepSeek 当时基础价为 +0.22／0.007／0.66 USD；路由、优惠和时段会影响实际报价。 + +如果每题累计输入 100 万、输出 3 万 tokens，输入缓存命中率 80%，20 题按上述两档 +价格估算约 0.4–1.4 USD；如果增至每题累计输入 300 万、输出 15 万 tokens，则约 +1.4–5 USD。它们是预算场景，不是实测均值或费用上限。50 轮限制也不是美元硬上限。 +这里只估算模型 API 费用，不含云容器、网络或本机运行成本。 + +本轮按以下顺序记录与复核： + +1. 保存 `jobs//config.json`、`lock.json`、任务 content hash 和 20 题清单。 +2. 保存每题 `result.json`、`agent/trajectory.json`、agent 文本日志和 verifier 日志。 +3. 分别统计 reward 为 1 的题数、reward 为 0 的题数、未产生评分的题数和基础设施 + 异常；报告通过题数／计划题数，不能静默缩小分母。 +4. 检查所有 trajectory 的 ATIF 校验、usage 与 cost 完整性，以及 Harbor 指标回填。 + 缺失或部分费用保持未知，已知小计不能当成完整总额。 +5. 记录总 input／cache／output tokens、实际 USD 费用、job 墙钟耗时和逐题耗时。 + Input 已包含 cache tokens，不再把两者相加。 +6. 对失败任务区分解答错误、轮数用尽、执行超时、API 错误、安装／镜像／verifier + 环境故障；保留首次结果，任何后续重跑另建记录。 +7. 用 pilot 的实测消耗调整完整 89 题预算，再建立单轮 baseline。未来如要评估波动， + 另做多次重复实验，并保留每次结果，不按重跑后的最好成绩替换首次结果。 + +`/jobs/` 已被 Git 忽略。原始结果应另行归档，开发笔记保存可审查的汇总、逐题结果及 +产物位置；不能只留下本机目录名就认为 baseline 已进入版本管理。 + +#### 20 题 pilot 的实际结果 + +本轮 job 为 `tb21-flash0731-pilot20-20260906`，20 题均已结束；完整数据见 +[机器可读结果](../../../benchmarks/harbor/results/tb21-flash0731-pilot20-20260906.json)，其中保存了逐题版本、指标、终止原因和费用补查记录。 + +| 指标 | 实测结果 | +| --- | --- | +| Job 开始／结束时间 | `2026-09-06T16:41:41.721662` ／ `2026-09-06T18:32:51.295051`（Harbor 宿主机本地时间，Asia/Shanghai） | +| Job 墙钟耗时 | 111 分 10 秒 | +| 通过／未通过／未评分 | 8 ／ 12 ／ 0 | +| 通过率（计划 20 题为分母） | 8 / 20 = 40% | +| 无 Harbor 异常通过 | 7 / 20 = 35% | +| Harbor 异常／自动重试 | 1 ／ 0 | +| 模型回复／工具调用 | 267 ／ 329 | +| Harbor 原始 input／cache／output tokens | 5,853,395 ／ 5,050,368 ／ 311,514 | +| 从最终 ATIF 重新汇总的 input／cache／output tokens | 6,207,708 ／ 5,362,944 ／ 331,041 | +| ATIF 文件校验 | 20 / 20 | +| Harbor 原始指标一致 | 19 / 20 | +| 补查前费用不完整的 trajectory | 13 / 20 | +| Harbor 原始 job 费用汇总 | 0.045695378 USD | +| 所有 trajectory 已知费用小计 | 0.11053603 USD | +| 独立补查后费用 | 0.119200332 USD（完整） | + +下表使用最终 ATIF 中的模型计量信息，费用采用独立补查后的值；原始 +`result.json` 和 trajectory 没有被改写。 + +| 任务 | Reward | 模型回复数 | 末次 stop reason | 费用（USD） | +| --- | ---: | ---: | --- | ---: | +| `write-compressor` | 0.0 | 2 | `max_tokens` | 0.00102374 | +| `torch-tensor-parallelism` | 0.0 | 1 | `max_tokens` | 0.00081648 | +| `schemelike-metacircular-eval` | 0.0 | 14 | `max_tokens` | 0.005474196 | +| `kv-store-grpc` | 1.0 | 11 | `end_turn` | 0.001279134 | +| `pypi-server` | 1.0 | 13 | `end_turn` | 0.001163556 | +| `dna-assembly` | 0.0 | 3 | `max_tokens` | 0.001096542 | +| `torch-pipeline-parallelism` | 0.0 | 5 | `max_tokens` | 0.002161215 | +| `qemu-alpine-ssh` | 1.0 | 32 | `end_turn` | 0.011533989 | +| `openssl-selfsigned-cert` | 1.0 | 14 | `end_turn` | 0.001429425 | +| `regex-chess` | 0.0 | 3 | `max_tokens` | 0.000980712 | +| `log-summary-date-ranges` | 1.0 | 6 | `end_turn` | 0.000608526 | +| `model-extraction-relu-logits` | 0.0 | 2 | `max_tokens` | 0.000850059 | +| `path-tracing` | 0.0 | 39 | `max_tokens` | 0.029570788 | +| `regex-log` | 0.0 | 1 | `max_tokens` | 0.00081279 | +| `caffe-cifar-10` | 0.0 | 6 | `max_tokens` | 0.001660104 | +| `mteb-leaderboard` | 0.0 | 50 | `tool_use` | 0.01964673 | +| `llm-inference-batching-scheduler` | 1.0 | 29 | `end_turn` | 0.029323242 | +| `pytorch-model-recovery` | 1.0 | 20 | `tool_use` | 0.006448779 | +| `circuit-fibsqrt` | 0.0 | 2 | `max_tokens` | 0.001051326 | +| `merge-diff-arc-agi-task` | 1.0 | 14 | `end_turn` | 0.002268999 | + +`pytorch-model-recovery` 的 reward=1 与 `AgentTimeoutError` 同时存在。因此 Harbor +原始通过率保留为 **8/20（40%）**，另报**无 Harbor 异常通过 7/20（35%）**，两者都 +以计划的 20 题为分母。这题不能作为正常完成的成功样本。 + +#### 本轮暴露的问题 + +1. **11 道未通过题的末次回复均为 `max_tokens`。** 当前每次回复最多 8192 tokens， + 部分回复的预算主要消耗在 thinking，尚未交付完整解答就结束。 + [`agent.py`](../../../src/nanopycodeagent/agent.py) 的模型循环把所有非 `tool_use` + 的 stop reason 都当作完成，所以这些轨迹的终态仍为 `completed`。这说明需要显式 + 处理截断，并评估 reasoning／output 预算；增加上限后的通过率和费用需要另行实测。 +2. **`mteb-leaderboard` 用尽 50 次模型回复。** 终态 outcome 为 + `max_turns_exhausted`，末次回复仍要求调用工具，verifier 所需的 `/app/result.txt` + 尚未生成。这与单次回复截断是两种不同的预算限制。 +3. **超时后 agent 仍在运行，且发生工具参数异常。** `pytorch-model-recovery` 在 + 2026-09-06 10:20:32 UTC 达到 900 秒限制，verifier 于 10:20:33 UTC 开始；内部 + journal 记录 agent 在 10:21:00 和 10:21:11 UTC 又启动两次模型调用。最后一次 + `edit` 只有 `old_text`、`new_text`，缺少 `path`，stdout 事件订阅者直接读取 + `arguments['path']`，触发 `KeyError`；`run.failed` 于 10:23:15 UTC 落盘。 + 因而该异常发生在超时之后，不能用它解释之前的超时。运行与验证发生重叠，该题的 + reward 需要带着这一限制解读。 +4. **Harbor 原始汇总漏掉超时题的计量，费用也有延迟。** 超时采集时 trajectory + 被标记为 missing，最终目录中却已有有效 ATIF，且与独立备份一致；Harbor 的 + tokens 仍为 null，所以原始指标只有 19/20 与最终 ATIF 一致。13 题原始费用为 + partial，共 18 个 generation 在原生对账中未取得费用。独立补查同一 OpenRouter + generation 接口后，18 个回执均成功，**267 次已记录模型回复全部获得费用**。 + 原始 job 的 0.045695378 USD 不能作为总费用；完整实测为 **0.119200332 USD**。 + 两次超时后的调用也计入实际消耗。 + +完整 input 为 6,207,708，其中 cache 为 5,362,944；output 为 331,041。费用依据 +generation 的 `total_cost`，没有按展示价格反推，也不是整个 OpenRouter 账户余额的 +差额。20 题均有评分且 ATIF 校验通过；除上述超时外，Harbor 没有记录其他 trial 异常。 + +原始 job 位于 `jobs/tb21-flash0731-pilot20-20260906/`。本地归档为 +`jobs/tb21-flash0731-pilot20-20260906-artifacts.tar.gz`，包含任务清单、日志、轨迹、 +generation 费用明细、补查回执、Docker 镜像标识和一次性运行／汇总脚本。归档未上传， +SHA-256 及文件校验值见机器可读结果。`torch-pipeline-parallelism` 的两份原始日志 +含运行时 API 凭据，原件权限已设为 0600，归档副本已脱敏；结果 JSON 分别记录原件与 +脱敏副本的校验值。版本管理保存汇总及补查回执，不包含凭据或完整对话日志。 + +#### 下一轮计划与费用判断 + +先修复截断处理、缺失工具参数导致的崩溃、超时停止及 trajectory 采集，再完善延迟 +费用补查；这些修复在新 revision 中进行。本轮固定源码版本且没有中途修改 agent。 +随后使用相同 20 个任务 hash 和新的 job 名复跑，对比无异常通过率、终止原因、token +和费用；保留本轮作为修复前的 pilot baseline。确认运行与计量可靠后，再运行完整 +89 题，并把结果单独记录为完整集合的 baseline。 + +本轮约 0.12 USD，机械地乘以 89/20 约为 0.53 USD，但 11 题提前截断，且题目取自 +列表前 20 项，这个外推不能作为修复后完整集合的预算。按前述每题 300 万 input、 +15 万 output、80% 输入缓存命中的场景，89 题约为 6–22 USD；仍需按当时供应商报价 +和修复后 pilot 的消耗调整。本轮只执行了 20 题，完整 89 题尚未运行。 From 1a7d818ffc5e5d36e8af627b1389efbd0ef6afd4 Mon Sep 17 00:00:00 2001 From: minixalpha Date: Sun, 6 Sep 2026 21:26:14 +0800 Subject: [PATCH 2/3] docs: clarify pilot terminology in chinese benchmark notes --- docs/dev_notes/zh-CN/0.8.x.md | 14 +++++++------- 1 file changed, 7 insertions(+), 7 deletions(-) diff --git a/docs/dev_notes/zh-CN/0.8.x.md b/docs/dev_notes/zh-CN/0.8.x.md index 41d30f3..ed94bc6 100644 --- a/docs/dev_notes/zh-CN/0.8.x.md +++ b/docs/dev_notes/zh-CN/0.8.x.md @@ -298,7 +298,7 @@ uv run pytest 单题验收已经证明任务容器、agent、trajectory、费用对账与官方 verifier 的链路可用。 下一步先扩大到 20 题，观察不同任务上的失败方式和实际消耗，再决定完整 89 题的 -baseline 运行预算。20 题是 pilot，不把它的通过率当作完整 Terminal-Bench 2.1 分数。 +baseline 运行预算。这 20 题用于小规模试跑，不把它的通过率当作完整 Terminal-Bench 2.1 分数。 #### 固定实验配置 @@ -314,7 +314,7 @@ baseline 运行预算。20 题是 pilot，不把它的通过率当作完整 Term | Model | `openrouter/deepseek/deepseek-v4-flash-0731` | | API endpoint | `https://openrouter.ai/api`，使用 Anthropic Messages 接口 | | Provider routing | 请求不额外指定供应商；账户路由设置也属于实验条件 | -| Pilot size | 数据集列表前 20 题，每题 1 次 | +| 试跑规模 | 数据集列表前 20 题，每题 1 次 | | Environment | 本地 Docker，并发 2 | | Agent limit | 每题最多 50 次模型回复 | | Per-response limit | `MAX_TOKENS = 8192`；请求未显式设置 reasoning effort 或 temperature | @@ -366,7 +366,7 @@ uv run --locked --project benchmarks/harbor harbor run \ 复跑时使用新的 `--job-name`，避免与既有实验目录混淆。完整 89 题运行时删除 `--n-tasks 20`，并另取 baseline job 名，其余条件保持一致；这会重新跑完整集合， -pilot 费用另计。暂不执行完整集合或增加每题 attempts，先分析本轮结果。 +小规模试跑费用另计。暂不执行完整集合或增加每题 attempts，先分析本轮结果。 #### 预算与记录计划 @@ -394,13 +394,13 @@ Baidu 当时的未缓存输入／缓存读取／输出价格分别为每百万 t Input 已包含 cache tokens，不再把两者相加。 6. 对失败任务区分解答错误、轮数用尽、执行超时、API 错误、安装／镜像／verifier 环境故障；保留首次结果，任何后续重跑另建记录。 -7. 用 pilot 的实测消耗调整完整 89 题预算，再建立单轮 baseline。未来如要评估波动， +7. 用小规模试跑的实测消耗调整完整 89 题预算，再建立单轮 baseline。未来如要评估波动， 另做多次重复实验，并保留每次结果，不按重跑后的最好成绩替换首次结果。 `/jobs/` 已被 Git 忽略。原始结果应另行归档，开发笔记保存可审查的汇总、逐题结果及 产物位置；不能只留下本机目录名就认为 baseline 已进入版本管理。 -#### 20 题 pilot 的实际结果 +#### 20 题小规模试跑的实际结果 本轮 job 为 `tb21-flash0731-pilot20-20260906`，20 题均已结束；完整数据见 [机器可读结果](../../../benchmarks/harbor/results/tb21-flash0731-pilot20-20260906.json)，其中保存了逐题版本、指标、终止原因和费用补查记录。 @@ -494,10 +494,10 @@ SHA-256 及文件校验值见机器可读结果。`torch-pipeline-parallelism` 先修复截断处理、缺失工具参数导致的崩溃、超时停止及 trajectory 采集，再完善延迟 费用补查；这些修复在新 revision 中进行。本轮固定源码版本且没有中途修改 agent。 随后使用相同 20 个任务 hash 和新的 job 名复跑，对比无异常通过率、终止原因、token -和费用；保留本轮作为修复前的 pilot baseline。确认运行与计量可靠后，再运行完整 +和费用；保留本轮作为修复前的小规模试跑基准。确认运行与计量可靠后，再运行完整 89 题，并把结果单独记录为完整集合的 baseline。 本轮约 0.12 USD，机械地乘以 89/20 约为 0.53 USD，但 11 题提前截断，且题目取自 列表前 20 项，这个外推不能作为修复后完整集合的预算。按前述每题 300 万 input、 15 万 output、80% 输入缓存命中的场景，89 题约为 6–22 USD；仍需按当时供应商报价 -和修复后 pilot 的消耗调整。本轮只执行了 20 题，完整 89 题尚未运行。 +和修复后小规模试跑的消耗调整。本轮只执行了 20 题，完整 89 题尚未运行。 From 41f81f98aa81ab7994ad24e3124ddbe560ad3981 Mon Sep 17 00:00:00 2001 From: minixalpha Date: Sun, 6 Sep 2026 22:09:35 +0800 Subject: [PATCH 3/3] docs(benchmarks): minimize public results and regenerate english notes --- .../tb21-flash0731-pilot20-20260906.json | 2151 +++++------------ docs/dev_notes/en/0.8.x.md | 408 ++-- docs/dev_notes/zh-CN/0.8.x.md | 26 +- 3 files changed, 743 insertions(+), 1842 deletions(-) diff --git a/benchmarks/harbor/results/tb21-flash0731-pilot20-20260906.json b/benchmarks/harbor/results/tb21-flash0731-pilot20-20260906.json index 6d462f8..1c63ed9 100644 --- a/benchmarks/harbor/results/tb21-flash0731-pilot20-20260906.json +++ b/benchmarks/harbor/results/tb21-flash0731-pilot20-20260906.json @@ -1,7 +1,8 @@ { - "schema_version": 1, + "schema_version": 2, "job_name": "tb21-flash0731-pilot20-20260906", - "job_id": "f81da209-ce59-4970-a5f4-32c8fee2bfaa", + "run_date": "2026-09-06", + "scope": "Single-attempt, first-20-task pilot baseline; not the full Terminal-Bench 2.1 score.", "experiment": { "agent_ref": "2b8309794cb9e00cb4d3b08baf1e4733153105e0", "harbor_version": "0.21.0", @@ -50,88 +51,40 @@ "reasoning_effort": "not explicitly set" } }, - "started_at": "2026-09-06T16:41:41.721662", - "finished_at": "2026-09-06T18:32:51.295051", - "job_duration_sec": 6669.573, - "planned_trials": 20, - "completed_trials": 20, "agent_versions": [ "0.8.1.dev3+g2b8309794" ], - "passed_trials": 8, - "failed_reward_trials": 12, - "unscored_trials": 0, - "exception_counts": { - "AgentTimeoutError": 1 - }, - "pass_rate_over_planned": 0.4, - "known_cost_usd": 0.11053603, - "cost_complete": false, - "total_cost_usd": null, - "post_run_reconciliation": { - "known_cost_usd": 0.119200332, - "cost_complete": true, - "total_cost_usd": 0.119200332, - "incomplete_cost_tasks": [], - "resolved_generations": 18, - "receipt_file": "jobs/tb21-flash0731-pilot20-20260906-record/generation-receipts.json" + "job_duration_sec": 6669.573, + "scores": { + "planned_trials": 20, + "completed_trials": 20, + "passed_trials": 8, + "failed_reward_trials": 12, + "unscored_trials": 0, + "exception_counts": { + "AgentTimeoutError": 1 + }, + "pass_rate_over_planned": 0.4, + "passes_without_harbor_exception": 7, + "pass_rate_without_harbor_exception_over_planned": 0.35 }, - "task_manifest_matches": true, - "observed_usage_totals": { + "usage": { "total_prompt_tokens": 6207708, "total_cached_tokens": 5362944, - "total_completion_tokens": 331041 + "total_completion_tokens": 331041, + "complete": true }, - "observed_usage_complete": true, - "artifacts": { - "config.json": { - "path": "jobs/tb21-flash0731-pilot20-20260906/config.json", - "sha256": "a83de26c5a9c36bca81d16b76a5bdcebf31a1a15e64335b4c9d6b10b389afb06" - }, - "lock.json": { - "path": "jobs/tb21-flash0731-pilot20-20260906/lock.json", - "sha256": "43351fe936aa44f20a2658b7efb4d180806c95571c8137afba4cfef21111425d" - }, - "result.json": { - "path": "jobs/tb21-flash0731-pilot20-20260906/result.json", - "sha256": "95a4e8f88fc91f41078ec1d4346d664ae9e2e91276855af1d9074c0b015c8ae2" - }, - "manifest.json": { - "path": "jobs/tb21-flash0731-pilot20-20260906-record/manifest.json", - "sha256": 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Use post_run_reconciliation.total_cost_usd for the reconciled cost.", - "cost_completeness": "All 267 distinct model generations recorded in the final ATIF files have a provider cost. This is the generation API cost, not a reconciliation of the entire OpenRouter account balance.", - "input_tokens": "Prompt totals include cached tokens; do not add the cached count again.", - "timezones": "Job start/finish are Harbor host local time in Asia/Shanghai. Per-trial timestamps and journal timestamps include UTC offsets.", - "exceptions_and_rewards": "Exception counts overlap reward counts. A reward of 1 with AgentTimeoutError remains in the Harbor score and is excluded only from passes_without_harbor_exception." + "cost": { + "currency": "USD", + "source": "OpenRouter generation total_cost for recorded model calls", + "original_harbor_aggregate_usd": 0.045695378, + "native_trajectory_known_subtotal_usd": 0.11053603, + "native_cost_complete": false, + "reconciled_total_usd": 0.119200332, + "reconciled_cost_complete": true, + "supplemental_receipt_count": 18, + "includes_recorded_calls_after_timeout": true }, - "passes_without_harbor_exception": 7, - "pass_rate_without_harbor_exception_over_planned": 0.35, "model_replies": 267, "tool_calls": 329, "last_model_stop_reason_counts": { @@ -160,601 +113,27 @@ "supplemental_receipts": 18, "per_generation_cost_and_usage_sums_match_trial_and_job_totals": true }, - "billing_receipts": { - "endpoint": "https://openrouter.ai/api/v1/generation", - "receipts": { - "gen-1788685602-GlDfIiZXs40PUnpDNfPJ": { - "trial_name": "kv-store-grpc__dqLAUiN", - 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"missing_generation_ids": null, - "artifacts": { - "result": "jobs/tb21-flash0731-pilot20-20260906/circuit-fibsqrt__758KQCh/result.json", - "result_sha256": "6d9360cab76c50a8d3a4f8168509af72b151ae2723615140f9138d1c1aa5a123", - "trajectory": "jobs/tb21-flash0731-pilot20-20260906/circuit-fibsqrt__758KQCh/agent/trajectory.json", - "trajectory_sha256": "8bc143ba6f59ba6ef2218cb890d1000ca4227ecce556a82252a0ddff9b9270be", - "native_trajectory_present": true - }, - "post_run_resolved_generations": [], - "post_run_unresolved_generations": [], - "post_run_known_cost_usd": 0.001051326, - "post_run_cost_usd": 0.001051326, - "atif_final_metrics": { - "total_steps": 3, - "total_prompt_tokens": 7590, - "total_completion_tokens": 8808, - "total_cached_tokens": 2304, - "total_cost_usd": 0.001051326 - }, "task_limits_and_environment": { "agent": { "timeout_sec": 3600.0 @@ -2338,42 +1346,44 @@ "allow_internet": true, "mcp_servers": [] } + }, + "task_image_digests": [ + "alexgshaw/circuit-fibsqrt@sha256:29783439f529eaed2145592f15af7a2281161528860392a382c825489030ae3a" + ], + "usage": { + "total_prompt_tokens": 7590, + "total_cached_tokens": 2304, + "total_completion_tokens": 8808, + "complete": true + }, + "cost": { + "native_total_usd": 0.001051326, + "native_known_subtotal_usd": 0.001051326, + "native_cost_is_partial": false, + "reconciled_total_usd": 0.001051326, + "reconciled_cost_complete": true + }, + "validation": { + "atif_valid": true, + "original_harbor_metrics_match": { + "n_input_tokens": true, + "n_cache_tokens": true, + "n_output_tokens": true, + "cost_usd": true + }, + "trajectory_arrived_after_missing_diagnostic": false } }, { "task": "terminal-bench/merge-diff-arc-agi-task", "task_ref": "sha256:6aab6511a5344ce87698293bb1ce4cc51d9a45f1ad9f0c075d2a83197b36727d", - "trial_name": "merge-diff-arc-agi-task__w5sZoRS", - "status": "finished", "reward": 1.0, - "agent_version": "0.8.1.dev3+g2b8309794", "exception_type": null, - "started_at": "2026-09-06T10:28:53.861023Z", - "finished_at": "2026-09-06T10:32:51.291945Z", "duration_sec": 237.431, "environment_setup_sec": 14.434, "agent_setup_sec": 66.768, "agent_execution_sec": 123.169, "verifier_sec": 21.67, - "agent_execution_started": true, - "n_input_tokens": 130371, - "n_cache_tokens": 121856, - "n_output_tokens": 8768, - "cost_usd": null, - "known_cost_usd": 0.002175354, - "cost_is_partial": true, - "usage_complete": true, - "trajectory_status": "partial", - "trajectory_valid": true, - "model_metrics_source": "native trajectory", - "trajectory_arrived_after_missing_diagnostic": false, - "trajectory_validation_errors": [], - "metrics_match": { - "n_input_tokens": true, - "n_cache_tokens": true, - "n_output_tokens": true, - "cost_usd": true - }, "steps": 15, "model_steps": 14, "model_stop_reasons": { @@ -2385,28 +1395,6 @@ "terminal_status": "completed", "terminal_outcome": "completed", "terminal_error_type": null, - "missing_generation_ids": [ - "gen-1788690615-MZxqjQeopSlBndazGddU" - ], - "artifacts": { - "result": "jobs/tb21-flash0731-pilot20-20260906/merge-diff-arc-agi-task__w5sZoRS/result.json", - "result_sha256": "afcd91b2f225a6f4882cafe499b8c396488e3a717201ebd9c3287b6772ca25e2", - "trajectory": "jobs/tb21-flash0731-pilot20-20260906/merge-diff-arc-agi-task__w5sZoRS/agent/trajectory.json", - "trajectory_sha256": "880b1bcdce351f4879dfefaa8d92ad666bc11c0cd7ba4465460328c318473b17", - "native_trajectory_present": true - }, - "post_run_resolved_generations": [ - "gen-1788690615-MZxqjQeopSlBndazGddU" - ], - "post_run_unresolved_generations": [], - "post_run_known_cost_usd": 0.002268999, - "post_run_cost_usd": 0.002268999, - "atif_final_metrics": { - "total_steps": 15, - "total_prompt_tokens": 130371, - "total_completion_tokens": 8768, - "total_cached_tokens": 121856 - }, "task_limits_and_environment": { "agent": { "timeout_sec": 900.0 @@ -2424,9 +1412,54 @@ "allow_internet": true, "mcp_servers": [] } + }, + "task_image_digests": [ + "alexgshaw/merge-diff-arc-agi-task@sha256:bfc2a235f2ceea64ffb1ffc5529c2acebf570bff61f3668f0e5669ef570c17d4" + ], + "usage": { + "total_prompt_tokens": 130371, + "total_cached_tokens": 121856, + "total_completion_tokens": 8768, + "complete": true + }, + "cost": { + "native_total_usd": null, + "native_known_subtotal_usd": 0.002175354, + "native_cost_is_partial": true, + "reconciled_total_usd": 0.002268999, + "reconciled_cost_complete": true + }, + "validation": { + "atif_valid": true, + "original_harbor_metrics_match": { + "n_input_tokens": true, + "n_cache_tokens": true, + "n_output_tokens": true, + "cost_usd": true + }, + "trajectory_arrived_after_missing_diagnostic": false } } ], + "timeout_observation": { + "task": "terminal-bench/pytorch-model-recovery", + "time_reference": "Seconds since Harbor agent execution started", + "timeout_at_sec": 900.029, + "verifier_started_at_sec": 901.366, + "model_calls_started_after_timeout_at_sec": [ + 928.758, + 939.104 + ], + "native_failure_recorded_at_sec": 1063.365, + "verifier_finished_at_sec": 1386.888, + "last_tool_name": "edit", + "last_tool_argument_keys": [ + "new_text", + "old_text" + ], + "native_terminal_error": "KeyError", + "interpretation": "Two new model calls started after the Harbor timeout, while the verifier was running. The later native terminal records KeyError for a missing edit path. Reward 1 is retained but excluded from passes without a Harbor exception. The timeout did not promptly stop the container agent; the late trajectory was not reflected in original Harbor metrics." + }, "follow_up_plan": [ "Handle max_tokens explicitly and evaluate the per-response reasoning/output budget.", "Make missing tool arguments recoverable without crashing stdout event projection.", @@ -2434,35 +1467,11 @@ "Support separate delayed cost reconciliation without replacing incomplete values with zero.", "After fixes, rerun these same 20 task hashes under a new agent revision and job name; use that pilot to budget the full 89-task run." ], - "raw_artifact_archive": { - "path": "jobs/tb21-flash0731-pilot20-20260906-artifacts.tar.gz", - "sha256": "680a59abb6d80b8f2d12b5c164219475eee6acf8665ee84f96d9211785fd2dc2", - "size_bytes": 861842, - "availability": "Local workspace archive under git-ignored jobs/; not uploaded or committed. This JSON summary and supplemental billing receipts are versioned.", - "contents": [ - "Harbor job with credential redactions listed below", - "Task manifest", - "Full derived summary", - "Generation cost ledger", - "Supplemental receipts", - "Environment metadata", - "Filtered timeout timeline", - "Independent trajectory backup", - "One-off run and analysis scripts" - ], - "redactions": [ - { - "path": "jobs/tb21-flash0731-pilot20-20260906/torch-pipeline-parallelism__9DcJ62N/agent/nanopycodeagent.txt", - "original_sha256": "c2cd573b34a1a76f5452463037301c2bfc302c9e42eae181efd60a2ef4b577c9", - "archived_sha256": "b43413be32f7c43f2793237d02d2be726b8586efd3b890d696bd1be46bbc7560", - "reason": "Runtime credential appeared in captured agent output; replaced only in archive copies. Local originals have mode 0600." - }, - { - "path": "jobs/tb21-flash0731-pilot20-20260906/torch-pipeline-parallelism__9DcJ62N/agent/trajectory.json", - "original_sha256": "caffe9c44221a892c76896f923aaf70527d6c7bf299e444c717f5b47d65c2218", - "archived_sha256": "8e776cf8055026812c76402ab44e8037ea84f92a84952ec74eac2ce86f15e0c2", - "reason": "Runtime credential appeared in captured agent output; replaced only in archive copies. Local originals have mode 0600." - } - ] + "local_artifacts": { + "archive_path": "jobs/tb21-flash0731-pilot20-20260906-artifacts.tar.gz", + "archive_sha256": "680a59abb6d80b8f2d12b5c164219475eee6acf8665ee84f96d9211785fd2dc2", + "archive_size_bytes": 861842, + "availability": "Local git-ignored archive; not uploaded. Raw logs, detailed receipts, request identifiers, exact event times, host hardware information, and per-file redaction checksums are kept in local records.", + "archive_credentials_redacted": true } } diff --git a/docs/dev_notes/en/0.8.x.md b/docs/dev_notes/en/0.8.x.md index c79baa1..8fb7b75 100644 --- a/docs/dev_notes/en/0.8.x.md +++ b/docs/dev_notes/en/0.8.x.md @@ -4,60 +4,60 @@ ## 0.8.0 - 2026.09.04 -We have now built several basic tools — bash, write, read, and edit — roughly matching Pi's basic toolset. The next step is to learn from practice which capabilities genuinely need improving and which new ones would strengthen the harness. To do that, we need to run public benchmarks. The goal is to use benchmarks reported in mainstream model releases so that, with the model held constant, we can compare different harnesses and identify concrete optimization targets. +The basic bash, write, read, and edit tools are now available, roughly matching Pi's basic toolset. The next step is to run public benchmarks to learn which existing capabilities need improvement and which new capabilities would strengthen the harness. Using benchmarks reported in mainstream model releases lets us hold the model constant, compare harnesses, and identify concrete optimization targets. -I first surveyed the landscape in [code_agent_benchmark](../../research/en/code_agent_benchmark.md). The survey showed that, regardless of which benchmark we choose, the first requirement is a non-interactive way to invoke the code agent. Only then can a program run the agent end to end on a task and evaluate the result. Based on the first benchmarks recommended by that survey, I then documented a practical non-interactive interface in [benchmark_headless_interface](../../research/en/benchmark_headless_interface.md). +I first surveyed the options in [code_agent_benchmark](../../research/en/code_agent_benchmark.md). Regardless of the benchmark, the first requirement is a non-interactive way to invoke the code agent: a program must be able to run a task end to end and evaluate its result. I then documented the non-interactive interface needed by the first recommended benchmarks in [benchmark_headless_interface](../../research/en/benchmark_headless_interface.md). -The first feature is therefore a **headless CLI**, the first step in the survey's minimum viable path. It is the only hard blocker: the first three requirements shared by all three benchmarks — accept task text in one command and run to completion, work in the process's current directory, and never prompt, ask questions, or wait for confirmation — all depend on it. The only existing entry point is the `input("You> ")` loop in `agent.py`; inside a container stdin is EOF, so the process immediately prints `Bye!` and exits without doing anything. Until this works, none of the later harness improvements can be verified programmatically, so there is no basis for deciding what to optimize. +The first feature is a **headless CLI**, the first step in the survey's minimum viable path. It is the only hard blocker. Three requirements shared by the benchmarks depend on it: accept a task and finish in one invocation, work in the process's current directory, and never prompt or wait for confirmation. The current entry point is the `input("You> ")` loop in `agent.py`. With EOF on stdin inside a container, it immediately prints `Bye!` and exits without doing any work. Until this is fixed, harness changes cannot be verified programmatically, so there is no basis for optimization. ### Basic headless CLI implementation -Before worrying about diagnostics or optimization, the first goal is to implement the smallest feature that can complete the simplest benchmark task. +Start with the minimum needed to complete one simple benchmark task. Debugging and performance improvements can follow. -**Command-line shape**: +**Command-line form:** ```text -nanoPyCodeAgent [-p/--prompt "" | --prompt-file | (piped stdin)] +nanoPyCodeAgent [-p/--prompt "" | --prompt-file | (stdin pipe)] [--max-turns N] [--version] ``` -`--version` is not decorative: Harbor uses an adapter's `get_version_command()` / `parse_version()` to probe and record the agent version on a best-effort basis. `_package_version()` already existed; it only needed to be exposed through the CLI. +`--version` has a practical purpose: Harbor uses the adapter's `get_version_command()` and `parse_version()` to detect and record the agent version on a best-effort basis. `_package_version()` already exists and only needs to be connected to the CLI. -**Headless detection**: passing `-p` or `--prompt-file` selects headless mode. When neither is present, the entire stdin stream becomes the task if `sys.stdin.isatty()` is false; only a tty with no supplied task starts the existing REPL. All three input forms are required. Harbor's two official examples already use two of them: Claude Code uses `printf … | claude --print` to avoid shell quoting and command-length limits, while mini-swe-agent passes `--task=` and explicitly connects stdin to `/dev/null`. This also fixes the behavior where EOF on stdin inside a container immediately printed `Bye!` and exited. +**Selecting headless mode:** Either `-p` or `--prompt-file` selects headless mode. Without either option, read all of stdin as the task when `sys.stdin.isatty()` is false. Enter the existing REPL only when stdin is a tty and no task was supplied. All three input forms are needed. Harbor's official Claude Code example uses `printf … | claude --print`, avoiding shell escaping and argument-length limits. Its mini-swe-agent example passes `--task=` and explicitly connects stdin to `/dev/null`. This also fixes the immediate `Bye!` on container stdin EOF. -**Exit codes**: +**Exit codes:** -| Exit code | Scenario | +| Exit code | Situation | | :-: | --- | -| 0 | The model declares completion; the turn budget is exhausted; the task is not completed | -| Non-zero | Missing API credentials; invalid arguments; API or transport errors | +| 0 | The model declares completion; the turn limit is reached; the task is not solved | +| Nonzero | Missing API credentials; invalid arguments; API or transport errors | -"Exit 0 even when unfinished" is counterintuitive. Harbor runs the agent under `set -o pipefail`; a non-zero code immediately raises `NonZeroAgentExitCodeError`, marks the entire trial as an agent failure, and may trigger a retry that only wastes money. Whether the task succeeded belongs to the verifier reading the reward file under `/logs/verifier/`, not to the agent's process exit code. +Returning 0 for an unfinished task may seem surprising. Harbor wraps the agent command in `set -o pipefail`; a nonzero exit raises `NonZeroAgentExitCodeError`, classifies the trial as an agent failure, and may trigger a paid retry. The verifier's reward files under `/logs/verifier/` should determine whether the task was solved. -This also exposes an existing bug: without API credentials, `run()` printed a message and returned, while `main()` had no concept of a return code, so the process ultimately exited 0. To the harness, that meant "the run completed but did not solve the task." An entire batch could quietly score zero with no indication that configuration was the problem. `main()` therefore has to return an integer that the generated console script can use as its exit code. +This exposes an existing bug: when credentials are missing, `run()` prints a message and returns, while `main()` has no return-code handling. The resulting exit code is 0. A harness can therefore score an entire batch as unsuccessful without revealing the configuration problem. `main()` needs to return an integer, which the console script will use as the exit code. -**Turn limit**: the inner tool-use loop currently has no bound. In interactive mode, someone watching can press Ctrl-C when the model gets stuck retrying the same command; in headless mode, nobody is there, so it can keep spending until the API refuses further calls. `--max-turns` costs only a counter but is essential for unattended runs, so it belongs in the CLI. Exhausting the budget still exits 0 under the table above. +**Turn limit:** The inner tool-use loop currently has no limit. In interactive mode a person can interrupt repeated commands with Ctrl-C; unattended execution would continue until the API fails. `--max-turns` costs only a counter to implement but is a prerequisite for unattended headless runs. Exhausting the budget returns 0 as described above. -**Headless system prompt**: the current prompt is written for a conversational assistant. In headless mode there is nobody to answer; if the model politely asks "should I continue?", that reply ends the run and the task scores zero. Headless mode therefore uses a separate system prompt that explicitly says not to ask the user questions, not to stop for confirmation, to make decisions independently through completion, and to state clearly when the work is done. +**Headless system prompt:** The current prompt is written for a conversational assistant. In headless mode, asking the user whether to continue ends the exchange and can fail the task. Use a separate prompt that explicitly requires the agent to make decisions, finish the work without questions or confirmation, and clearly state completion. -**Print API errors unchanged**: Harbor classifies errors by applying regular expressions to the agent's stdout and stderr — rate limits, usage limits, `Overloaded`, context exhaustion, authentication failures, connection drops, and so on. That classification, together with options such as `--max-retries 3 --retry-include ApiRateLimitError`, decides whether a retry is worthwhile. Error text should therefore be preserved rather than swallowed or rewritten, which removes half of the retry work from the agent itself. +**Preserve API error text:** Harbor scans stdout/stderr with regular expressions to classify rate limits, usage limits, overload, context limits, authentication failures, network interruptions, and similar errors. That classification works with options such as `--max-retries 3 --retry-include ApiRateLimitError`. Preserving the original error message lets the harness handle much of the retry policy. -**Acceptance**: one command is enough to show whether this step truly works: +**Acceptance:** One command demonstrates the basic path: ```bash printf "%s" "create hello.py that prints hi" | nanoPyCodeAgent; echo $? ``` -If the task arrives through the pipe, the file is created, and the exit code is 0, the step is complete. This is the first point at which nanoPyCodeAgent can be called by a script, and only then can it be connected to a benchmark. +Receiving the piped task, creating the file, and exiting with 0 establishes that nanoPyCodeAgent can be called by a script. Benchmark integration can then begin. -After this, the next work is: `--output-format stream-json` and `--trajectory` (step 4 of the minimum viable path, once failures need attribution), API retry and backoff (step 2), context compaction (step 5), `--workdir` (downgraded from P0 by the survey because all three benchmarks pass their working directory through the container's default `WORKDIR`; the agent only needs to operate in the current process directory), and `--timeout` (the harness owns the wall-clock timeout — Harbor stores it in `[agent].timeout_sec` in `task.toml` — so an agent-side timeout is a safeguard rather than an integration prerequisite). +Later work includes `--output-format stream-json` and `--trajectory` for failure analysis (step 4 of the minimum viable path), API retries and backoff (step 2), and context compression (step 5). The survey lowered the priority of `--workdir`: all three benchmarks set the container's working directory, so the agent can use its process cwd. A separate agent `--timeout` would be an additional safeguard; Harbor already enforces wall-clock limits through `[agent].timeout_sec` in `task.toml`. ### First end-to-end validation with Terminal-Bench -The `hello.py` smoke test only proves that the CLI can accept a task and exit; it does not prove that the CLI can participate in a real benchmark. The goal of this run was not to produce a formal benchmark score, but to choose a task simple enough for an initial attempt while still exercising command execution, file writes, recovery from mistakes, and official verification, then run the full path for real. +The `hello.py` smoke test only proves that the CLI accepts a task and exits. To verify real benchmark integration, choose a simple task that still requires commands, file writes, recovery from mistakes, and official verification. The aim is to establish the full execution path before collecting a formal score. -The selected task was `terminal-bench/openssl-selfsigned-cert` from Terminal-Bench 2.1. Harbor 0.21.0 and the nanoPyCodeAgent adapter were both placed temporarily under `/tmp`; no benchmark-specific code was added to the project. The adapter inherits Harbor's `BaseInstalledAgent`. The `--agent nanopy_harbor_agent:NanoPyCodeAgent` argument uses the dynamic import form `module:ClassName`, allowing Harbor on the host to execute this chain: +The chosen task is Terminal-Bench 2.1's `terminal-bench/openssl-selfsigned-cert`. Harbor 0.21.0 and the initial adapter were placed temporarily under `/tmp`, without adding benchmark-specific code to the project. The adapter inherits Harbor's `BaseInstalledAgent`. The `module:ClassName` import syntax in `--agent nanopy_harbor_agent:NanoPyCodeAgent` lets host Harbor execute this sequence: ```text Harbor @@ -66,10 +66,10 @@ Harbor -> install and invoke the nanoPyCodeAgent headless CLI in /app -> wait for the agent to exit -> run the official verifier - -> save logs, test results, and the reward + -> save logs, test results, and reward ``` -The core run configuration was as follows. The API key, base URL, and model configuration were still injected through environment variables and were not written into the command or logs: +The core configuration follows. Credentials, base URL, and model configuration were injected through environment variables and were not written into this run's command or logs: ```bash harbor run \ @@ -81,7 +81,7 @@ harbor run \ --n-attempts 1 ``` -During the first environment preparation, the container's initial Python and dependency downloads exceeded the default setup timeout. The model had not been called yet, so this was not a benchmark result. After increasing only the installation-phase timeout while keeping the task, model, turn limit, and verifier unchanged, the final trial completed end to end: +The first environment setup exceeded the default setup timeout while downloading Python and dependencies. No model call had occurred, so that setup attempt is not a benchmark result. Increasing only the installation timeout, while preserving the task, model, turn budget, and verifier, produced a complete trial: | Metric | Result | | --- | --- | @@ -91,43 +91,41 @@ During the first environment preparation, the container's initial Python and dep | Tool calls | 16 bash, 2 write, 1 edit | | Agent / verifier exceptions | 0 | -This result does not prove that nanoPyCodeAgent can run an entire leaderboard workload reliably. It proves that the minimum closed loop works: Harbor can deliver a real task to the headless CLI; the CLI can invoke the model and tools unattended in the isolated container's current working directory; the model can recover from mistakes; and after the process exits normally, the official verifier can independently produce a reward. At this point, the simplest benchmark can run all the way from task input to final scoring. +This establishes the minimum execution loop. Harbor delivers a real task to the headless CLI; the CLI invokes the model and tools unattended in the isolated container's current directory; the model can recover from mistakes; and the official verifier independently scores the artifacts after the process exits normally. It does not yet demonstrate reliable performance across a full benchmark suite. -### Dependency and observability boundaries exposed by the run +### Dependency and observability limits exposed by the run -One container installation reported a missing `httpx` dependency when starting the CLI. Adding `uv tool install --with httpx` to the temporary adapter allowed the evaluation to continue. Two later clean installations using the same uv version, Python version, and project revision did not reproduce the omission because `anthropic` still installs `httpx` as a transitive dependency. It would therefore be inaccurate to claim that every clean installation fails. The confirmed issue is that nanoPyCodeAgent directly imports `httpx` without declaring it as a direct dependency. The project currently works only by relying on the `anthropic -> httpx` transitive relationship, and that dependency ownership should still be corrected. +One container installation reported missing `httpx` when starting the CLI. The temporary adapter continued after adding `uv tool install --with httpx`. Two later clean installations with the same uv, Python, and project revision did not reproduce the omission because `anthropic` still installs `httpx` transitively. The confirmed issue is dependency ownership: nanoPyCodeAgent directly imports `httpx` without declaring it as a direct dependency. Its current operation relies on the `anthropic -> httpx` relationship, which should be corrected. -Although trajectory output does not exist yet, the tool calls could still be counted from the plain-text log. Before every tool call, the CLI prints a prefix such as `[bash]`, `[write]`, or `[edit]`, so the completed evaluation log can be counted with text matching. This count has no turn boundaries, tool call IDs, input or output token counts, cost, duration, or structured error status, which is why Harbor still reports token and cost fields as `null`. +Even without a trajectory, tool calls could be counted from the ordinary text log because the CLI prints prefixes such as `[bash]`, `[write]`, and `[edit]` before execution. Such counts lack turn boundaries, tool-call IDs, input/output tokens, costs, durations, and structured error states. Harbor's token and cost fields consequently remained `null`. -The [agent_output_and_trajectory](../../research/en/agent_output_and_trajectory.md) survey makes clear that this text log must not simply be renamed a trajectory. Run output is what a single invocation publicly delivers, while a trajectory is a structured execution path bounded by one benchmark task or trial and intended for benchmark and offline analysis. In the future, `--output-format text|json|stream-json` should control only the public stdout representation, while `--trajectory PATH` independently stores analysis data such as observations, actions, tool results, outcomes, and usage. Both should be projected from the same canonical internal events, but they must remain separate interfaces. +As explained in [agent_output_and_trajectory](../../research/en/agent_output_and_trajectory.md), renaming a text log does not make it a trajectory. Run output is the public content delivered by an invocation; a trajectory is a structured execution path for a task or trial, intended for benchmarking and offline analysis. `--output-format text|json|stream-json` should control stdout's public representation. A separate `--trajectory PATH` should save observations, actions, tool results, outcomes, and usage. Both should be projected from the same internal events while remaining separate interfaces. -The next work therefore has three independent parts: +The next work therefore has three parts: -- Add `httpx` as a direct dependency so container installation does not rely on a transitive relationship. -- Add the Harbor adapter to the project, fixing and versioning its installation command, headless CLI invocation, environment-variable forwarding, and version probing so future benchmark runs can reuse it instead of rewriting a temporary adapter each time. -- Design canonical internal events and a trajectory writer so the agent produces ATIF directly and the adapter can read it to populate token, cost, and step statistics reliably; implement `stream-json` later as separate run output. These changes improve the installability, reproducibility, and observability of the working benchmark path. +- Declare `httpx` directly so container installation does not depend on a transitive relationship. +- Bring the Harbor adapter into the repository and version its installation command, headless invocation, environment forwarding, and version detection for reuse. +- Design internal events and a trajectory writer so the agent emits ATIF directly and the adapter reliably reports tokens, costs, and steps. Implement `stream-json` later as separate run output. These changes improve installation, reproducibility, and observability of the established execution path. -### Adding the Harbor adapter to the project +### Bringing the Harbor adapter into the project -The temporary script used for the end-to-end validation now lives in an isolated `benchmarks/harbor/` workspace in the repository under the public import path `harbor_adapter:NanoPyCodeAgent`. The adapter exists only for development and evaluation: it does not live under `src/nanopycodeagent` and does not add Harbor to the wheel installed by regular users. The workspace's own `pyproject.toml` and `uv.lock` pin Harbor to 0.21.0, the version used for this implementation and its tests. Running `uv run --project benchmarks/harbor harbor ...` from the repository root loads the adapter without relying on `/tmp` or a manually configured `PYTHONPATH`. +The temporary adapter now lives in the independent `benchmarks/harbor/` workspace, exposed as `harbor_adapter:NanoPyCodeAgent`. It serves development and evaluation, stays outside `src/nanopycodeagent`, and does not add Harbor to the ordinary user wheel. Its own `pyproject.toml` and `uv.lock` pin Harbor to 0.21.0, the version used for implementation and testing. Running `uv run --project benchmarks/harbor harbor ...` from the repository root loads the adapter without relying on `/tmp` or a manually configured `PYTHONPATH`. -The installation path supports two reproducible pins. `--agent-kwarg version=X.Y.Z` runs `uv tool install --force nanoPyCodeAgent==X.Y.Z` inside the task container. `--agent-kwarg git_ref=` installs an exact GitHub revision for benchmarking code that has not yet been released. The options are mutually exclusive; only when neither is supplied does the adapter install the latest PyPI release. The uv bootstrap URL is pinned to 0.9.11, and installation immediately runs `nanoPyCodeAgent --version` as a self-check. The temporary adapter's `--with httpx` workaround is gone because the project now owns `httpx` as a direct dependency. +Installation supports two pins. `--agent-kwarg version=X.Y.Z` runs `uv tool install --force nanoPyCodeAgent==X.Y.Z` in the task container. `--agent-kwarg git_ref=` installs an exact GitHub revision for benchmarks before a release. These options are mutually exclusive; omitting both installs the latest PyPI version. The uv bootstrap URL is pinned to 0.9.11, and installation immediately runs `nanoPyCodeAgent --version` as a self-check. The temporary `--with httpx` workaround is gone because the project now declares `httpx` directly. -At runtime, the instruction is no longer interpolated into a shell command. The adapter injects the complete task through a one-use environment variable and pipes it into the headless CLI with `printf`, so quotes, newlines, dollar signs, and long instructions never become shell syntax. The CLI keeps its default 50-turn limit, configurable through `--agent-kwarg max_turns=N`. Under `pipefail`, stdout and stderr are combined and saved with `tee` to `/logs/agent/nanopycodeagent.txt`, so Harbor can still classify API errors and the pipeline cannot swallow a non-zero CLI exit code. +The adapter no longer interpolates the instruction into shell syntax. It injects the complete task through a temporary environment variable, then pipes it into the headless CLI with `printf`. Quotes, newlines, dollar signs, and long task text are kept out of shell syntax. The CLI defaults to 50 turns, overridable with `--agent-kwarg max_turns=N`. Under `pipefail`, stdout/stderr are combined and saved with `tee` to `/logs/agent/nanopycodeagent.txt`. Harbor can still classify API errors, and the pipeline preserves nonzero CLI exits. -The configuration boundary is fixed as well. `ANTHROPIC_API_KEY`, `ANTHROPIC_BASE_URL`, and `ANTHROPIC_MODEL` pass directly into the container. When Harbor supplies a `provider/model` name and the provider's own key and base-URL variables, the adapter normalizes the credentials and endpoint into the names expected by the Anthropic SDK and removes the first provider prefix when no explicit `ANTHROPIC_MODEL` exists. An explicit `ANTHROPIC_MODEL` takes precedence, preserving the proxy-endpoint configuration used by the first real run. Version probing converts output such as `nanoPyCodeAgent 0.8.0` to the plain version `0.8.0` before reporting it to Harbor. +The configuration boundary is also explicit. `ANTHROPIC_API_KEY`, `ANTHROPIC_BASE_URL`, and `ANTHROPIC_MODEL` are forwarded into the container. When Harbor supplies a `provider/model` name and the provider's own key/base URL, the adapter normalizes credentials and endpoint into the Anthropic SDK variables. Unless `ANTHROPIC_MODEL` is explicitly set, it removes the first provider prefix. An explicit `ANTHROPIC_MODEL` takes precedence, preserving the proxy-endpoint model selection used in the first run. Version detection extracts `0.8.0` from `nanoPyCodeAgent 0.8.0` for Harbor. -The formal adapter's container boundary is first covered by five contract tests built on Harbor 0.21.0's real base class: published-release pins, Git revision pins, mutually exclusive source arguments, instruction piping and logging, and both environment-variable mappings. The same task was then rerun once through the formal adapter in a real Docker environment. Harbor completed the full path — starting the container, installing the specified Git revision, injecting model configuration, invoking the agent, collecting logs, and running the official verifier — with zero agent or verifier infrastructure exceptions, demonstrating that the adapter itself works end to end. The task received a reward of 0, with 5 of 6 official verifier tests passing. The only failure was that the agent's `check_cert.py` depended on `cryptography`, which was unavailable in the verifier's Python environment. This was a portability defect in the task solution, not an adapter infrastructure failure. +Five contract tests using Harbor 0.21.0's real base class initially cover the container boundary: release pinning, Git revision pinning, mutually exclusive options, the instruction pipeline and logs, and both environment mappings. The same task was then rerun in Docker with the formal adapter. Harbor started the container, installed the pinned Git revision, injected model configuration, called the agent, collected logs, and ran the official verifier. Agent/verifier infrastructure exceptions were zero. The reward was 0 with 5/6 verifier tests passing: the generated `check_cert.py` depended on `cryptography`, which was absent from the verifier's Python environment. That failure concerns solution portability rather than adapter infrastructure. -In addition, the Harbor CLI version command and dynamic import in the isolated workspace verify the host-side integration, while a build of the root project confirms that its wheel contains neither the adapter nor a Harbor dependency. The first two follow-up items — owning `httpx` directly and adding the formal adapter — are therefore complete. The remaining trajectory work is to design canonical internal events and an Event Journal, have the agent produce ATIF directly, and let the adapter read it to populate token, cost, and step statistics reliably. `stream-json` is a separate form of run output and will be implemented in a later PR. +Host integration was also checked through the workspace's Harbor version command and dynamic import. Building the root project wheel confirmed that it includes neither the adapter nor a Harbor dependency. The direct `httpx` dependency and formal adapter are complete. Remaining trajectory work is to define internal events and an Event Journal, emit ATIF from the agent, and reliably populate tokens, costs, and steps in the adapter. `stream-json` is separate run output planned for a later PR. -### Implementing Trajectory +### Implementing trajectories -Based on the research ([the boundary between agent output and trajectory](../../research/en/agent_output_and_trajectory.md), [mapping agent events to ATIF](../../research/en/agent_events_to_atif_examples.md), [OpenRouter cost accounting](../../research/en/openrouter_cost_accounting.md), and [the unified OpenRouter model protocol](../../research/en/openrouter_unified_protocol.md)), the implementation path has converged on a replayable internal **Event Journal**, while the public `--trajectory` option produces only **ATIF-v1.7**. `stream-json` remains a separate form of run output that shares the same runtime facts with trajectory; it and the corresponding `--output-format` CLI interface are outside this trajectory implementation and will be delivered in a later PR. +The research on [agent output and trajectory boundaries](../../research/en/agent_output_and_trajectory.md), [agent event mapping to ATIF](../../research/en/agent_events_to_atif_examples.md), [OpenRouter cost accounting](../../research/en/openrouter_cost_accounting.md), and [OpenRouter's unified model protocol](../../research/en/openrouter_unified_protocol.md) leads to a replayable internal **Event Journal** and an external `--trajectory` that emits only **ATIF-v1.7**. `stream-json` shares the same underlying facts but remains separate run output. Its `--output-format` CLI belongs to another control surface outside trajectory implementation. -One completed `read` tool execution illustrates the distinction between a `Native Event` and a `Journal Entry`. - -The agent loop first produces a Native Event that describes only what happened: +A completed `read` call illustrates the distinction between a `Native Event` and a `Journal Entry`. The agent loop first emits only the facts of what happened: ```json { @@ -142,7 +140,7 @@ The agent loop first produces a Native Event that describes only what happened: } ``` -After receiving it, the journal writer adds the identity, order, and record time needed for persistence, producing a Journal Entry: +The journal writer adds identity, ordering, and persistence time to form a `Journal Entry`: ```json { @@ -161,24 +159,24 @@ After receiving it, the journal writer adds the identity, order, and record time } ``` -These describe the same occurrence rather than two events. A `Native Event` is the runtime fact produced by core; a `Journal Entry` is the persistable record of that fact after it enters the Event Journal, additionally answering which run it belongs to, where it appears in the sequence, and when it was recorded. The ATIF projector consumes Journal Entries ordered by `seq` and folds multiple facts into trajectory steps. +Both describe the same event. The `Native Event` carries facts produced by the core; the `Journal Entry` persists those facts with the run identity, sequence, and recording time. The ATIF projector consumes entries ordered by `seq` and folds multiple facts into trajectory steps. -Rather than splitting the implementation into seven technically layered and disconnected steps, the work is organized as independently useful and independently testable capabilities. Each capability defines its implementation and acceptance criteria together instead of postponing all validation until the end: +Implementation is organized around independently usable and verifiable capabilities, with acceptance criteria attached to each capability: -1. **Runtime facts and the Event Journal.** Establish versioned contracts for `Native Event` and `Journal Entry`. The agent loop emits facts only at user, model, tool, and run boundaries, and appends them to an internal Event Journal. Text output is projected from those same facts so existing user-visible behavior remains unchanged. -2. **Public ATIF trajectory.** Implement a one-way Event Journal to ATIF-v1.7 projector and expose it for headless runs through an independent `--trajectory PATH`. `PATH` identifies one complete ATIF JSON snapshot rather than the internal Journal and does not change stdout. The projector maps messages, tools, timestamps, durations, token and cache usage, and terminal state already defined by the Journal. Cost mapping depends on a separate actual-cost contract and reconciliation mechanism. -3. **Provider-reported cost collection and reconciliation.** Prefer the provider's actual charge from the model response's `usage.cost`. When a response has no charge but does include a generation ID, mark the cost as pending and query the provider's Generation API for `total_cost` while the run is finalizing. Save query results as appended events without modifying existing Journal Entries, and do not change the task result if a query fails. The projector maps known charges to their steps and the run total, explicitly describes whether cost is complete, and never records an unknown charge as zero. -4. **Harbor integration and end-to-end acceptance.** The Harbor adapter only passes a trajectory path, declares and reads the agent-produced ATIF, then populates steps, tokens, and cost. It no longer converts a native trajectory. Harbor contract tests verify collection and statistics first, followed by one real trial of the complete path. +1. **Runtime facts and Event Journal.** Version the `Native Event` and `Journal Entry` contracts. Emit facts at user, model, tool, and run boundaries and append them to the internal journal. Project text output from the same facts while preserving visible behavior. +2. **Public ATIF trajectory.** Implement a one-way Event Journal to ATIF-v1.7 projector enabled in headless runs by `--trajectory PATH`. The path names a complete ATIF JSON snapshot, not the internal journal, and does not change stdout. Map defined messages, tools, timestamps, durations, token/cache usage, and terminal state. Cost mapping depends on the separate provider-cost contract and reconciliation mechanism. +3. **Provider cost collection and reconciliation.** Prefer actual costs in model response `usage.cost`. When costs are absent but a generation ID exists, mark them pending and query the provider's Generation API for `total_cost` during run finalization. Append results without rewriting journal entries or changing task outcomes. Map known costs into steps and run totals, explicitly track completeness, and never substitute zero for an unknown cost. +4. **Harbor integration and end-to-end validation.** The adapter supplies the trajectory path, declares and reads agent-generated ATIF, and populates steps, tokens, and costs. It does not convert a native trajectory. Verify collection and metrics with contract tests, then confirm the full path in a real trial. -`--output-format` and `stream-json` are separate run-output capabilities and are outside the trajectory work above. +`--output-format` and `stream-json` belong to separate run output and are outside these trajectory capabilities. -#### Runtime facts and the Event Journal +#### Runtime facts and Event Journal -**Goal:** establish versioned Native Event and Journal Entry contracts so the agent loop produces only runtime facts, then append a replayable internal Event Journal for each Agent Run. Existing stdout text is projected from the same facts, but the Event Journal itself is sensitive internal reconstruction data rather than public run output or a trajectory. +**What to build:** Version the Native Event and Journal Entry contracts, keep the agent loop concerned with runtime facts, and append a replayable internal Event Journal for each Agent Run. Existing text stdout is another projection of those facts. The Event Journal is sensitive reconstruction data, not public run output or a trajectory. -**Protocol:** the [Event Journal implementation protocol v1](../../dev_docs/en/event-journal-protocol-v1.md) is the single definition of the schema-version-1 wire contract, event semantics, persistence behavior, and compatibility boundaries. These development notes do not repeat its field-level validation details. +**Protocol:** [Event Journal implementation protocol v1](../../dev_docs/en/event-journal-protocol-v1.md) defines schema version 1's wire contract, event semantics, persistence behavior, and compatibility boundary. These development notes do not duplicate its field definitions and validation rules. -**Validation:** behavioral tests demonstrate that event contracts and ordering are validated, Journals can be appended and replayed, interruption does not damage complete records, sensitive-data permissions are restricted, and event-based projection leaves stdout unchanged: +**Validation:** Behavioral tests should establish valid contracts and ordering, append/replay support, survival of complete records after interruption, restricted access to sensitive data, and unchanged stdout behavior after event integration: ```bash uv run pytest \ @@ -187,19 +185,19 @@ uv run pytest \ tests/test_agent.py ``` -Development acceptance requires these tests to return zero and every mandatory protocol behavior to have a corresponding test. +Acceptance requires successful tests and coverage for every mandatory behavior in the protocol document. #### Public ATIF trajectory -**Goal:** implement a one-way Event Journal to ATIF-v1.7 projector that exports one headless Agent Run as a complete ATIF JSON document. The CLI gains an independent `--trajectory PATH`; `PATH` selects the output file while stdout keeps its existing text behavior. The internal Event Journal remains private. Actual-cost collection and reconciliation, Harbor adapter collection, `stream-json`, and multi-run trajectories for interactive sessions are outside this capability. +**What to build:** Implement a one-way Event Journal to ATIF-v1.7 projector that represents one headless Agent Run as a complete ATIF JSON document. Add a separate `--trajectory PATH` option to enable the projector and choose the output file while preserving text stdout. Do not expose the internal Event Journal. Provider cost collection/reconciliation, Harbor adapter collection, `stream-json`, and interactive trajectories spanning multiple runs are separate capabilities. -**Protocol:** the target format is ATIF-v1.7 as implemented by Harbor 0.21.0: +**Protocol:** Use ATIF-v1.7 as implemented in Harbor 0.21.0: -- [Harbor's ATIF documentation](https://www.harborframework.com/docs/agents/trajectory-format) -- [ATIF-v1.7 RFC](https://github.com/harbor-framework/harbor/blob/v0.21.0/rfcs/0001-trajectory-format.md) -- [Pydantic reference implementation](https://github.com/harbor-framework/harbor/tree/v0.21.0/src/harbor/models/trajectories) and [trajectory validator](https://github.com/harbor-framework/harbor/blob/v0.21.0/src/harbor/utils/trajectory_validator.py) +- [Official Harbor ATIF documentation](https://www.harborframework.com/docs/agents/trajectory-format). +- [ATIF-v1.7 RFC](https://github.com/harbor-framework/harbor/blob/v0.21.0/rfcs/0001-trajectory-format.md). +- [Pydantic reference implementation](https://github.com/harbor-framework/harbor/tree/v0.21.0/src/harbor/models/trajectories) and [trajectory validator](https://github.com/harbor-framework/harbor/blob/v0.21.0/src/harbor/utils/trajectory_validator.py). -**Validation:** automated tests demonstrate that the projector preserves representative user, model, tool, usage, and terminal facts from the Journal; CLI stdout remains unchanged; and a file is created only when `--trajectory` is supplied. Every generated trajectory must also pass the repository-pinned Harbor 0.21.0 validator: +**Validation:** Automated tests should show that representative journals preserve user/model/tool information, usage, and run terminal state in projection; stdout remains unchanged; and files are created only when `--trajectory` is supplied. Every generated trajectory must also pass the repository's pinned Harbor 0.21.0 validator: ```bash nanoPyCodeAgent -p "read README.md and summarize it" \ @@ -209,60 +207,60 @@ uv run --project benchmarks/harbor \ /tmp/nanopycodeagent-trajectory.json ``` -Development acceptance requires the validator to return zero, stdout to retain its existing text format, and the target to contain complete JSON. Harbor adapter collection and a real trial belong to the later Harbor integration acceptance work. +Acceptance requires validator exit code 0, unchanged text stdout, and a complete JSON output file. Adapter reading and a real trial are covered by the later Harbor integration acceptance. -#### Provider-reported cost collection and reconciliation +#### Provider cost collection and reconciliation -**Goal:** trajectories record the actual model-call charges reported by the provider. Three similarly named layers serve different purposes: +**What to build:** Record the provider's actual reported model-call costs in trajectories. Three layers have similar terminology but different roles: -- **Provider responses** are the source of truth for cost. `usage.cost` is the `cost` field in the response's `usage` object; `X-Generation-Id` is the generation ID in an HTTP response header; and `data.total_cost` is the total-charge field inside the Generation API response's `data` object. -- **The Event Journal** preserves runtime facts collected by the agent. `model.completed` is the event emitted when a model call finishes, and its `payload.cost` stores the call's cost state. `model.cost_resolved` is a separate event appended after a deferred lookup succeeds; its `payload` stores the generation ID, amount, currency, and source. -- **The ATIF trajectory** is the public result projected from the Event Journal. A model step's `metrics.cost_usd` represents the cost of one call, while the trajectory-level `final_metrics` stores the run-wide cost summary and completeness state. +- **Provider responses** supply the cost facts. `usage.cost` is the `cost` field inside the model response's `usage` object. `X-Generation-Id` is the generation ID in a response HTTP header. `data.total_cost` is the total cost inside the Generation API response body's `data` object. +- **Event Journal** persists facts collected by the agent. `model.completed` is emitted when a model call finishes; its `payload.cost` records that call's cost status. A successful delayed lookup appends `model.cost_resolved`, whose `payload` contains the generation ID, amount, currency, and source. +- **ATIF trajectory** is the public projection of those facts. A model step's `metrics.cost_usd` is its call cost. The top-level `final_metrics` contains run cost totals and completeness information. There are two collection paths: -- **Synchronous response accounting.** OpenRouter's [Usage Accounting](https://openrouter.ai/docs/cookbook/administration/usage-accounting) defines a complete `usage` object for OpenAI-compatible Chat Completions and streaming responses. Its `usage.cost` is the total amount charged for the request, delivered in the complete non-streaming response or the final SSE message. When present, nanoPyCodeAgent immediately records it in `model.completed.payload.cost` as a resolved, provider-reported USD amount. Token usage does not imply that cost is present: OpenRouter's [Anthropic Messages API](https://openrouter.ai/docs/api/api-reference/anthropic-messages/create-messages) preserves an Anthropic-compatible usage schema containing token, cache, service-tier, and speed fields but does not define `cost`. nanoPyCodeAgent currently calls this endpoint through the Anthropic SDK, so a response may contain token counts and an extension such as `speed: "standard"` without a cost. -- **Asynchronous generation reconciliation.** When `usage.cost` is absent but the HTTP headers include `X-Generation-Id`, nanoPyCodeAgent records the generation ID in `model.completed.payload.generation_id` and marks `payload.cost.status` as `pending`. This covers the current OpenRouter Anthropic Messages path and compatible APIs whose usage record becomes visible after the model response. OpenRouter's documented alternative is to retain the generation ID, then call [`GET /api/v1/generation?id=...`](https://openrouter.ai/docs/api/api-reference/generations/get-generation) and read `data.total_cost`. The endpoint documents 404, 429, and 5xx responses, so possession of an ID does not guarantee that its billing record is immediately queryable. +- **Synchronous collection from responses.** OpenRouter's [Usage Accounting](https://openrouter.ai/docs/cookbook/administration/usage-accounting) defines complete `usage` for OpenAI-compatible Chat Completions and streaming responses. `usage.cost` is the total account charge for that request, returned in the complete non-streaming response or final streaming SSE message. When present, record it immediately in `model.completed.payload.cost` as a provider-reported USD amount with status `resolved`. Token usage alone does not establish that cost is present. For Anthropic schema compatibility, OpenRouter's [Anthropic Messages API](https://openrouter.ai/docs/api/api-reference/anthropic-messages/create-messages) defines token, cache, service-tier, and speed fields in `usage`, but no `cost`. This project currently calls that interface through the Anthropic SDK, so observed usage may contain tokens and extensions such as `speed: "standard"` without a cost. +- **Asynchronous reconciliation by generation ID.** When `usage.cost` is absent but the HTTP response has `X-Generation-Id`, save the identifier in `model.completed.payload.generation_id` and set `payload.cost.status` to `pending`. This covers the current OpenRouter Anthropic Messages path and compatible interfaces that return an identity before the usage record is ready. OpenRouter documents saving the generation ID, calling [`GET /api/v1/generation?id=...`](https://openrouter.ai/docs/api/api-reference/generations/get-generation), and reading `data.total_cost`. The endpoint documents `404`, `429`, and 5xx responses, so a generation ID does not imply an immediately queryable billing record. -The reconciliation endpoint is derived from the current Anthropic SDK base URL on the same origin as `v1/generation`. It is not tied to a provider hostname and never sends API credentials to a hard-coded destination; OpenRouter is currently used to validate this extension protocol. Reconciliation makes six bounded attempts with exponential delays of 1, 2, 4, 8, and 15 seconds, for about 30 seconds of total waiting. A 404, 408, 409, 429, common 5xx response, network error, or HTTP 200 response without `total_cost` is retryable; permanent failures such as 400, 401, 402, and 403 stop immediately. A successful lookup appends `model.cost_resolved`, storing the amount in `payload.amount`, the association key in `payload.generation_id`, and the currency and source in `payload.currency` and `payload.source`. Existing `model.completed` events are never rewritten, so the Event Journal remains append-only. +Derive the same-origin `v1/generation` endpoint from the current Anthropic SDK base URL. The implementation does not bind a provider by hostname or send credentials to a hardcoded destination; OpenRouter currently verifies this extension protocol. Queries make up to six attempts with `1, 2, 4, 8, 15` seconds of exponential backoff, totaling about 30 seconds of waits. Retry `404`, `408`, `409`, `429`, common 5xx responses, network errors, and HTTP 200 responses that still lack `total_cost`. Stop immediately for permanent failures such as `400`, `401`, `402`, and `403`. On success, append `model.cost_resolved`: put the amount in `payload.amount`, the association key in `payload.generation_id`, and the currency/source in `payload.currency` and `payload.source`. Persisted `model.completed` entries remain unchanged, preserving append-only journal semantics. -Reconciliation must be observable instead of collapsing every failure into `None`. The terminal event for each run carries an optional `cost_reconciliation` list. For every generation it records a final `resolved` or `unresolved` state and classifies every attempt as `resolved`, HTTP 200 with `cost_unavailable`, `http_error` with a status code, `request_error` with an exception type, or `unsupported_endpoint` when the base URL cannot produce a lookup endpoint. Credentials and provider response bodies are never persisted. The ATIF projector copies this list to `extra.terminal.cost_reconciliation`, allowing incomplete costs to be diagnosed from either the internal Journal or the public trajectory. +Reconciliation observability needs more than `None`. The terminal event optionally carries a `cost_reconciliation` list. Each generation records its final `resolved` or `unresolved` status and individual attempt results: `resolved`, `cost_unavailable` for HTTP 200 without a cost, `http_error` with a status code, `request_error` with an exception type, or `unsupported_endpoint` when the base URL cannot produce a lookup endpoint. Credentials and provider response bodies are excluded. The ATIF projector copies this list into `extra.terminal.cost_reconciliation`, supporting diagnosis from either the internal journal or public trajectory. -Cost collection is best-effort enrichment: HTTP errors, billing records that are not yet available, and invalid responses never change whether the agent's original task succeeded or failed. The ATIF projector associates deferred results with model steps through the matching `payload.generation_id` and emits: +Cost collection is best-effort enrichment. HTTP errors, unavailable billing records, and invalid responses do not change the original task's success or failure. The ATIF projector joins delayed results to model steps through `payload.generation_id` and emits: -- The cost of each model call in that ATIF step's `metrics.cost_usd`, with the cost source and generation ID in `metrics.extra.cost_source` and `metrics.extra.generation_id`. -- The run total in the trajectory-level `final_metrics.total_cost_usd` when every billable call has a complete cost. -- When cost is incomplete, the known subtotal and incompleteness marker in `final_metrics.extra.known_cost_usd` and `final_metrics.extra.cost_is_partial`; if any recognizable calls are still pending reconciliation, their generation IDs appear in `final_metrics.extra.missing_generation_ids`. +- Per-call cost in the step's `metrics.cost_usd`, with source and generation ID in `metrics.extra.cost_source` and `metrics.extra.generation_id`. +- Run total in `final_metrics.total_cost_usd` when every billable call has a known cost. +- Otherwise, a known subtotal in `final_metrics.extra.known_cost_usd` and an incomplete flag in `final_metrics.extra.cost_is_partial`. Identifiable unresolved calls are listed in `final_metrics.extra.missing_generation_ids`. -Unknown costs always remain unknown and are never written as zero. +Unknown costs remain unknown and are never written as zero. -**Automated acceptance:** cost unit tests cover synchronous response accounting, pending and unknown states, successful Generation API retries, permanent HTTP-error short circuiting, bounded failure, and per-attempt diagnostics. Agent event tests verify that reconciliation occurs before the run's terminal event and that failure diagnostics are persisted. ATIF tests verify step costs, complete totals, partial totals, and terminal diagnostics. The complete test suite is: +**Automated acceptance:** Cost unit tests cover direct response collection, pending/unknown states, successful Generation API retries, permanent HTTP failures, bounded unsuccessful lookups, and per-attempt diagnostics. Agent event tests establish that reconciliation happens before the terminal event and that failure diagnostics are persisted. ATIF tests cover step costs, complete and partial totals, and terminal diagnostics. Run the complete suite with: ```bash uv run pytest ``` -#### Harbor integration and end-to-end acceptance +#### Harbor integration and end-to-end validation -**Goal:** +**What to build:** -1. Have the repository's Harbor adapter specify a trajectory output path inside the container for every trial. After the agent exits, tell Harbor the file's path and format so Harbor can read the ATIF-v1.7 document generated by the agent. -2. Populate Harbor with the complete ATIF steps and with the token, cache-token, cost, and completeness state from step metrics and final metrics. -3. Define the adapter's responsibility and failure semantics clearly: it only coordinates the path and data handoff; it does not parse the Event Journal or maintain a native-trajectory conversion. A missing, invalid, or partial trajectory must produce an explicit diagnostic instead of being misreported as zero usage or a complete result. +1. Have the repository's Harbor adapter specify a container trajectory path for each trial, then tell Harbor the path and format after the agent ends so Harbor can read agent-generated ATIF-v1.7. +2. Populate Harbor with complete ATIF steps and the tokens, cache tokens, costs, and completeness states in step/final metrics. +3. Define the adapter boundary and failure semantics. It hands off paths and data, without parsing the Event Journal or maintaining native-trajectory conversion. Missing, invalid, and partial trajectories need explicit diagnostics rather than zero-consumption or complete-result claims. **Validation:** -1. Extend the adapter contract tests under `benchmarks/harbor/tests` to verify that the trajectory path is passed into the container correctly and that the ATIF can be declared and read. -2. Use contract tests to verify population of steps, tokens, and cost, including behavior for a missing trajectory file, validation failure, and incomplete metrics. -3. Run one real Terminal-Bench trial with the Harbor version pinned by the repository. Compare the agent log, Harbor's saved trajectory, steps, tokens, cost, and reward to confirm the complete path from container installation and task execution through trajectory persistence, Harbor collection, and official verifier scoring, while distinguishing a failed task solution from an adapter infrastructure failure. +1. Extend the adapter contract tests in `benchmarks/harbor/tests` to verify that trajectory paths reach the container and that ATIF can be declared and read. +2. Verify step/token/cost population through contract tests, including missing files, validation failures, and incomplete metrics. +3. Run a real Terminal-Bench trial with the pinned Harbor version. Compare the agent log, collected trajectory, steps, tokens, costs, and reward across container installation, task execution, trajectory writing, Harbor collection, and official verification. Distinguish unsuccessful solutions from adapter infrastructure failures. -**End-to-end test procedure:** +**End-to-end procedure:** -1. On a host with Docker installed, run `sudo systemctl start docker.service` to start the Docker daemon. -2. If the current user cannot yet access the Docker socket, run `sudo usermod -aG docker "$USER"` to add that user to the `docker` group. This system configuration is required only once. -3. Log in again, or run `newgrp docker` in the current terminal to activate the group membership. Then run `docker info` and confirm that it can connect to the daemon. -4. Set `ANTHROPIC_API_KEY` and `ANTHROPIC_BASE_URL`, and prepare a complete 40-character commit SHA that can be installed from the remote repository. Select the model only through `--model` in the next step; the adapter converts it into the `ANTHROPIC_MODEL` used by nanoPyCodeAgent. -5. From the repository root, run the pinned task. With `--env docker`, Harbor pulls or builds the image, starts the task container, and cleans up the environment when it finishes; there is no need to run `docker compose` manually: +1. On a host with Docker installed, run `sudo systemctl start docker.service` to start the daemon. +2. If the current user cannot access the Docker socket, run `sudo usermod -aG docker "$USER"` to join the `docker` group. This system configuration is needed only once. +3. Log in again or run `newgrp docker` in the current terminal, then confirm daemon access with `docker info`. +4. Set `ANTHROPIC_API_KEY` and `ANTHROPIC_BASE_URL`, and prepare a remotely installable full 40-character commit SHA. Specify the model only through the next command's `--model`; the adapter converts it to `ANTHROPIC_MODEL` for nanoPyCodeAgent. +5. Run the pinned task from the repository root. `--env docker` lets Harbor pull or build the image, start the task container, and clean up afterward; manual `docker compose` invocation is unnecessary: ```bash uv run --project benchmarks/harbor harbor run \ @@ -275,38 +273,32 @@ uv run pytest --n-attempts 1 ``` -6. Read the result directory `jobs/2026-09-03__21-42-56/openssl-selfsigned-cert__5p9AFiW/` from Harbor's output and inspect these four artifacts: +6. Find the run directory `jobs/2026-09-03__21-42-56/openssl-selfsigned-cert__5p9AFiW/` in Harbor's output and check four artifacts: - - `agent/nanopycodeagent.txt`: the agent's stdout/stderr text log. - - `agent/trajectory.json`: the agent-generated ATIF steps, metrics, and terminal state. - - `verifier/test-stdout.txt`: the official tests' execution details. - - `result.json`: Harbor's aggregate agent metrics, reward, and exception information. + - `agent/nanopycodeagent.txt`: agent stdout/stderr text log. + - `agent/trajectory.json`: agent-generated ATIF steps, metrics, and terminal state. + - `verifier/test-stdout.txt`: official test details. + - `result.json`: Harbor's agent metrics, reward, and exception information. -7. Run `uv run --project benchmarks/harbor python -m harbor.utils.trajectory_validator jobs/2026-09-03__21-42-56/openssl-selfsigned-cert__5p9AFiW/agent/trajectory.json`. This uses the repository-pinned Harbor 0.21.0 to check the trajectory's ATIF schema and cross-field constraints; an exit code of 0 means the format and semantics are valid. -8. Compare `agent/trajectory.json` with `result.json`. Confirm that the schema is ATIF-v1.7, steps are complete, `final_metrics` agrees with `agent_result` for tokens, cache tokens, and cost, `exception_info` is empty, and the verifier produced a reward. +7. Run `uv run --project benchmarks/harbor python -m harbor.utils.trajectory_validator jobs/2026-09-03__21-42-56/openssl-selfsigned-cert__5p9AFiW/agent/trajectory.json`. The pinned Harbor 0.21.0 validator checks ATIF schema and cross-field constraints; exit code 0 indicates successful format and semantic validation. +8. Compare `agent/trajectory.json` with `result.json`. Confirm ATIF-v1.7, complete steps, matching token/cache-token/cost values between `final_metrics` and `agent_result`, empty `exception_info`, and a verifier reward. -**Result of this run:** +**Measured result:** - Official verifier: 6/6. - Reward: 1.0. - Harbor exceptions: 0. - Trajectory: 11 steps, including 10 model steps. - Tokens: 49,878 input, 42,240 cache, and 5,845 output. -- Cost: USD 0.00222441; all 10 reconciliation lookups succeeded on their first attempt. +- Cost: 0.00222441 USD; all 10 reconciliation lookups succeeded on their first attempt. ### Running Terminal-Bench in batches and establishing a baseline -The single-task validation has established that the task container, agent, -trajectory, cost reconciliation, and official verifier work together. The next -step is a 20-task pilot to observe failure modes and actual usage across different -tasks, then decide the budget for a baseline over the full 89-task set. The -20-task pass rate is a pilot result, not a full Terminal-Bench 2.1 score. +The single-task validation established the path through the task container, agent, trajectory, cost reconciliation, and official verifier. Next, expand to 20 tasks to observe failure modes and actual consumption before deciding the budget for a baseline across all 89 tasks. These 20 tasks are a small-scale trial run; their pass rate is not the full Terminal-Bench 2.1 score. #### Fixed experiment configuration -This run was prepared on 2026-09-06 with the following conditions. Future harness -comparisons should reuse the tasks and execution limits while recording each new -agent revision separately. +This run was prepared on 2026-09-06 with the following fixed conditions. Future harness comparisons should reuse the tasks and execution limits while recording each new agent revision separately. | Item | Configuration for this run | | --- | --- | @@ -317,31 +309,20 @@ agent revision separately. | Model | `openrouter/deepseek/deepseek-v4-flash-0731` | | API endpoint | `https://openrouter.ai/api`, using the Anthropic Messages API | | Provider routing | No request-level provider override; account routing settings are also an experiment condition | -| Pilot size | First 20 tasks in the dataset list, one attempt each | +| Trial-run size | First 20 tasks in the dataset list, one attempt each | | Environment | Local Docker, concurrency 2 | | Agent limit | At most 50 model replies per task | | Per-response limit | `MAX_TOKENS = 8192`; requests do not explicitly set reasoning effort or temperature | | Harbor retries | 0; this does not disable request retries inside the Anthropic SDK | | Timeouts and resources | Preserve the task defaults | -In Harbor 0.21.0, `--n-tasks 20` takes the first 20 entries after filtering; it is -not random sampling. Pinning the dataset hash fixes task versions, but the actual -selected task list should still be saved. Repeated `--include-task-name` options -can select those exact tasks later. Provider routing and server behavior on -OpenRouter may still change, so a fixed model slug does not make the experiment -fully deterministic. +In Harbor 0.21.0, `--n-tasks 20` selects the first 20 entries after filtering rather than sampling randomly. Pinning the dataset hash fixes task versions, but the actual task list should also be retained. Repeated `--include-task-name` options can select those exact tasks later. Provider routing and server behavior on OpenRouter may change, so pinning the model slug does not make the experiment fully deterministic. -Host Harbor dependencies use the lockfile. Inside containers, `uv tool install` -pins the agent source revision but does not lock dependency resolution. This run -also records the Docker version and task images' local IDs/RepoDigests to help -compare environments in later experiments. +Host Harbor uses a lockfile. Inside containers, `uv tool install` pins only the agent source revision, without locking dependency resolution. Public results retain the Docker version and task images' RepoDigests for comparing later environments. Host hardware information stays in local records. #### How to run -First confirm that `docker info` succeeds, then run the following from the -repository root. Credentials are supplied through environment variables, not -command arguments or experiment records. `ANTHROPIC_MODEL` overrides the model -name the adapter derives from `--model`, so this run explicitly removes it. +First confirm that `docker info` succeeds, then execute the command from the repository root. Supply credentials through environment variables without writing them into command arguments or experiment records. `ANTHROPIC_MODEL` overrides the model derived by the adapter from `--model`, so explicitly remove it for this run. ```bash export ANTHROPIC_API_KEY="${OPENROUTER_API_KEY:?Set OPENROUTER_API_KEY first}" @@ -365,75 +346,35 @@ uv run --locked --project benchmarks/harbor harbor run \ --job-name tb21-flash0731-pilot20-20260906 ``` -If credentials are already stored in `~/.nanoPyCodeAgent/settings.json` on the -host, the Python process that launches Harbor can call -`nanopycodeagent.settings.load_settings_env()`, remove `ANTHROPIC_MODEL`, and -start the same Harbor command. Having only the CLI inside the task container -read its configuration is insufficient: the host configuration file is not -automatically mounted into the container, so Harbor must first receive the -connection environment variables on the host. This run uses that approach in a -new right split in the current Herdr tab, preserving focus in the original pane. - -Use a new `--job-name` for a rerun to keep experiments distinct. For the full -89-task run, remove `--n-tasks 20` and choose a separate baseline job name while -keeping the other conditions unchanged. This reruns the entire set, with pilot -costs counted separately. The full set and additional attempts are deferred -until the pilot results have been analyzed. +If credentials already reside in the host's `~/.nanoPyCodeAgent/settings.json`, the Python process launching Harbor can call `nanopycodeagent.settings.load_settings_env()`, remove `ANTHROPIC_MODEL`, and start the same command. Reading settings only from the CLI inside the task container is insufficient: the host settings file is not automatically mounted there. Host Harbor must first receive the connection environment variables. This run used that approach in a new right split in the current Herdr tab, preserving focus in the original pane. + +Use a new `--job-name` for each rerun. To run all 89 tasks, remove `--n-tasks 20` and choose a separate baseline job name, retaining the other conditions. That runs the complete set again, with the small-scale trial run charged separately. Analyze these results before running the full set or increasing attempts per task. #### Budget and recording plan -The earlier certificate task cost 0.00222441 USD, but a simple task cannot -represent average spending across the full set. According to the -[OpenRouter model prices](https://openrouter.ai/deepseek/deepseek-v4-flash-0731) -and [provider quotes](https://openrouter.ai/api/v1/models/deepseek/deepseek-v4-flash-0731/endpoints) -checked on 2026-09-06, Baidu's uncached input, cache read, and output rates were -0.04998, 0.009996, and 0.09996 USD per million tokens; DeepSeek's base rates were -0.22, 0.007, and 0.66 USD. Routing, discounts, and time of day affect actual rates. - -Assuming cumulative usage per task of one million input tokens and 30,000 output -tokens with an 80% input cache hit rate, 20 tasks would cost approximately -0.4–1.4 USD at those two price levels. With three million input tokens and -150,000 output tokens per task, the estimate becomes 1.4–5 USD. These are budget -scenarios, not measured averages or spending caps. The 50-turn limit is not a -hard dollar limit either. These estimates cover model API charges only, excluding -cloud containers, networking, and local operating costs. - -Record and review this run in the following order: - -1. Save `jobs//config.json`, `lock.json`, task content hashes, and the - list of 20 tasks. -2. Save each task's `result.json`, `agent/trajectory.json`, agent text log, and - verifier log. -3. Count tasks with reward 1, tasks with reward 0, tasks without a score, and - infrastructure exceptions separately. Report passes over the planned task - count without silently reducing the denominator. -4. Check every trajectory's ATIF validation, usage and cost completeness, and - Harbor metric population. Missing or partial costs remain unknown; a known - subtotal is not a complete total. -5. Record total input, cache, and output tokens, actual USD cost, job wall time, - and per-task durations. Input already includes cached tokens, so do not add - the two together. -6. Distinguish incorrect solutions, exhausted turn budgets, execution timeouts, - API errors, and installation, image, or verifier environment failures. Preserve - first-attempt results and record any subsequent rerun separately. -7. Use measured pilot usage to adjust the full 89-task budget, then establish a - single-attempt baseline. To assess variability later, run separate repeated - experiments and retain every result instead of replacing the first attempt - with the best rerun. - -`/jobs/` is ignored by Git. Archive raw results separately, and keep reviewable -aggregate metrics, per-task results, and artifact locations in the development -notes. A local directory name alone does not put a baseline under version control. - -#### Actual results of the 20-task pilot - -The job `tb21-flash0731-pilot20-20260906` has finished all 20 tasks. The -[machine-readable result](../../../benchmarks/harbor/results/tb21-flash0731-pilot20-20260906.json) preserves per-task versions, metrics, -termination reasons, and separate cost lookup receipts. +The earlier certificate task cost 0.00222441 USD, but one simple task cannot represent the full dataset's average. According to [OpenRouter model prices](https://openrouter.ai/deepseek/deepseek-v4-flash-0731) and [provider quotes](https://openrouter.ai/api/v1/models/deepseek/deepseek-v4-flash-0731/endpoints) checked on 2026-09-06, Baidu's uncached input, cache-read, and output rates were 0.04998, 0.009996, and 0.09996 USD per million tokens. DeepSeek's base rates were 0.22, 0.007, and 0.66 USD. Routing, discounts, and time of day affect actual quotes. + +At cumulative per-task usage of one million input tokens and 30,000 output tokens with an 80% input cache hit rate, 20 tasks would cost about 0.4–1.4 USD at those price levels. At three million input tokens and 150,000 output tokens per task, the estimate becomes 1.4–5 USD. These are budget scenarios, not measured averages or spending caps. The 50-turn limit is not a hard dollar cap. Estimates cover model API charges, excluding cloud containers, networking, and local operating costs. + +Record and review the run as follows: + +1. Save `jobs//config.json`, `lock.json`, task content hashes, and the list of 20 tasks. +2. Save each task's `result.json`, `agent/trajectory.json`, agent text log, and verifier log. +3. Count rewards of 1, rewards of 0, unscored tasks, and infrastructure exceptions separately. Report passes over the planned task count without silently reducing the denominator. +4. Check each trajectory's ATIF validity, usage and cost completeness, and Harbor metric population. Missing or partial costs remain unknown; a known subtotal must not be presented as a complete total. +5. Record input/cache/output token totals, actual USD cost, job wall time, and per-task durations. Input includes cached tokens, so do not add them again. +6. Distinguish incorrect solutions, exhausted turn budgets, execution timeouts, API errors, and installation/image/verifier environment failures. Retain first results and create separate records for reruns. +7. Use the small-scale trial run's measured consumption to adjust the full 89-task budget, then establish a single-attempt baseline. To assess variation later, retain separate repeated experiments rather than replacing first results with the best rerun. + +`/jobs/` is ignored by Git. Archive raw results separately and retain reviewable summaries, per-task results, and artifact locations in the development notes. A local directory name alone does not put a baseline under version control. + +#### Actual results of the 20-task small-scale trial run + +The job `tb21-flash0731-pilot20-20260906` has finished all 20 tasks. The [machine-readable result](../../../benchmarks/harbor/results/tb21-flash0731-pilot20-20260906.json) preserves per-task versions, metrics, termination reasons, and cost completeness. Individual billing receipts are stored locally. | Metric | Measured result | | --- | --- | -| Job start / finish | `2026-09-06T16:41:41.721662` / `2026-09-06T18:32:51.295051` (Harbor host local time, Asia/Shanghai) | +| Run date | 2026-09-06 | | Job wall time | 111 minutes 10 seconds | | Passed / failed / unscored | 8 / 12 / 0 | | Pass rate over 20 planned tasks | 8 / 20 = 40% | @@ -449,9 +390,7 @@ termination reasons, and separate cost lookup receipts. | Known cost subtotal across all trajectories | 0.11053603 USD | | Cost after separate lookups | 0.119200332 USD (complete) | -The table uses model metrics from the final ATIF files, with costs from -the supplemental lookups. Original `result.json` files and trajectories have not -been rewritten. +The table uses model metrics from final ATIF files and costs after separate lookups. Original `result.json` files and trajectories have not been rewritten. | Task | Reward | Model replies | Last stop reason | Cost (USD) | | --- | ---: | ---: | --- | ---: | @@ -476,76 +415,25 @@ been rewritten. | `circuit-fibsqrt` | 0.0 | 2 | `max_tokens` | 0.001051326 | | `merge-diff-arc-agi-task` | 1.0 | 14 | `end_turn` | 0.002268999 | -`pytorch-model-recovery` has both reward=1 and `AgentTimeoutError`. Preserve the -original Harbor pass rate of **8/20 (40%)** and separately report **7/20 (35%) -passing without a Harbor exception**, both over the 20 planned tasks. This trial -cannot be treated as a normally completed success. +`pytorch-model-recovery` has both reward=1 and `AgentTimeoutError`. Preserve Harbor's original **8/20 (40%)** pass rate and separately report **7/20 (35%) passing without a Harbor exception**, both over the 20 planned tasks. This trial is not a normally completed success. #### Problems exposed by this run -1. **The final reply in 11 failed tasks has `stop_reason=max_tokens`.** Each reply - currently has an 8192-token limit. Some replies spend most of that budget on - thinking and end before delivering a complete solution. The model loop in - [`agent.py`](../../../src/nanopycodeagent/agent.py) treats every stop reason - other than `tool_use` as completion, so these trajectories still end with - `completed`. Truncation needs explicit handling, and the reasoning/output - budget needs evaluation. Pass rate and cost with a higher limit require a - separate experiment. -2. **`mteb-leaderboard` exhausts 50 model replies.** Its terminal outcome is - `max_turns_exhausted`, its final reply still requests tool use, and the - `/app/result.txt` required by the verifier has not been created. This is a - different budget limit from truncation within one reply. -3. **The agent continues after timeout and later fails on missing tool input.** - `pytorch-model-recovery` reaches its 900-second limit at 10:20:32 UTC on - 2026-09-06; verification starts at 10:20:33 UTC. The internal journal records - two new model calls starting at 10:21:00 and 10:21:11 UTC. The last `edit` has - only `old_text` and `new_text`, with no `path`. The stdout event subscriber - directly reads `arguments['path']` and raises `KeyError`; `run.failed` is - persisted at 10:23:15 UTC. That error occurs after the timeout and cannot - explain the earlier timeout. Agent execution overlaps verification, which - limits how this trial's reward can be interpreted. -4. **The original Harbor aggregate misses the timed-out trial's metrics, and - billing data arrives late.** The trajectory is marked missing when metrics - are collected at timeout, but a valid ATIF file later appears in the final - directory and matches the independent backup. Harbor token fields remain - null, so original metrics agree with final ATIF files in only 19/20 trials. - Original costs are partial in 13 trials, with 18 generations unresolved by - native reconciliation. Separate lookups against the same OpenRouter - generation endpoint resolve all 18 receipts: **all 267 recorded model replies - now have costs**. The original job value of 0.045695378 USD is incomplete; - the complete measured total is **0.119200332 USD**. It includes the two calls - made after timeout. - -Total input is 6,207,708 tokens, including 5,362,944 cached tokens; output is -331,041 tokens. Costs use generation `total_cost` values, without deriving them -from advertised prices or comparing the entire OpenRouter account balance. -All 20 tasks have rewards and valid ATIF files. Harbor records no other trial -exceptions besides the timeout above. - -The original job is at `jobs/tb21-flash0731-pilot20-20260906/`. The local archive -`jobs/tb21-flash0731-pilot20-20260906-artifacts.tar.gz` contains the task manifest, -logs, trajectories, generation cost ledger, supplemental receipts, Docker image -identifiers, and one-off run/analysis scripts. It has not been uploaded; its -SHA-256 and file checksums are in the machine-readable result. Two original -`torch-pipeline-parallelism` logs contain the runtime API credential. Their local -permissions are now 0600, and archive copies are redacted; the result JSON records -checksums for both originals and redacted copies. The summary and supplemental -receipts are versioned, without credentials or full conversation logs. +1. **The last reply in 11 failed tasks has `stop_reason=max_tokens`.** The current limit is 8192 tokens per response. Some replies spend most of it on thinking and finish before delivering a complete solution. The model loop in [`agent.py`](../../../src/nanopycodeagent/agent.py) treats every stop reason other than `tool_use` as completion, so these trajectories still have terminal status `completed`. Truncation needs explicit handling and the reasoning/output budget needs evaluation. Pass rate and cost under a higher limit require a separate experiment. +2. **`mteb-leaderboard` exhausts 50 model replies.** Its terminal outcome is `max_turns_exhausted`. Its final reply still requests tool use, and `/app/result.txt`, required by the verifier, has not been created. This differs from the budget limit within a single reply. +3. **The agent continues after timeout and later fails on missing tool input.** Relative to Harbor starting agent execution, `pytorch-model-recovery` reaches its limit at about 900 seconds and verification starts at about 901 seconds. The internal journal records two new model calls starting at about 929 and 939 seconds. The last `edit` contains only `old_text` and `new_text`, with no `path`. The stdout event subscriber directly reads `arguments['path']` and raises `KeyError`; `run.failed` is persisted at about 1063 seconds. The error occurs after the timeout and cannot explain the earlier timeout. Agent execution overlaps verification, which limits interpretation of the reward. +4. **The original Harbor aggregate misses the timed-out task's metrics, and billing records arrive late.** At timeout collection, the trajectory is marked missing. A valid ATIF file later appears in the final directory and matches the independent backup, but Harbor tokens remain null. Original metrics therefore agree with final ATIF in only 19/20 trials. Costs are originally partial in 13 tasks, with 18 generations unresolved by native reconciliation. Independent queries against the same OpenRouter generation endpoint resolve all 18 receipts, so **all 267 recorded model replies have costs**. The original job's 0.045695378 USD is incomplete; the complete measured amount is **0.119200332 USD**, including both calls made after timeout. + +Total input is 6,207,708 tokens, including 5,362,944 cached tokens; output is 331,041 tokens. Costs come from generation `total_cost` values, without deriving them from displayed prices or calculating a change in the entire OpenRouter account balance. All 20 tasks have rewards and valid ATIF files. Harbor records no other trial exceptions besides the timeout above. + +The original job is at `jobs/tb21-flash0731-pilot20-20260906/`. The local archive `jobs/tb21-flash0731-pilot20-20260906-artifacts.tar.gz` contains task lists, logs, trajectories, generation cost details, supplemental receipts, Docker image identifiers, and one-off run/analysis scripts. It has not been uploaded; the machine-readable result records its SHA-256. Two original `torch-pipeline-parallelism` logs contain the runtime API credential. Their local permissions are 0600 and archive copies are redacted. Checksums of originals and redacted copies are kept in local records. + +Version control retains only compact experiment configuration, per-task scores, tokens, costs, durations, and exception summaries. Request IDs, internal run IDs, individual billing receipts, exact call timestamps, host hardware information, and full conversation logs remain local. This minimization affects the latest version. Earlier pushed commits still contain accounting metadata; Git history has not been rewritten. #### Next run and cost assessment -First fix truncation handling, crashes on missing tool arguments, timeout -termination, and trajectory collection, then improve delayed cost reconciliation. -Those fixes belong to a new revision; the agent source was fixed throughout this -run. Rerun the same 20 task hashes under a new job name and compare passes without -exceptions, termination reasons, tokens, and costs. Preserve this run as the -pilot baseline before those fixes. Once execution and accounting are reliable, -run all 89 tasks and record a separate baseline for the full dataset. - -This pilot costs about 0.12 USD. Multiplying by 89/20 gives about 0.53 USD, but -11 tasks truncate early and the sample consists of the first 20 list entries, so -that extrapolation cannot budget the full dataset after fixes. Under the earlier -scenario of 3 million input tokens, 150,000 output tokens, and 80% input cache hits -per task, 89 tasks cost roughly 6–22 USD. Adjust that estimate using provider -prices at the time and the next pilot's measured consumption. Only the 20-task -pilot was executed; the full 89-task run remains pending. +First fix truncation handling, crashes on missing tool arguments, timeout termination, and trajectory collection, then improve delayed cost lookups. These fixes belong to a new revision. The agent source stayed pinned throughout this run. + +Rerun the same 20 task hashes under a new job name and compare passes without exceptions, termination reasons, tokens, and costs. Preserve this run as the small-scale baseline before those fixes. Once execution and accounting are reliable, run all 89 tasks and record a separate baseline for the full dataset. + +The current run costs about 0.12 USD. Multiplying by 89/20 gives about 0.53 USD, but 11 tasks truncate early and the sample consists of the first 20 list entries. That extrapolation cannot budget the full dataset after fixes. Under the earlier scenario of three million input tokens, 150,000 output tokens, and 80% input cache hits per task, 89 tasks cost about 6–22 USD. Adjust the estimate to provider prices at the time and consumption in the next small-scale trial run. Only 20 tasks have been executed; the full 89-task run is still pending. diff --git a/docs/dev_notes/zh-CN/0.8.x.md b/docs/dev_notes/zh-CN/0.8.x.md index ed94bc6..fb276c4 100644 --- a/docs/dev_notes/zh-CN/0.8.x.md +++ b/docs/dev_notes/zh-CN/0.8.x.md @@ -327,8 +327,8 @@ hash 可以固定任务版本，但仍应保存实际选中的任务清单，后 变化，固定 model slug 不等于完全确定性的实验。 宿主机 Harbor 使用 lockfile；容器内 `uv tool install` 只固定 agent 源码 revision， -没有锁定依赖解析。本轮另外保存了 Docker 版本及任务镜像的本地 ID／RepoDigests， -用于核对后续实验环境。 +没有锁定依赖解析。公开结果保存 Docker 版本及任务镜像的 RepoDigests，用于核对后续 +实验环境；宿主机硬件信息仅保存在本地记录中。 #### 运行方法 @@ -403,11 +403,11 @@ Baidu 当时的未缓存输入／缓存读取／输出价格分别为每百万 t #### 20 题小规模试跑的实际结果 本轮 job 为 `tb21-flash0731-pilot20-20260906`，20 题均已结束；完整数据见 -[机器可读结果](../../../benchmarks/harbor/results/tb21-flash0731-pilot20-20260906.json)，其中保存了逐题版本、指标、终止原因和费用补查记录。 +[机器可读结果](../../../benchmarks/harbor/results/tb21-flash0731-pilot20-20260906.json)，其中保存了逐题版本、指标、终止原因和费用完整性；逐次费用回执保存在本地。 | 指标 | 实测结果 | | --- | --- | -| Job 开始／结束时间 | `2026-09-06T16:41:41.721662` ／ `2026-09-06T18:32:51.295051`（Harbor 宿主机本地时间，Asia/Shanghai） | +| 运行日期 | 2026-09-06 | | Job 墙钟耗时 | 111 分 10 秒 | | 通过／未通过／未评分 | 8 ／ 12 ／ 0 | | 通过率（计划 20 题为分母） | 8 / 20 = 40% | @@ -463,11 +463,11 @@ Baidu 当时的未缓存输入／缓存读取／输出价格分别为每百万 t 2. **`mteb-leaderboard` 用尽 50 次模型回复。** 终态 outcome 为 `max_turns_exhausted`，末次回复仍要求调用工具，verifier 所需的 `/app/result.txt` 尚未生成。这与单次回复截断是两种不同的预算限制。 -3. **超时后 agent 仍在运行，且发生工具参数异常。** `pytorch-model-recovery` 在 - 2026-09-06 10:20:32 UTC 达到 900 秒限制，verifier 于 10:20:33 UTC 开始；内部 - journal 记录 agent 在 10:21:00 和 10:21:11 UTC 又启动两次模型调用。最后一次 +3. **超时后 agent 仍在运行，且发生工具参数异常。** 以 Harbor 开始执行 agent 为 + 起点，`pytorch-model-recovery` 在约第 900 秒达到限制，verifier 约第 901 秒开始； + 内部 journal 记录 agent 在约第 929 秒和第 939 秒又启动两次模型调用。最后一次 `edit` 只有 `old_text`、`new_text`，缺少 `path`，stdout 事件订阅者直接读取 - `arguments['path']`，触发 `KeyError`；`run.failed` 于 10:23:15 UTC 落盘。 + `arguments['path']`，触发 `KeyError`；`run.failed` 约在第 1063 秒落盘。 因而该异常发生在超时之后，不能用它解释之前的超时。运行与验证发生重叠，该题的 reward 需要带着这一限制解读。 4. **Harbor 原始汇总漏掉超时题的计量，费用也有延迟。** 超时采集时 trajectory @@ -485,9 +485,13 @@ generation 的 `total_cost`，没有按展示价格反推，也不是整个 Open 原始 job 位于 `jobs/tb21-flash0731-pilot20-20260906/`。本地归档为 `jobs/tb21-flash0731-pilot20-20260906-artifacts.tar.gz`，包含任务清单、日志、轨迹、 generation 费用明细、补查回执、Docker 镜像标识和一次性运行／汇总脚本。归档未上传， -SHA-256 及文件校验值见机器可读结果。`torch-pipeline-parallelism` 的两份原始日志 -含运行时 API 凭据，原件权限已设为 0600，归档副本已脱敏；结果 JSON 分别记录原件与 -脱敏副本的校验值。版本管理保存汇总及补查回执，不包含凭据或完整对话日志。 +其 SHA-256 见机器可读结果。`torch-pipeline-parallelism` 的两份原始日志含运行时 +API 凭据，原件权限已设为 0600，归档副本已脱敏；原件与脱敏副本的校验值保存在 +本地记录中。 + +版本管理仅保存精简的实验配置、逐题评分、token、费用、耗时和异常摘要。请求 ID、 +内部运行 ID、逐次账单回执、精确调用时间、宿主机硬件信息及完整对话日志留在本地。 +这一精简修改作用于最新版；之前已推送的提交仍包含对账元数据，未改写 Git 历史。 #### 下一轮计划与费用判断