diff --git a/docs/dev_notes/en/0.8.x.md b/docs/dev_notes/en/0.8.x.md index 8fb7b75..9890d06 100644 --- a/docs/dev_notes/en/0.8.x.md +++ b/docs/dev_notes/en/0.8.x.md @@ -4,15 +4,15 @@ ## 0.8.0 - 2026.09.04 -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. +The basic bash, write, read, and edit tools are now implemented, providing a toolset similar to Pi's. The next step is to run public benchmarks to learn which features need improvement or addition and how to strengthen the harness. Benchmarks used in mainstream model releases provide a way to compare different harnesses with the same model and identify optimization targets. -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 step was research, documented in [code_agent_benchmark](../../research/en/code_agent_benchmark.md). Whichever benchmark we choose, it first needs a non-interactive way to invoke the code agent. Only then can a program run the agent on a task from start to finish and evaluate the result. The interface requirements for the first recommended benchmarks are collected in [benchmark_headless_interface](../../research/en/benchmark_headless_interface.md). -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. +The first feature is a **headless CLI**, step one of the research's minimum viable path. It is the only hard blocker: the first three requirements shared by the three benchmarks all depend on it—receive a task and finish in one command, work in the process's current directory, and run without interaction, questions, or confirmation. The current sole entry point is the `input("You> ")` loop in `agent.py`. When container stdin is EOF, the process immediately prints `Bye!` and exits without doing anything. Until this path works, later harness changes cannot be verified programmatically, leaving no basis for optimization. ### Basic headless CLI implementation -Start with the minimum needed to complete one simple benchmark task. Debugging and performance improvements can follow. +Start with the minimum functionality needed to complete one simple benchmark task. Troubleshooting and performance improvements can follow. **Command-line form:** @@ -22,42 +22,42 @@ nanoPyCodeAgent [-p/--prompt "" | --prompt-file | (stdin pipe)] [--version] ``` -`--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. +`--version` serves a concrete purpose: Harbor uses the adapter's `get_version_command()` and `parse_version()` to discover and record the agent version on a best-effort basis. Failure does not raise an error. `_package_version()` already exists and only needs a CLI option. -**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. +**Headless detection:** Supplying `-p` or `--prompt-file` selects headless mode. If neither is supplied and `sys.stdin.isatty()` is False, read all of stdin as the task. Enter the existing REPL only when connected to a tty with no task supplied. All three input paths are needed: Harbor's official Claude Code example uses a stdin pipe, `printf … | claude --print`, to avoid shell escaping and command-length limits; mini-swe-agent uses `--task=` and explicitly connects stdin to `/dev/null`. This also fixes the current behavior of immediately printing `Bye!` and exiting when container stdin is EOF. **Exit codes:** | Exit code | Situation | | :-: | --- | -| 0 | The model declares completion; the turn limit is reached; the task is not solved | +| 0 | The model declares completion; the turn budget is exhausted; the task is not solved | | Nonzero | Missing API credentials; invalid arguments; API or transport errors | -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. +Exiting with 0 despite unfinished work may seem counterintuitive. Harbor wraps the agent command in `set -o pipefail`; a nonzero exit raises `NonZeroAgentExitCodeError`, marks the trial as an agent failure, and may trigger a costly retry. The verifier should decide whether the task was solved using reward files under `/logs/verifier/`. The agent's exit code should not make that decision. -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. +This also exposes an existing bug: without API credentials, `run()` prints a message and returns, while `main()` has no return-code handling. The process therefore exits with 0. To the harness, that looks like a completed run that failed to solve the task. A whole batch could silently score zero without revealing the configuration error. `main()` must return an int, which the console script uses as the exit code. -**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. +**Turn limit:** The inner tool-use loop currently has no limit. In interactive mode, a person can press Ctrl-C when the model repeatedly tries the same command. In headless mode, it can continue spending until an API error stops it. `--max-turns` requires only a counter but is essential for unattended operation, so it belongs with the CLI. Exhausting the budget exits with 0, as above. -**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. +**Headless system prompt:** The current prompt is written for a conversational assistant. A headless model has nobody to ask. A polite request such as “Should I continue?” ends its turn and leaves the task scoring zero. Headless mode needs a separate system prompt that explicitly requires autonomous decisions, no questions or pauses for confirmation, and a clear declaration when finished. -**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. +**Preserve API error text:** Harbor scans stdout/stderr with regular expressions to classify rate limits, usage limits, overloaded services, context overflow, missing authentication, network interruptions, and other errors. These classifications combine with options such as `--max-retries 3 --retry-include ApiRateLimitError` to decide whether to retry. Emit the original error rather than swallowing or rewriting it so the harness can handle part of the retry work. -**Acceptance:** One command demonstrates the basic path: +**Acceptance:** One command demonstrates whether this step works: ```bash printf "%s" "create hello.py that prints hi" | nanoPyCodeAgent; echo $? ``` -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. +The task must arrive through the pipe, create the file, and exit with 0. This is the first point at which nanoPyCodeAgent can be called from scripts and connected to benchmarks. -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`. +Later work includes `--output-format stream-json` and `--trajectory` (step four of the minimum viable path, when failures need attribution), API retries and backoff (step two), context compaction (step five), `--workdir` (downgraded from P0 because all three benchmarks supply a container WORKDIR, so using the process cwd suffices), and `--timeout`. The harness controls wall-clock timeouts through settings such as `[agent].timeout_sec` in Harbor's `task.toml`; an agent timeout is an additional safeguard rather than an integration prerequisite. -### First end-to-end validation with Terminal-Bench +### First end-to-end acceptance with Terminal-Bench -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 `hello.py` smoke test establishes that the CLI can accept a task and exit, but does not establish integration with a real benchmark. The goal here is to run the whole path once on a sufficiently simple task that still exercises command execution, file writing, recovery from mistakes, and official verification. -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: +The selected task is `terminal-bench/openssl-selfsigned-cert` from Terminal-Bench 2.1. Harbor 0.21.0 and the nanoPyCodeAgent adapter were temporarily placed under `/tmp`, without adding benchmark-specific code to the project. The adapter inherits Harbor's `BaseInstalledAgent`. The `module:ClassName` dynamic import syntax in `--agent nanopy_harbor_agent:NanoPyCodeAgent` lets host Harbor execute this sequence: ```text Harbor @@ -69,7 +69,7 @@ Harbor -> save logs, test results, and reward ``` -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: +The core configuration follows. Credentials, base URL, and model configuration were still injected through environment variables rather than written into the command or logs: ```bash harbor run \ @@ -81,7 +81,7 @@ harbor run \ --n-attempts 1 ``` -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: +The first setup exceeded the default setup timeout while the container downloaded Python and dependencies. No model call had occurred, so this was not a benchmark result. Increasing only the installation timeout, while keeping the task, model, turn budget, and verifier unchanged, produced a complete trial: | Metric | Result | | --- | --- | @@ -91,41 +91,41 @@ The first environment setup exceeded the default setup timeout while downloading | Tool calls | 16 bash, 2 write, 1 edit | | Agent / verifier exceptions | 0 | -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. +This establishes the minimum execution loop: Harbor can deliver a real task to the headless CLI; the CLI can call the model and tools unattended in the isolated container's current directory; the model can recover from mistakes; and the official verifier can independently assign a reward after the process exits normally. The simplest benchmark now works from task input through final scoring. Reliable performance across a full suite remains to be established. ### Dependency and observability limits exposed by the run -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. +One container installation reported missing `httpx` when starting the CLI. The temporary adapter continued after adding `uv tool install --with httpx`. Two subsequent clean installations with the same uv, Python, and project commit did not reproduce the omission because `anthropic` still installs `httpx` transitively. This does not establish that clean installations always fail. The confirmed issue is that nanoPyCodeAgent directly imports `httpx` without declaring it as a direct dependency. Its operation currently borrows the `anthropic -> httpx` relationship, and that dependency ownership should be corrected. -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`. +Even without a trajectory, tool calls could be counted from ordinary text logs because the CLI prints prefixes such as `[bash]`, `[write]`, and `[edit]` before each call. Such counts lack turn boundaries, tool-call IDs, input/output tokens, costs, durations, and structured error states, leaving Harbor's token and cost fields `null`. -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. +As explained in [agent_output_and_trajectory](../../research/en/agent_output_and_trajectory.md), a text log cannot simply be renamed a trajectory. Run output is the public content delivered by an invocation; a trajectory is a structured execution path bounded by one 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 retaining distinct interfaces. The next work therefore has three parts: - 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. +- Bring the Harbor adapter into the repository and version its installation commands, headless invocation, environment forwarding, and version detection for reuse in future benchmarks. +- Design internal events and a trajectory writer so the agent emits ATIF directly and the adapter reliably populates tokens, costs, and steps. Implement `stream-json` later as separate run output. These changes improve installation, reproducibility, and observability of the established execution path. ### Bringing the Harbor adapter into the project -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 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 tests. Running `uv run --project benchmarks/harbor harbor ...` from the repository root loads the adapter without `/tmp` or a manually configured `PYTHONPATH`. -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. +Installation 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 before release. The two 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 has been removed because the project now declares `httpx` directly. -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 instruction is no longer interpolated into shell syntax. The adapter injects the complete task through a temporary environment variable, then pipes it into the headless CLI with `printf`. Quotes, newlines, dollar signs, and arbitrary task lengths do not become shell syntax. The CLI retains its default limit of 50 turns, overridable with `--agent-kwarg max_turns=N`. Under `pipefail`, stdout/stderr are combined and saved with `tee` to `/logs/agent/nanopycodeagent.txt`, allowing Harbor to classify API errors and preserving nonzero CLI exits through the pipeline. -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 configuration boundary is also fixed. `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 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. -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. +Five contract tests using Harbor 0.21.0's real base class first cover the container boundary: release pinning, Git revision pinning, mutually exclusive arguments, 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, invoked the agent, collected logs, and ran the official verifier. Agent/verifier infrastructure exceptions were zero, establishing end-to-end adapter integration. 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. This concerns solution portability rather than adapter infrastructure. -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. +Host integration was also checked through the independent workspace's Harbor version command and dynamic import. Building the root project wheel confirmed that it contains neither the adapter nor a Harbor dependency. The direct `httpx` dependency and formal adapter are now 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 trajectories -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. +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 runtime facts but remains separate run output. Its `--output-format` CLI belongs to another control surface outside trajectory implementation. -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: +A completed `read` call illustrates the concepts of a `Native Event` and a `Journal Entry`. The agent loop first emits a `Native Event` describing only what happened: ```json { @@ -140,7 +140,7 @@ A completed `read` call illustrates the distinction between a `Native Event` and } ``` -The journal writer adds identity, ordering, and persistence time to form a `Journal Entry`: +The journal writer adds the identity, ordering, and recording time needed for persistence, producing a `Journal Entry`: ```json { @@ -161,18 +161,18 @@ The journal writer adds identity, ordering, and persistence time to form a `Jour 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. -Implementation is organized around independently usable and verifiable capabilities, with acceptance criteria attached to each capability: +Implementation is organized around independently usable and verifiable capabilities, rather than seven disconnected technical layers. Each capability defines its implementation and acceptance criteria together: -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. +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 Event 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. +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 existing 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` belong to separate run output and are outside these trajectory capabilities. #### Runtime facts and Event Journal -**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. +**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 internal reconstruction data, not public run output or a trajectory. **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. @@ -185,11 +185,11 @@ uv run pytest \ tests/test_agent.py ``` -Acceptance requires successful tests and coverage for every mandatory behavior in the protocol document. +Acceptance requires these tests to return 0 and a corresponding test for every mandatory behavior in the protocol document. #### Public ATIF trajectory -**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. +**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. The internal Event Journal stays private. Provider cost collection/reconciliation, Harbor adapter collection, `stream-json`, and interactive trajectories spanning multiple runs are separate capabilities. **Protocol:** Use ATIF-v1.7 as implemented in Harbor 0.21.0: @@ -207,7 +207,7 @@ uv run --project benchmarks/harbor \ /tmp/nanopycodeagent-trajectory.json ``` -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. +Acceptance requires validator exit code 0, unchanged text stdout, and a complete JSON output file. Adapter reading and a real trial belong to the later Harbor integration acceptance. #### Provider cost collection and reconciliation @@ -219,10 +219,10 @@ Acceptance requires validator exit code 0, unchanged text stdout, and a complete There are two collection paths: -- **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. +- **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. To preserve 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. -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 derives a same-origin `v1/generation` endpoint from the current Anthropic SDK base URL. It 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 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. @@ -244,23 +244,23 @@ uv run pytest **What to build:** -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. +1. Have the repository's Harbor adapter specify an in-container trajectory output path for each trial. After the agent finishes, report the path and format so Harbor reads the agent-generated ATIF-v1.7 file. +2. Populate Harbor with complete ATIF steps and the token, cache-token, cost, and completeness information in step and final metrics. +3. Make the adapter's responsibilities and failure semantics explicit. It handles paths and data handoff, without parsing the Event Journal or maintaining a native trajectory conversion. Missing, invalid, or partial trajectories need clear diagnostics and must not appear as zero consumption or complete results. **Validation:** -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. +1. Extend adapter contract tests in `benchmarks/harbor/tests` to verify that the trajectory path reaches the container and that ATIF can be declared and read. +2. Verify step, token, and cost reporting with contract tests, including missing files, failed validation, and incomplete metrics. +3. Run a real Terminal-Bench trial using the pinned Harbor version. Compare agent logs, Harbor's saved trajectory, steps, tokens, cost, and reward to establish the entire path from container installation and task execution through trajectory persistence, Harbor collection, and official scoring. Distinguish solution failures from adapter infrastructure failures. -**End-to-end procedure:** +**End-to-end test procedure:** 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: +3. Log in again, or run `newgrp docker` in the current terminal to activate group permissions. Run `docker info` to confirm access to the daemon. +4. Set `ANTHROPIC_API_KEY` and `ANTHROPIC_BASE_URL`, and prepare a full 40-character commit SHA that can be installed remotely. Specify the model only through the next command's `--model`; the adapter converts it into `ANTHROPIC_MODEL` for nanoPyCodeAgent. +5. Run the fixed task from the repository root. `--env docker` lets Harbor pull or build the image, start the task container, and clean up afterward, without manually running `docker compose`: ```bash uv run --project benchmarks/harbor harbor run \ @@ -273,26 +273,26 @@ uv run pytest --n-attempts 1 ``` -6. Find the run directory `jobs/2026-09-03__21-42-56/openssl-selfsigned-cert__5p9AFiW/` in Harbor's output and check four artifacts: +6. Obtain the result directory from Harbor output, `jobs/2026-09-03__21-42-56/openssl-selfsigned-cert__5p9AFiW/`, and inspect four artifacts: - `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. + - `result.json`: Harbor's aggregate 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`. 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. +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`: require ATIF-v1.7, complete steps, matching tokens/cache tokens/cost between `final_metrics` and `agent_result`, an empty `exception_info`, and a verifier reward. -**Measured result:** +**Observed results:** - 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: 0.00222441 USD; all 10 reconciliation lookups succeeded on their first attempt. +- Tokens: 49,878 input, 42,240 cache, 5,845 output. +- Cost: 0.00222441 USD; all 10 cost lookups succeeded on their first attempt. -### Running Terminal-Bench in batches and establishing a baseline +### Running a Terminal-Bench batch and establishing a baseline 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. @@ -307,22 +307,22 @@ This run was prepared on 2026-09-06 with the following fixed conditions. Future | 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 | -| Trial-run size | First 20 tasks in the dataset list, one attempt each | +| API endpoint | `https://openrouter.ai/api`, using Anthropic Messages | +| Provider routing | No provider override in requests; account routing settings are also an experimental condition | +| Trial size | First 20 dataset entries, one attempt per task | | 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 | +| Per-response limit | `MAX_TOKENS = 8192`; reasoning effort and temperature are not explicitly set | +| Harbor retries | 0; this does not disable the Anthropic SDK's internal request retries | +| Timeouts and resources | Preserve task defaults | -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. +Harbor 0.21.0's `--n-tasks 20` takes the first 20 entries after filtering; it is not random sampling. Pinning the dataset hash fixes task versions, but the selected task list should still be saved. Repeated `--include-task-name` options can select those exact tasks later. OpenRouter provider routing and server behavior can still change, so pinning a model slug does not make the experiment fully deterministic. -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. +Host Harbor uses a lockfile. Inside containers, `uv tool install` pins only the agent source revision and leaves dependency resolution unlocked. Public results retain the Docker version and task image RepoDigests for checking later environments. Host hardware details remain in local records. -#### How to run +#### Running the experiment -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. +First confirm that `docker info` succeeds, then run from the repository root. Supply credentials through environment variables rather than command arguments or experiment records. `ANTHROPIC_MODEL` overrides the model derived by the adapter from `--model`, so this run explicitly removes it. ```bash export ANTHROPIC_API_KEY="${OPENROUTER_API_KEY:?Set OPENROUTER_API_KEY first}" @@ -346,51 +346,51 @@ uv run --locked --project benchmarks/harbor harbor run \ --job-name tb21-flash0731-pilot20-20260906 ``` -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. +If credentials are already stored in local `~/.nanoPyCodeAgent/settings.json`, call `nanopycodeagent.settings.load_settings_env()` in the Python process launching Harbor, remove `ANTHROPIC_MODEL`, and start the same Harbor command. Relying on the CLI inside the task container to read configuration is insufficient: the host configuration file is not automatically mounted into the container, so host Harbor must first receive the connection environment variables. The actual run used this method, opening a right split in the current Herdr tab while 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. +Use a new `--job-name` for a rerun to distinguish it from existing experiment directories. For all 89 tasks, remove `--n-tasks 20` and choose a separate baseline job name while retaining the remaining conditions. This reruns the full dataset, with the small-scale trial cost counted separately. Analyze this run before executing the full dataset or increasing attempts per task. #### Budget and recording plan -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. +The earlier certificate task cost 0.00222441 USD, but one simple task cannot represent the dataset's average cost. 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/output prices were 0.04998/0.009996/0.09996 USD per million tokens. DeepSeek's base prices were 0.22/0.007/0.66 USD. Routing, promotions, and time affect actual prices. -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. +At one million input tokens and 30,000 output tokens per task with 80% input cache hits, those two price bands imply about 0.4–1.4 USD for 20 tasks. At three million input tokens and 150,000 output tokens per task, the range is about 1.4–5 USD. These are budget scenarios, not measured means or cost ceilings. A 50-turn limit is not a hard dollar cap either. The estimate covers model API charges only, excluding cloud containers, networking, and local execution costs. -Record and review the run as follows: +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. +1. Save `jobs//config.json`, `lock.json`, task content hashes, and the selected 20-task list. 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. +3. Count reward-1 tasks, reward-0 tasks, unscored tasks, and infrastructure exceptions separately. Report passes divided by planned tasks 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. +5. Record total input/cache/output tokens, actual USD charges, job wall-clock duration, and per-task durations. Input already includes cached tokens; do not add them again. +6. Distinguish incorrect solutions, exhausted turns, execution timeouts, API errors, and installation/image/verifier environment failures. Preserve the first result and record every rerun separately. +7. Use measured trial consumption to adjust the full 89-task budget, then establish a single-run baseline. To assess variability later, perform additional independent repetitions and preserve every result instead of replacing the first attempt 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. +`/jobs/` is Git-ignored. Archive raw results separately and retain a reviewable summary, per-task results, and artifact locations in the development notes. A local directory name alone does not place the baseline under version control. -#### Actual results of the 20-task small-scale trial run +#### Actual results of the 20-task 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. +The job is `tb21-flash0731-pilot20-20260906`, and all 20 tasks have finished. The [machine-readable results](../../../benchmarks/harbor/results/tb21-flash0731-pilot20-20260906.json) retain per-task versions, metrics, termination reasons, and cost completeness. Individual billing receipts remain local. | Metric | Measured result | | --- | --- | | Run date | 2026-09-06 | -| Job wall time | 111 minutes 10 seconds | +| Job wall-clock duration | 111 minutes 10 seconds | | Passed / failed / unscored | 8 / 12 / 0 | -| Pass rate over 20 planned tasks | 8 / 20 = 40% | +| Pass rate, denominator of 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 | +| Input / cache / output tokens reaggregated from final ATIF | 6,207,708 / 5,362,944 / 331,041 | +| Valid ATIF files | 20 / 20 | +| Original Harbor metrics matching final ATIF | 19 / 20 | +| Trajectories with incomplete costs before supplemental lookup | 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) | +| Cost after independent supplemental lookup | 0.119200332 USD, complete | -The table uses model metrics from final ATIF files and costs after separate lookups. Original `result.json` files and trajectories have not been rewritten. +The table below uses model metrics from final ATIF files and independently reconciled costs. Original `result.json` and trajectory files were not rewritten. | Task | Reward | Model replies | Last stop reason | Cost (USD) | | --- | ---: | ---: | --- | ---: | @@ -415,25 +415,150 @@ The table uses model metrics from final ATIF files and costs after separate look | `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 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. +`pytorch-model-recovery` has both reward=1 and `AgentTimeoutError`. Preserve Harbor's original pass rate of **8/20 (40%)** and separately report **7/20 (35%) passes without a Harbor exception**, both using the planned 20 tasks as the denominator. This task is not a normally completed success sample. #### Problems exposed by this run 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. +2. **`mteb-leaderboard` exhausts 50 model replies.** Its terminal outcome is `max_turns_exhausted`; the final reply still requests tool use, and `/app/result.txt`, required by the verifier, has not been created. This is a different budget limit from truncating one response. +3. **The agent continues after timeout and encounters a tool argument exception.** Relative to the start of Harbor's agent execution, `pytorch-model-recovery` reaches its limit around second 900 and the verifier starts around second 901. The internal journal records two more model calls starting around seconds 929 and 939. The final `edit` has only `old_text` and `new_text`, with no `path`. The stdout event subscriber directly accesses `arguments['path']`, raising `KeyError`; `run.failed` is persisted around second 1063. This exception occurs after timeout and cannot explain the earlier timeout. Agent execution overlaps verification, limiting how the reward can be interpreted. +4. **Harbor's original aggregate omits the timed-out task's metrics, and costs are delayed.** The trajectory is marked missing during timeout collection. 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. +Complete input is 6,207,708 tokens, including 5,362,944 cache tokens; output is 331,041. Costs come from generation `total_cost`, not displayed-price estimates or the difference in the entire OpenRouter account balance. All 20 tasks have rewards and valid ATIF. Apart from the timeout above, Harbor reports no other trial exceptions. -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. +The original job is under `jobs/tb21-flash0731-pilot20-20260906/`. A local archive at `jobs/tb21-flash0731-pilot20-20260906-artifacts.tar.gz` contains the task list, logs, trajectories, generation-level costs, supplemental receipts, Docker image identifiers, and one-off execution and aggregation scripts. It has not been uploaded; its SHA-256 is in the machine-readable results. Two original `torch-pipeline-parallelism` logs contain runtime API credentials. Their permissions are 0600 and archived copies are redacted. Checksums for original and redacted copies remain 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. +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 times, host hardware details, and full conversations remain local. This minimization applies to the latest revision; earlier pushed commits still contain reconciliation metadata, and 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 lookups. These fixes belong to a new revision. The agent source stayed pinned throughout this run. +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 remained pinned throughout this run. Rerun the same 20 task hashes under a new job name and compare passes without exceptions, termination reasons, tokens, and cost. Preserve this run as the small-scale baseline before fixes. Once execution and accounting are reliable, run all 89 tasks and record that result separately as the full-dataset baseline. -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. +This 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 trial run. Only 20 tasks have been executed; the full 89-task run is still pending. -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. +### Addressing problems encountered in the Terminal-Bench batch + +The explanations below record what the problems mean, the current implementation, and proposed fixes. They do not indicate that the corresponding code has already been fixed. + +#### Truncation handling + +Each model reply is currently limited to 8192 tokens. If the reply exhausts this budget before finishing, the API returns `stop_reason="max_tokens"`, indicating that generation was forced to stop at its length limit. For example, the model may still be analyzing how to modify code when the budget runs out, before generating the subsequent tool call or complete solution. Some replies in this run spent most of their budget on thinking; the final replies in 11 failed tasks all had `max_tokens`. + +Currently, `_run_model_loop()` in [`agent.py`](../../../src/nanopycodeagent/agent.py) decides whether the model has ended normally using only this condition: + +```python +if message.stop_reason != "tool_use": + return True +``` + +As a result, `max_tokens` also causes the agent to leave the loop and record `completed` in the trajectory. This conflates an interrupted reply with a normal model finish. `completed` itself does not establish that the solution is correct; the verifier still decides whether the task passes. + +At minimum, the fix must recognize `max_tokens` separately and accurately record truncation and the stopping reason. Further strategies could attempt to resume generation within the available budget or explicitly record truncation and stop. The recovery method still needs design; unlimited continuation or retries should not be the default. + +The value 8192 is currently hardcoded as a module constant in `agent.py`: + +```python +MAX_TOKENS = 8192 +``` + +There is currently no environment variable or CLI option for adjusting it. + +**Even after increasing this value, `stop_reason="max_tokens"` still needs handling.** The two changes serve different purposes: + +| Change | Purpose | +| --- | --- | +| Increase `MAX_TOKENS` | Give each reply more room to generate, reducing truncation caused by insufficient budget | +| Handle `stop_reason="max_tokens"` | Recognize truncation, decide whether to recover or stop, and accurately record the reason | + +Any finite limit can be exhausted. Increasing the number alone leaves the incorrect completion classification in place. The proposed order is to implement truncation handling first, then use small-scale trial runs to evaluate whether to raise the limit, by how much, and how to allocate reasoning and output budgets. Truncation in 11 failed tasks does not mean that a higher limit will make all 11 pass. Pass rate, token consumption, and cost require a new experiment. + +#### Crashes caused by missing tool arguments + +The model omitted a required tool argument. Instead of returning a tool-call error to the model, the agent raised a Python exception and failed the entire run. In this trial, the failure occurred in the final `edit` call of `pytorch-model-recovery`. + +The [`edit` tool definition](../../../src/nanopycodeagent/edit_tool.py) requires three arguments: + +- `path`: the file to modify. +- `old_text`: the original text to replace. +- `new_text`: the replacement text. + +This call supplied only `old_text` and `new_text`, omitting `path`. It described the replacement without identifying the file. Although the tool schema already declares `path` as required, the actual input was missing it, so the program needs to check arguments before using them. + +Before executing a tool, the agent emits `tool.started`, and `_TextOutputProjector` prints a preview. The `edit` preview directly reads `arguments['path']`; because that key is absent, it raises `KeyError: 'path'`. The failure occurred while printing the preview, before any file modification. It happened after the Harbor timeout and cannot explain the earlier timeout. + +Both preview and execution need protection. The preview must tolerate missing arguments; execution must validate them and return a clear `tool_result` with `is_error=True` instead of executing an invalid call. For example: + +```text +Missing required argument: path. Provide the file path and retry. +``` + +The model can then see the error in a later turn, supply the missing argument, and continue within the remaining budget. Fixing the preview alone is insufficient: `_run_one_tool()` still directly reads `block.input["path"]` in its execution branch, and its exception handler records the failure and re-raises it, failing the run. Missing required arguments should be recoverable tool-call errors returned to the model. Other tools should have the same argument-checking and error-return paths. + +#### Timeout termination + +Timeout termination means ensuring that the agent inside the container actually stops working when the task reaches its allotted runtime, before verification begins. In this run, Harbor had already declared a timeout while the agent continued making model calls. + +This occurred in `pytorch-model-recovery`, whose agent runtime limit was 900 seconds, or 15 minutes. Relative to the start of agent execution, the timeline was: + +| Time | What happened | +| --- | --- | +| Around second 900 | Harbor declared a timeout and recorded `AgentTimeoutError` | +| Around second 901 | The verifier started checking the task result | +| Around seconds 929 and 939 | The agent started two more model calls | +| Around second 1063 | The agent failed because `edit` lacked `path`, recording `run.failed` | + +Harbor's timeout decision did not promptly stop the container agent, with two consequences: + +- The agent remained active during verification and could continue changing files being checked by the verifier, affecting the reliability of the result. Although the task received `reward=1`, it cannot be treated as a normally completed success sample. +- New model calls and charges occurred after the deadline, so the task's time boundary was not enforced. + +The proposed fix should: + +1. Stop agent execution at the deadline and prevent new model and tool calls. +2. Clean up tool child processes that need termination so commands cannot continue modifying files after the agent exits. +3. Confirm that execution has stopped before starting the verifier, and accurately record the timeout state. + +The confirmed observation is that the agent continued after timeout. The specific layer where cancellation or process cleanup failed still needs investigation. The purpose is to enforce the time limit; simply extending the 900-second allowance does not solve continued execution after timeout. + +#### Trajectory collection + +Trajectory collection means saving the agent's structured execution record and making it available to Harbor in time to aggregate steps, tokens, and costs. The issue in this run was that the file appeared later, after Harbor had already tried to collect it for aggregation. + +Normally, the agent continuously appends model replies, tool calls, execution results, and usage to its internal Event Journal. During finalization, `_run_exchange()` in [`agent.py`](../../../src/nanopycodeagent/agent.py) projects the journal into a public ATIF `trajectory.json`. The [Harbor adapter](../../../benchmarks/harbor/src/harbor_adapter/adapter.py) reads and validates the file in `populate_context_post_run()`, records diagnostics such as step counts, and populates tokens and costs in the evaluation result. + +Collection for `pytorch-model-recovery` encountered a timing problem: + +1. Harbor began collection after declaring timeout, could not obtain the trajectory, and recorded it as `missing`. +2. The agent continued running and wrote a valid ATIF file during its later finalization. +3. The file eventually appeared in the result directory, but tokens in Harbor's original result remained `null`; the file's arrival did not automatically populate them. + +Thus, all 20/20 final ATIF files passed validation, while original Harbor metrics matched final ATIF for only 19/20 tasks. A file's eventual existence and validity do not establish that it was collected when evaluation metrics were aggregated. + +The proposed fix should preserve as much execution history as possible through the stopping point for normal completion, exceptions, and timeouts, and make it available to Harbor before aggregation. Incomplete records should be explicitly identified. + +This needs to be designed together with timeout termination: stop execution, complete finalization within a bounded time, then collect and aggregate. ATIF writing currently depends on `finally` during finalization. Forcibly killing the process does not guarantee that this code can run. Options such as periodic snapshots or recovering a trajectory from the persisted journal therefore need consideration. The specific solution is undecided; the objective is to keep actual execution records, saved trajectories, and Harbor aggregates consistent. + +#### Improving delayed cost lookups + +Improving delayed cost lookups means being able to query model-call costs that remain unavailable when a task ends and complete the accounting later. + +A model reply may already have arrived without an actual cost, while the billing endpoint still cannot provide an amount. The agent saves that call's generation ID to associate a later billing lookup with the call and query its `total_cost`. A temporarily unavailable charge must remain pending or unknown, not be treated as free. + +The existing `_reconcile_costs()` in [`agent.py`](../../../src/nanopycodeagent/agent.py) automatically checks calls with missing costs and known generation IDs one by one during finalization. By default, `resolve_generation_cost()` in [`cost.py`](../../../src/nanopycodeagent/cost.py) makes up to six attempts per generation, with retry delays of `1, 2, 4, 8, 15` seconds. Those delays total 30 seconds, in addition to the time spent on requests themselves, so 30 seconds is not a limit on the entire lookup process. Exhausting the attempts leaves costs incomplete. This finalization lookup is already a supported agent capability; there is no supported independent entry point for subsequent lookups yet. + +In this run, 13 tasks had incomplete costs in their original trajectories, with 18 calls unresolved during finalization. During and after the batch on 2026-09-06, an independent script queried the same cost endpoint in batches and eventually resolved all 18 charges. + +**Those independent lookups used a temporary script that is not part of the regular workflow.** The local script is `jobs/tb21-flash0731-pilot20-20260906-record/workflow/reconcile.py`. It lives under Git-ignored `jobs/` and is archived as a one-off workflow record for this trial. It must be run separately and is not integrated into the supported CLI, Harbor adapter, or a standard evaluation post-processing step. Obtaining complete costs through this script does not mean that the agent already performs subsequent lookups automatically. + +Costs before and after the supplemental lookups were: + +| Accounting basis | Cost (USD) | +| --- | ---: | +| Known cost subtotal in final trajectories | 0.11053603 | +| Complete cost after independent lookups | 0.119200332 | + +The reconciled amount covers all 267 recorded model replies. Bounded attempts during finalization can leave charges requiring later lookup; the subtotal known at that point must not be treated as the complete total. + +The proposed direction is to turn the temporary subsequent lookup work into a reusable supported capability that can integrate into the evaluation workflow and run independently after a task ends. Read generation IDs with unresolved costs, query them later, save receipts, and generate updated cost aggregates. Preserve original results, avoid counting the same charge twice, and explicitly distinguish known subtotals from complete totals. Unavailable amounts stay unknown rather than zero, and lookup failures must not change task scores. + +Whether this becomes an independent command or an automatic step after batch evaluation remains undecided. The objective is to allow lookups to continue beyond task finalization without delaying agent exit for a long time while waiting for billing records. diff --git a/docs/dev_notes/zh-CN/0.8.x.md b/docs/dev_notes/zh-CN/0.8.x.md index fb276c4..6cb6e04 100644 --- a/docs/dev_notes/zh-CN/0.8.x.md +++ b/docs/dev_notes/zh-CN/0.8.x.md @@ -505,3 +505,195 @@ API 凭据，原件权限已设为 0600，归档副本已脱敏；原件与脱 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。 + +### 修复批量运行 Terminal-Bench 时遇到的问题 + +以下问答记录本轮问题的含义、当前实现与拟议修复方向，尚不表示对应代码已修复。 + +#### 截断处理 + + +当前每次模型回复最多生成 8192 tokens。模型尚未输出完就用尽这次预算时，接口 +返回 `stop_reason="max_tokens"`，表示达到生成长度上限，被迫停止。例如，模型 +可能仍在分析如何修改代码，后面的工具调用或完整解答还没有生成，预算就耗尽了。 +本轮部分回复的预算主要消耗在 thinking；11 道未通过题的末次回复均为 `max_tokens`。 + +当前 [`agent.py`](../../../src/nanopycodeagent/agent.py) 的 `_run_model_loop()` +只用以下判断决定模型是否正常结束： + +```python +if message.stop_reason != "tool_use": + return True +``` + +因此，`max_tokens` 也会使 agent 退出循环，并在 trajectory 中记录 `completed`。 +这里混淆了“回复被迫中断”和“模型正常结束”。`completed` 本身也不代表题目做对了， +题目是否通过仍由 verifier 判断。 + +修复的最低要求是单独识别 `max_tokens`，准确记录截断事实和停止原因。进一步的 +策略可以是在预算允许时尝试恢复生成，或者明确记录截断后停止；具体采用何种恢复 +方式仍需设计，不能把无限续写或重试当成默认方案。 + +8192 目前直接写在 `agent.py` 的模块常量中： + +```python +MAX_TOKENS = 8192 +``` + +当前没有环境变量或 CLI 参数可以调整它。 + +**即使增大这个值，也必须处理 `stop_reason="max_tokens"`。** 两项工作解决的问题不同： + +| 工作 | 作用 | +| --- | --- | +| 增大 `MAX_TOKENS` | 给一次回复更多生成空间，减少因预算不足而截断的情况 | +| 处理 `stop_reason="max_tokens"` | 发生截断时，正确识别、决定恢复或停止，并准确记录原因 | + +任何有限上限都有可能被用尽。只调大数值，当前“截断也当作正常完成”的错误判断 +仍然存在。拟议顺序是先补上截断处理，再通过小规模试跑评估是否提高上限、提高到 +多少，以及如何分配思考与输出预算。11 道失败题出现截断，不等于提高上限后它们就 +一定能通过；通过率、token 消耗和费用都需要重新实验验证。 + +#### 缺失工具参数导致的崩溃 + +模型调用工具时漏传了必填参数，agent 没有把它作为可反馈给模型的调用错误处理， +而是抛出 Python 异常，导致整次运行失败。本轮具体发生在 `pytorch-model-recovery` +任务的最后一次 `edit` 调用中。 + +[`edit` 工具定义](../../../src/nanopycodeagent/edit_tool.py) 要求三个必填参数： + +- `path`：修改哪个文件。 +- `old_text`：要替换的原文。 +- `new_text`：替换后的内容。 + +本次调用只有 `old_text` 和 `new_text`，缺少 `path`，相当于模型说明了如何替换 +文字，却没有指定文件。工具 schema 已经把 `path` 声明为必填，但本次收到的实际 +参数仍然缺失，因此程序需要在使用参数前检查。 + +agent 执行工具前会先发出 `tool.started` 事件，由 `_TextOutputProjector` 打印调用 +预览。打印 `edit` 预览时，它直接读取 `arguments['path']`；字典里没有这个字段， +于是抛出 `KeyError: 'path'`。这次尚未执行文件修改，就在打印预览时失败了。 +异常发生在 Harbor 超时之后，不能用它解释此前的超时。 + +需要同时覆盖预览和执行两处：打印预览时能容忍缺失参数；执行工具前校验参数， +缺少必填项时不执行该工具，而是返回明确的 `tool_result`，标记 `is_error=True`， +例如： + +```text +Missing required argument: path. Provide the file path and retry. +``` + +这样模型可以在后续轮次看到错误，补齐参数重新调用，agent 在剩余预算内继续工作。 +只修打印逻辑不够：`_run_one_tool()` 的执行分支仍然直接读取 `block.input["path"]`， +其异常处理会记录失败后重新抛出，依然导致整次运行失败。缺失必填参数应作为可恢复 +的工具调用错误反馈给模型；其他工具也应覆盖相同的参数检查与错误返回路径。 + +#### 超时停止 + +“超时停止”指的是：任务达到规定的运行时间后，要确保容器里的 agent 真正停止 +工作，再开始验证结果。本轮 Harbor 已经判定任务超时，但 agent 仍在继续调用模型。 + +具体发生在 `pytorch-model-recovery`，该题的 agent 运行时限为 900 秒，即 15 分钟。 +以 agent 开始执行为起点，实际时间线如下： + +| 时间 | 实际发生的事情 | +| --- | --- | +| 约第 900 秒 | Harbor 判定超时，记录 `AgentTimeoutError` | +| 约第 901 秒 | verifier 开始验证任务结果 | +| 约第 929、939 秒 | agent 又启动了两次模型调用 | +| 约第 1063 秒 | agent 因 `edit` 缺少 `path` 异常而失败，记录 `run.failed` | + +Harbor 的“已经超时”没有及时转化为容器内 agent 的“停止执行”，带来两个后果： + +- 验证期间 agent 仍在运行，可能继续修改 verifier 正在检查的文件，影响结果的 + 可靠性。因此这题虽然得到 `reward=1`，也不能当作正常完成的成功样本。 +- 超过时限后仍产生新的模型调用和费用，任务的时间边界没有得到落实。 + +拟议修复目标是： + +1. 到达任务时限后，停止 agent 的执行，阻止它继续发起模型和工具调用。 +2. 清理需要终止的工具子进程，避免 agent 退出后仍有命令继续修改文件。 +3. 确认执行已停止，再启动 verifier，并准确记录超时状态。 + +目前确认的是超时后 agent 仍在运行；具体是哪一层的取消或进程清理没有生效， +还需要进一步定位。这项修复的重点是让时间限制真正生效，单纯延长 900 秒无法 +解决超时后继续执行的问题。 + +#### trajectory 采集 + +“trajectory 采集”指的是：确保 agent 的结构化执行记录能被保存，并被 Harbor +及时读取，用来汇总执行步骤、token 和费用。本轮暴露的问题是文件后来有了， +但 Harbor 汇总时没有拿到。 + +正常情况下，agent 运行时持续写入内部 Event Journal，记录模型回复、工具调用、 +执行结果和用量；运行收尾时，再由 +[`agent.py`](../../../src/nanopycodeagent/agent.py) 的 `_run_exchange()` 将 Journal +转换成公开的 ATIF 格式 `trajectory.json`。 +[`Harbor adapter`](../../../benchmarks/harbor/src/harbor_adapter/adapter.py) 的 +`populate_context_post_run()` 读取并校验这个文件，记录步骤数等诊断信息，并把 +token 和费用回填到评测结果中。 + +本轮 `pytorch-model-recovery` 的采集过程出现了时序问题： + +1. Harbor 判定超时后开始采集，此时没有拿到 trajectory，记录为 `missing`。 +2. agent 仍在运行，后来收尾才写出有效的 ATIF 文件。 +3. 文件最终出现在结果目录里，但 Harbor 原始结果中的 token 仍为 `null`，没有随 + 文件出现而自动补齐。 + +因此，最终 20/20 份 ATIF 都通过校验，但只有 19/20 题的 Harbor 原始指标与最终 +ATIF 一致。文件最终存在、格式有效，并不表示评测汇总时已经成功采集到它。 + +拟议修复目标是：正常完成、发生异常或超时时，都应尽可能保留截至停止时的执行 +记录，并让 Harbor 在汇总前读到它；记录不完整时，也要明确标注。 + +这需要与“超时停止”一起设计：先让执行停止并在有限时间内完成收尾，再采集和 +汇总。当前 ATIF 写入依赖收尾时的 `finally`；如果直接强制杀死进程，不能保证这段 +代码有机会执行。因此还需考虑定期保存快照，或从已落盘的 Journal 恢复轨迹等 +方案。具体方案尚未确定，修复目标是让实际执行记录、保存的 trajectory 和 Harbor +汇总指标能够对应起来。 + +#### 完善延迟费用补查 + +“完善延迟费用补查”指的是：任务结束时还没查到的模型调用费用，要能在稍后继续 +查询，并补齐费用统计。 + +模型回复已经返回时，响应里可能没有实际费用，账单查询接口也暂时查不到金额。 +此时 agent 保存该次调用的 generation ID，作为后续关联账单的标识，再用它查询 +这次调用的 `total_cost`。暂时查不到只能标记为待查或未知，不能当成没有费用。 + +当前 [`agent.py`](../../../src/nanopycodeagent/agent.py) 的 `_reconcile_costs()` +已经会在运行收尾时，自动对缺少费用且有 generation ID 的调用逐个补查。 +[`cost.py`](../../../src/nanopycodeagent/cost.py) 中的 `resolve_generation_cost()` +默认对每个 generation 最多尝试 6 次，重试间隔为 `1、2、4、8、15` 秒。这些间隔 +累计为 30 秒，还要加上查询请求本身的耗时，因此 30 秒并非整个补查过程的耗时 +上限。超过尝试次数仍未查到，就保留费用不完整的状态。上述收尾补查已经是 agent +的正式能力；当前尚未提供正式的独立后续补查入口。 + +本轮 13 道题的原始 trajectory 费用不完整，共有 18 次调用未在收尾补查中取得 +费用。在 2026-09-06 本轮批量运行期间及结束后，通过独立脚本分批查询同一个费用 +接口，最终补齐了这 18 笔费用。 + +**这次独立补查使用的是临时脚本，尚未纳入正式流程。** 脚本为本地 +`jobs/tb21-flash0731-pilot20-20260906-record/workflow/reconcile.py`，保存在被 Git +忽略的 `jobs/` 目录中，作为本次试跑的一次性工作流记录归档。它需要单独运行， +没有接入正式 CLI、Harbor adapter 或固定的评测后处理步骤。因此，这次通过临时 +脚本查全费用，并不表示 agent 已具备自动完成后续补查的能力。 + +补查前后的费用为： + +| 统计口径 | 费用（USD） | +| --- | ---: | +| 最终 trajectory 中的已知费用小计 | 0.11053603 | +| 独立补查后的完整费用 | 0.119200332 | + +补查后的金额覆盖全部 267 次已记录模型回复。这说明收尾阶段的有限尝试仍会留下 +需要稍后补查的费用，不能把当时已知的小计视为完整总额。 + +拟议方向是把本次临时完成的后续补查做成可重复使用、能接入评测流程的正式能力， +并支持任务结束后独立运行：读取尚未取得费用的 +generation ID，稍后重新查询，保存补查回执，再生成更新后的费用汇总。该流程 +需要保留原始结果，避免同一笔费用被重复计入，并明确区分已知费用小计和完整 +总费用。仍查不到的金额保持未知，不能填成零；补查失败也不应改变题目的评分。 + +具体采用独立命令还是批量评测后的自动步骤，尚未确定。重点是让补查可以跨越任务 +收尾阶段继续进行，避免为了等待账单而长时间拖住 agent 退出。