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feat: report complete and observed model usage - #442
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The Deep Agents adapter summed usage_metadata from the AI messages that reached graph state through the updates stream. Three cases came out wrong: - Deep Agents adds SummarizationMiddleware to every agent and subagent. Its summary call returns only text, so the summary model's message and usage never enter graph state, and an invocation that summarized under-reported input, output and total tokens. - When the invocation raised, including GraphRecursionError, the adapter returned no messages, so usage was None even though model calls had finished. - A count was summed over the calls that reported it and returned as known. ChatOpenAI also turns an omitted Chat Completions count into 0 and derives a missing total, so a partial provider report came out as measured zeros. The adapter now records usage from LangChain's chat-model callbacks, which fire for every chat-model call that runs in the adapter process: the main agent, declarative subagents and summarization. Agent Protocol subagents run remotely and are not included. A new recorder is created for each invocation, so earlier turns are never counted again. It outlives an exception, so a failed invocation still reports the fields that every counted call reported. Each token count and the cost are known only when every counted call in the invocation reported them, and an explicit zero stays a known zero. A request the provider rejects with a 4xx status (other than 408) contributes no usage. A 5xx response, timeout, cancellation or unfinished call leaves the invocation's counts and cost unknown, because the provider may have done work without reporting usage. OpenAI SDK retries happen inside one LangChain call, so a 5xx the SDK retries to success is not visible to the recorder, and only the final response's reported usage counts. When a response carries OpenAI-shaped raw usage, the raw counts decide what was reported: an omitted count stays unknown, the provider's total is used when present, and a derived total counts only when input and output were both reported. The graph tests run the real Deep Agents graph and ChatOpenAI against a scripted Chat Completions endpoint for summarization, parallel task subagents, 400, 408, 503 and timeout failures, the recursion limit, partial reports and explicit zeros. Separate ChatOpenAI.with_retry tests cover 429 then success and 503 then success. The existing usage tests now produce their messages through a LangChain chat-model call so the callbacks fire. Signed-off-by: mnajafian-nv <mnajafian@nvidia.com>
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WalkthroughThe change adds invocation-scoped model usage accounting across adapters, shared contracts, Fabric Core, and SDK results. It preserves unknown counts, records gaps, freezes evidence at invocation return, and exposes normalized usage through ChangesUsage accounting
Runtime operations
Priority: ➖ Normal Estimated code review effort: 4 (Complex) | ~60 minutes Merge Risk: 🔵 Low · up to
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| Check name | Status | Explanation | Resolution |
|---|---|---|---|
| Docstring Coverage |  |
Docstring coverage is 21.03% which is insufficient. The required threshold is 80.00%. Docstring coverage is scoped to functions touched by this diff. Analyzed 233 functions across 39 files. | Write docstrings for the functions missing them to satisfy the coverage threshold. |
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Check name Status Explanation Title check The title "feat: report complete and observed model usage" follows Conventional Commits (type feat, lowercase, imperative, no trailing period, 46 characters). It matches the main change: shared invoca… Linked Issues check Check skipped because no linked issues were found for this pull request. Out of Scope Changes check Check skipped because no linked issues were found for this pull request. Description check The PR description follows the template sections: Overview, Where should the reviewer start, and Related Issues with checkboxes. The Overview explains the problem (harnesses report cache and coverage … Full details: Title check
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Explanation
The title "feat: report complete and observed model usage" follows Conventional Commits (type feat, lowercase, imperative, no trailing period, 46 characters). It matches the main change: shared invocation accounting that separates complete token counts from observations, surfaced as RunResult.model_usage.
Full details: Description check
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Explanation
The PR description follows the template sections: Overview, Where should the reviewer start, and Related Issues with checkboxes. The Overview explains the problem (harnesses report cache and coverage conventions inconsistently), the proposed shared model-usage contract, and the Deep Agents collector. It also states the dependency on
#476and gives validation notes across Rust, Python, TypeScript, and Deep Agents tests. The Related Issues entry is "Relates to: none", which is a valid action keyword choice. The description does not list a Closes/Fixes/Resolves target, which is acceptable because it is marked as related only. The template's "Details" and "Validation" headings are extra sections that fit the change. The description is thorough and specific.
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Fern docs preview: https://nvidia-preview-pull-request-442.docs.buildwithfern.com/nemo/fabric |
Signed-off-by: mnajafian-nv <mnajafian@nvidia.com>
LangChain carries cache reads per model call in usage_metadata.input_token_details.cache_read, and OpenAI raw usage carries them in prompt_tokens_details.cached_tokens, but the adapter never reported cached_input_tokens. Sum cached input per call with the same rule as the other counts: one call without a cache count leaves the sum unknown, and a reported 0 stays 0. Raw OpenAI usage is read first because ChatOpenAI renames the cache key for priority and flex service tiers. Report input_tokens_include_cache=True whenever input tokens are known. LangChain's UsageMetadata.input_tokens counts every input token type; langchain-anthropic adds cache reads and writes back into it, and OpenAI's prompt_tokens already includes cached tokens. Signed-off-by: mnajafian-nv <mnajafian@nvidia.com>
The adapter drops a model call that the provider rejects with a 4xx status other than 408, because the provider spent no tokens on it. When every call was rejected, or no call started, the call list was empty and the usage was reported as unknown, although the invocation provably spent no tokens. The adapter now reports 0 for every token count and the cost in that case. Signed-off-by: mnajafian-nv <mnajafian@nvidia.com>
Deep Agents routes a subagent spec with graph_id to its async subagent middleware, which starts and updates tasks on a remote Agent Protocol server. Those model calls never reach the adapter's callbacks, so the reported counts left them out while appearing complete. The adapter blocked such subagents only when a tools policy was configured. When the configured subagents include one with graph_id, the adapter now reports the invocation's token counts and cost as unknown. The local sums stay in output.usage. Signed-off-by: mnajafian-nv <mnajafian@nvidia.com>
The usage notes said that after a 5xx response, timeout, or cancelled call the provider may have done work. The reason the counts stay unknown is narrower: the provider may have billed the call without reporting usage. The README, the recorder docstring, and a test comment now say that. Signed-off-by: mnajafian-nv <mnajafian@nvidia.com>
Apply ruff format to the check added in the previous commit. Signed-off-by: mnajafian-nv <mnajafian@nvidia.com>
…gent The adapter reports 0 when its callbacks observed no billed model call. That 0 holds for the invocation only when every model call runs through those callbacks. Deep Agents runs declarative subagents and summarization inside the graph, where the run config's callbacks reach them, but an Agent Protocol subagent (graph_id) runs on a remote server that the callbacks never see. The adapter already reports unknown usage for a runtime with such a subagent; this test pins that for an invocation whose only observed call was rejected, and the docstring and README state the condition. Signed-off-by: mnajafian-nv <mnajafian@nvidia.com>
mnajafian-nv
marked this pull request as draft
October 20, 2026 19:53
Signed-off-by: mnajafian-nv <mnajafian@nvidia.com>
Signed-off-by: mnajafian-nv <mnajafian@nvidia.com>
Signed-off-by: mnajafian-nv <mnajafian@nvidia.com>
Separate complete invocation totals from available observations and preserve accounting gaps. Add the first collector in Deep Agents. Signed-off-by: mnajafian-nv <mnajafian@nvidia.com>
Signed-off-by: mnajafian-nv <mnajafian@nvidia.com>
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This was referenced Oct 22, 2026
Return the documented JSON-compatible rate snapshot instead of exposing a frozen internal mapping. Preserve optional cache rates and snapshot isolation. Signed-off-by: mnajafian-nv <mnajafian@nvidia.com>
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🟡 Minor · Materialize accepted optional fields as None. · types.py:2551-2552
sdk/python/nemo-fabric-runtime/src/nemo_fabric/types.py:2551-2552
🎯 Functional Correctness | 🟡 Minor | ⚡ Quick winMaterialize accepted optional fields as
None.
PricingBasis._normalizeandPerModelUsage._normalizeaccept omitted optional fields but leave them absent.FabricMapping.__getattr__then raisesAttributeErrorfor valid accesses touser_catalog_sha256andprovider, although both fields are typed as nullable.Use
setdefault(..., None)or equivalent normalization, and add attribute-access regression tests.As per path instructions, Python SDK typed fields must remain consistent with accepted mappings and runtime access.
Also applies to: 2815-2816
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Outside diff comments:
Review comments at @sdk/python/nemo-fabric-runtime/src/nemo_fabric/types.py:
- Around line 2551-2552: Update PricingBasis._normalize and
PerModelUsage._normalize to materialize omitted nullable fields as None,
including user_catalog_sha256 and provider, while preserving supplied values.
Add regression tests confirming these fields can be accessed as attributes when
omitted from accepted mappings.
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GitHub Actions: Check / 0_Pre-commit.txt: feat: report complete and observed model usage
Conclusion: failure
##[group]Run bail() {
�[36;1mbail() {�[0m
�[36;1m printf '::error::install-action: %s\n' "$*"�[0m
...
##[group]Run uv run --no-sync pre-commit run --all-files --show-diff-on-failure
...
^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
File "/usr/lib/python3.12/urllib/request.py", line 1347, in do_open
raise URLError(err)
urllib.error.URLError: <urlopen error [Errno 104] Connection reset by peer>
##[error]Process completed with exit code 1.
GitHub Actions: Check / Pre-commit: feat: report complete and observed model usage
Conclusion: failure
##[group]Run bail() {
�[36;1mbail() {�[0m
�[36;1m printf '::error::install-action: %s\n' "$*"�[0m
GitHub Actions: Check / Pre-commit: feat: report complete and observed model usage
Conclusion: failure
##[group]Run uv run --no-sync pre-commit run --all-files --show-diff-on-failure
�[36;1muv run --no-sync pre-commit run --all-files --show-diff-on-failure�[0m
shell: /usr/bin/bash --noprofile --norc -e -o pipefail {0}
env:
UV_PYTHON_INSTALL_DIR: /home/runner/work/_temp/uv-python-dir
UV_CACHE_DIR: /home/runner/work/_temp/setup-uv-cache
GOTOOLCHAIN: local
CARGO_INCREMENTAL: 0
CARGO_PROFILE_DEV_DEBUG: 0
CARGO_TERM_COLOR: always
RUST_BACKTRACE: short
RUSTFLAGS: -D warnings
CARGO_UNSTABLE_SPARSE_REGISTRY: true
CARGO_REGISTRIES_CRATES_IO_PROTOCOL: sparse
##[endgroup]
copyright header..........................................................Passed
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Cargo.lock is up to date..................................................Passed
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ATTRIBUTIONS-Rust.md (Cargo.lock).........................................Passed
ATTRIBUTIONS-Python.md (uv.lock)..........................................Passed
ATTRIBUTIONS-Node.md (TypeScript package-lock.json).......................Passed
Dependency license diff...................................................Failed
- hook id: license-diff
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[license-diff] selected languages: rust, node, python
[license-diff] generating current inventory
[license-diff] current: generating Rust inventory
[license-diff] current: Rust inventory complete (343 packages)
[license-diff] current: generating Node inventory
[license-diff] current: Node inventory complete (569 packages)
[license-diff] current: generating Python inventory
Traceback (most recent call last):
File "/usr/lib/python3.12/urllib/request.py", line 1344, in do_open
h.request(req.get_method(), req.selector, req.data, headers,
File "/usr/lib/python3.12/http/client.py", line 1365, in r...
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Review Python SDK changes for typed API consistency, import-time dependency neutrality, async/session behavior, and parity with the native extension.
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Tests should cover the behavior promised by the changed API surface, including error paths, lifecycle cleanup, and SDK/native parity where relevant.
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tests/python/test_model_usage.py
🪛 ast-grep (0.45.3)
tests/python/test_model_usage.py
[info] 496-496: use jsonify instead of json.dumps for JSON output
Context: json.dumps(rates)
Note: [CWE-116] Improper Encoding or Escaping of Output.
(use-jsonify)
🔇 Additional comments: 2
tests/python/test_model_usage.py: 1
489-498: LGTM!scripts/contracts/generate_usage_accounting.py: 1
37-39: 🎯 Functional CorrectnessThe two descriptors that contain
nemo_fabric.usage_accounting.v1each have one exact"extension_schemas":occurrence. That occurrence is the top-level property consumed bydocument.get("extension_schemas", {}). The whitespace and duplicate-key cases do not occur in the current matched descriptors.
2 tasks done
Signed-off-by: mnajafian-nv <mnajafian@nvidia.com>
Signed-off-by: mnajafian-nv <mnajafian@nvidia.com>
Signed-off-by: mnajafian-nv <mnajafian@nvidia.com>
Signed-off-by: mnajafian-nv <mnajafian@nvidia.com>
Signed-off-by: mnajafian-nv <mnajafian@nvidia.com>
Signed-off-by: mnajafian-nv <mnajafian@nvidia.com>
Signed-off-by: mnajafian-nv <mnajafian@nvidia.com>
Signed-off-by: mnajafian-nv <mnajafian@nvidia.com>
Signed-off-by: mnajafian-nv <mnajafian@nvidia.com>
mnajafian-nv
marked this pull request as ready for review
October 23, 2026 09:28
mnajafian-nv
marked this pull request as draft
October 23, 2026 15:53
Signed-off-by: mnajafian-nv <mnajafian@nvidia.com>
mnajafian-nv
marked this pull request as ready for review
October 23, 2026 16:23
Use the system Bash path so macOS tests exercise Bash 3.2 even when PATH contains a newer Bash. This keeps the empty-array regression meaningful in CI. Signed-off-by: mnajafian-nv <mnajafian@nvidia.com>
Signed-off-by: mnajafian-nv <mnajafian@nvidia.com>
Signed-off-by: mnajafian-nv <mnajafian@nvidia.com>
Signed-off-by: mnajafian-nv <mnajafian@nvidia.com>
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Overview
Harnesses expose token counts with different cache conventions and different coverage. Some fill missing fields with zero or omit failed attempts. Adding those counters without shared rules can make incomplete runs appear cheaper.
Add a shared model-usage contract to NVIDIA NeMo Fabric and connect Deep Agents as its first collector. Results distinguish complete totals from available observations and explain which counts remain unknown. Users do not need recording or Relay to read this accounting.
Dependency: Merge after #476, which supplies the Relay startup compatibility changes. The diff against
mainincludes unmerged prerequisites.Details
Python and TypeScript helpers preserve reported fields, model identity and gaps. Core validates and normalizes that evidence into
RunResult.model_usage. Input includes cache tokens; output includes reasoning tokens. Omitted values remain unknown, while a reported zero remains zero. Complete counts, observations and provider-reported cost remain separate. Per-model entries retain identities established by the source. Catalog pricing is added separately in #478.Deep Agents combines model callbacks with passive HTTP-attempt observations. It covers adapter-created ChatOpenAI clients, including summarization and in-process subagents using the observed clients. Custom model overrides, unsupported clients, actual remote dispatch and unfinished work leave the relevant coverage unverified. Graph-message subtotals remain labeled diagnostics in
output.usage.Each model call stays with the invocation that started it. Accounting freezes when that invocation returns. Later reports cannot move into another invocation. Observations are not guaranteed lower bounds. A non-model failure preserves settled usage. Empty source coverage does not establish a measured zero. If validly shaped evidence cannot be normalized, retain the execution result and raw evidence with an accounting diagnostic; schema-invalid extensions still fail validation.
Consumers with older strict result decoders must upgrade before reading the new
model_usagefield.Validation
Shared fixtures check normalization across Rust, Python and TypeScript. Regression tests cover independent dimensions, reported zero, omissions, invalid evidence and schema-copy parity. Deep Agents tests cover retries, summaries, parallel in-process subagents, concurrent runtimes and late callbacks.
Where should the reviewer start?
Start with
crates/fabric-core/src/model_usage.rsandusage_normalization.rsfor complete versus observed values. Then read Deep Agentsusage.pyand the shared Python, Rust and TypeScript contract vectors.Related Issues: (use one of the action keywords Closes / Fixes / Resolves / Relates to)
Relates to: none
I confirm this contribution is my own work, or I have the right to submit it under this project's license.
I searched existing issues and open pull requests, and this does not duplicate existing work.