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fix: preserve usage from OpenClaw and remote agents - #452
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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>
A RunResult says which agent and adapter ran, but not which Fabric version, models or MCP servers were behind it, so a rerun weeks later cannot tell what changed. Add an optional, versioned what_ran object. Each fact holds a value, or a state and a reason, plus how Fabric learned it: its source, collector and method. Core fills the facts it holds before it sends the request: its own version, the adapter ID, the requested models, the configured MCP servers with secret-free endpoints, and the environment provider. Core reads no adapter report yet, so adapter-reported facts read unsupported with reason key_not_declared. On the OpenShell host path, the host relabels the task core's facts as task_core, records its own version as host_core_version and sets the environment provider to openshell. A task result without a readable what_ran keeps what_ran absent. The Rust and Python readers apply the same structural checks, tested against one shared vector file. A malformed what_ran reads as absent and never fails the enclosing result. Core gains a direct dependency on sha2 0.10, already in the lockfile, for the string length rule. Rust code that builds RunResult with a struct literal must add the what_ran field. Python's RunResult.to_mapping() now always includes it. Signed-off-by: mnajafian-nv <mnajafian@nvidia.com>
Explain the fact envelope, the four no-value states, the endpoint rule, the OpenShell attribution and the structural read, and keep the integration skill's RunResult table in step with the guide. Signed-off-by: mnajafian-nv <mnajafian@nvidia.com>
Regenerate the Rust library reference with Rust 1.94.0 for the new what_ran module and the RunResult field, and the Python types reference for RunResult.what_ran. Only the pages this change affects are included. Signed-off-by: mnajafian-nv <mnajafian@nvidia.com>
A plan with many model roles or MCP servers, or very long names, could make what_ran arbitrarily large, and every RunResult carries it. Keep it bounded without ever failing a run. Core keeps the first 16 model roles and 32 MCP servers in sorted key order, and drops entries whose key is longer than 128 characters. While the compact JSON of what_ran is larger than 128 KiB, it drops MCP servers from the end of the sorted order, then model roles. If plan identifiers alone still exceed the budget, what_ran becomes a minimal object with only the schema name, the version and what_ran_truncated. A marker next to each map counts the dropped entries and names the last rule applied. The OpenShell host applies the same bounds again after it attributes the task core's facts, so they hold for a task core of any version. The six what_ran sections become optional in the Rust type so the minimal object can be represented. Every section is still required unless what_ran_truncated is present, and no section may be null. The Rust reader, the generated schema and the Python reader check this against the shared vectors. Signed-off-by: mnajafian-nv <mnajafian@nvidia.com>
Explain the entry caps, the key length rule, the byte budget, the truncation markers and the minimal object, and tell readers to check what_ran_truncated before reading a section. Keep the integration skill's RunResult notes in step. Signed-off-by: mnajafian-nv <mnajafian@nvidia.com>
Regenerate the Rust library reference pages of the what_ran module for TruncatedEntries and the optional sections, and update the Python types reference for RunResult.what_ran. Only the pages this change affects are included. Signed-off-by: mnajafian-nv <mnajafian@nvidia.com>
Signed-off-by: mnajafian-nv <mnajafian@nvidia.com>
The shared Chat Completions helper, used by the OpenClaw adapter and by the remote-agent adapter's openai-completions mode, read only prompt_tokens, completion_tokens and total_tokens. It dropped prompt_tokens_details.cached_tokens and left input_tokens_include_cache unset, so a run that read most of its prompt from cache reported an input count with unknown cache semantics and no cached count. Chat Completions prompt_tokens already counts cached prompt tokens, and OpenClaw 2026.9.4 builds it as input plus cache reads. The helper now reports the cached count as cached_input_tokens and sets input_tokens_include_cache when prompt_tokens is reported. An omitted count stays unknown, a reported zero stays 0, and a null details object means no cached count. A report whose only count is the cached count keeps it. Signed-off-by: mnajafian-nv <mnajafian@nvidia.com>
The remote-agent adapter had three usage gaps outside the shared Chat Completions helper: - openai-responses dropped input_tokens_details.cached_tokens and left input_tokens_include_cache unset, although Responses input_tokens includes cached tokens. - anthropic-messages reported Anthropic's input_tokens, which excludes cache reads and cache writes, so a prompt-cached endpoint reported only the uncached remainder. It also ignored the cumulative input counts a message_delta event can carry. - A "usage": null object in response.completed, message_start or message_delta raised AttributeError, which the invoke error handling does not catch. Responses now reports the cached count with the inclusive flag. Messages adds each reported cache count to input_tokens, reports cache reads as cached_input_tokens, and sets input_tokens_include_cache, the inclusive approach NVIDIA#419 takes for Hermes. A missing cache field adds nothing: the Anthropic API always sends both fields, and vLLM's /v1/messages omits them by default and then reports the full prompt in input_tokens, subtracting a cache count only when it sends that field. Without input_tokens, input stays unknown, and the reported cache reads and output are kept. Counts in a later message_delta replace those from message_start. A negative or non-integer input or cache count is an invalid response, because the sum would otherwise hide it from the contract's count validation. A report whose only count is the cached count keeps it. A null usage object reads as no usage, and a null response or message object is reported as an invalid response instead of raising AttributeError. The shared mock Anthropic endpoint now sends the two cache fields that the Anthropic API includes in message_start. Signed-off-by: mnajafian-nv <mnajafian@nvidia.com>
Run a local host invocation with two model roles and three MCP servers (npx with arguments, streamable-http and sse) and no environment section, and assert the exact what_ran a user gets, including the origin of every unsupported adapter-reported fact and that no configured secret appears. Signed-off-by: mnajafian-nv <mnajafian@nvidia.com>
Resolve a plan from each of the 16 bundled descriptors with a model and, where the adapter accepts MCP, three servers, and check that what_ran passes the schema, carries no truncation marker, and reads every adapter-reported fact as unsupported/key_not_declared. Also show that 16 roles and 32 servers with 128-character keys fit the byte budget, and that stdio placeholders read executable_unparsed. Signed-off-by: mnajafian-nv <mnajafian@nvidia.com>
Loop over whatever the catalog lists without pinning its size, so adding or removing a bundled adapter doesn't break the test. Name the size test after what it builds: ASCII keys and model names at their maximum length. Signed-off-by: mnajafian-nv <mnajafian@nvidia.com>
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WalkthroughThe PR adds shared usage-accounting contracts and reducers, integrates usage collection across adapters, and exposes normalized usage and execution provenance in run results. It also updates adapter descriptors, SDK types, documentation, tests, and Relay gateway readiness handling. ChangesUsage accounting
Execution provenance
Startup and maintenance
Priority: Estimated code review effort: 5 (Critical) | ~120 minutes Sequence Diagram(s)sequenceDiagram
participant Adapter
participant UsageAccounting
participant FabricCore
participant RunResult
Adapter->>UsageAccounting: Submit usage observations and gaps
UsageAccounting->>FabricCore: Provide accounting evidence
FabricCore->>FabricCore: Validate and normalize usage
FabricCore->>RunResult: Set model_usage
sequenceDiagram
participant Adapter
participant Descriptor
participant FabricCore
participant RunResult
Adapter->>FabricCore: Return versions report
Descriptor->>FabricCore: Declare report schema
FabricCore->>FabricCore: Validate and ingest declared facts
FabricCore->>RunResult: Set what_ran
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Check name Status Explanation Title check The title follows Conventional Commits format with the allowed lowercase type, an imperative summary, no trailing period, and 51 characters. It accurately describes the usage-preservation changes. 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 description includes the required Overview, reviewer starting point, Related Issues section, validation details, and both contribution checkboxes. It is specific and aligned with the pull request … Full details: Description check
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@adapters/python/common/src/nemo_fabric_adapters/common/openai_chat.py:
- Around line 152-154: Update the prompt_tokens_details handling in the usage
parsing logic to treat only None as absent and default it to an empty
dictionary; preserve the TypeError validation for all other non-dictionary
values, matching the remote-agent adapter’s _usage_object behavior.
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tests/adapters/test_adapters_common_openai_chat.py
[info] 29-29: use jsonify instead of json.dumps for JSON output
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adapters/python/common/src/nemo_fabric_adapters/common/openai_chat.py: 2
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AgentUsagerejects boolean counters before_bounded_intruns.ContractModel.__post_init__callsvalidate_dataclass, and the contract decoder requirestype(value) is intfor fields annotated asint. Therefore,cached_tokens=Truecannot be stored incached_input_tokensthrough either adapter path.tests/_utils/mock_openclaw.py: 1
117-124: LGTM!tests/adapters/test_adapters_common_openai_chat.py: 1
1-154: LGTM!tests/adapters/test_openclaw.py: 1
29-29: LGTM!Also applies to: 290-296
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45-68: LGTM!
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Fern docs preview: https://nvidia-preview-pull-request-452.docs.buildwithfern.com/nemo/fabric |
The Chat Completions helper read prompt_tokens_details with `or {}`, so
an empty list, 0, false or an empty string became an empty object and
skipped the object check. The remote-agent adapter treats only a null
usage or details object as unreported and rejects any other non-object
value. The helper now checks for null explicitly, so a non-object
details value takes the existing invalid-report path in both adapters.
Signed-off-by: mnajafian-nv <mnajafian@nvidia.com>
Adapters had no way to report their own facts, so the adapter package version, the harness name and version, the served model and the MCP runtime facts always read unsupported with reason key_not_declared. Core now reads nemo_fabric.versions.v1 and nemo_fabric.mcp.v1 from a result's extensions after the descriptor's extension check, when the descriptor's run_result schema names the key under properties. Only adapter-owned facts with source reported and collector adapter are kept; a malformed or forged fact reads invalid_report, an omitted one fact_omitted, an omitted key key_omitted, and a key over 64 KiB too_large. A reported server the plan does not configure becomes a harness entry. tool_names keeps 64 sorted names and counts the rest in a new tool_names_truncated marker, added to the schema and both readers. Ingestion never changes the run's status or error. Signed-off-by: mnajafian-nv <mnajafian@nvidia.com>
A what_ran could not say which harness build ran. Six adapters now declare nemo_fabric.versions.v1 and report their package version and the harness name and version from a source that names the build that ran: - Claude Code: claude_code_version from the CLI's init message. - Codex: the thread's cli_version from one bounded thread/read request. - Hermes: get_version_info(), a blocking call that runs git, on a daemon thread with a 5 second limit. - OpenClaw: the --version check, or an attached gateway's /startupz version, both already read at startup. - Deep Agents and mini-swe-agent: package metadata, accepted only when the distribution installed the module the adapter loaded. A shared helper builds the report, applies core's string length rule, and runs blocking calls with at most one outstanding probe per runtime; stopping a runtime never waits for it. A source that times out, fails or answers nothing reads a state with a reason, never a substitute value. The adapter catalog is regenerated for the six changed descriptors. Signed-off-by: mnajafian-nv <mnajafian@nvidia.com>
what_ran.models.<role>.served stayed unsupported for Claude Code even though each assistant message names the model that answered. The Claude Code adapter now reports, for the role it runs (default, or the sole configured role), the one distinct model named by this invocation's main-thread assistant messages. Messages with a parent_tool_use_id belong to subagents and are skipped, and so are Claude Code's <synthetic> API-error messages. Two distinct models read not_attributed, none reads empty_response, a failed stream reads collection_failed, and every invocation starts from its own messages. Tests feed saved Claude Code CLI streams through the SDK's parser. Signed-off-by: mnajafian-nv <mnajafian@nvidia.com>
The SDK guide and the integration reference said every adapter-reported fact reads key_not_declared. They now name the six adapters that report harness versions, the Claude Code served-model rule, the states a report key can produce and the tool_names cap. The adapter tutorial and the build-adapter skill show adapter authors how to declare and fill nemo_fabric.versions.v1. Signed-off-by: mnajafian-nv <mnajafian@nvidia.com>
Regenerated with just docs for the new report-key constants and the tool_names_truncated field. Signed-off-by: mnajafian-nv <mnajafian@nvidia.com>
NOOA, NOOA Bench, OpenHands and Remote Agent left adapter.package_version at key_not_declared, so a run with them could not say which adapter build ran. Each now declares nemo_fabric.versions.v1 and attaches its installed package version to every result. These adapters have no harness version or served-model source yet, so those facts read unsupported with reason no_source. The catalog is regenerated. Signed-off-by: mnajafian-nv <mnajafian@nvidia.com>
Cline, Droid, Kilo Code, OpenCode, Pi and Qwen Code left adapter.package_version at key_not_declared. A shared withPackageVersion wrapper in the TypeScript common package reads the adapter's own package.json synchronously at start and adds nemo_fabric.versions.v1 to every result, with the harness and served model facts unsupported/no_source until their sources land. Each CLI wraps its runtime, each descriptor declares the key, and the catalog is regenerated. A catalog test checks that all bundled descriptors declare the key. Signed-off-by: mnajafian-nv <mnajafian@nvidia.com>
The SDK guide and the integration reference named only the six adapters with a harness version source. The TypeScript common README documents withPackageVersion. Signed-off-by: mnajafian-nv <mnajafian@nvidia.com>
The Claude and Hermes adapters attach nemo_fabric.versions.v1 to every result, and core rejects a result extension that the descriptor's run_result schema does not declare. The Harbor SWE-bench copies of both descriptors lacked the block, so runs through them failed. A new test finds every checked-in copy of a bundled descriptor and checks that it declares the key. Signed-off-by: mnajafian-nv <mnajafian@nvidia.com>
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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>
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Overview
OpenClaw and remote-agent responses can contain useful usage even when they do not account for every inner model call. A remote stream can also fail after a usage snapshot arrives.
Retain those reports for the initiating invocation. Keep observations separate from complete totals, so missing inner coverage does not look like lower measured usage.
Dependencies: #442 supplies shared accounting; #458 supplies adapter-reported execution facts. Both branches are included. The diff against
mainincludes unmerged prerequisites.Details
The shared response parser preserves reported fields and independent cache counts. Core uses the accounting contract from #442. OpenClaw fills omitted values with zero and can return a latest-call snapshot, so positive values remain observations with unverified coverage. Its partially inclusive cache convention is not forced into a guessed complete input count.
Remote-agent reports also leave inner-model coverage unverified. Preserve the last usage snapshot before a stream failure; only an event with usable usage updates that snapshot. A completed response with absent, null or empty usage keeps the earlier snapshot as an observation and marks the missing usage. A reported zero is usable evidence. Malformed events follow the adapter's invalid-response path.
OpenClaw failures, including a gateway exit after a usage report, retain available usage and harness-version facts. The agent's name does not identify the models responsible for aggregate usage. Missing evidence never moves into a later invocation, and estimates do not become provider-reported cost.
Canonical descriptors and the generated catalog declare the shared usage extension.
Validation
The TypeScript adapter checks remain blocked by the shared dependency audit tracked in #484.
Regression tests cover independent cache fields, omitted counts, malformed stream events and usage received before a process or stream failure.
Where should the reviewer start?
Start with
_usageand stream parsing inadapters/python/common/src/nemo_fabric_adapters/common/openai_chat.py, then the OpenClaw and remote-agent adapters. Reviewtests/adapters/test_adapters_common_openai_chat.pyand the stream-failure cases.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.