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feat: include execution facts in run results - #443
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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>
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WalkthroughThe change adds versioned ChangesRun provenance
Relay gateway startup
Priority: Estimated code review effort: 4 (Complex) | ~60 minutes Change: Feature Merge Risk:
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Fern docs preview: https://nvidia-preview-pull-request-443.docs.buildwithfern.com/nemo/fabric |
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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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>
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Enforce the product name in user-facing prose: use "NVIDIA NeMo Fabric" on first use and "NeMo Fabric" thereafter.
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October 23, 2026 09:44
Signed-off-by: mnajafian-nv <mnajafian@nvidia.com>
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
A result's status and token counts do not identify the configuration behind the run. Callers need the requested models, adapter identity and environment context to investigate differences between runs.
Add an optional
RunResult.what_ranobject containing the facts NVIDIA NeMo Fabric knows from its core and resolved configuration. Typed facts identify their source and collection method, or explain why no value is available. Reading these facts requires neither recording nor Relay.Dependencies: #442 supplies shared usage accounting; #476 supplies Relay startup compatibility. The diff against
mainincludes these unmerged prerequisites.Details
Collect the core version, adapter identity, requested model roles, sanitized Model Context Protocol (MCP) endpoints and environment provider. Adapter and harness version collection is not included; those fields remain
unsupportedwith reasonkey_not_declared.Bound strings, map keys, model roles, server entries and total serialized size. Use deterministic retention and explicit truncation markers. Add core linkage to the already locked
sha2version for digest suffixes on shortened values; the standard library has no SHA-256 implementation. No locked package versions change. OpenShell keeps task-core facts distinct from observations made by the host.Readers accept older results without
what_ran. Malformed or unsupported execution facts are treated as absent without discarding the rest of the decoded result. These facts do not prove that two runs executed under equivalent conditions. Readers that reject unknown result fields need the updated SDK or schema. Rust struct literals that list everyRunResultfield must addwhat_ran.Validation
Passed
just test-rust, Rust formatting,just no_uv=true build-python,cargo check -p fabric-python --lockedandpre-commit run --all-files. Focused Python checks passed for shared model usage, SDK contracts, the native provenance result and generated API references. References were regenerated from the combined source; Fern configuration and strict link checks passed.The full Python suite was not run locally; full-suite result pending.
The TypeScript adapter checks remain blocked by the shared dependency audit tracked in #484.
Regression tests cover a realistic multi-role configuration, secret exclusion, all bundled descriptors, entry limits and serialized-size limits. Shared reader fixtures cover valid, absent and malformed execution facts.
Where should the reviewer start?
Start with collection and bounds in
crates/fabric-core/src/what_ran.rs, then the collection points inruntime.rs. Review realistic-configuration, descriptor, secret-exclusion and shared-reader tests.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.