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fix(agent-sessions): session checks, vitals and turn labels - #1120
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…le prompts Symptom: the Prompt cache check warned "Cache hit rate 0% over N calls; N missed the cache" on sessions whose prompts were all too short to cache. Seen on DSPy (prompts peak 870 tokens, OpenAI minimum 1024), Google ADK, LangChain, Haystack, CrewAI and Claude Agent SDK (Haiku 4.5 prompts of 1.2K-2.3K tokens against its 4,096-token minimum) test sessions. Cause: promptCacheCheck judged every call after the first, whatever its prompt size. Fix: calls under 1024 prompt tokens (the smallest minimum OpenAI, Anthropic and Gemini cache) are left out of the judgement, and a session whose calls report a cache-write bucket but never wrote or read the cache is skipped: nothing reached the model's minimum, or caching is off. The "too few calls" message now counts the calls actually judged.
…adline
Symptom: the Provider errors check read "All 48 model calls were answered
first time" on an OpenRouter Broadcast session of 16 calls ("All 30" for
10), and "All 16 model calls" on a Google ADK session with llmCalls 8.
Cause: buildSessionChecks passed the count of every inference-classified
span (session-checks.ts providerCheck call), so OpenRouter's `provider
attempt N` and `generation` children and ADK's `call_llm` wrapper were
counted beside the call they belong to.
Fix: the headline uses summary.work.llmCalls, the netted count the
session header already shows.
Symptom: on Google ADK sessions the Prompt cache check read "0% over 15 calls" (a1) and "over 19 calls" (b) while the session had 8 and 10 model calls: the checks did not net ADK's duplicate reporter pair. Cause: promptCacheCheck took every inference-classified span, so ADK's `call_llm` wrapper and the `generate_content` span beneath it, both stamped with the same usage, were judged as two calls each. Fix: session-summary exports sessionLlmCalls, the netted call list behind work.llmCalls, and the cache check reads it. The provider headline half of the same symptom is fixed in the previous commit.
Symptom: vitals read 204.8 s of agent time in a 125.5 s OpenRouter Broadcast session with no parallel work. Cause: computeAgentTime charged every inference span its full duration, and Broadcast nests a `provider attempt N` and a `generation` span (both op `chat`) under each `LLM Generation`, so one call was charged up to three times. Google ADK's `call_llm` over `generate_content` doubles the same way. Fix: an inference span inside another inference span is the same call observed twice; only the outermost is charged. A TTFT that only a nested level reported still splits the call.
…rk spans Symptom: every turn of a checkpointed LangGraph thread (OpenInference LangChain dual-write) was labeled with turn 1's prompt, e.g. all a1 turns 1-6 read "Hi! Briefly introduce yourself.". Cause: turnLabel (session-turns.ts) fell back to the first span in start order with any user message. The LangGraph `model` node CHAIN span starts before its ChatOpenAI call and carries only the thread's first message. Fix: after the anchor, model calls are asked first, as the transcript's userRows already does; other spans remain the last resort.
Symptom: every smolagents turn label and the session title read "New task:". Cause: proseLine (session-turns.ts) labels a turn with the first non-empty line of the user message, and smolagents sends every task as "New task:\n<task>". Fix: a first line of at most three words ending in a colon is a lead-in; the line after it labels the turn. A longer sentence ending in a colon, or a lead-in with nothing after it, is kept as before.
Symptom: a Microsoft Agent Framework workflow session showed its one-span `workflow.build` trace as Turn 1 (no label, no agent, 1 span), the real run as Turn 2, and get_agent_session returned no title. Cause: `workflow.build` carries the session's id in its own trace and is read as an agent root by its name, so buildSessionTurns opened a turn on it; the summary title is turn 1's label. Fix: on the agent-root and per-trace rules, where the boundary is a heuristic, an anchor whose spans hold no model or tool call, no user message and nothing failed joins the next turn, as spans before the first anchor join turn 1. Conversation-id turns are explicit and kept, and a session with no work anywhere keeps its anchors.
A captured prompt can be tens of kilobytes; the label reads at most two non-empty lines, so it stops collapsing whitespace after them.
…t call, in the prompt-cache check Follow-up to the uncacheable-prompt fix: the size filter ran after dropping the first call, so a short opening call (a title or router call) was dropped instead of the first long call, which writes the cache and then read as a miss.
…rn labels Follow-up to the lead-in fix: any first line of three words ending in a colon was skipped, so a user's "Fix this:" over pasted code labeled the turn with the code's first line. The rule now matches the framework's literal heading.
Maple reviewConfidence 4/5 · likely safe to merge Seven read-path fixes in
What was checked
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Navigate logical layers of code changes, visualize relationships, and explore their blast radius. 📝 WalkthroughWalkthroughThe changes deduplicate inference-time accounting across nested spans, expose counted model-call spans for prompt-cache checks, and update turn grouping and prompt-label extraction. ChangesModel-call accounting and prompt-cache checks
Session-turn grouping and labels
Priority: ➖ Normal Estimated code review effort: 3 (Moderate) | ~25 minutes Change: Bug fix Merge Risk: 🔵 Low · up to Some session turn summaries may be inaccurate, and unusually large sessions may take longer to construct. These issues warrant fixes or owner acceptance but do not appear to block merging. Security Architecture ReviewSecurity architecture risk: 🔵 Low · up to The reviewed changes affect how existing sessions are interpreted, not who can access them. No material security regression was established in the inspected path, though downstream coverage is incomplete. Retained concerns Security review detailsSecurity Blast Radius
Security Findings and Attack Paths
Trust Boundaries and Controls
🚥 Pre-merge checks | ✅ 4 | ❌ 1❌ Failed checks (1 warning)
✅ Passed checks (4 passed)
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🧪 Generate unit tests (beta)
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| const opened = (bucket: readonly AiSessionSpan[]) => | ||
| bucket.some( | ||
| (span) => | ||
| WORK_CATEGORIES.has(classifyAiSpan(span)) || | ||
| spanFailed(span) || | ||
| lastUserMessageText(span.genAi.inputMessages) !== undefined, | ||
| ) | ||
| if (anchors[0]?.kind !== "conversation" && buckets.some(opened)) { | ||
| for (let i = 0; i < buckets.length - 1; i++) { | ||
| if (opened(buckets[i])) continue | ||
| buckets[i + 1] = [...buckets[i], ...buckets[i + 1]] | ||
| buckets[i] = [] | ||
| } |
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🔴 Agent-only turns merge into later turns
When an agent invocation has no model call, opened merges its turn into the next busy turn. Distinct invocations disappear, and pauses between turns become false mid-turn stalls.
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Agent-root boundaries represent distinct invocations even when an invocation has no model or tool spans. The new opened predicate treats such a turn as empty and folds it forward if any later turn has work. stallFindings then examines both invocations as one turn and marks their idle gap as a stall. The session also loses a turn in its work count and transcript.
Example: An invocation at 0–1 seconds has no child call; another invocation at 60–70 seconds has a model call. Both are agent roots, but the first is removed and the 59-second pause is reported as a stall inside the remaining turn.
Recommended fix: Distinguish setup-only roots such as workflow.build from genuine invocation roots before folding buckets. Preserve genuine agent invocations as boundaries even without captured calls, and keep between-turn gaps outside stallFindings.
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Fixed in efd5c06: a workless anchor now folds only when the next turn starts within 5 s of it ending (setup such as MAF's workflow.build). One followed by a pause stays its own turn, so the pause is never read as a stall. Test: "keeps a workless anchor followed by a pause as its own turn".
| const outermostCall = (span: AiSessionSpan): AiSessionSpan => { | ||
| let call = span | ||
| const seen = new Set<string>([span.spanId]) | ||
| let parent = byId.get(span.parentSpanId) | ||
| while (parent !== undefined && !seen.has(parent.spanId)) { | ||
| seen.add(parent.spanId) | ||
| if (classifyAiSpan(parent) === "inference") call = parent | ||
| parent = byId.get(parent.parentSpanId) | ||
| } | ||
| return call |
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🔴 Nested model calls lose inference time
When a model call runs beneath another inference span, outermostCall treats both as one call. Distinct nested calls lose their inference time even when separated by an agent or tool.
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Agent time previously summed every inference span. The new ancestor walk assumes every nested inference span describes its outermost inference ancestor, even when the spans represent separate requests. It also crosses agent and tool spans, where a child request can be independent of its enclosing request. The existing countedLlmCalls deliberately counts multiple children under one SDK wrapper as separate calls when the wrapper rolls them up.
Example: A three-second SDK generateText wrapper contains two one-second doGenerate requests with separate usage. The call counter reports two calls, but agent time reports only the wrapper's three seconds and never charges either request separately; with a ten-second wrapper around two concurrent ten-second requests, the reported time is ten instead of twenty seconds.
Recommended fix: Identify observation pairs using the same per-call evidence as usage netting instead of suppressing all inference descendants by ancestry. Retain separate request durations when an inference parent wraps multiple calls or a nested agent/tool starts its own inference.
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Partly fixed in 429379c: the walk now stops at an agent or tool span, so a model call made by a tool or sub-agent inside another call is charged again. Nesting directly under an inference span still charges only the outer span, by design. Its duration already covers its children, and summing them double counts the call: the bug here was OpenRouter's LLM Generation + provider attempt + generation reading 204.8 s in a 125.5 s session. Usage netting can't pair these: Broadcast's nested spans carry no usage. Concurrent calls under a single inference span don't appear in any of the 53 captures.
| // Per call, so a model call its framework also rolled up (ADK `call_llm` over | ||
| // `generate_content`) is judged once. | ||
| promptCacheCheck(sessionLlmCalls(spans)), |
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🟡 Gateway cache usage disappears from checks
When app and gateway spans share a response ID, sessionLlmCalls can retain the app span without cache buckets. The cache check then skips recorded gateway hits and misses.
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The check now consumes one representative span per model call. countedLlmCalls resolves duplicate response IDs using a claimed-usage preference and keeps the first observation when both claim usage. The repository's app-and-gateway example gives the app and gateway the same response ID and token total. If the gateway records cache usage but the earlier app span only reports total input tokens, the app wins and the cache check no longer sees the gateway measurement.
Example: Four app calls each report 2,000 input tokens without cache fields; their gateway mirrors share response IDs and each report 1,500 cached tokens. The app observations win ties, leaving zero reporting calls and a skipped cache check instead of the measured hit rate.
Recommended fix: Select a cache-reporting observation for each response ID specifically for promptCacheCheck, or aggregate the cache fields across observations without counting a call twice. Keep the session's token representative unchanged if its current selection is needed elsewhere.
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Fixed in 3b9a6e5: when the span counted for a call reported no cache fields, the check reads the same response id's observation that did. Test: "judges the prompt cache off a gateway mirror when the counted span has none".
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Actionable comments posted: 2
- 🪄 Fix CodeRabbit comments on this PR
🤖 Prompt to fix review comments
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only still-valid issues, skip the rest with a brief reason, keep changes
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Inline comments:
Review comments at @packages/agent-sessions/src/session-turns.ts:
- Line 233: Update the work check in the bucket-merging logic in
session-turns.ts to use isLlmCall(span) and an explicit classifyAiSpan(span) ===
"tool" check instead of WORK_CATEGORIES.has(classifyAiSpan(span)). Preserve the
existing behavior for model calls and tools while excluding retrieval spans from
this check.
- Line 240: Update the bucket-merging loop in the session-turn construction flow
to collect consecutive unopened buckets and prepend their spans once to the next
opened bucket, or to the final bucket if none opens. Avoid repeatedly copying
accumulated spans on each iteration while preserving their order.
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packages/agent-sessions/src/session-checks.test.tspackages/agent-sessions/src/session-checks.tspackages/agent-sessions/src/session-summary.test.tspackages/agent-sessions/src/session-summary.tspackages/agent-sessions/src/session-turns.test.tspackages/agent-sessions/src/session-turns.ts
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… on Anthropic only Follow-up to the uncacheable-prompt fix (#25). The never-written skip read a zero cache-write bucket as "nothing was cacheable", but OpenAI- model emitters (CrewAI, Haystack, LiteLLM, Mastra, Microsoft Agent Framework, Strands, Vercel AI SDK) stamp a zero write bucket on every call, so an OpenAI session with long prompts and a real 0% hit rate was skipped instead of warned. The rule now applies only when every reporting call is an Anthropic call. The separate "too few calls" early return is dropped: the cacheable-call gate below it covers it.
…ot that it is one call Follow-up to the agentTime fix (#39): an inference span inside another is only the same model call under direct nesting; the rule holds because the nested span's time is inside the outer span's. Comment only.
Symptom: Strands TS replies cut off at the output limit were never flagged; the Reply length check passed. Cause: Strands TS writes its finish reasons camelCase (`maxTokens`), and the truncation and refusal matchers (session-findings.ts TRUNCATION_FINISH_REASONS, session-summary.ts REFUSAL_FINISH_REASONS) only lowercased before comparing against `max_tokens`/`content_filter`. Fix: a shared finishReasonsIn helper matches on the reason lowercased with separators removed, so `maxTokens`, `MAX_TOKENS` and `max_tokens` are one reason.
…ht into the next turn Follow-up to the empty-turn fix (#41), from review. A workless agent invocation followed by a pause was folded into the next turn, which then read the pause as a stall inside it. The fold now needs the next turn to start within 5 s of the workless anchor ending (setup such as MAF's `workflow.build`), and merging moves the accumulated bucket forward in place instead of re-copying it for each workless anchor in a row.
…n agentTime Follow-up to the agentTime fix (#39), from review. The walk to the outermost inference span crossed agent and tool spans, so a model call a tool or sub-agent made inside another call was charged nothing. The walk now stops at an agent or tool: only inference spans nested directly (or through non-work spans) share the outer span's time.
…that reported it Follow-up to the per-call cache fix (#40), from review. When an app span and its gateway's mirror share a response id, the netted call list can keep the app span, which may carry no cache fields, and the cache check then skipped a session the mirror measured. For such a call the check now reads the same response's observation that reported cache usage.
Maple reviewConfidence 4/5 · likely safe to merge Seven read-path fixes to the agent-session heuristics: the prompt-cache check ignores calls too short to cache and judges one netted span per model call, the provider headline uses
What was checked
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…mpt-cache check Follow-up to the uncacheable-prompt fix (#25). The never-written rule was session-wide and keyed on provider `anthropic` with a write bucket of 0, so it missed Claude through OpenRouter (flue: provider `openrouter`, prompts 1,043-1,392 tokens under Haiku 4.5's 4,096 minimum) and OpenRouter Broadcast (provider `anthropic`, write key absent, prompts 1.2K-2.6K), and a mixed session fell back to judging every call. A call is now left out on its own when it is Claude (provider `anthropic` or a `claude` model) and neither wrote nor read the cache, like the 1024-token cut; the rest of the session is still judged.
…the finish-reason match Follow-up to the finish-reason fix. The case it fixes today is Vercel AI SDK's kebab-case ai.response.finishReason: a `content-filter` refusal read as passed and now warns. Strands TS camelCase reasons live in output-message parts and reach the checks only once those are read.
Maple reviewConfidence 4/5 · likely safe to merge Read-path fixes to the agent-session checks, vitals and turn labels: the prompt-cache check now nets calls, ignores uncacheable prompts and uncached Claude calls, and the provider headline, finish-reason match and turn labels are corrected. Logic and tests hold up; only the prompt-cache skip wording misleads on never-cached Claude calls.
FindingsNote · F1 · Cache-check skip message calls never-cached Claude prompts too shortcorrectness · When every reporting call is an Anthropic call that wrote and read no cache, the What was checked
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…sses every Claude minimum Follow-up to the uncacheable-prompt fix (#25). Leaving out every Claude call that neither wrote nor read the cache also hid real misses when the emitter does not report writes: 8 Claude calls with 10K-token prompts and 4 misses read "passed 90% over 3 calls" instead of "51% over 7". Such a call is now left out only when its prompt is also under 4,096 tokens, the largest Claude minimum; above it, reading nothing is a miss. The cold/warm cache fixture is back on a Claude model with no write bucket and 10K prompts, and must warn.
Maple reviewConfidence 3/5 · needs attention Warning This review ended early; what follows is what it established. Reworks the agent-session checks and turns: the prompt-cache check skips prompts below the provider's cache minimum and judges each model call once, agentTime charges an outer inference span once, the provider headline counts calls rather than observations, and workless setup anchors fold into the next turn. Contained to analysis code and well covered by new tests.
Still open from earlier reviews
What was checked
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…blind runs Blind runs of 11 frameworks (a fresh agent applying only the skill to an open-source example) surfaced stale Maple limitations and missing setup guidance. - Remove statements fixed by #1120, #1121, #1122 and #1127 (2x token totals, Unidentified vendors, dropped plain-text tool payloads, OpenInference transcripts, check-headline caveats) from the skills and docs pages. - Add to every skill: wrong-region 401 hint, load .env before the exporter, fail fast on a missing key, verification without Maple access, a driver for apps without a scriptable entry point, and a non-crashing TS shutdown. - Apply the verified framework-specific fixes for vercel-ai-sdk, cloudflare-agents, mastra, langchain, openai-agents, google-adk, claude-agent-sdk and pydantic-ai.
…1115) * docs(agent-tracing): per-framework agent tracing guides and skills (WIP) * docs(agent-tracing): apply verifier fixes from end-to-end runs of every guide * docs(agent-tracing): editorial pass, cross-links from instrumentation and onboarding * docs(agent-tracing): align guides with what Agent Sessions shows for each framework * docs(agent-tracing): cut the human guides to a 5-minute setup, move detail into the skills * docs(agent-tracing): trim the Vercel AI SDK setup to three packages, keep the span processor variant for serverless * docs(agent-tracing): drop package trivia from the Vercel AI SDK install step * docs(agent-tracing): say when to use the OpenTelemetry guide instead of listing languages * docs(agent-tracing): cut claims a reader setting up tracing doesn't need * docs(agent-tracing): one quick-setup wording across guides, with the EU region hint * feat(docs): render install commands as npm/pnpm/bun and pip/uv tabs * docs(agent-tracing): TypeScript for LangChain.js, OpenAI Agents, ADK, Cloudflare Agents and Genkit; filter guides by language * docs(agent-tracing): list guides per language on the overview without a selector * docs(agent-tracing): list the any-language guide once, under other languages and frameworks * docs(agent-tracing): say what each Cloudflare Agents package is for * skills(agent-tracing): tell agents how to send redacted feedback on a skill * skills(agent-tracing): send feedback through the MCP only * skills(agent-tracing): drop the feedback section for now * skills(agent-tracing): drop fixed Maple gaps, add setup gotchas from blind runs Blind runs of 11 frameworks (a fresh agent applying only the skill to an open-source example) surfaced stale Maple limitations and missing setup guidance. - Remove statements fixed by #1120, #1121, #1122 and #1127 (2x token totals, Unidentified vendors, dropped plain-text tool payloads, OpenInference transcripts, check-headline caveats) from the skills and docs pages. - Add to every skill: wrong-region 401 hint, load .env before the exporter, fail fast on a missing key, verification without Maple access, a driver for apps without a scriptable entry point, and a non-crashing TS shutdown. - Apply the verified framework-specific fixes for vercel-ai-sdk, cloudflare-agents, mastra, langchain, openai-agents, google-adk, claude-agent-sdk and pydantic-ai. * docs(agent-tracing): drop stale payload rules, build the Claude SDK env per call - opentelemetry: tool results may be plain strings; Maple no longer drops plain-text tool payloads. - claude-agent-sdk: build the telemetry env per query() and fail fast on a missing key, matching the skill. * docs(agent-tracing): drop setup steps Maple no longer needs - GenAI semconv flag is recommended, not required, for LangChain (Python), LlamaIndex and smolagents; openai-agents keeps it for agent lanes and finish reasons - smolagents: stop zeroing run-span token usage - Vercel AI SDK / Cloudflare Agents: runtimeContext groups sessions, drop enrichSpan - Genkit: pass string tool results through unwrapped - LangChain.js: correct the GenAiSpans rationale - Strands TS: note zero tokens with api: "chat" behind OpenAI-compatible gateways * skills(agent-tracing): pass LangChain.js tool results through as plain text * docs(agent-tracing): drop workarounds and caveats the agent-session fixes made stale - Session keys: every vendor now falls back to gen_ai.conversation.id; drop the "Maple ignores gen_ai.conversation.id" lines (agno, crewai, dspy, smolagents, spring-ai, strands) and the OpenInference Haystack session.id caveat. - Tokens: usage counts only on the model-call span, so drop Strands' gen_ai_use_latest_invocation_tokens, the per-request TS agent rationale and the 1.54 floor, pydantic-ai's aggregated-usage warning and the Anthropic cache double-count caveats. Hand-written spans send semconv totals (input includes cache, output includes reasoning); the Anthropic/Gemini mappings and the ADK TS processor follow that. - Cost: LiteLLM's litellm.cost.total and Pydantic AI's operation.cost are read; drop the LiteLLM turn-cost recipe. - Detection: LangChain.js, Genkit and .NET Semantic Kernel get their framework label; OpenAI Agents TS gets agent lanes; LangChain.js groups by session.id without GenAiSpans' conversation-id copy. - Classification: drop DSPy's adapter marker, Spring AI's advisor rename, LangChain's ChatPromptTemplate step, the MAF workflow.build instruction, Mastra scorer and LangChain turn-label caveats, and MapleSpanFixes' tool argument fix (the tool-errors view decodes arguments like the session page). * skills(agent-tracing): leave ADK TS output tokens as reported; Maple adds thinking * skills(agent-tracing): trim to what an implementing agent needs (#1174) - cut human-guide links, backend background, tested-version notes, restated code - drop the Go reference; other languages follow the generic steps - Do-not lists keep only silent, non-obvious mistakes not stated in the steps - inline the GenkitForMaple processor instead of pointing at the guide - OTLP header: quoted literal space everywhere (every targeted SDK accepts it) - add Cloudflare Agents and Genkit to the OpenTelemetry skill's framework list - smolagents: enable_genai_semconv is required * docs(agent-tracing): ADK header uses a quoted literal space, not %20 * docs(agent-tracing): keep tool error text out of Haystack spans with content off, note Spring AI's * docs(agent-tracing): export ADK env vars, name MAPLE_INGEST_KEY, drop the removed Go reference * skills(agent-tracing): tolerate malformed tool arguments, route provider SDKs through the router, raw skill URL * docs(agent-tracing): tighten the OpenTelemetry guide's intro, content and check wording * docs(agent-tracing): use the private ingest key from the environment, never inline Agent tracing runs server-side, so guides and skills now use the private key (maple_sk_) as MAPLE_INGEST_KEY in the repo's secret/env convention. The user sets it themselves instead of pasting it into the prompt; skills create a gitignored .env and .env.example when the repo has none. * docs(onboard): servers use the private ingest key from MAPLE_INGEST_KEY, browsers the public key maple-onboard and the language style skills now read the private key (maple_sk_) from MAPLE_INGEST_KEY on servers, with the agent-tracing secret rules: repo secret/env convention, gitignored .env plus .env.example when there is none, fail fast when unset, never in source or asked for in chat. Browser and mobile code keep the inline public key. Landing docs and the agent-tracing overview prompt follow. * docs(agent-tracing): warn and disable export when MAPLE_INGEST_KEY is unset Instrumentation must never crash or block the app. Replace every throw, exit, panic and ${VAR:?} on a missing key with one warning plus a skipped Maple exporter, and never send an empty bearer. * skills(agent-tracing): read the Spring key from Boot's environment so .env works * Revert "skills(agent-tracing): read the Spring key from Boot's environment so .env works" This reverts commit dc45d34. * Revert "docs(agent-tracing): warn and disable export when MAPLE_INGEST_KEY is unset" This reverts commit 307aa7a. * Revert "docs(onboard): servers use the private ingest key from MAPLE_INGEST_KEY, browsers the public key" This reverts commit 814e177. * Revert "docs(agent-tracing): use the private ingest key from the environment, never inline" This reverts commit 821d778. * docs(agent-tracing): warn and disable export when the ingest key is unset or setup fails Instrumentation must never crash or block the app. Code that reads MAPLE_INGEST_KEY logs one warning and skips the Maple exporter instead of throwing, exiting or panicking, and Go/Rust setup errors are logged, not fatal.
1 participant
Eight Agent Sessions fixes in the session checks, vitals and turn labels (
packages/agent-sessions). Read path only: no warehouse, schema or migration change, so the fixes apply to existing sessions as soon as this deploys. One commit per bug, each with its regression test; follow-up commits from review and verification are listed under their bug.Prompt-cache check warns on prompts too short to cache (#25)
promptCacheCheck(session-checks.ts) judged every call after the first, whatever its size.anthropic, or aclaudemodel through any gateway) that neither wrote nor read the cache must also reach 4,096 prompt tokens to be judged. That is the largest Claude minimum (Haiku 4.5, Opus 4.5), which covers model-specific minimums without a per-model table.session-checks.test.ts"skips the prompt cache when no prompt could have been cached". It covers:anthropicand viaopenrouter;Provider-errors headline counts chat spans, not model calls (#38)
llmCalls8.buildSessionCheckspassed the count of every inference-classified span toproviderCheck. That included Broadcast'sprovider attempt Nandgenerationchildren and ADK'scall_llmwrapper.summary.work.llmCalls, the netted count the session header already shows.session-checks.test.ts"counts model calls in the provider headline once per call, not per chat span". It uses the Broadcast shape from the capture:LLM Generation+provider attempt 1+generation.Checks don't net duplicate reporter pairs (#40)
promptCacheChecktook every inference-classified span. ADK stamps the same usage oncall_llm(no operation, a model) and on thegenerate_contentbeneath it.session-summary.tsexportssessionLlmCalls(spans), the netted call list behindwork.llmCalls, and the cache check reads it. Context-window and reply-length still read every inference span: duplicates change neither the peak nor whether a finish reason was recorded.session-checks.test.ts:agentTimeMssums nested inference spans (#39)computeAgentTime(session-summary.ts) charged every inference span its full duration.provider attempt N(2,019 ms) andgeneration(4,402 ms) under eachLLM Generation(4,842 ms), all opchat.call_llmovergenerate_contentdoubles the same way.session-summary.test.ts:Turn label taken from a non-inference span (#26)
turnLabel(session-turns.ts) fell back to the first span in start order that carried any user message. The LangGraphmodelnode CHAIN span starts before itsChatOpenAIcall, and itsgen_ai.input.messagesholds only the thread's first message.isLlmCall) are asked first, as the transcript'suserRowsalready does. Other spans stay the last resort.session-turns.test.ts"labels from a model call before a framework span that started earlier".Turn label is smolagents' "New task:" (#33)
proseLinelabels with the first non-empty line, and smolagents sends every task asNew task:\n<task>.New task:(case-insensitive) is a framework heading, so the next line labels the turn.Fix this:over pasted code into a label made of the code's first line.session-turns.test.ts"reads past smolagents' lead-in".An AI-less trace becomes an empty turn 1 and takes the title (#41)
workflow.buildtrace as Turn 1 (no label, no agent, 1 span) and the real run as Turn 2.get_agent_sessionreturned no title.workflow.buildcarries the session id in a trace of its own and is read as an agent root by its name, sobuildSessionTurnsopened a turn on it. The session title is turn 1's label.session-turns.test.ts:wfspans. It also asserts the summary title.Finish reasons in other spellings never match
ai.response.finishReason="content-filter"left the Refusals check passed.session-findings.tsTRUNCATION_FINISH_REASONS) and the refusal matcher (session-summary.tsREFUSAL_FINISH_REASONS) only lowercased the reason before comparing it withmax_tokens/content_filter.finishReasonsInhelper matches on the reason lowercased with separators removed, socontent-filter,content_filter,maxTokensandMAX_TOKENSeach read as one reason.maxTokens) inside output-message parts, not ingen_ai.response.finish_reasons.session-checks.test.ts:Verification
session-checks,session-summary,session-turns,session-findings,session-transcript. Each new test was confirmed to fail with its fix reverted.packages/agent-sessionstypecheck.Merge order
session-turns.ts.vendorIdand no operation (e.g.makeSpanwith onlyspanName) will classify asotherinstead of by name.main.