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fix(agent-sessions): transcript and tool payload decoding - #1121
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…butes Symptom: tool results that are plain strings or numbers (OpenAI Agents `deleted /tmp/scratch-notes.txt`, `391`, a 503 error text, a handoff summary) read "result: not captured", and Mastra's plain-text `gen_ai.system_instructions` never reached the transcript. Cause: the mapper decoded every `json` catalog field with JSON.parse and kept only objects and arrays, so anything else was dropped. The convention types `gen_ai.tool.call.arguments` / `.result` as any value. Fix: JSON null still means not captured; objects, arrays and JSON strings decode as before; everything else (plain text, numbers, booleans, truncated JSON) is kept as the text it arrived as. Seen in: openai-agents (docs_openai-agents_a, spans bf977b7555424c18, bda436b865ce7528, 189ec7a1efd8b75b) and mastra (docs_mastra_a).
Symptom: a model call whose reply only calls a tool showed no output row
and no tool-call request (LiteLLM a1 turn-3 `chat` span 7072bdfe10b317a7);
in the proxy capture the assistant tool-call message was missing from the
input history (6 of 7 messages shown). OpenInference dual-write histories
lose the same messages.
Cause: `messageParts` (span-detail.ts) read only `parts` / `content`, so an
OpenAI chat message with `content: null` and a `tool_calls` array had no
parts and was skipped.
Fix: read `tool_calls` (`{ id, function: { name, arguments } }`) as
tool_call parts beside the content, and a `{ role: "tool", tool_call_id }`
message as the tool_result for that call id, which also lets the session
result index match it.
Seen in: litellm (docs_litellm_a, docs_litellm_proxy), OpenInference TS
(docs_openai-agents_ts input.value).
Symptom: every OpenRouter Broadcast `LLM Generation` span showed two raw
JSON messages (span a04a15db8c490dbb: input/user = the whole
`{"messages":[...]}` blob, output/assistant = the `{"completion":"",
"reasoning":...}` blob); the 16 turns read "Segment 1..16" with no turn
label and the session had no title.
Cause: Broadcast sends `gen_ai.prompt` as `{ messages }` and
`gen_ai.completion` as `{ completion, reasoning, rawRequest }`. The mapper
passed both through, and every reader (turn label, transcript, span
detail) expects the documented message array.
Fix: the default integration's refine unwraps a `{ messages: [...] }`
envelope on either message field, and turns `{ completion, reasoning }`
into one assistant message with a reasoning part and a text part. Shape
keyed, so OpenInference's `input.value` request body benefits too.
Seen in: openrouter (capture `openrouter`, 1595 prompts / 1594
completions in these shapes).
Symptom: no model reply and no tool-call request on any OpenInference TS
model span (docs_openai-agents_ts generations fa84aae0ec2fa65d,
9e250b5c18100b86), so the last reply of each turn was absent; the
reply-length check was skipped because no finish reason was read.
Cause: the OpenInference integration reads `llm.output_messages`, a key
OpenInference never writes (it flattens to `llm.output_messages.N.*`),
then `output.value`, which OpenInference TS sets to the raw
`[{"object":"chat.completion","choices":[...]}]`; the message walker
skips entries without role/parts/content. `llm.finish_reason` was not
mapped.
Fix: the default refine turns a `chat.completion` object (alone or in an
array) into its choices' messages, carrying each choice's finish reason
onto the message; the OpenInference integration reads
`llm.finish_reason` as the response finish reason. The flattened keys are
left unread: `output.value` carries the same reply, and reading them would
need a prefix projection on the span read.
Seen in: openai-agents TS (docs_openai-agents_ts, vendor
unknown:openinference); OpenRouter's test generation uses the same shape.
Symptom: the reply-length check was always skipped for Strands sessions
("No model call recorded a finish reason"), and call meta lines showed no
stop reason, although every `chat` span recorded one.
Cause: the mapper reads finish reasons only from
`gen_ai.response.finish_reasons` (and aliases). Strands (Python and TS)
writes them only where the convention also allows them, as
`finish_reason` on each `gen_ai.output.messages` entry.
Fix: when the span carries no finish reason attribute, the default refine
collects the non-empty `finish_reason` values of its output messages. The
attribute still wins when present; a streamed chunk's empty reason is
ignored. This also picks up the reasons the chat.completion unwrap
carries onto each message.
Seen in: strands (docs_strands_a, docs_strands_g, docs_strands_ts).
Symptom: a streamed Google ADK turn rendered its reply as 8 assistant messages, 7 one-token chunks followed by the full text (trace 1feecb5b2b06551a5c1ce516083635cc, span 32be028f453b9eb6). Cause: ADK writes every streamed chunk into `gen_ai.output.messages` with an empty `finish_reason`, then the aggregate with the real one, and the message walker rendered each entry. Fix: output messages whose `finish_reason` is the empty string are dropped when a finished message is present to stand for them. Chunks with no aggregate behind them are kept. Seen in: google-adk (docs_google-adk_a, `generate_content` span 8f487cfcae3893f5 in the capture).
Symptom: every LangChain tool result rendered as the serialised
ToolMessage (`{"type": "tool", "data": {"content": "391",
"additional_kwargs": {}, ..., "status": "success"}}`) instead of what the
tool returned.
Cause: OpenInference's LangChain instrumentor dual-writes the whole
ToolMessage into `gen_ai.tool.call.result`, and the mapper passed it
through.
Fix: the default refine replaces a result of exactly that shape
(`type: "tool"` with a `data` object of `type: "tool"` carrying
`content`) by `data.content`. Any other object stays whole.
Seen in: langchain (docs_langchain_a, docs_langchain_b: every TOOL span).
Symptom: the session detail page showed no messages, usage or cost for spans the gateway stamps as dspy, agno, smolagents, openai_agents_sdk or crewai unless the app turned on OpenInference's GenAI dual-write (`TraceConfig(enable_genai_semconv=True)`, the workaround in all five guides). The list read the same spans' `llm.*` keys, so list and detail disagreed (agno: list $0.0042, detail none). Cause: the OpenInference integration was registered only under `openinference-openai` and `unknown:openinference` (ai-vendors.ts AI_VENDOR_INTEGRATIONS). The gateway stamps the framework id for `openinference.instrumentation.<framework>` scopes, and those ids fell through to the default integration, which reads only `gen_ai.*`. Fix: register the OpenInference integration under agno, crewai, dspy, openai_agents_sdk and smolagents, plus langchain and llamaindex, whose OpenInference scopes the gateway may fingerprint as those ids. Their native spans carry no OpenInference keys, and the canonical `gen_ai.*` keys keep priority, so nothing a native span decodes changes. Seen in: agno, crewai, dspy, openai-agents, smolagents (docs_* captures).
…chema
Symptom: crewai and llamaindex tool spans showed the tool's JSON schema
(`{"properties": {"city": {...}}, "required": ["city"], "type":
"object"}`) as the call's arguments instead of `{"city": "Berlin"}`.
Cause: OpenInference's GenAI dual-write (`enable_genai_semconv`) copies
`tool.parameters` into `gen_ai.tool.call.arguments` for these
instrumentors (an upstream bug), and the canonical key wins over the
dialect's keys.
Fix: the OpenInference integration's refine replaces
`gen_ai.tool.call.arguments` by `input.value` when its raw value is
exactly the span's `tool.parameters`. Exact equality, so arguments that
merely resemble a schema are never touched. The refine context gains
`read(field, key)`, the mapper's own decoder, so a refine can read another
key the way the source lists do.
Seen in: crewai (docs_crewai_a `get_weather.run` 0a327721d936828a),
llamaindex (docs_llamaindex_a `FunctionTool.acall` 8dc64cacfbc534dc).
Maple reviewConfidence 4/5 · likely safe to merge Read-path decoding fixes for Agent Sessions: plain-text JSON attributes are kept, OpenRouter/OpenAI/OpenInference envelopes and OpenAI tool calls are unwrapped, and streamed Google ADK chunks are collapsed. Contained, well tested, safe to merge.
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📝 WalkthroughWalkthroughAI span parsing now handles additional message and tool-result shapes, streamed output chunks, and OpenInference framework attributes. Integration refinement can read decoded fields and normalize supported message envelopes. ChangesAI Integration Normalization
Span Detail Message Parsing
Priority: ⬇️ Low Estimated code review effort: 3 (Moderate) | ~25 minutes Change: Bug fix Sequence Diagram(s)sequenceDiagram
participant AttributeMapper
participant OpenInferenceIntegration
participant genAiRefine
AttributeMapper->>OpenInferenceIntegration: invoke refinement with decoded read callback
OpenInferenceIntegration->>genAiRefine: normalize input and output messages
genAiRefine-->>OpenInferenceIntegration: refined messages and finish reasons
OpenInferenceIntegration-->>AttributeMapper: refined span fields
Merge Risk: 🟡 Moderate · up to Some streamed session transcripts and completion outputs can lose recorded content. Fix both read paths before merging. Security Architecture ReviewSecurity architecture risk: 🔵 Low · up to The changes remain on the read path, with no demonstrated access-control bypass. One streaming-output shape can lose transcript content, which may make affected sessions harder to investigate. Retained concerns
Security review detailsSecurity Blast Radius
Trust Boundaries and Controls
Resilience and Maintainability Implications
Hardening Proposals
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✅ Passed checks (4 passed)
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🛠️ Fix failing CI checks 💡
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| if (parsed === null) return undefined | ||
| if (typeof parsed === "object" || typeof parsed === "string") return parsed | ||
| return raw |
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🔴 Valid fallback messages disappear behind malformed JSON
When a canonical message attribute contains malformed JSON, decodeAttribute accepts it instead of trying a valid alias. The source-key loop stops at that value, so the transcript loses the valid messages.
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The mapper tries each source key for a field until decoding returns a value mapAiSpan. Previously malformed JSON returned undefined, allowing a later legacy or vendor key to supply captured messages. The new raw-text fallback claims the field first, even when that field expects a structured message array. The transcript then receives a string instead of the valid history or reply.
Example: With gen_ai.output.messages = '[{"role":' and gen_ai.completion = '[{"role":"assistant","content":"Done"}]', the mapper keeps the truncated canonical string and never reads the completed reply.
Recommended fix: Preserve raw text for tool payloads and plain-text system instructions, but defer malformed canonical message values until all candidate keys have been tried. Use the raw value only when no source supplies a decodable message.
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Intended and documented in the PR body: a JSON-typed attribute that is not valid JSON now takes the field as its raw text instead of falling through to the next alias. The trade-off is deliberate: a truncated canonical value is shown rather than silently replaced, and no capture from the verified frameworks sets both a canonical message key and a legacy alias on one span. Splitting the rule by field would bring back the per-field special cases this change removes.
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| const finished = entries.filter((entry) => !(isRecord(entry) && entry.finish_reason === "")) | ||
| return finished.length > 0 ? finished : entries |
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🔴 Unfinished streamed replies vanish beside other output
When output contains unfinished chunks and any other message, withoutStreamedChunks drops every chunk. An unrelated message does not contain their text or tool calls, so the transcript loses that reply.
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Output messages can contain multiple distinct messages, while a streaming emitter can leave only chunks when generation stops before an aggregate arrives. This filter assumes every non-chunk entry represents every chunk. If an independent output message is present, the chunks are deleted even though no completed equivalent exists. The same parser feeds the transcript and spanToolCalls, so deleted tool calls also disappear from the call list.
Example: Output [ {role:'assistant', parts:[{type:'text',content:'Partial'}], finish_reason:''}, {role:'tool', content:'Result'} ] renders only Result; Partial was never aggregated.
Recommended fix: Drop chunks only when a demonstrable matching aggregate exists for that stream. Keep unmatched chunks even when other output messages exist.
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Fixed in 7bc1a37 (same change as the CodeRabbit thread): chunks are kept unless a finished aggregate is present.
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| const choices = (Array.isArray(value) ? value : [value]).map(completionChoices) | ||
| if (choices.length > 0 && choices.every((entry) => entry !== undefined)) { | ||
| return choices | ||
| .flat() | ||
| .flatMap((choice) => | ||
| isRecord(choice) && isRecord(choice.message) | ||
| ? [{ ...choice.message, finish_reason: choice.finish_reason }] | ||
| : [], | ||
| ) |
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🟡 Empty completion choices hide captured output
When a captured completion has choices: [], unwrapOutputMessages returns an empty array. The response disappears from the span detail instead of remaining visible as raw captured output.
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The message walker preserves unknown payloads as raw JSON so the reader can inspect what an emitter captured parseMessages. An OpenAI-format completion can have no choices, for example when a capture contains only the response envelope. The new unwrapping recognizes the empty array as a valid choices list and produces no output messages. The previously visible envelope therefore disappears altogether.
Example: output.value = '{"object":"chat.completion","id":"req-1","choices":[]}' maps to outputMessages: [], so the expanded span shows no captured response instead of the response envelope.
Recommended fix: Fall back to the captured envelope whenever unwrapping yields no messages, including empty choices or choices lacking a message.
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Fixed in dcad4b6: empty or message-less choices now leave the captured envelope visible.
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Inline comments:
Review comments at @packages/agent-sessions/src/span-detail.ts:
- Around line 337-338: Update the `finished` filtering logic to drop streamed
chunks only when an entry has a nonempty `finish_reason`; when no such completed
aggregate exists, retain all `entries`.
Review comments at
@packages/query-engine-integrations/src/ai/ai-integrations.ts:
- Line 216: Update the choices handling in the output-mapping flow so it
replaces output.value only when choices contain messages. When choices contain
streaming deltas instead, preserve the original value so the payload and
choice-level finish reason are retained.
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) Follow-up to the OpenAI tool_calls fix. smolagents' model spans write `output.value` as one OpenAI message object (`{role, content: null, tool_calls: [...]}`), not an array, so the walker rendered it as raw JSON and the tool-call request was lost. The default refine now wraps a single record carrying a `role` into a one-element message array; the tool_calls reading then applies. Seen in: smolagents (docs_smolagents_a `OpenAIModel.generate` db63f17aa5554acf).
…ind (#3) Follow-up to registering the OpenInference integration for framework vendor ids. Once smolagents and agno spans decoded `input.value` / `output.value` as message fields, an agent run's arguments became the turn's user row: smolagents' `CodeAgent.run` anchor rendered `{"task": "Hi! Briefly introduce yourself.", "stream": false, ...}` as the user message, and agno's plain-text agent input came before the system row and suppressed the model call's own user row. `input.value` and `output.value` are whatever the span's function took and returned. They leave the source lists and are read in the refine: on a model call (or a span naming no kind) they are the request and reply as before; on any other kind they are messages only when they unwrap to a role-carrying message list (LangChain's `{messages}` chain input); on a TOOL span they fill the call's arguments and result when the GenAI attributes are absent. The schema-as-arguments replacement moves under the TOOL branch. The envelope normalisers move to `ai-messages.ts` so the vendor refine can unwrap the same way the default one does. The integration's doc comment no longer claims the gateway already stamps langchain / llamaindex for OpenInference scopes; that takes effect with the ingest detection batch. Seen in: smolagents (docs_smolagents_a/b `assistant.run` a92540159081b72d), agno (docs_agno_a).
…ys (#30) Follow-up to the chat.completion decoding. Every Python OpenInference instrumentor writes model messages only as flattened keys (`llm.input_messages.N.message.role`, `….contents.M.message_content.text`, `….tool_calls.M.tool_call.function.name`, `….tool_call_id`); the unflattened `llm.input_messages` / `llm.output_messages` keys the integration listed never appear. Without the GenAI dual-write, dspy, smolagents and llamaindex model calls fell back to `input.value`, a Python repr (`ChatMessage(role=<MessageRole.USER: 'user'>, ...)`), and agno and crewai sessions had no transcript and no turn labels at all. The span read now also projects key prefixes an integration declares (`refinePrefixes`, beside the existing prompt-variable family), and the OpenInference refine rebuilds the flattened families as OpenAI chat messages. Preference per message field: the GenAI dual-write, then `input.value` / `output.value` when it is a message list (the exact capture), then the flattened keys, then the bare value on a model call. With tool calls now decoded from OpenInference outputs, a call whose tool span recorded no call id (OpenInference never stamps one) rendered twice: from the span and from the message. The transcript's name fallback for unmatched `tool_call` parts now also covers parts whose id no tool span carries. Seen in: dspy, smolagents, llamaindex, agno, crewai, langchain (docs_* captures, with and without the GenAI dual-write), openai-agents TS.
…an projection The session span read now projects llm.finish_reason, tool.parameters and the OpenInference llm.*_messages prefixes; the recorded SQL baseline follows.
…egate (#20) Review follow-up: the chunk filter dropped empty-finish-reason messages whenever any other message was present, including one that names no finish reason at all, so an unfinished stream beside an ordinary message lost its chunks. Chunks are now dropped only when a message with a non-empty finish reason is there to stand for them.
…#30) Review follow-up: a chat.completion whose choices hold no `message` (a streamed chunk's `delta`, an empty `choices`) was unwrapped to an empty list, which erased the captured output. The capture is now left as it was unless the choices yield at least one message.
Maple reviewConfidence 4/5 · likely safe to merge Read-path fixes to agent-session decoding: plain-text JSON-typed attributes, OpenAI
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Follow-up to the tool-row dedupe. Once an OpenInference tool call renders once, from its span, a failed span that recorded no result attribute and no call id lost the only error text the session had (the history echo under the message's call id): crewai_b, langchain_b and llamaindex_b rendered result "not captured ... Whether it succeeded is unknown" for a call that failed with "transport data service unavailable (503)". A failed tool span with no captured or echoed result now falls back to its failure text (`rawFailureText`: the status message unless generic). The transcript never tells a reader a failed call's outcome is unknown: when even that is absent the note says the span failed without recording a result or an error message.
llamaindex writes `llm.output_messages.N.message.contents.0.message_content.text = ""`
beside its tool calls. The empty value is skipped, which left a bare
`{type: "text"}` part that rendered as raw assistant text (7 rows in
docs_llamaindex_a/b without the GenAI dual-write). A content part carrying
nothing but its type is now dropped.
Maple reviewConfidence 4/5 · likely safe to merge Read-path fixes for Agent Sessions decoding: OpenInference flattened messages, OpenAI tool calls, vendor envelopes, and failed tool spans whose reason only lives in their status message. Contained to the read path, no migration; the recent commits are safe to merge.
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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.
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…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.
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Transcript and tool payload decoding fixes for the Agent Sessions read path, found while verifying the agent-tracing guides against replayed captures. One commit per bug, each with its regression test built from trimmed capture values; follow-up commits after an independent replay of every
docs_*capture through the mapper, turns and transcript (base vs head, with and without the OpenInference GenAI dual-write) are named after the bug they extend. All fixes are read-path only (mapper refine hooks, the span-detail message walker, and the span read's attribute projection): no materialized view or migration change, so they apply to existing rows as well as new ones.Where a fix normalises a vendor envelope, it lives in the mapper's default refine (
ai-integrations.ts), so the turn label, transcript, span detail and MCP tools all read the same normalised message array. Message-level parsing (OpenAI tool calls, streamed chunks) lives inspan-detail.ts, which every reader already goes through.#17 + #29: plain-text JSON-typed attributes dropped (
b84fc90)deleted /tmp/scratch-notes.txt,391, a 503 error text, a handoff summary; spans bf977b7555424c18, bda436b865ce7528, 189ec7a1efd8b75b). Mastra's plain-textgen_ai.system_instructions(7 spans in the capture) never reached the transcript.packages/query-engine-integrations/src/ai/ai-integrations.ts:113-118kept only objects and arrays forjsoncatalog fields. The convention typesgen_ai.tool.call.arguments/.resultas any value.nullstill means not captured; objects, arrays and JSON strings decode as before; anything else (plain text, numbers, booleans, truncated JSON) is kept as the text it arrived as. Every consumer already renders a string value.jsoncatalog field, not only the tool payloads: a plain"0"/"false"now renders as that text, and a value that is not valid JSON (truncated, for example) now takes the field as its raw text instead of falling through to the next alias.ai-integrations.test.ts"keeps plain-text values of a JSON field as their text", "treats JSON null as not captured", "keeps malformed JSON as its raw text rather than throwing" (was: yields no field).#14: OpenAI-format
content: null+tool_callsdropped (9e572d2)chatspan 7072bdfe10b317a7 showed no output row and no tool-call request; in the proxy capture the assistant tool-call message was missing from the history (6 of 7 messages shown).packages/agent-sessions/src/span-detail.ts:330-337messagePartsread onlyparts/content, so a message withcontent: nullandtool_callshad no parts and was skipped.tool_calls({ id, function: { name, arguments } }) become tool_call parts beside the content; a{ role: "tool", tool_call_id, content }message becomes the tool_result for that id (which the session result index then matches).span-detail.test.ts"reads OpenAI-format tool calls and tool messages".66ceedf): smolagents writesoutput.valueas one bare OpenAI message ({role, content: null, tool_calls}), not an array; a single record carrying aroleis now read as a one-message reply.#15: OpenRouter Broadcast
{"messages"}/{"completion"}wrappers (60cff82)LLM Generationspan showed two raw JSON messages (span a04a15db8c490dbb); the 16 turns read "Segment 1..16" with no label and the session had no title.gen_ai.promptas{ messages }andgen_ai.completionas{ completion, reasoning, rawRequest }(1595 / 1594 spans in the capture); the mapper passed both through, and every reader expects the message array (session-turns.tslastUserMessageTextreturns nothing for a non-array).{ messages: [...] }on either message field and turns{ completion, reasoning }into one assistant message with a reasoning part and a text part (an empty completion, the tools-only case, adds no text part). Shape keyed, so OpenInferenceinput.valuerequest bodies benefit too.ai-integrations.test.ts"unwraps the OpenRouter Broadcast message envelopes".#30: OpenInference
chat.completionoutput andllm.finish_reason(f110484)ai-vendors.ts:101readsllm.output_messages(never written; OpenInference flattens tollm.output_messages.N.message.*), thenoutput.value, which OpenInference TS sets to the raw[{"object":"chat.completion","choices":[...]}];span-detail.ts:323-324skips entries without role/parts/content.llm.finish_reasonwas not mapped.chat.completionobject (alone or in an array) into its choices' messages, carrying each choice'sfinish_reasononto its message; the OpenInference integration readsllm.finish_reason. The flattenedllm.output_messages.N.*keys stay unread:output.valuecarries the same reply, and reading them would need a prefix projection on the span read.ai-vendors.test.ts"decodes the chat.completion array OpenInference TS writes to output.value" (also a lone object, the OpenRouter test-generation shape).44354c2): every Python OpenInference instrumentor writes messages only as flattened keys (llm.input_messages.N.message.role,….contents.M.message_content.text,….tool_calls.M.tool_call.function.*,….tool_call_id); the unflattened keys never appear. The span read now also projects key prefixes an integration declares (refinePrefixes, beside the prompt-variable family,ai-sessions.tsspan projection), and the OpenInference refine rebuilds these families as OpenAI chat messages. Per message field: the GenAI dual-write, theninput.value/output.valuewhen it is a message list, then the flattened keys, then the bare value on a model call. Without the dual-write, dspy / smolagents / llamaindex model calls no longer showChatMessage(...)Python reprs, and agno / crewai sessions get transcripts and turn labels. With tool calls now decoded from OpenInference outputs, a call whose tool span recorded no call id (OpenInference stamps none) rendered twice; the transcript's name fallback for unmatchedtool_callparts (session-transcript.tscoveredBySpan) now covers parts whose id no tool span carries. Cost: the projection carries each OpenInference model span's message content once more.dcad4b6): choices that carry nomessage(a streamed chunk'sdelta, emptychoices) leave the capture as it was.f02f604): a failed tool span that recorded no result and no call id now shows its failure text (rawFailureText: the status message unless generic) as the result, so the single remaining row keeps the error (crewai_b, langchain_b, llamaindex_b: "transport data service unavailable (503)"). The web transcript never says a failed call's outcome is unknown.11b138b): a flattened content part carrying nothing but its type (llamaindex writestext = ""beside its tool calls) is dropped instead of rendering as raw{"type":"text"}.#32: finish reasons inside output messages (
454e3cf)chatspan recorded one.gen_ai.response.finish_reasonsand aliases (ai-integrations.tssource lists); Strands (Python and TS) writes them only asfinish_reasonon eachgen_ai.output.messagesentry, where the convention also keeps them.finish_reasonvalues of its output messages. The attribute wins when present; an empty (streamed chunk) reason is ignored.ai-integrations.test.ts"reads finish reasons off the output messages when the span has none of its own".#20: Google ADK streamed chunks as separate assistant messages (
eb4a1cc)gen_ai.output.messageswithfinish_reason: "", then the aggregate with the real reason;span-detail.ts:300-328parseMessagesrendered every entry.finish_reasonare dropped when a finished message is present to stand for them; chunks with no aggregate are kept.span-detail.test.ts"reads a streamed Google ADK reply as its aggregate, not its chunks".7bc1a37): chunks are dropped only when a message with a non-emptyfinish_reasonis present, not beside any other message.#27: LangChain ToolMessage wrapper as the tool result (
061cc4d){"type": "tool", "data": {"content": "391", "additional_kwargs": {}, ..., "status": "success"}}).gen_ai.tool.call.result; the mapper passed it through.type: "tool"with adataobject oftype: "tool"carryingcontent) bydata.content. Any other object stays whole. The tool-errors modal reads raw attributes in SQL (ai-tools.ts) and still shows the wrapper there.ai-integrations.test.ts"unwraps a LangChain ToolMessage tool result to what the tool returned".#3: OpenInference ignored on the detail page for framework vendor ids (
29acd37)TraceConfig(enable_genai_semconv=True)(the workaround in the dspy, agno, smolagents, openai-agents and crewai guides) the detail page showed no messages, usage or cost for those vendors, while the list read the samellm.*keys (agno: list $0.0042, detail none).ai-vendors.ts:159-165registered the OpenInference integration only underopeninference-openaiandunknown:openinference; the gateway stamps the framework id foropeninference.instrumentation.<framework>scopes, which fell to the default integration.gen_ai.*keeps priority. This also makes the detail page readllm.cost.totalfor these vendors (chore(ci): SHA-pin actions, gate publish/deploy with environments, fix RCE in tag input #31 for the OpenInference-scoped ones).ai-vendors.test.ts"is registered under every vendor id the gateway stamps from an OpenInference scope", "decodes a framework's OpenInference span without the GenAI dual-write".a8791fd): with the framework ids mapped, an agent run'sinput.valuebecame the turn's user row (smolagentsCodeAgent.run:{"task": …, "stream": false}; agno's plain-text agent input landed before the system row and suppressed the model call's user row).input.value/output.valuenow leave the source lists and are read in the refine by span kind: request and reply on a model call (or a span naming no kind); on any other kind only when they unwrap to a role-carrying message list (LangChain's{messages}); on a TOOL span they fill the call's arguments and result when the GenAI attributes are absent. The envelope normalisers move toai-messages.tsso the vendor refine unwraps the same way. Thelangchain/llamaindexentries take effect once the ingest detection batch fingerprints those OpenInference scopes; today they stampunknown:openinference, which was already mapped.#19: tool JSON schema in
gen_ai.tool.call.arguments(be780c5){"properties": {"city": {...}}, "required": ["city"], "type": "object"}) as the arguments (docs_crewai_a 0a327721d936828a, docs_llamaindex_a 8dc64cacfbc534dc).tool.parametersintogen_ai.tool.call.arguments(upstream), and the canonical key wins.input.valuewhen the rawgen_ai.tool.call.argumentsis exactly the span'stool.parameters. Exact equality, so real arguments are never touched.AiRefineContextgainsread(field, key), the mapper's own decoder, so a refine reads another key the way the source lists do. For llamaindex,input.valueis{"kwargs": {...}}, shown as captured.ai-vendors.test.ts"reads the real arguments when the dual-write put the tool's schema there".Behaviour changes worth knowing
JSON-typed attributes: see docker-compose not working #17 + Replace Tinybird SQL client with ClickHouse HTTP client #29 above (plain text on every
jsonfield; malformed JSON no longer falls through to the next alias).Tool rows: an OpenInference tool call now renders once (from its span), where it rendered twice before; a failed one carries its failure text as the result.
A
jsonattribute whose value is truncated or otherwise not valid JSON now shows as its raw text instead of being dropped (one test changed accordingly).Follow-ups (not in this PR)
ai-tools.ts:982-983coalesce over the raw attributes), so it still shows the New query engine #19 dual-written schema and the feat: add Hazel as an alert destination type #27 ToolMessage wrapper.maxTokens), which the reply-length check's truncation set does not match (checks batch).Verification
docs_*capture throughmapAiSpans→buildSessionTurns→buildTranscriptat the merge base and at head, with and without the GenAI dual-write; no user, system or assistant row regressed.vitestonai-integrations,ai-vendors,ai-sessions,ai-span-columns,ai-tools,benchmark/catalog(SQL baseline refreshed for the projection),span-detail,session-transcript,session-turnstest files;typecheckof@maple/query-engine-integrationsand@maple/agent-sessions;oxfmtand baseoxlinton the touched files.