From 0444136fc0841286aebb1156d1e2e31f84c302f0 Mon Sep 17 00:00:00 2001 From: m-peko Date: Thu, 30 Apr 2026 11:14:48 +0200 Subject: [PATCH] Update trace samples --- samples/data/README.md | 10 +- samples/data/traces/batch_traces.jsonl | 100 +++--- samples/data/traces/error_trace.json | 322 +++++++++++--------- samples/data/traces/multi_agent_trace.json | 284 +++++++++-------- samples/data/traces/rag_pipeline_trace.json | 258 ++++++++++------ samples/data/traces/simple_llm_trace.json | 118 ++++--- 6 files changed, 646 insertions(+), 446 deletions(-) diff --git a/samples/data/README.md b/samples/data/README.md index ab293fe7..eed25c76 100644 --- a/samples/data/README.md +++ b/samples/data/README.md @@ -25,12 +25,12 @@ Expected output: the trace ID of the newly uploaded trace record. | File | Description | |------|-------------| -| `traces/simple_llm_trace.json` | A single LLM call trace (OpenAI support assistant) with prompt, completion, and token usage. The simplest trace format for getting started. | -| `traces/rag_pipeline_trace.json` | A RAG pipeline trace (LangChain) with retrieval and LLM spans, including document references. Useful for testing multi-span evaluation. | -| `traces/multi_agent_trace.json` | A multi-agent trace (CrewAI) with sequential researcher, analyst, and writer collaboration spans. | -| `traces/error_trace.json` | A failed trace containing a TimeoutError after retry attempts. Useful for testing error handling and investigation workflows. | +| `traces/simple_llm_trace.json` | A single-agent OpenAI support-assistant trace with one LLM call and a cost record. The simplest structured trace for getting started. | +| `traces/rag_pipeline_trace.json` | A LangChain retrieval-augmented generation trace with retriever, reranker, and answer-synthesis agents handed off across multiple spans. | +| `traces/multi_agent_trace.json` | A CrewAI multi-agent trace where a researcher, fact-verifier, and analyst collaborate on an executive brief with peer review and one corrected error. | +| `traces/error_trace.json` | A failed LangChain trace covering context-length errors, rate-limit retries with exponential backoff, fallback agent handoff, and final failure with diagnostic guidance. | | `traces/example_traces.jsonl` | A collection of example traces in JSONL format for batch processing samples. | -| `traces/batch_traces.jsonl` | Ten traces across multiple frameworks, models, and statuses. Designed for batch ingestion testing. | +| `traces/batch_traces.jsonl` | Fifty structured traces across multiple frameworks, models, and statuses. Designed for batch ingestion testing. | ## Datasets diff --git a/samples/data/traces/batch_traces.jsonl b/samples/data/traces/batch_traces.jsonl index 27690c64..02184860 100644 --- a/samples/data/traces/batch_traces.jsonl +++ b/samples/data/traces/batch_traces.jsonl @@ -1,50 +1,50 @@ -{"trace_id":"tr_00007","timestamp":"2026-03-13T10:22:00.913Z","framework":"langchain","agent_name":"data-analyst","status":"ok","duration_ms":3857,"metadata":{"sdk_version":"1.3.0","environment":"production","seed":42},"events":[{"span_id":"sp_00007_001","span_name":"llm_call","span_type":"llm","start_time":"2026-03-13T10:22:00.089Z","end_time":"2026-03-13T10:22:03.604Z","status":"ok","attributes":{"llm.model":"gemini-3.1-pro","llm.temperature":1.0,"llm.token_usage":{"prompt_tokens":889,"completion_tokens":1405,"total_tokens":2294},"llm.latency_ms":3820}}]} 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That arbitrary length stuck and became the official distance worldwide."}},{"type":"cost.record","dt":2,"payload":{"total_tokens":140,"total_cost_usd":0.0017,"model":"gpt-4o","agents_invoked":["qa-bot"],"resolution":"Marathon distance with the 1908 royal-box origin story"}}]} +{"trace_id":"tr-scn-batch-038","agent_name":"task-router","framework":"langgraph","status":"completed","description":"Notification preferences","event_count":5,"metadata":{"synthetic":true,"agents":["task-router"],"scenario_type":"batch_sample","batch_index":38},"start_time":"2026-04-14T23:18:30Z","end_time":"2026-04-14T23:18:30.180Z","duration_ms":180,"events":[{"type":"environment.config","dt":0,"payload":{"agent_name":"orchestrator","config":{"session_type":"single-agent","framework":"langgraph","model_default":"gpt-4o","agents_available":["task-router"]}}},{"type":"agent.input","dt":3,"payload":{"agent_name":"task-router","input":"Mute Slack notifications during my focus block today (10 AM - 12 PM)."}},{"type":"model.invoke","dt":3,"payload":{"agent_name":"task-router","model":"gpt-4o","provider":"openai","prompt_tokens":58,"completion_tokens":54,"duration_ms":180,"purpose":"notification mute set"}},{"type":"agent.output","dt":2,"payload":{"agent_name":"task-router","output":"Slack notifications muted from 10:00 AM to 12:00 PM today. Direct messages from your manager and on-call escalations will still get through (per your default DND override settings). Calendar shows your focus block already on it - status will sync to 'in focus' automatically."}},{"type":"cost.record","dt":2,"payload":{"total_tokens":112,"total_cost_usd":0.0011,"model":"gpt-4o","agents_invoked":["task-router"],"resolution":"DND set with override clarity"}}]} +{"trace_id":"tr-scn-batch-039","agent_name":"agent-pair","framework":"autogen","status":"completed","description":"Memory leak debugging","event_count":5,"metadata":{"synthetic":true,"agents":["agent-pair"],"scenario_type":"batch_sample","batch_index":39},"start_time":"2026-04-14T23:19:00Z","end_time":"2026-04-14T23:19:00.620Z","duration_ms":620,"events":[{"type":"environment.config","dt":0,"payload":{"agent_name":"orchestrator","config":{"session_type":"single-agent","framework":"autogen","model_default":"gpt-4o","agents_available":["agent-pair"]}}},{"type":"agent.input","dt":3,"payload":{"agent_name":"agent-pair","input":"My Node.js service memory keeps growing slowly. It restarts every 18 hours from OOM. 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If similar timing regardless of ceiling, you might have a one-shot allocator.\n4. **Common culprits**:\n - express.js with custom session middleware NOT using a TTL store\n - axios or http library keeping kept-alive connections + response bodies in memory\n - winston/bunyan logging chunks not being released\n5. **Tooling**: clinic.js doctor + clinic heapprofiler are excellent for first-pass diagnosis.\n\nIf you can share a heap snapshot, I can usually point at the leak class quickly."}},{"type":"cost.record","dt":2,"payload":{"total_tokens":330,"total_cost_usd":0.0039,"model":"gpt-4o","agents_invoked":["agent-pair"],"resolution":"Node memory-leak triage with concrete tooling and common-culprit list"}}]} +{"trace_id":"tr-scn-batch-040","agent_name":"doc-qa","framework":"llamaindex","status":"completed","description":"Compensation review timing","event_count":5,"metadata":{"synthetic":true,"agents":["doc-qa"],"scenario_type":"batch_sample","batch_index":40},"start_time":"2026-04-14T23:19:30Z","end_time":"2026-04-14T23:19:30.320Z","duration_ms":320,"events":[{"type":"environment.config","dt":0,"payload":{"agent_name":"orchestrator","config":{"session_type":"single-agent","framework":"llamaindex","model_default":"gpt-4o","kb":"hr_handbook","agents_available":["doc-qa"]}}},{"type":"agent.input","dt":3,"payload":{"agent_name":"doc-qa","input":"When does our annual compensation review happen?"}},{"type":"model.invoke","dt":3,"payload":{"agent_name":"doc-qa","model":"gpt-4o","provider":"openai","prompt_tokens":162,"completion_tokens":102,"duration_ms":320,"purpose":"comp-review calendar lookup"}},{"type":"agent.output","dt":2,"payload":{"agent_name":"doc-qa","output":"Annual comp review runs end of Q1 each year. 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Source: comp-handbook 2026 sec 4."}},{"type":"cost.record","dt":2,"payload":{"total_tokens":264,"total_cost_usd":0.0024,"model":"gpt-4o","agents_invoked":["doc-qa"],"resolution":"Comp review timing with effective date"}}]} +{"trace_id":"tr-scn-batch-041","agent_name":"general-assistant","framework":"openai","status":"completed","description":"Travel timezone math","event_count":5,"metadata":{"synthetic":true,"agents":["general-assistant"],"scenario_type":"batch_sample","batch_index":41},"start_time":"2026-04-14T23:20:00Z","end_time":"2026-04-14T23:20:00.220Z","duration_ms":220,"events":[{"type":"environment.config","dt":0,"payload":{"agent_name":"orchestrator","config":{"session_type":"single-agent","framework":"openai","model_default":"gpt-4o","agents_available":["general-assistant"]}}},{"type":"agent.input","dt":3,"payload":{"agent_name":"general-assistant","input":"If I leave SFO at 10 PM on May 12 and the flight to Tokyo (Haneda) is 11 hours, what time and date will I arrive local Tokyo time?"}},{"type":"model.invoke","dt":3,"payload":{"agent_name":"general-assistant","model":"gpt-4o","provider":"openai","prompt_tokens":78,"completion_tokens":98,"duration_ms":220,"purpose":"timezone math for travel"}},{"type":"agent.output","dt":2,"payload":{"agent_name":"general-assistant","output":"You arrive at 4:00 AM local Tokyo time on May 14.\n\nMath: SFO 10 PM PT on May 12 + 11 hours flight = 9 AM PT on May 13. Tokyo is 16 hours ahead of PT (during Pacific Daylight Time, May is in PDT). 9 AM PT + 16 hours = 1 AM Tokyo on May 14? Let me recheck. PT is UTC-7 in May; Tokyo is UTC+9; so Tokyo is 16 hours ahead. SFO 10 PM PT May 12 = 3 PM JST May 13. Plus 11 hours flight = 2 AM JST May 14. So **2 AM May 14 Tokyo time**, not 4 AM. (My first math was wrong - corrected.)"}},{"type":"cost.record","dt":2,"payload":{"total_tokens":176,"total_cost_usd":0.0019,"model":"gpt-4o","agents_invoked":["general-assistant"],"resolution":"Timezone math with self-correction"}}]} +{"trace_id":"tr-scn-batch-042","agent_name":"qa-bot","framework":"langchain","status":"completed","description":"Photography technical","event_count":5,"metadata":{"synthetic":true,"agents":["qa-bot"],"scenario_type":"batch_sample","batch_index":42},"start_time":"2026-04-14T23:20:30Z","end_time":"2026-04-14T23:20:30.220Z","duration_ms":220,"events":[{"type":"environment.config","dt":0,"payload":{"agent_name":"orchestrator","config":{"session_type":"single-agent","framework":"langchain","model_default":"gpt-4o","agents_available":["qa-bot"]}}},{"type":"agent.input","dt":3,"payload":{"agent_name":"qa-bot","input":"What is f-stop in photography?"}},{"type":"model.invoke","dt":3,"payload":{"agent_name":"qa-bot","model":"gpt-4o","provider":"openai","prompt_tokens":42,"completion_tokens":98,"duration_ms":220,"purpose":"photography concept"}},{"type":"agent.output","dt":2,"payload":{"agent_name":"qa-bot","output":"f-stop is the ratio of the lens focal length to the aperture diameter - written as f/N where N is the number. So f/2 means the aperture is half the focal length wide. Smaller numbers = wider aperture = more light + shallower depth of field. Larger numbers = narrower aperture = less light + deeper depth of field. The common scale (f/1.4, f/2, f/2.8, f/4, f/5.6, f/8, f/11) doubles the light each step down."}},{"type":"cost.record","dt":2,"payload":{"total_tokens":140,"total_cost_usd":0.0017,"model":"gpt-4o","agents_invoked":["qa-bot"],"resolution":"f-stop concept with the ratio explanation"}}]} +{"trace_id":"tr-scn-batch-043","agent_name":"task-router","framework":"langgraph","status":"completed","description":"Calendar block scheduling","event_count":5,"metadata":{"synthetic":true,"agents":["task-router"],"scenario_type":"batch_sample","batch_index":43},"start_time":"2026-04-14T23:21:00Z","end_time":"2026-04-14T23:21:00.280Z","duration_ms":280,"events":[{"type":"environment.config","dt":0,"payload":{"agent_name":"orchestrator","config":{"session_type":"single-agent","framework":"langgraph","model_default":"gpt-4o","agents_available":["task-router"]}}},{"type":"agent.input","dt":3,"payload":{"agent_name":"task-router","input":"Block 9 AM - 11 AM every day this week for deep work."}},{"type":"model.invoke","dt":3,"payload":{"agent_name":"task-router","model":"gpt-4o","provider":"openai","prompt_tokens":58,"completion_tokens":78,"duration_ms":280,"purpose":"recurring calendar block"}},{"type":"agent.output","dt":2,"payload":{"agent_name":"task-router","output":"Created 5 calendar blocks (Mon-Fri April 14-18, 9 AM - 11 AM) labeled 'Deep work - do not schedule.' Status auto-set to 'busy.' Slack DND will activate at the start of each block (linked to calendar status). One conflict: you already had a 10:30 AM standup Wednesday - I left that as-is rather than moving it without asking. Want me to push the standup to 11 AM Wednesday?"}},{"type":"cost.record","dt":2,"payload":{"total_tokens":136,"total_cost_usd":0.0015,"model":"gpt-4o","agents_invoked":["task-router"],"resolution":"Recurring blocks created with conflict surfaced"}}]} +{"trace_id":"tr-scn-batch-044","agent_name":"agent-pair","framework":"autogen","status":"completed","description":"API design question","event_count":5,"metadata":{"synthetic":true,"agents":["agent-pair"],"scenario_type":"batch_sample","batch_index":44},"start_time":"2026-04-14T23:21:30Z","end_time":"2026-04-14T23:21:30.480Z","duration_ms":480,"events":[{"type":"environment.config","dt":0,"payload":{"agent_name":"orchestrator","config":{"session_type":"single-agent","framework":"autogen","model_default":"gpt-4o","agents_available":["agent-pair"]}}},{"type":"agent.input","dt":3,"payload":{"agent_name":"agent-pair","input":"Should my new API use REST or GraphQL?"}},{"type":"model.invoke","dt":3,"payload":{"agent_name":"agent-pair","model":"gpt-4o","provider":"openai","prompt_tokens":48,"completion_tokens":172,"duration_ms":480,"purpose":"API style choice"}},{"type":"agent.output","dt":2,"payload":{"agent_name":"agent-pair","output":"Default to REST unless you have a specific GraphQL fit. Honest reasoning:\n\n**REST wins by default:**\n- Better tooling, observability (every CDN, log analyzer, browser dev tool understands HTTP).\n- Easier to cache (HTTP-level caching is mature; GraphQL caching requires more work).\n- Smaller learning curve for consumers.\n- Easier rate limiting and authorization (per-endpoint).\n\n**GraphQL wins when:**\n- You have many client types with different data needs (mobile + web + admin), and they all hit the same backend.\n- You have deeply nested object graphs where REST creates N+1 round trips.\n- Your clients are internal (you control them) and benefit from schema introspection.\n\nGraphQL is genuinely better for that one shape but the operational tax is real - schema management, query depth limiting, performance tuning are all extra work. For a brand-new API without those specific needs, REST is the lower-risk default.\n\nWhat is the consumer profile - who calls this API and how many client types?"}},{"type":"cost.record","dt":2,"payload":{"total_tokens":220,"total_cost_usd":0.0028,"model":"gpt-4o","agents_invoked":["agent-pair"],"resolution":"REST-vs-GraphQL with the specific-fit framing"}}]} +{"trace_id":"tr-scn-batch-045","agent_name":"doc-qa","framework":"llamaindex","status":"completed","description":"Reimbursement form question","event_count":5,"metadata":{"synthetic":true,"agents":["doc-qa"],"scenario_type":"batch_sample","batch_index":45},"start_time":"2026-04-14T23:22:00Z","end_time":"2026-04-14T23:22:00.380Z","duration_ms":380,"events":[{"type":"environment.config","dt":0,"payload":{"agent_name":"orchestrator","config":{"session_type":"single-agent","framework":"llamaindex","model_default":"gpt-4o","kb":"finance_handbook","agents_available":["doc-qa"]}}},{"type":"agent.input","dt":3,"payload":{"agent_name":"doc-qa","input":"How do I submit an expense report for last week's conference?"}},{"type":"model.invoke","dt":3,"payload":{"agent_name":"doc-qa","model":"gpt-4o","provider":"openai","prompt_tokens":162,"completion_tokens":138,"duration_ms":380,"purpose":"expense process lookup"}},{"type":"agent.output","dt":2,"payload":{"agent_name":"doc-qa","output":"Per finance-handbook ch 3:\n\n1. Open Concur via the SSO portal (concur.acme.com).\n2. Create a new report titled '[Conference Name] - April 2026.'\n3. Attach receipts via the Concur mobile app or web upload (any over $25 require receipt; under $25 do not). Photo receipts are accepted.\n4. Code each line to project code 'CONF-2026' if you have travel pre-approval, or to your team cost-center otherwise.\n5. Submit for approval. Manager has 5 business days; reimbursement direct-deposit lands ~7 business days after approval.\n\nReceipts older than 60 days are not reimbursable per policy - so submit within that window. Source: finance-handbook ch 3."}},{"type":"cost.record","dt":2,"payload":{"total_tokens":300,"total_cost_usd":0.0029,"model":"gpt-4o","agents_invoked":["doc-qa"],"resolution":"Expense submission process with deadlines"}}]} +{"trace_id":"tr-scn-batch-046","agent_name":"general-assistant","framework":"openai","status":"completed","description":"Wine recommendation","event_count":5,"metadata":{"synthetic":true,"agents":["general-assistant"],"scenario_type":"batch_sample","batch_index":46},"start_time":"2026-04-14T23:22:30Z","end_time":"2026-04-14T23:22:30.380Z","duration_ms":380,"events":[{"type":"environment.config","dt":0,"payload":{"agent_name":"orchestrator","config":{"session_type":"single-agent","framework":"openai","model_default":"gpt-4o","agents_available":["general-assistant"]}}},{"type":"agent.input","dt":3,"payload":{"agent_name":"general-assistant","input":"What wine pairs well with grilled salmon?"}},{"type":"model.invoke","dt":3,"payload":{"agent_name":"general-assistant","model":"gpt-4o","provider":"openai","prompt_tokens":48,"completion_tokens":138,"duration_ms":380,"purpose":"wine pairing"}},{"type":"agent.output","dt":2,"payload":{"agent_name":"general-assistant","output":"Salmon is interesting because it works with both whites and lighter reds. Default safe picks:\n\n**White**: Oregon or Russian River Pinot Gris ($18-25), or a New Zealand Sauvignon Blanc ($15-22) - both are bright enough to cut through salmon's richness without overpowering.\n\n**Red, lighter side**: Pinot Noir from Oregon or Burgundy ($25-40 for something good). One of the few fish-friendly reds because of its low tannin. The Willamette Valley style is a particularly good salmon pairing.\n\n**If grilled with charred flavor**: skip the lighter whites and go for an oaked Chardonnay or even a richer Pinot - the smoke and oak echo each other well.\n\nWhat are you serving with it?"}},{"type":"cost.record","dt":2,"payload":{"total_tokens":186,"total_cost_usd":0.0023,"model":"gpt-4o","agents_invoked":["general-assistant"],"resolution":"Wine pairing across white/red/grilled with follow-up question"}}]} +{"trace_id":"tr-scn-batch-047","agent_name":"qa-bot","framework":"langchain","status":"completed","description":"Geography question","event_count":5,"metadata":{"synthetic":true,"agents":["qa-bot"],"scenario_type":"batch_sample","batch_index":47},"start_time":"2026-04-14T23:23:00Z","end_time":"2026-04-14T23:23:00.220Z","duration_ms":220,"events":[{"type":"environment.config","dt":0,"payload":{"agent_name":"orchestrator","config":{"session_type":"single-agent","framework":"langchain","model_default":"gpt-4o","agents_available":["qa-bot"]}}},{"type":"agent.input","dt":3,"payload":{"agent_name":"qa-bot","input":"What is the largest desert in the world?"}},{"type":"model.invoke","dt":3,"payload":{"agent_name":"qa-bot","model":"gpt-4o","provider":"openai","prompt_tokens":42,"completion_tokens":68,"duration_ms":220,"purpose":"geography fact with the technicality"}},{"type":"agent.output","dt":2,"payload":{"agent_name":"qa-bot","output":"By the technical definition (a region with very low precipitation, regardless of temperature): **Antarctica is the largest desert** at ~14 million sq km. Among hot deserts, the **Sahara** is largest at ~9 million sq km. The 'desert' definition catches people off guard - it is about precipitation, not heat or sand."}},{"type":"cost.record","dt":2,"payload":{"total_tokens":110,"total_cost_usd":0.0012,"model":"gpt-4o","agents_invoked":["qa-bot"],"resolution":"Largest desert with the technical-vs-popular framing"}}]} +{"trace_id":"tr-scn-batch-048","agent_name":"task-router","framework":"langgraph","status":"completed","description":"Status update composition","event_count":5,"metadata":{"synthetic":true,"agents":["task-router"],"scenario_type":"batch_sample","batch_index":48},"start_time":"2026-04-14T23:23:30Z","end_time":"2026-04-14T23:23:30.380Z","duration_ms":380,"events":[{"type":"environment.config","dt":0,"payload":{"agent_name":"orchestrator","config":{"session_type":"single-agent","framework":"langgraph","model_default":"gpt-4o","agents_available":["task-router"]}}},{"type":"agent.input","dt":3,"payload":{"agent_name":"task-router","input":"Draft a quick status update for my manager - I am behind on the analytics dashboard project, hit a snag with the data pipeline, but on track for the customer rollout."}},{"type":"model.invoke","dt":3,"payload":{"agent_name":"task-router","model":"gpt-4o","provider":"openai","prompt_tokens":108,"completion_tokens":138,"duration_ms":380,"purpose":"status update with honest framing"}},{"type":"agent.output","dt":2,"payload":{"agent_name":"task-router","output":"**Subject:** Status update - dashboard + customer rollout\n\nHi [Manager],\n\nQuick status:\n\n- **Analytics dashboard**: behind by ~3 days. Hit a snag with the data-pipeline integration (specific issue: [describe]). I have an unblock plan, but want to flag the slip rather than surprise you. New target: [date].\n- **Customer rollout**: on track for original date. The two workstreams are independent, so the dashboard slip does not push customer rollout.\n\nLet me know if you want to dive deeper on either, or if the dashboard slip needs me to reprioritize anything.\n\nThanks,\n[Your Name]"}},{"type":"cost.record","dt":2,"payload":{"total_tokens":246,"total_cost_usd":0.0026,"model":"gpt-4o","agents_invoked":["task-router"],"resolution":"Honest status update with bad-news-flagged-clearly framing"}}]} +{"trace_id":"tr-scn-batch-049","agent_name":"agent-pair","framework":"autogen","status":"completed","description":"Hiring question","event_count":5,"metadata":{"synthetic":true,"agents":["agent-pair"],"scenario_type":"batch_sample","batch_index":49},"start_time":"2026-04-14T23:24:00Z","end_time":"2026-04-14T23:24:00.520Z","duration_ms":520,"events":[{"type":"environment.config","dt":0,"payload":{"agent_name":"orchestrator","config":{"session_type":"single-agent","framework":"autogen","model_default":"gpt-4o","agents_available":["agent-pair"]}}},{"type":"agent.input","dt":3,"payload":{"agent_name":"agent-pair","input":"How do I write a good engineering interview rubric for system design questions?"}},{"type":"model.invoke","dt":3,"payload":{"agent_name":"agent-pair","model":"gpt-4o","provider":"openai","prompt_tokens":58,"completion_tokens":188,"duration_ms":520,"purpose":"interview rubric guidance"}},{"type":"agent.output","dt":2,"payload":{"agent_name":"agent-pair","output":"A good system-design rubric scores by signals, not by vocabulary. Three dimensions:\n\n1. **Requirements gathering** (1-4): does the candidate ask about scale, constraints, and goals before diving into solution? Strong candidates spend 5-10 min here without prompting.\n\n2. **Tradeoff reasoning** (1-4): when they propose an architecture, do they articulate what they are giving up? 'I will use Kafka here because we need durability and ordering, even though it adds operational complexity vs SQS.' Strong candidates volunteer the alternatives they considered.\n\n3. **Depth on demand** (1-4): when you ask 'walk me through how the database scales here,' can they go 2-3 levels deep with specifics? Strong candidates have firsthand pain rather than textbook recall.\n\n**What NOT to score**: matching a 'correct' architecture from a course. There is no canonical right answer for design problems - the signal is in the reasoning, not the picture they draw.\n\nFinal score is the average of the three dimensions, rounded conservatively. 'Strong hire' threshold: average above 3.0 with no dimension below 2.5."}},{"type":"cost.record","dt":2,"payload":{"total_tokens":246,"total_cost_usd":0.0031,"model":"gpt-4o","agents_invoked":["agent-pair"],"resolution":"3-dimension system-design rubric with concrete scoring guidance"}}]} +{"trace_id":"tr-scn-batch-050","agent_name":"doc-qa","framework":"llamaindex","status":"completed","description":"Conference room booking","event_count":5,"metadata":{"synthetic":true,"agents":["doc-qa"],"scenario_type":"batch_sample","batch_index":50},"start_time":"2026-04-14T23:24:30Z","end_time":"2026-04-14T23:24:30.220Z","duration_ms":220,"events":[{"type":"environment.config","dt":0,"payload":{"agent_name":"orchestrator","config":{"session_type":"single-agent","framework":"llamaindex","model_default":"gpt-4o","kb":"facilities_handbook","agents_available":["doc-qa"]}}},{"type":"agent.input","dt":3,"payload":{"agent_name":"doc-qa","input":"How do I book a conference room for 8 people in the SF office?"}},{"type":"model.invoke","dt":3,"payload":{"agent_name":"doc-qa","model":"gpt-4o","provider":"openai","prompt_tokens":162,"completion_tokens":78,"duration_ms":220,"purpose":"facilities lookup"}},{"type":"agent.output","dt":2,"payload":{"agent_name":"doc-qa","output":"In Google Calendar, click 'Add rooms' on the meeting. Filter by SF office and capacity 8+. Recommended for your group size: 'Yosemite' (room for 10, SF 4th floor), 'Tahoe' (room for 12, SF 5th floor), or 'Sequoia' (room for 8 exactly, SF 4th floor). All have video conferencing and a screen share. Booking up to 30 days in advance, no admin approval needed for under 4 hours. Source: facilities-handbook ch 5."}},{"type":"cost.record","dt":2,"payload":{"total_tokens":240,"total_cost_usd":0.0019,"model":"gpt-4o","agents_invoked":["doc-qa"],"resolution":"Conference room booking process with specific room recommendations"}}]} diff --git a/samples/data/traces/error_trace.json b/samples/data/traces/error_trace.json index 7f7e2634..298bac1c 100644 --- a/samples/data/traces/error_trace.json +++ b/samples/data/traces/error_trace.json @@ -1,162 +1,204 @@ { - "trace_id": "tr_00005", - "timestamp": "2026-03-13T14:08:22.004Z", - "framework": "langchain", + "trace_id": "tr-scn-error-handling-001", "agent_name": "document-summarizer", - "status": "error", - "duration_ms": 47231, + "framework": "langchain", + "status": "failed", + "description": "Failure scenario - context-length error then rate-limit, exponential backoff retry, fallback agent invoked, ultimately fails after 3 attempts with diagnostic guidance", + "event_count": 12, "metadata": { - "sdk_version": "1.3.0", - "environment": "staging", - "seed": 42, - "error_summary": "TimeoutError after 3 attempts; partial result returned" + "synthetic": true, + "agents": [ + "primary-summarizer", + "fallback-summarizer" + ], + "scenario_type": "failure_with_retry_and_fallback", + "retry_policy": "exponential_backoff_max_3", + "evaluation_dimensions": [ + "error_recovery", + "tool_usage", + "helpfulness" + ], + "failure_mode": "persistent_token_budget_exhaustion" }, + "start_time": "2026-04-14T08:20:00Z", + "end_time": "2026-04-14T08:21:02.620Z", + "duration_ms": 62620, "events": [ { - "span_id": "sp_00005_001", - "span_name": "llm_call_attempt_1", - "span_type": "llm", - "parent_span_id": null, - "start_time": "2026-03-13T14:08:22.004Z", - "end_time": "2026-03-13T14:08:52.004Z", - "status": "error", - "attributes": { - "llm.model": "gpt-5.3", - "llm.temperature": 0.4, - "llm.max_tokens": 2048, - "llm.prompt": [ - { - "role": "system", - "content": "Summarize the provided document in a structured format with key findings, methodology, and conclusions." - }, - { - "role": "user", - "content": "[Document: 47-page quarterly financial report for FY2025-Q4. Content truncated for trace storage. Total input tokens: 28,412]" - } - ], - "llm.response": null, - "llm.token_usage": null, - "error": { - "type": "TimeoutError", - "message": "Request timed out after 30000ms", - "timestamp": "2026-03-13T14:08:52.004Z" - }, - "retry.attempt": 1, - "retry.max_attempts": 3, - "retry.delay_ms": 0, - "llm.latency_ms": 30000 + "type": "environment.config", + "dt": 0, + "payload": { + "agent_name": "orchestrator", + "config": { + "session_type": "multi-agent", + "framework": "langchain", + "retry_policy": "exponential_backoff", + "max_retries": 3, + "backoff_base_ms": 1000, + "backoff_factor": 2, + "fallback_enabled": true, + "agents_available": [ + "primary-summarizer", + "fallback-summarizer" + ] + } } }, { - "span_id": "sp_00005_002", - "span_name": "retry_backoff", - "span_type": "event", - "parent_span_id": null, - "start_time": "2026-03-13T14:08:52.010Z", - "end_time": "2026-03-13T14:08:57.010Z", - "status": "ok", - "attributes": { - "retry.attempt": 1, - "retry.strategy": "exponential_backoff", - "retry.delay_ms": 5000, - "retry.jitter_ms": 0 + "type": "agent.input", + "dt": 3, + "payload": { + "agent_name": "primary-summarizer", + "input": "Summarize this 240-page financial filing PDF into a 500-word executive memo highlighting risks, capex, and forward guidance: s3://acme-docs/Q4-2025-10K-Acme-Corp.pdf (8.4 MB, 240 pages, ~168K extracted tokens).", + "document_id": "doc-2026-0414-7714", + "page_count": 240, + "extracted_tokens": 168420, + "output_word_target": 500, + "focus_areas": [ + "risks", + "capex", + "forward_guidance" + ] } }, { - "span_id": "sp_00005_003", - "span_name": "llm_call_attempt_2", - "span_type": "llm", - "parent_span_id": null, - "start_time": "2026-03-13T14:08:57.012Z", - "end_time": "2026-03-13T14:09:27.012Z", - "status": "error", - "attributes": { - "llm.model": "gpt-5.3", - "llm.temperature": 0.4, - "llm.max_tokens": 2048, - "llm.prompt": [ - { - "role": "system", - "content": "Summarize the provided document in a structured format with key findings, methodology, and conclusions." - }, - { - "role": "user", - "content": "[Document: 47-page quarterly financial report for FY2025-Q4. Content truncated for trace storage. Total input tokens: 28,412]" - } - ], - "llm.response": null, - "llm.token_usage": null, - "error": { - "type": "TimeoutError", - "message": "Request timed out after 30000ms", - "timestamp": "2026-03-13T14:09:27.012Z" - }, - "retry.attempt": 2, - "retry.max_attempts": 3, - "retry.delay_ms": 5000, - "llm.latency_ms": 30000 + "type": "model.invoke", + "dt": 4, + "payload": { + "agent_name": "primary-summarizer", + "model": "gpt-4o", + "provider": "openai", + "prompt_tokens": 168420, + "completion_tokens": 0, + "duration_ms": 2400, + "purpose": "first attempt - summarize full document in single context window", + "attempt": 1 + } + }, + { + "type": "agent.output", + "dt": 1, + "payload": { + "agent_name": "primary-summarizer", + "output": "ERROR: context_length_exceeded. Input tokens 168,420 exceeded model context limit 128,000. Document is too large for single-pass summarization. Attempting retry with map-reduce chunking strategy.", + "error": true, + "error_code": "context_length_exceeded", + "tokens_attempted": 168420, + "tokens_limit": 128000, + "retry_attempt": 1, + "backoff_ms": 1000 + } + }, + { + "type": "model.invoke", + "dt": 2, + "payload": { + "agent_name": "primary-summarizer", + "model": "gpt-4o", + "provider": "openai", + "prompt_tokens": 42000, + "completion_tokens": 1840, + "duration_ms": 18200, + "purpose": "second attempt - map-reduce: chunk document into 8 sections, summarize each", + "attempt": 2 + } + }, + { + "type": "agent.output", + "dt": 2, + "payload": { + "agent_name": "primary-summarizer", + "output": "PARTIAL SUCCESS then ERROR: rate_limit_exceeded. Successfully processed 6 of 8 chunks (~$0.84 spent). On chunk 7, hit Tier 1 rate limit (10K tokens/min on this account, 42K already consumed in this minute). Backing off 4.2 seconds before final attempt. Note: account is rate-limit constrained - escalating to fallback-summarizer with smaller-model strategy.", + "error": true, + "error_code": "rate_limit_exceeded", + "chunks_processed": 6, + "chunks_remaining": 2, + "tokens_spent": 42000, + "retry_attempt": 2, + "backoff_ms": 4200 + } + }, + { + "type": "agent.handoff", + "dt": 4, + "payload": { + "from_agent": "primary-summarizer", + "to_agent": "fallback-summarizer", + "reason": "Primary agent hit rate limit on a tier-constrained account - fallback uses gpt-4o-mini which has higher rate limits and lower cost, can complete the remaining 2 chunks plus reduce phase", + "context": { + "chunks_processed": 6, + "chunks_remaining": 2, + "chunk_summaries_available": true, + "reduce_phase_pending": true + } + } + }, + { + "type": "model.invoke", + "dt": 2, + "payload": { + "agent_name": "fallback-summarizer", + "model": "gpt-4o-mini", + "provider": "openai", + "prompt_tokens": 18000, + "completion_tokens": 620, + "duration_ms": 9800, + "purpose": "complete remaining 2 chunks with smaller model", + "attempt": 1 + } + }, + { + "type": "agent.output", + "dt": 2, + "payload": { + "agent_name": "fallback-summarizer", + "output": "Successfully summarized chunks 7 and 8 with gpt-4o-mini. All 8 chunk summaries now in hand (~14,000 tokens combined). Proceeding to reduce phase to compose final 500-word executive memo.", + "chunks_processed": 2, + "all_chunks_summarized": true, + "reduce_phase_starting": true + } + }, + { + "type": "model.invoke", + "dt": 2, + "payload": { + "agent_name": "fallback-summarizer", + "model": "gpt-4o-mini", + "provider": "openai", + "prompt_tokens": 14000, + "completion_tokens": 0, + "duration_ms": 24000, + "purpose": "reduce phase - synthesize 8 chunk summaries into final 500-word memo", + "attempt": 1 } }, { - "span_id": "sp_00005_004", - "span_name": "retry_backoff", - "span_type": "event", - "parent_span_id": null, - "start_time": "2026-03-13T14:09:27.015Z", - "end_time": "2026-03-13T14:09:37.015Z", - "status": "ok", - "attributes": { - "retry.attempt": 2, - "retry.strategy": "exponential_backoff", - "retry.delay_ms": 10000, - "retry.jitter_ms": 0 + "type": "agent.output", + "dt": 2, + "payload": { + "agent_name": "fallback-summarizer", + "output": "ERROR: rate_limit_exceeded AGAIN. Reduce phase requires 14K tokens at once, account is now in throttle window from cumulative usage. Waited 30s, retried, throttled again. RECOMMENDATION TO USER: this account is on Tier 1 rate limits which are insufficient for a 240-page document workflow even with chunking. Three paths forward: (1) upgrade to Tier 2 ($50 paid + 7 days from first success - 24h wait) or higher to raise the rate ceiling, (2) split this job across 2 hours with explicit 5-minute pacing between chunks, (3) use the offline batch API which has separate rate limits and is 50% cheaper but takes up to 24h. Job has been paused with checkpoint ckpt-2026-0414-7714 - the 8 chunk summaries are saved and can resume from reduce phase once rate ceiling is raised.", + "error": true, + "error_code": "rate_limit_exceeded", + "final_attempt": true, + "checkpoint_saved": "ckpt-2026-0414-7714", + "recovery_options": 3, + "chunks_preserved": 8 } }, { - "span_id": "sp_00005_005", - "span_name": "llm_call_attempt_3_partial", - "span_type": "llm", - "parent_span_id": null, - "start_time": "2026-03-13T14:09:37.018Z", - "end_time": "2026-03-13T14:09:49.235Z", - "status": "error", - "attributes": { - "llm.model": "gpt-5.3", - "llm.temperature": 0.4, - "llm.max_tokens": 1024, - "llm.prompt": [ - { - "role": "system", - "content": "Summarize the provided document in a structured format with key findings, methodology, and conclusions. Provide a partial summary if the document is too long." - }, - { - "role": "user", - "content": "[Document: First 15 pages of 47-page quarterly financial report for FY2025-Q4. Content truncated. Total input tokens: 9,104]" - } + "type": "cost.record", + "dt": 2, + "payload": { + "total_tokens": 74420, + "total_cost_usd": 0.651, + "model": "gpt-4o + gpt-4o-mini", + "agents_invoked": [ + "primary-summarizer", + "fallback-summarizer" ], - "llm.response": { - "role": "assistant", - "content": "## Partial Summary (Pages 1-15 of 47)\n\n**Key Findings**:\n- Revenue: $142.3M (+18% YoY)\n- Operating margin: 12.4% (up from 9.1%)\n- ARR: $487M, net retention 128%\n\n**Note**: This is a partial summary covering only the first 15 pages. Full financial analysis, segment breakdowns, and forward guidance were not processed due to timeout constraints.", - "finish_reason": "stop" - }, - "llm.token_usage": { - "prompt_tokens": 9104, - "completion_tokens": 97, - "total_tokens": 9201 - }, - "error": { - "type": "PartialResultError", - "message": "Only 32% of document processed after retry exhaustion. Returning partial result.", - "timestamp": "2026-03-13T14:09:49.235Z", - "original_error": "TimeoutError", - "pages_processed": 15, - "pages_total": 47 - }, - "retry.attempt": 3, - "retry.max_attempts": 3, - "retry.strategy": "reduced_input", - "llm.latency_ms": 12217 + "resolution": "FAILED after 3 attempts and fallback escalation - root cause is account rate-limit tier insufficient for document size, not a model or code defect. Checkpoint saved with 8 chunk summaries; user has 3 actionable recovery paths. Demonstrates: graceful degradation, error classification, fallback agent handoff, and actionable failure messaging." } } ] -} +} \ No newline at end of file diff --git a/samples/data/traces/multi_agent_trace.json b/samples/data/traces/multi_agent_trace.json index 56a3a213..6f610da1 100644 --- a/samples/data/traces/multi_agent_trace.json +++ b/samples/data/traces/multi_agent_trace.json @@ -1,141 +1,185 @@ { - "trace_id": "tr_00003", - "timestamp": "2026-03-13T11:05:44.290Z", - "framework": "crewai", + "trace_id": "tr-scn-multi-agent-001", "agent_name": "market-research-crew", - "status": "ok", - "duration_ms": 18742, + "framework": "crewai", + "status": "completed", + "description": "Multi-agent crew - researcher gathers, fact-verifier challenges, analyst synthesizes into executive brief with peer review and one corrected error", + "event_count": 11, "metadata": { - "sdk_version": "1.3.0", - "environment": "development", - "seed": 42, - "crew": { - "crew_id": "crew_00001", - "name": "Market Research Crew", - "process": "sequential", - "max_iterations": 10, - "verbose": true - } + "synthetic": true, + "agents": [ + "researcher-agent", + "fact-verifier", + "analyst-agent" + ], + "scenario_type": "multi_agent_crew", + "crew_name": "market-research-crew", + "collaboration_pattern": "research_verify_synthesize_with_peer_review", + "evaluation_dimensions": [ + "factual_accuracy", + "helpfulness", + "tool_usage", + "instruction_following" + ] }, + "start_time": "2026-04-14T08:15:00Z", + "end_time": "2026-04-14T08:15:08.840Z", + "duration_ms": 8840, "events": [ { - "span_id": "sp_00003_001", - "span_name": "agent:researcher", - "span_type": "agent", - "parent_span_id": null, - "start_time": "2026-03-13T11:05:44.290Z", - "end_time": "2026-03-13T11:05:52.118Z", - "status": "ok", - "attributes": { - "agent.name": "researcher", - "agent.role": "Senior Market Researcher", - "agent.goal": "Gather comprehensive data on the AI observability market landscape", - "agent.backstory": "You are an experienced market researcher specializing in enterprise AI and ML infrastructure.", - "agent.model": "gpt-5.3", - "agent.tools": ["web_search", "document_reader"], - "agent.task": { - "description": "Research the current state of the AI observability and evaluation market. Identify key players, market size, growth trends, and emerging technologies.", - "expected_output": "A structured market research brief with data points and sources." - }, - "agent.output": "## AI Observability Market Research Brief\n\n**Market Size**: The AI observability market reached $2.8B in 2025, growing at 34% CAGR. Projected to reach $8.1B by 2028.\n\n**Key Players**:\n- Arize AI: Focus on ML monitoring, $62M Series B\n- Weights & Biases: Experiment tracking, $250M valuation\n- Datadog ML Monitoring: Enterprise integration play\n- LayerLens Stratix: Full-lifecycle AI governance\n- Langfuse: Open-source LLM observability\n\n**Growth Drivers**:\n1. Regulatory pressure (EU AI Act enforcement 2026)\n2. Enterprise LLM adoption reaching production scale\n3. Multi-agent system complexity\n4. Cost optimization needs (token spend visibility)\n\n**Emerging Trends**:\n- Agent-as-a-Judge evaluation frameworks\n- Real-time policy enforcement\n- Automated remediation pipelines\n- Cross-framework observability standards", - "agent.token_usage": { - "prompt_tokens": 1847, - "completion_tokens": 623, - "total_tokens": 2470 - }, - "agent.iterations": 3, - "agent.duration_ms": 7828 + "type": "environment.config", + "dt": 0, + "payload": { + "agent_name": "orchestrator", + "config": { + "session_type": "multi-agent", + "framework": "crewai", + "crew_name": "market-research-crew", + "model_default": "gpt-4o", + "tools_available": [ + "web_search", + "financial_data_api", + "scientific_paper_search" + ], + "agents_available": [ + "researcher-agent", + "fact-verifier", + "analyst-agent" + ], + "peer_review_enabled": true + } + } + }, + { + "type": "agent.input", + "dt": 3, + "payload": { + "agent_name": "researcher-agent", + "input": "Brief me on the EV battery cell-to-pack (CTP) and cell-to-chassis (CTC) market. I need: top 5 manufacturers by 2025 volume share, current pack-level cost trajectory, where structural-battery technology differentiation is heading for 2026-2028, and any geopolitical supply concentration risk. Output as a 200-word executive brief suitable for a board pre-read.", + "requestor": "product-strategy-team", + "output_format": "executive_brief", + "word_target": 200, + "deadline_minutes": 15 + } + }, + { + "type": "model.invoke", + "dt": 4, + "payload": { + "agent_name": "researcher-agent", + "model": "gpt-4o", + "provider": "openai", + "prompt_tokens": 312, + "completion_tokens": 482, + "duration_ms": 2840, + "purpose": "comprehensive market research using web_search and financial_data_api tools across 4 sub-questions" + } + }, + { + "type": "agent.output", + "dt": 3, + "payload": { + "agent_name": "researcher-agent", + "output": "Research complete across 4 sub-questions, 18 sources consulted (BloombergNEF, S&P Global Mobility, IEA, Reuters, company filings).\n\nManufacturer share 2025 (CTP/CTC pack volumes for EV passenger vehicles): CATL 35.2%, BYD 16.8%, LG Energy Solution 13.1%, Panasonic 6.4%, Samsung SDI 4.9%. Note CATL share includes Qilin platform; BYD share reflects vertical integration with own EVs (not merchant sales).\n\nCost: pack-level $115/kWh (2022) -> $98/kWh (2023) -> $89/kWh (2024) -> $82/kWh (2025), tracking $70-72/kWh by 2027 per BNEF Lithium-Ion Battery Price Survey 2025. The $100/kWh barrier breached 2024.\n\nStructural battery: Tesla 4680 + Cybertruck shipping; BYD Seal e-Platform 3.0 in production; Leapmotor B10 launched late 2025. Direction: cell-to-chassis (CTC) eliminates pack housing, 30-40% parts reduction, but ties customer to single-vendor stack and complicates collision repair (pack cannot be replaced as unit).\n\nGeopolitical: 2025 China-origin share of CTP/CTC supply ~78% (CATL+BYD+CALB+EVE). U.S. IRA Section 30D tightens 'foreign entity of concern' rules for 2025+, reducing eligibility for Chinese-content packs. EU Critical Raw Materials Act (eff 2024) targets 65% from any single non-EU country by 2030.", + "sources_consulted": 18, + "sub_questions_addressed": 4, + "confidence_high": true + } + }, + { + "type": "agent.handoff", + "dt": 3, + "payload": { + "from_agent": "researcher-agent", + "to_agent": "fact-verifier", + "reason": "Research complete with specific numeric claims (volume share, cost figures, regulatory dates) that must be verified before being put in front of the board - peer review is mandatory in this crew", + "context": { + "specific_claims_to_verify": [ + "CATL 35.2 pct", + "pack cost $82/kWh 2025", + "CTC parts reduction 30-40 pct", + "China origin share 78 pct" + ] + } + } + }, + { + "type": "model.invoke", + "dt": 3, + "payload": { + "agent_name": "fact-verifier", + "model": "gpt-4o", + "provider": "openai", + "prompt_tokens": 612, + "completion_tokens": 288, + "duration_ms": 1680, + "purpose": "independent verification of 4 numeric claims against primary sources" + } + }, + { + "type": "agent.output", + "dt": 3, + "payload": { + "agent_name": "fact-verifier", + "output": "Verification complete. THREE claims verified, ONE correction needed.\n\n(1) CATL 2025 share 35.2% - VERIFIED against SNE Research October 2025 monthly report (35.1%) and BNEF Q3 2025 (35.4%) - the 35.2% figure is within the source range, accept.\n\n(2) Pack-level $82/kWh 2025 - VERIFIED against BNEF Lithium-Ion Battery Price Survey 2025 (volume-weighted average $82/kWh) and Goldman Sachs December 2025 estimate ($83/kWh).\n\n(3) CTC parts reduction 30-40% - VERIFIED in concept but the specific 30-40% range comes from Tesla Battery Day 2020 and BYD whitepaper - it is the manufacturer's own claim, not independent measurement. Recommend qualifying as 'manufacturer-claimed' rather than asserting as fact.\n\n(4) China-origin CTP/CTC supply 78% - INCORRECT. Researcher conflated cell production share (~78%) with pack-level CTP/CTC supply share (~62% per BNEF supply chain report). The 78% figure is correct for cells, not for finished CTP/CTC packs because LG Energy Solution and Panasonic also produce pack-level CTP designs. Correction required.", + "claims_verified": 3, + "claims_corrected": 1, + "correction_severity": "material" } }, { - "span_id": "sp_00003_002", - "span_name": "task_delegation", - "span_type": "event", - "parent_span_id": null, - "start_time": "2026-03-13T11:05:52.120Z", - "end_time": "2026-03-13T11:05:52.122Z", - "status": "ok", - "attributes": { - "delegation.from": "researcher", - "delegation.to": "analyst", - "delegation.message": "Research complete. Passing market research brief to analyst for competitive analysis and strategic recommendations.", - "delegation.data_size_bytes": 1842 + "type": "agent.handoff", + "dt": 3, + "payload": { + "from_agent": "fact-verifier", + "to_agent": "analyst-agent", + "reason": "Verification surfaced one material correction (China supply share 78% -> 62%) and one nuance (manufacturer-claimed parts reduction) - analyst must integrate corrections into the final brief, not reproduce the original error", + "context": { + "corrections_required": 1, + "qualifications_required": 1, + "original_research_otherwise_sound": true + } } }, { - "span_id": "sp_00003_003", - "span_name": "agent:analyst", - "span_type": "agent", - "parent_span_id": null, - "start_time": "2026-03-13T11:05:52.125Z", - "end_time": "2026-03-13T11:05:58.603Z", - "status": "ok", - "attributes": { - "agent.name": "analyst", - "agent.role": "Strategic Analyst", - "agent.goal": "Analyze market data and produce competitive intelligence with actionable recommendations", - "agent.backstory": "You are a strategic analyst with deep expertise in SaaS competitive landscapes and go-to-market strategy.", - "agent.model": "gpt-5.3", - "agent.tools": ["calculator", "chart_generator"], - "agent.task": { - "description": "Analyze the market research brief. Compare competitive positioning, identify whitespace opportunities, and produce a SWOT analysis for LayerLens Stratix.", - "expected_output": "A competitive analysis document with SWOT matrix and three strategic recommendations." - }, - "agent.input_from": "researcher", - "agent.output": "## Competitive Analysis: LayerLens Stratix\n\n**SWOT Analysis**:\n\n| | Positive | Negative |\n|---|---|---|\n| Internal | **Strengths**: Full-lifecycle coverage, Agent-as-a-Judge, multi-framework support (13 adapters), policy engine | **Weaknesses**: Smaller brand recognition, Python-first SDK limits polyglot shops |\n| External | **Opportunities**: EU AI Act compliance demand, multi-agent systems growth, enterprise migration from point tools | **Threats**: Datadog bundling, open-source alternatives gaining traction, price pressure |\n\n**Strategic Recommendations**:\n1. **Lead with compliance**: Position AI governance and audit trail as primary differentiator for EU AI Act readiness\n2. **Expand protocol support**: Add Go and Java SDKs to capture enterprise segments using JVM/Go stacks\n3. **Partner program**: Build SI partnerships for enterprise deployment, leveraging the platform's white-label capabilities", - "agent.token_usage": { - "prompt_tokens": 2214, - "completion_tokens": 487, - "total_tokens": 2701 - }, - "agent.iterations": 2, - "agent.duration_ms": 6478 + "type": "model.invoke", + "dt": 3, + "payload": { + "agent_name": "analyst-agent", + "model": "gpt-4o", + "provider": "openai", + "prompt_tokens": 1240, + "completion_tokens": 312, + "duration_ms": 2240, + "purpose": "synthesize verified research into 200-word board-pre-read brief with strategic frame" } }, { - "span_id": "sp_00003_004", - "span_name": "task_delegation", - "span_type": "event", - "parent_span_id": null, - "start_time": "2026-03-13T11:05:58.605Z", - "end_time": "2026-03-13T11:05:58.607Z", - "status": "ok", - "attributes": { - "delegation.from": "analyst", - "delegation.to": "writer", - "delegation.message": "Analysis complete. Passing competitive analysis and SWOT to writer for final executive summary.", - "delegation.data_size_bytes": 2156 + "type": "agent.output", + "dt": 3, + "payload": { + "agent_name": "analyst-agent", + "output": "**EV Battery CTP/CTC Market - Board Pre-Read**\n\n**Players:** CATL leads at 35% global share (Qilin platform), BYD second at 17% (vertically integrated, not merchant), LG Energy Solution 13%, Panasonic 6%, Samsung SDI 5%. The market is consolidating.\n\n**Cost:** Pack-level cost has fallen from $115/kWh (2022) to $82/kWh (2025), with a clear trajectory toward $70-72/kWh by 2027 per BNEF. The $100/kWh barrier - long held as the threshold for EV TCO parity with ICE - was decisively breached in 2024.\n\n**Tech direction:** Structural battery (cell-to-chassis) is the 2026-2028 frontier. Tesla, BYD, and Leapmotor have shipping products. Manufacturers claim 30-40% parts reduction, though this is vendor-asserted rather than independently measured. CTC reduces unit cost but creates customer lock-in to a single vendor stack and complicates collision repair.\n\n**Geopolitical risk:** China supplies ~78% of cells globally and ~62% of finished CTP/CTC packs. The U.S. Inflation Reduction Act Section 30D and the EU Critical Raw Materials Act both restrict China-origin content for compliance, creating real near-term pressure on non-China supply build-out (LGES, Panasonic, Samsung SDI).\n\n*Source-verified by independent peer-review pass; one figure corrected from initial draft.*", + "brief_word_count": 206, + "corrections_integrated": 1, + "qualifications_integrated": 1, + "peer_reviewed": true } }, { - "span_id": "sp_00003_005", - "span_name": "agent:writer", - "span_type": "agent", - "parent_span_id": null, - "start_time": "2026-03-13T11:05:58.610Z", - "end_time": "2026-03-13T11:06:03.032Z", - "status": "ok", - "attributes": { - "agent.name": "writer", - "agent.role": "Executive Communications Writer", - "agent.goal": "Produce a polished executive summary suitable for C-suite presentation", - "agent.backstory": "You are a senior communications professional who translates complex market data into clear executive narratives.", - "agent.model": "gpt-5.3", - "agent.tools": [], - "agent.task": { - "description": "Write a one-page executive summary combining the market research and competitive analysis. Target audience: VP of Product and CEO. Tone: authoritative, data-driven, action-oriented.", - "expected_output": "A polished executive summary with key metrics, competitive position, and recommended next steps." - }, - "agent.input_from": "analyst", - "agent.output": "# Executive Summary: AI Observability Market Position\n\n**Date**: March 2026 | **Classification**: Internal\n\n## Market Landscape\nThe AI observability market has reached $2.8B (2025) with a 34% CAGR trajectory toward $8.1B by 2028. Growth is accelerated by EU AI Act enforcement (H2 2026), enterprise LLM production deployments, and the rising complexity of multi-agent architectures.\n\n## Our Position\nLayerLens Stratix is uniquely positioned with full-lifecycle AI governance -- the only platform combining observability, evaluation, policy enforcement, and compliance in a single solution. Our 13 framework adapters and Agent-as-a-Judge capability represent technical moats that competitors have not replicated.\n\n## Competitive Dynamics\nArize and Weights & Biases lead in brand recognition but focus on narrow ML monitoring. Datadog poses the primary bundling threat. Open-source alternatives (Langfuse) compete on price but lack enterprise governance features.\n\n## Recommended Actions\n1. **Compliance-first GTM** — Lead positioning around EU AI Act readiness (Q2 2026)\n2. **SDK expansion** — Ship Go and Java SDKs to unlock JVM-heavy enterprise accounts (Q3 2026)\n3. **SI channel** — Launch partner program for enterprise deployment scale (Q3 2026)\n\n## Key Metric Targets\n- Market share target: 4% by EOY 2026 (~$140M ARR)\n- Enterprise logo target: 50 net-new accounts\n- NRR target: 135%", - "agent.token_usage": { - "prompt_tokens": 3102, - "completion_tokens": 541, - "total_tokens": 3643 - }, - "agent.iterations": 1, - "agent.duration_ms": 4422 + "type": "cost.record", + "dt": 2, + "payload": { + "total_tokens": 3246, + "total_cost_usd": 0.026, + "model": "gpt-4o", + "agents_invoked": [ + "researcher-agent", + "fact-verifier", + "analyst-agent" + ], + "resolution": "Board-quality 206-word executive brief delivered after research, peer-review verification (1 material correction), and synthesis. Demonstrates the value of fact-verifier in catching the cell-share-vs-pack-share conflation that would have shipped to leadership unchecked." } } ] -} +} \ No newline at end of file diff --git a/samples/data/traces/rag_pipeline_trace.json b/samples/data/traces/rag_pipeline_trace.json index 34598ee5..e4054934 100644 --- a/samples/data/traces/rag_pipeline_trace.json +++ b/samples/data/traces/rag_pipeline_trace.json @@ -1,107 +1,179 @@ { - "trace_id": "tr_00002", - "timestamp": "2026-03-13T10:22:08.117Z", + "trace_id": "tr-scn-rag-pipeline-001", + "agent_name": "enterprise-rag-stack", "framework": "langchain", - "agent_name": "knowledge-base-qa", - "status": "ok", - "duration_ms": 2814, + "status": "completed", + "description": "Retrieval-augmented generation with reranker - retriever, reranker, and grounded answer synthesizer", + "event_count": 11, "metadata": { - "sdk_version": "1.3.0", - "environment": "development", - "pipeline_type": "rag", - "seed": 42 + "synthetic": true, + "agents": [ + "knowledge-retriever", + "relevance-reranker", + "answer-synthesizer" + ], + "scenario_type": "retrieval_augmented_generation", + "evaluation_dimensions": [ + "rag_quality", + "factual_accuracy", + "relevance", + "groundedness" + ] }, + "start_time": "2026-04-14T08:05:00Z", + "end_time": "2026-04-14T08:05:02.880Z", + "duration_ms": 2880, "events": [ { - "span_id": "sp_00002_001", - "span_name": "retriever", - "span_type": "retrieval", - "parent_span_id": null, - "start_time": "2026-03-13T10:22:08.117Z", - "end_time": "2026-03-13T10:22:09.341Z", - "status": "ok", - "attributes": { - "retriever.engine": "pgvector", - "retriever.query": "What are the SLA guarantees for the enterprise tier?", - "retriever.top_k": 5, - "retriever.documents": [ - { - "doc_id": "doc_0041", - "title": "Enterprise SLA Agreement v3.2", - "content": "Enterprise tier customers receive a 99.95% uptime SLA with automated credits. Incident response times: P1 critical within 15 minutes, P2 high within 1 hour, P3 medium within 4 hours. Monthly uptime is calculated excluding scheduled maintenance windows announced 72 hours in advance.", - "score": 0.94, - "source": "legal/sla-enterprise-v3.2.pdf", - "chunk_index": 2 - }, - { - "doc_id": "doc_0042", - "title": "Enterprise SLA Agreement v3.2", - "content": "SLA credit schedule: Below 99.95% but above 99.9% — 10% credit. Below 99.9% but above 99.0% — 25% credit. Below 99.0% — 50% credit. Credits are applied to the next billing cycle and do not exceed the monthly subscription fee.", - "score": 0.91, - "source": "legal/sla-enterprise-v3.2.pdf", - "chunk_index": 3 - }, - { - "doc_id": "doc_0108", - "title": "Pricing and Plans Overview", - "content": "Enterprise plans include dedicated infrastructure, custom data residency, SSO/SCIM provisioning, and a named account manager. SLA-backed uptime guarantees apply to all production endpoints.", - "score": 0.82, - "source": "marketing/pricing-overview.md", - "chunk_index": 1 - }, - { - "doc_id": "doc_0073", - "title": "Support Tiers Comparison", - "content": "Enterprise support includes 24/7 coverage with phone, email, and Slack channels. Dedicated support engineer assigned within 48 hours of contract signing. Quarterly business reviews included.", - "score": 0.76, - "source": "support/tiers-comparison.md", - "chunk_index": 4 - }, - { - "doc_id": "doc_0019", - "title": "Platform Architecture Overview", - "content": "The platform is deployed across three AWS regions (us-east-1, eu-west-1, ap-southeast-1) with active-active replication. Failover is automatic with sub-30-second DNS propagation.", - "score": 0.68, - "source": "engineering/architecture-overview.md", - "chunk_index": 7 - } + "type": "environment.config", + "dt": 0, + "payload": { + "agent_name": "orchestrator", + "config": { + "session_type": "multi-agent", + "framework": "langchain", + "vector_store": "pinecone-serverless", + "index": "acme-policy-2026", + "embedding_model": "text-embedding-3-large", + "reranker_model": "cohere-rerank-3", + "top_k_initial": 12, + "top_k_after_rerank": 4, + "agents_available": [ + "knowledge-retriever", + "relevance-reranker", + "answer-synthesizer" + ] + } + } + }, + { + "type": "agent.input", + "dt": 3, + "payload": { + "agent_name": "knowledge-retriever", + "input": "What is the SLA for production incidents on our enterprise tier during business hours, and what are the specific response and resolution targets per incident severity?", + "customer_tier": "enterprise", + "customer_id": "cust-acme-corp-0017", + "query_id": "q-2026-0414-7711" + } + }, + { + "type": "model.invoke", + "dt": 4, + "payload": { + "agent_name": "knowledge-retriever", + "model": "text-embedding-3-large", + "provider": "openai", + "prompt_tokens": 48, + "completion_tokens": 0, + "duration_ms": 180, + "purpose": "embed user query into 3072-dim vector for similarity search" + } + }, + { + "type": "agent.output", + "dt": 2, + "payload": { + "agent_name": "knowledge-retriever", + "output": "Retrieved 12 candidate chunks across 5 source documents. Top scores by document: enterprise-sla-2026-q1.md (0.91, 0.87, 0.82), incident-response-runbook.md (0.84, 0.78), pricing-tier-comparison.md (0.78), severity-classification.md (0.74, 0.71), legacy-sla-2024.md (0.68 - flagged as outdated). Median relevance 0.79. Sending 12-chunk candidate set to reranker.", + "retrieval_count": 12, + "sources_consulted": 5, + "top_score": 0.91, + "flagged_outdated": 1 + } + }, + { + "type": "agent.handoff", + "dt": 3, + "payload": { + "from_agent": "knowledge-retriever", + "to_agent": "relevance-reranker", + "reason": "Vector retrieval cast a wide net - need cross-encoder reranking to identify the chunks that actually answer this multi-part question (SLA + response + resolution + severity)", + "context": { + "query_id": "q-2026-0414-7711", + "candidate_count": 12 + } + } + }, + { + "type": "model.invoke", + "dt": 3, + "payload": { + "agent_name": "relevance-reranker", + "model": "cohere-rerank-3", + "provider": "cohere", + "prompt_tokens": 1840, + "completion_tokens": 0, + "duration_ms": 420, + "purpose": "cross-encoder rerank to surface chunks that answer all four parts of the question" + } + }, + { + "type": "agent.output", + "dt": 2, + "payload": { + "agent_name": "relevance-reranker", + "output": "Reranked candidates against compound query 'enterprise tier business-hours SLA + response targets + resolution targets + per-severity'. Top 4 selected: (1) enterprise-sla-2026-q1.md#sla-table covering all severities (rerank 0.96), (2) severity-classification.md#definitions defining P1-P4 (rerank 0.92), (3) enterprise-sla-2026-q1.md#business-hours defining 8AM-8PM PT window (rerank 0.89), (4) incident-response-runbook.md#escalation tying response to severity (rerank 0.85). Discarded the 2024 outdated chunk and 7 lower-relevance fragments.", + "reranked_count": 4, + "discarded_count": 8, + "top_rerank_score": 0.96 + } + }, + { + "type": "agent.handoff", + "dt": 3, + "payload": { + "from_agent": "relevance-reranker", + "to_agent": "answer-synthesizer", + "reason": "Top-4 chunks span SLA table, severity definitions, business hours, and escalation - sufficient context for a fully-grounded multi-part answer with citations", + "context": { + "context_chunk_count": 4, + "primary_source": "enterprise-sla-2026-q1.md" + } + } + }, + { + "type": "model.invoke", + "dt": 4, + "payload": { + "agent_name": "answer-synthesizer", + "model": "gpt-4o", + "provider": "openai", + "prompt_tokens": 2280, + "completion_tokens": 348, + "duration_ms": 2280, + "purpose": "synthesize grounded multi-part answer with explicit per-severity targets and source citations" + } + }, + { + "type": "agent.output", + "dt": 2, + "payload": { + "agent_name": "answer-synthesizer", + "output": "Enterprise tier SLA during business hours (Monday-Friday 8:00 AM - 8:00 PM Pacific Time):\n\n**P1 (Critical)** - Service unavailable or core functionality broken for all users.\n Response: 15 minutes. Resolution target: 4 hours.\n\n**P2 (High)** - Significant degradation or core functionality broken for a subset of users.\n Response: 1 hour. Resolution target: 8 business hours.\n\n**P3 (Medium)** - Non-critical feature broken or workaround exists.\n Response: 4 hours. Resolution target: 2 business days.\n\n**P4 (Low)** - Cosmetic or documentation issues.\n Response: 1 business day. Resolution target: scheduled in next release cycle.\n\nAfter business hours, only P1 and P2 are continuously monitored: P1 response shifts to 30 minutes with 8-hour resolution target; P2 escalates to next-business-day morning queue.\n\nSeverity is determined by the customer in the initial report and may be reclassified by the on-call engineer within 30 minutes if scope assessment differs (per incident-response-runbook.md section 3.2). All P1 incidents trigger automatic VP-of-Engineering paging.\n\nSources: enterprise-sla-2026-q1.md (sections 1-3), severity-classification.md, incident-response-runbook.md (section 3.2).", + "citations": [ + "enterprise-sla-2026-q1.md", + "severity-classification.md", + "incident-response-runbook.md" ], - "retriever.latency_ms": 1224 + "groundedness_score": 0.97, + "answer_completeness": "all_4_subquestions_addressed" } }, { - "span_id": "sp_00002_002", - "span_name": "llm_call", - "span_type": "llm", - "parent_span_id": null, - "start_time": "2026-03-13T10:22:09.345Z", - "end_time": "2026-03-13T10:22:10.931Z", - "status": "ok", - "attributes": { - "llm.model": "gpt-5.3", - "llm.temperature": 0.3, - "llm.max_tokens": 1024, - "llm.prompt": [ - { - "role": "system", - "content": "You are a helpful assistant that answers questions using only the provided context. If the context does not contain enough information, say so. Cite document sources." - }, - { - "role": "user", - "content": "Context:\n[1] Enterprise SLA Agreement v3.2 (chunk 2): Enterprise tier customers receive a 99.95% uptime SLA with automated credits. Incident response times: P1 critical within 15 minutes, P2 high within 1 hour, P3 medium within 4 hours. Monthly uptime is calculated excluding scheduled maintenance windows announced 72 hours in advance.\n\n[2] Enterprise SLA Agreement v3.2 (chunk 3): SLA credit schedule: Below 99.95% but above 99.9% — 10% credit. Below 99.9% but above 99.0% — 25% credit. Below 99.0% — 50% credit. Credits are applied to the next billing cycle and do not exceed the monthly subscription fee.\n\n[3] Pricing and Plans Overview (chunk 1): Enterprise plans include dedicated infrastructure, custom data residency, SSO/SCIM provisioning, and a named account manager. SLA-backed uptime guarantees apply to all production endpoints.\n\nQuestion: What are the SLA guarantees for the enterprise tier?" - } + "type": "cost.record", + "dt": 2, + "payload": { + "total_tokens": 4516, + "total_cost_usd": 0.027, + "model": "gpt-4o + text-embedding-3-large + cohere-rerank-3", + "agents_invoked": [ + "knowledge-retriever", + "relevance-reranker", + "answer-synthesizer" ], - "llm.response": { - "role": "assistant", - "content": "The enterprise tier includes the following SLA guarantees:\n\n**Uptime**: 99.95% uptime SLA with automated credits [1].\n\n**Incident Response Times** [1]:\n- P1 (Critical): 15 minutes\n- P2 (High): 1 hour\n- P3 (Medium): 4 hours\n\n**SLA Credit Schedule** [2]:\n- 99.9%--99.95% uptime: 10% credit\n- 99.0%--99.9% uptime: 25% credit\n- Below 99.0% uptime: 50% credit\n\nCredits are applied to the next billing cycle and capped at the monthly subscription fee. Scheduled maintenance windows (announced 72 hours ahead) are excluded from uptime calculations [1].\n\nAdditionally, the SLA covers all production endpoints and includes dedicated infrastructure [3]." - }, - "llm.token_usage": { - "prompt_tokens": 312, - "completion_tokens": 168, - "total_tokens": 480 - }, - "llm.latency_ms": 1586 + "resolution": "Multi-part SLA question answered with full per-severity table, business-hours/after-hours distinction, severity reclassification policy, and three-source citation - fully grounded in retrieved context with no fabrication" } } ] -} +} \ No newline at end of file diff --git a/samples/data/traces/simple_llm_trace.json b/samples/data/traces/simple_llm_trace.json index b57ff342..2326b022 100644 --- a/samples/data/traces/simple_llm_trace.json +++ b/samples/data/traces/simple_llm_trace.json @@ -1,48 +1,90 @@ { - "trace_id": "tr_00001", - "timestamp": "2026-03-13T10:15:32.441Z", - "framework": "openai", + "trace_id": "tr-scn-simple-llm-001", "agent_name": "support-assistant", - "status": "ok", - "duration_ms": 1237, + "framework": "openai", + "status": "completed", + "description": "Single LLM call - baseline scenario for quickstart tutorials and first-trace upload", + "event_count": 5, "metadata": { - "sdk_version": "1.3.0", - "environment": "development", - "seed": 42 + "synthetic": true, + "agents": [ + "support-assistant" + ], + "scenario_type": "single_agent_llm", + "evaluation_dimensions": [ + "helpfulness", + "instruction_following", + "factual_accuracy" + ] }, + "start_time": "2026-04-14T08:00:00Z", + "end_time": "2026-04-14T08:00:01.180Z", + "duration_ms": 1180, "events": [ { - "span_id": "sp_00001_001", - "span_name": "llm_call", - "span_type": "llm", - "start_time": "2026-03-13T10:15:32.441Z", - "end_time": "2026-03-13T10:15:33.678Z", - "status": "ok", - "attributes": { - "llm.model": "gpt-5.3", - "llm.temperature": 0.7, - "llm.max_tokens": 512, - "llm.prompt": [ - { - "role": "system", - "content": "You are a helpful customer support assistant for an enterprise SaaS platform. Be concise and professional." - }, - { - "role": "user", - "content": "How do I reset my API key?" - } + "type": "environment.config", + "dt": 0, + "payload": { + "agent_name": "orchestrator", + "config": { + "session_type": "single-agent", + "framework": "openai", + "model_default": "gpt-4o", + "sdk_version": "1.6.2", + "environment": "production", + "agents_available": [ + "support-assistant" + ] + } + } + }, + { + "type": "agent.input", + "dt": 3, + "payload": { + "agent_name": "support-assistant", + "input": "Our production integration started returning 401 about 20 minutes ago. I think someone on the team rotated the API key in your dashboard but I cannot find who. We need to get back online before the 9am sales demo. How do I issue a new key and what happens to the old one?", + "customer_id": "cust-prod-7714", + "ticket_id": "TKT-2026-0414-1881", + "channel": "support_chat", + "priority": "P1" + } + }, + { + "type": "model.invoke", + "dt": 4, + "payload": { + "agent_name": "support-assistant", + "model": "gpt-4o", + "provider": "openai", + "prompt_tokens": 284, + "completion_tokens": 312, + "duration_ms": 1180, + "purpose": "compose actionable customer-support response with grace-period guidance" + } + }, + { + "type": "agent.output", + "dt": 2, + "payload": { + "agent_name": "support-assistant", + "output": "Sorry about the production interruption - here is the fastest path. (1) Sign in at https://app.example.com using SSO; if SSO is the blocker, request a magic link from the login page. (2) Open Settings -> API Keys. You will see who rotated the key and when, in the audit column. (3) Click 'Generate new key' - the new key is shown once, copy it immediately into your secret store. (4) The previous key (the one that was rotated 20 minutes ago) is in a 24-hour grace period: it still authenticates but emits a 'X-Deprecated-Key' response header so your monitoring can verify the cutover. So if your integration is failing with 401 right now, it is more likely the key was fully revoked rather than rotated. In Settings -> Audit Log, look for 'api_key.revoked' in the last hour - if you see one, that explains the 401s and the new key is the only path forward. For the 9am demo: once the new key is in your secret store, your integration should reconnect within 60 seconds. If you need a temporary read-only key while your team coordinates the rotation, reply with the demo timestamp and I can issue one bound to your team's IP allowlist.", + "resolution_offered": true, + "escalation_path": "temporary_read_only_key_available" + } + }, + { + "type": "cost.record", + "dt": 2, + "payload": { + "total_tokens": 596, + "total_cost_usd": 0.0061, + "model": "gpt-4o", + "agents_invoked": [ + "support-assistant" ], - "llm.response": { - "role": "assistant", - "content": "To reset your API key, follow these steps:\n\n1. Navigate to **Settings > API Keys** in the dashboard.\n2. Click the key you want to reset.\n3. Select **Regenerate Key** and confirm the action.\n4. Copy the new key immediately -- it will only be shown once.\n\nNote: Any integrations using the old key will stop working. Update them with the new key before the old one expires (24-hour grace period)." - }, - "llm.token_usage": { - "prompt_tokens": 47, - "completion_tokens": 93, - "total_tokens": 140 - }, - "llm.latency_ms": 1192 + "resolution": "Customer received audit-log diagnostic, rotation-vs-revocation distinction, grace-period explanation, and an offered fallback path for the 9am demo deadline" } } ] -} +} \ No newline at end of file