{"slug": "show-hn-tracegen-realistic-opentelemetry-traces-incl-ai-agent-one-binary", "title": "Show HN: TraceGen – realistic OpenTelemetry traces, incl. AI-agent, one binary", "summary": "Immersive Fusion released TraceGen, a single-binary distributed trace generator that produces realistic OpenTelemetry traces and correlated logs for up to 28 services, including AI agentic scenarios with full OTel GenAI semantic conventions. The tool, available as a standalone executable or 5.7 MB container, aims to fill a gap in the LLM observability market by combining traditional APM with AI agent tracing, enabling testing of observability platforms and load testing of trace pipelines without complex infrastructure.", "body_md": "A single-binary distributed trace generator that produces realistic, topology-rich OTLP traces and correlated logs. No Docker Compose, no microservices to deploy, no infrastructure - just one executable (or a 5.7 MB container) that simulates a full e-commerce platform with up to 28 services, dozens of pods that scale with the topology, and 40 scenario flows - including AI agentic scenarios with full OTel GenAI semantic conventions. Three complexity levels (light/normal/heavy) let you scale from a clean 10-service demo to the full topology with AI.\n\nBuilt for testing observability platforms, load testing trace pipelines, and showcasing distributed system visualizations - for both traditional APM and LLM observability.\n\nEvery existing trace generator falls into one of two categories:\n\n**Flat span generators**(telemetrygen, tracepusher) - produce uniform, identical spans with no service topology** Full demo apps**(OTel Astronomy Shop, Jaeger HotROD) - require Docker Compose with 15+ containers and ~6GB RAM\n\nAnd none of them generate **AI agentic traces**. The LLM observability market has no standalone tool that combines traditional APM with LLM observability. Every specialized LLM tool (Langfuse, LangSmith, Helicone, Arize, Traceloop, Portkey, Galileo) tracks token usage, model costs, and agent tool calls - but none of them provide traditional distributed tracing.\n\nThis tool generates **topology-rich, failure-injectable traces from a single binary** - covering both traditional microservice flows AND AI agentic patterns with OTel GenAI semantic conventions. One binary proves that a platform can visualize both.\n\n```\n# Download the latest release (or build from source)\ngo install github.com/ImmersiveFusion/if-opentelemetry-tracegen/cmd/tracegen@latest\n\n# Send to a local OTLP collector (Jaeger, Tempo, etc.)\ntracegen -insecure\n\n# Send to a remote endpoint with auth headers\ntracegen -endpoint your-otlp-endpoint:443 -headers \"api-key=YOUR_KEY\"\n\n# Or set headers via the standard OTel environment variable\nexport OTEL_EXPORTER_OTLP_HEADERS=\"api-key=YOUR_KEY\"\ntracegen -endpoint your-otlp-endpoint:443\n```\n\nSee it in 3D- Send traces to[IAPM](`tracegen -endpoint otlp.iapm.app:443 -headers \"api-key=YOUR_KEY\"`\n\n) to explore them as a 3D force-directed graph, drill into conventional trace waterfalls for detailed analysis, and get AI-assisted insights from[Tessa]. For a ready-made example without any setup, try the[OpenTelemetry Chaos Simulator]at[demo.iapm.app]- a fully interactive sandbox with visual failure injection.\n\nSeven always-on demo grids stream live OpenTelemetry traces into IAPM's 3D player right now — a clean baseline, an AI-native app, a blended environment, phantom-service detection, an AI-outage, and a full incident. Each grid is this container, deployed declaratively via GitOps (Argo CD) in the Immersive Fusion cloud — multi-arch and distroless, one matrix row per grid, shipping to `otlp.iapm.app:443`\n\n.\n\n**See them in 3D:** the full experience is the **IAPM 3D client** — install it and open a grid to walk the live traces. On mobile or can't install right now? **IAPM Web** runs the same grids in your browser at [portal.iapm.app](https://portal.iapm.app).\n\n** Where else does TraceGen run? →** — a community board of deployments. Add yours.\n\n| Service | Pods | Role |\n|---|---|---|\n| web-frontend | 2 | Browser client, SPA |\n| api-gateway | 3 | HTTP routing, auth |\n| order-service | 3 | Order lifecycle |\n| payment-service | 2 | Stripe integration |\n| inventory-service | 2 | Stock management |\n| notification-service | 2 | Event-driven notifications |\n| user-service | 2 | Auth, profiles |\n| cache-service | 3 | Redis cluster |\n| search-service | 2 | Elasticsearch queries |\n| scheduler-service | 1 | Cron jobs (singleton) |\n| auth-service | 3 | JWT, webhook verification |\n| recommendation-service | 2 | ML-based recommendations |\n| cart-service | 2 | Shopping cart |\n| product-service | 3 | Product catalog |\n| shipping-service | 2 | Rates, labels, tracking |\n| fraud-service | 2 | ML fraud scoring |\n| email-service | 2 | SMTP relay (SendGrid) |\n| tax-service | 1 | Tax calculation |\n| analytics-service | 3 | Event tracking (Kafka) |\n| config-service | 1 | Feature flags |\n\n| Service | Pods | Role |\n|---|---|---|\n| llm-gateway | 3 | OpenAI API routing, token tracking |\n| embedding-service | 2 | Text-to-vector operations |\n| vector-db-service | 2 | Qdrant similarity search |\n| ai-agent-service | 2 | Agent orchestration (plan/act/reflect) |\n| content-moderation-service | 2 | Safety classifiers, PII detection |\n| model-registry-service | 1 | Model versioning (singleton) |\n| feature-store-service | 2 | ML feature serving |\n| data-pipeline-service | 2 | Batch embedding, retraining |\n\nAll 59 pods are distributed across 5 AKS VMSS nodes (2 node pools) with realistic `service.instance.id`\n\nand `host.name`\n\nresource attributes.\n\n| Scenario | Graph Shape | Key Pattern |\n|---|---|---|\nCreate Order |\nLong chain (8 services, 14+ spans) | Producer/consumer with queue delays |\nSearch & Browse |\nLinear with cache | Elasticsearch + Redis |\nUser Login |\nBranching (success/failure) | Auth with session creation |\nFailed Payment |\nError chain | Stripe 402 + error propagation |\nBulk Notifications |\nFan-out (3-5 parallel) | Batch email processing |\nHealth Check |\nStar topology (6 parallel) | Concurrent health pings |\nInventory Sync |\nFan-out + reindex | Parallel cache warming |\nScheduled Report |\nHeadless chain (no UI) | Cron job entry point |\nStripe Webhook |\nHeadless chain (no gateway) | External callback entry |\nRecommendations |\nScatter-gather / bowtie | Fan-out to 3, gather, cache |\nAdd to Cart |\nCross-service with feature flags | Config service + analytics |\nFull Checkout |\nMonster chain (15 services) | Tax+shipping parallel, fraud ML |\nShipping Update |\nCarrier webhook (headless) | External webhook + email relay |\nSaga Compensation |\nForward chain + 4-way compensation fan-out | Payment retries + rollback |\nTimeout Cascade |\nBranching with circuit breaker | Stale cache fallback |\nUser Registration |\nLinear with async branch | Email verification token, duplicate detection |\nProduct Review |\nWrite + async moderation | Optimistic write + background processing |\nReturn/Refund |\nParallel reverse flow (16-18 spans) | Parallel refund + restock, reverse money flow |\nWishlist + Price Alert |\nWrite-through with async | Write-through cache, async price monitoring |\nCoupon Application |\nValidation chain | Cart recalculation, validation branch |\nGift Card Purchase |\nPayment splitting | Balance check, payment splitting |\nSubscription Management |\nWebhook-driven lifecycle | Stripe subscription, renewal webhook |\nA/B Test Exposure |\nFeature flag branch | Variant assignment, sticky session |\nRate Limiting |\nEarly termination (4-6 spans) | Redis sliding window, 429 response |\nAdmin Product CRUD |\nWrite-amplification fan-out | Cache + search reindex on write |\nOrder History |\nPaginated read | Keyset pagination, cursor-based |\nSupport Ticket |\nCross-domain trace | SLA assignment, team routing |\nMulti-Currency Checkout |\nExternal API chain | FX rate API, cache hit ratio |\n\n| Scenario | Graph Shape | Key Pattern |\n|---|---|---|\nSemantic Search (RAG) |\nLinear with 2 LLM calls (14-16 spans) | Embedding + vector search + LLM reranking |\nAI Chatbot with Tool Use |\nDouble bowtie (18-22 spans) | Plan -> fan-out tool calls -> synthesize |\nAI Content Moderation |\nParallel classifiers + 3-way branch (12-16 spans) | Safety/spam scoring, guardrail decisions |\nMulti-Step Agent |\nIterative loop (28-40 spans) | Plan -> act -> reflect cycle (3-5 iterations) |\nAI Customer Support |\nBranching with escalation (16-20 spans) | Sentiment classification, intent detection |\nAI Content Generation |\nLinear with safety filter (12-15 spans) | Temperature-controlled generation, content safety |\nEmbedding Pipeline |\nHigh fan-out batch (25-40 spans) | Batch chunking, parallel embedding, vector upsert |\nDynamic Pricing Agent |\nHeadless agent (14-18 spans) | Feature store lookup, autonomous price updates |\nFraud with Explainability |\nLinear with LLM explanation (10-12 spans) | SHAP-style feature attribution via LLM |\nInventory Reorder Agent |\nAutonomous agent (16-20 spans) | Demand forecast, autonomous purchase orders |\nModel Retraining Pipeline |\nBatch pipeline (14-18 spans) | ML training spans, model registry, quality gate |\nConversational Commerce |\nMulti-turn session (10-14 spans/turn) | Growing context tokens, session continuity |\n\nNote:Failed Payment, Saga Compensation, Timeout Cascade, lost messages, and retry storms only activate when`-errors > 0`\n\n. AI error scenarios (rate limits, hallucinated tool calls, token budget exceeded, content filter blocks) also require`-errors > 0`\n\n.\n\nEvery service emits OTel log records via OTLP alongside traces. Logs are automatically correlated with the active span context (trace_id, span_id), so your APM platform can link logs to the exact span that produced them.\n\n**ERROR** logs are emitted alongside every exception event (cache failures, DB errors, payment declines, LLM rate limits, agent failures)**WARN** logs fire on auth failures, content moderation flags, payment retries, and LLM fallbacks**INFO** logs cover request entry points, payment processing, fraud analysis, agent invocations, and iteration progress\n\nDisable with `-no-logs`\n\nto emit traces only.\n\nAll AI scenarios emit spans following [OTel GenAI Semantic Conventions](https://opentelemetry.io/docs/specs/semconv/gen-ai/) and matching the exact span shapes produced by [Microsoft Semantic Kernel](https://learn.microsoft.com/en-us/semantic-kernel/concepts/enterprise-readiness/observability/) and [Microsoft Agent Framework](https://learn.microsoft.com/en-us/semantic-kernel/frameworks/agent/agent-observability).\n\n**Span types:**\n\n| Span Name Pattern | SpanKind | Example |\n|---|---|---|\n`chat {model}` |\nCLIENT | `chat gpt-4o` |\n`embedding {model}` |\nCLIENT | `embedding text-embedding-3-small` |\n`invoke_agent {name}` |\nCLIENT | `invoke_agent CustomerSupportAgent` |\n`execute_tool {name}` |\nINTERNAL | `execute_tool get_order_status` |\n`{operation} {collection}` |\nCLIENT | `query product-embeddings` |\n\n**Attributes on every LLM span:**\n\n`gen_ai.system`\n\n- LLM provider (e.g.,`openai`\n\n)`gen_ai.request.model`\n\n/`gen_ai.response.model`\n\n- model requested and used`gen_ai.usage.input_tokens`\n\n/`gen_ai.usage.output_tokens`\n\n- token consumption`gen_ai.response.finish_reasons`\n\n- completion reason (`stop`\n\n,`tool_calls`\n\n,`length`\n\n,`content_filter`\n\n)`gen_ai.response.id`\n\n- unique response identifier`gen_ai.request.temperature`\n\n,`gen_ai.request.max_tokens`\n\n- request parameters\n\n**Agent-specific attributes:**\n\n`gen_ai.agent.id`\n\n/`gen_ai.agent.name`\n\n/`gen_ai.agent.description`\n\n- agent identity`gen_ai.conversation.id`\n\n- session linking for multi-turn interactions`gen_ai.tool.name`\n\n/`gen_ai.tool.type`\n\n/`gen_ai.tool.call.id`\n\n- tool call tracking`gen_ai.data_source.id`\n\n- RAG data source identifier`gen_ai.request.embedding.dimensions`\n\n- embedding dimensions\n\nThese attributes match what every LLM observability tool on the market tracks - enabling direct comparison of visualization capabilities.\n\n| Feature | Description |\n|---|---|\nLost messages |\n5% chance per queue hop that the consumer never fires - trace ends abruptly |\nDead consumer mode |\n`-no-consumers` flag: producers fire, consumers never pick up |\nRetry storms |\nPayment retries 3x with exponential backoff before saga compensation |\nTimeout cascades |\nSearch service times out, gateway returns 504, circuit breaker serves stale cache |\nSaga compensation |\nPayment fails after order+inventory committed - triggers 4-way parallel rollback |\nLLM rate limits |\nOpenAI 429 with token budget details, fallback to text search |\nHallucinated tool calls |\nAgent requests non-existent tool, triggers error handling |\nToken budget exceeded |\nAgent exceeds iteration token limit, graceful degradation |\nContent filter blocks |\nSafety classifier blocks content, alternate flow triggered |\nTunable error rate |\n`-errors 0` (none) to `-errors 10` (chaos) with realistic .NET stack traces |\n\nThe generated traces simulate a .NET-based e-commerce platform with AI capabilities. Stack traces and library names reflect the .NET ecosystem by design.\n\n**Stack traces**: Npgsql, StackExchange.Redis, Stripe SDK, Elasticsearch.Net, System.Net.Http, OpenAI SDK, Qdrant client** Database operations**: PostgreSQL INSERT/SELECT/UPDATE with semantic conventions** Cache operations**: Redis GET/SET/HSET/MSET/DEL with TTL and key attributes** Messaging**: RabbitMQ and Kafka with producer/consumer span kinds and queue delays** External APIs**: Stripe charges, SendGrid email, UPS shipping, OpenAI chat/embeddings** LLM operations**: Chat completions, embeddings, agent tool calls with token tracking** Vector search**: Qdrant similarity search with cosine distance, dimension validation** Agent orchestration**: Plan/act/reflect loops, tool dispatch, session management** Content moderation**: Safety classifiers, PII detection, guardrail enforcement** ML inference**: Fraud detection model scoring with feature counts** Feature flags**: Config service checks that gate behavior\n\n```\ntracegen [flags]\n\nFlags:\n  -endpoint string     OTLP gRPC endpoint host:port (default \"localhost:4317\")\n  -headers string      OTLP headers as key=value pairs, comma-separated (or set OTEL_EXPORTER_OTLP_HEADERS)\n  -complexity string   Topology complexity: light, normal, heavy (default \"normal\")\n  -level int           Aggressiveness 1-10 (default 1)\n  -errors int          Error rate 0-10 (default 0)\n  -no-consumers        Disable all async consumers\n  -no-ai-backends      Disable LLM/AI backends (AI spans emit errors)\n  -ai-only             Only run AI agentic scenarios\n  -no-logs             Disable OTel log record emission (traces only)\n  -insecure            Use plaintext gRPC (no TLS) for local backends\n  -log-level string    Console verbosity: silent, error, info, debug (default \"info\")\n  -quiet               Errors only (alias for -log-level=error); silences the periodic \"traces sent\" heartbeat\n```\n\nConsole verbosity.`silent`\n\n/`error`\n\nsuppress the periodic heartbeat. The startup banner (\"what it's doing\") and genuine errors/fatal exits always print to stderr at any level. You can also set it with the`TRACEGEN_LOG_LEVEL`\n\nenv var (handy for containers). Precedence:`-quiet`\n\n>`-log-level`\n\n>`TRACEGEN_LOG_LEVEL`\n\n> default (`info`\n\n). The published container image defaults to`TRACEGEN_LOG_LEVEL=error`\n\nso it doesn't spam logs; the bare CLI stays`info`\n\n. Override with`-e TRACEGEN_LOG_LEVEL=info`\n\n(or`-log-level`\n\n/`-quiet`\n\n).\n\n| Complexity | Services | Pods | Scenarios | Best for |\n|---|---|---|---|---|\nlight |\n10 core | ~20 (min replicas) | 6 | Clean demos, small graphs |\nnormal |\n20 traditional | ~40 | 16 | General testing, full e-commerce |\nheavy |\n28 (+ AI) | 59 | 20 (of 40 defined) | Full topology with AI agentic flows |\n\n**Light** includes only the e-commerce backbone: web-frontend, api-gateway, order-service, payment-service, inventory-service, user-service, cache-service, auth-service, product-service, and cart-service. Scenarios are limited to the core flows (Create Order, Search & Browse, User Login, Add to Cart, Full Checkout, Health Check).\n\n**Normal** (default) adds all remaining traditional services and scenarios including chaos/failure modes.\n\n**Heavy** adds all 8 AI services and 4 of the 12 AI agentic scenarios (RAG Search, AI Chatbot, Content Moderation, Multi-Step Agent).\n\n| Level | Label | Rate |\n|---|---|---|\n| 1 | whisper | ~2 traces/s |\n| 2 | gentle | ~3 traces/s |\n| 3 | calm | ~3 traces/s |\n| 4 | moderate | ~5 traces/s |\n| 5 | steady | ~7 traces/s |\n| 6 | brisk | ~15 traces/s |\n| 7 | aggressive | ~21 traces/s |\n| 8 | intense | ~40 traces/s |\n| 9 | firehose | ~83 traces/s |\n| 10 | SCREAM | ~350 traces/s |\n\n```\n# Send to a local Jaeger/Tempo/Collector (default endpoint localhost:4317)\ntracegen -insecure\n\n# Clean demo with minimal services - great for presentations\ntracegen -complexity light -level 1 -insecure\n\n# Full e-commerce topology (default)\ntracegen -level 1 -insecure\n\n# Everything including AI agentic scenarios\ntracegen -complexity heavy -level 3 -insecure\n\n# Moderate load with normal error rates\ntracegen -level 5 -errors 5 -insecure\n\n# Simulate dead consumers (messages pile up, consumers never fire)\ntracegen -level 3 -no-consumers -insecure\n\n# AI scenarios only - great for LLM observability testing\ntracegen -level 3 -ai-only -insecure\n\n# Simulate AI backend outage (LLM rate limits, timeouts)\ntracegen -level 5 -no-ai-backends -errors 5 -insecure\n\n# Chaos mode - maximum load and errors\ntracegen -level 10 -errors 10 -insecure\n\n# Send to a remote endpoint with authentication\ntracegen -endpoint otlp.example.com:443 -headers \"api-key=YOUR_KEY\"\n\n# Multiple headers via environment variable\nexport OTEL_EXPORTER_OTLP_HEADERS=\"api-key=SECRET,x-team=platform\"\ntracegen -endpoint otlp.example.com:443\n\n# Send to IAPM (3D trace visualization)\ntracegen -endpoint otlp.iapm.app:443 -headers \"API-Key=YOUR_IAPM_KEY\"\n```\n\n| Capability | tracegen | OTel telemetrygen | OTel Astronomy Shop | Jaeger HotROD | k6 + xk6-tracing |\n|---|---|---|---|---|---|\n| Single binary, zero infra | Yes |\n1 binary | 15+ containers, ~6GB | 4 containers | k6 + extension |\n| Services | 28 |\n1 | ~22 | 4 | User-defined |\n| Pod instances | 59 |\n0 | 1/svc | 0 | 0 |\n| Scenario flows | 40 |\n0 | ~10 | 1 | User-defined |\n| AI agentic scenarios | 12 |\nNo | No | No | No |\n| OTel GenAI conventions | Yes |\nNo | No | No | No |\n| Agent tool call traces | Yes |\nNo | No | No | No |\n| RAG pipeline traces | Yes |\nNo | No | No | No |\n| Diamond dependencies | Yes |\nNo | Implicit | No | No |\n| Scatter-gather | Yes |\nNo | No | No | No |\n| Lost messages | Yes |\nNo | No | No | No |\n| Dead consumer mode | Yes |\nNo | No | No | No |\n| Saga compensation | Yes |\nNo | No | No | No |\n| Retry storms | Yes |\nNo | No | No | No |\n| Timeout cascade | Yes |\nNo | No | No | No |\n| LLM failure injection | Yes |\nNo | No | No | No |\n| Tunable error rate | 0-10 |\nNo | Fixed | No | No |\n| Tunable throughput | 2-350/s |\nRate flag | Locust | Fixed | k6 VUs |\n| Headless flows (webhook/cron) | 3 |\nNo | No | No | No |\n| Startup time | <1s |\n<1s | 3-5 min | 30s | <5s |\n\nWorks with any OTLP gRPC-compatible backend:\n\n[IAPM](https://immersivefusion.com)(3D visualization)- Jaeger\n- Grafana Tempo\n- Honeycomb\n- New Relic\n- Datadog (with OTLP endpoint)\n- Splunk Observability\n- Elastic APM\n- Any OpenTelemetry Collector\n\nThe AI agentic traces are also compatible with LLM-specialized observability tools that accept OTel input:\n\n- Langfuse (OTel-native since SDK v3)\n- Arize Phoenix (OTel instrumentation)\n- Traceloop / OpenLLMetry (built on OTel)\n\n- Interactive chaos engineering sandbox with visual failure injection. Complements tracegen: generate topology-rich traces here, inject chaos there,[OpenTelemetry Chaos Simulator](https://github.com/ImmersiveFusion/if-opentelemetry-chaos-simulator-sample)[visualize both in 3D](https://demo.iapm.app).\n\n```\ngit clone https://github.com/ImmersiveFusion/if-opentelemetry-tracegen.git\ncd if-opentelemetry-tracegen\ngo build -o tracegen ./cmd/tracegen\n# Linux\nGOOS=linux GOARCH=amd64 go build -o tracegen ./cmd/tracegen\n\n# macOS (Apple Silicon)\nGOOS=darwin GOARCH=arm64 go build -o tracegen ./cmd/tracegen\n\n# Windows\nGOOS=windows GOARCH=amd64 go build -o tracegen.exe ./cmd/tracegen\n```\n\nThe LLM observability market is growing rapidly, but every specialized tool focuses exclusively on LLM workloads. No standalone LLM observability tool provides traditional APM capabilities. The only platforms addressing both are legacy APM giants (Datadog, New Relic, Dynatrace) adding LLM features to existing products.\n\nThis trace generator produces both traditional distributed traces AND AI agentic traces from the same binary - proving that a single platform can visualize both. The AI scenarios emit the exact same telemetry signals that Langfuse, LangSmith, Helicone, Arize, Traceloop, Portkey, and Galileo track.\n\nMicrosoft's Semantic Kernel and Agent Framework are the most widely adopted .NET AI frameworks. Their OTel instrumentation emits exactly three span types: `invoke_agent {name}`\n\n, `chat {model}`\n\n, and `execute_tool {function}`\n\n. Our AI scenarios produce traces structurally identical to what a real Semantic Kernel / Agent Framework application would emit - so observability platforms can be tested against realistic .NET AI workloads.\n\nThe [OTel GenAI Semantic Conventions](https://opentelemetry.io/docs/specs/semconv/gen-ai/) are being adopted across the ecosystem. Langfuse SDK v3 is OTel-native, LangSmith added OTel support, Arize Phoenix uses OTel instrumentation, and Traceloop's OpenLLMetry conventions were adopted into the official OTel spec. Building on these conventions ensures the generated traces are compatible with every tool that adopts the standard.\n\n| Source | Decision Informed |\n|---|---|\n|\n\n[OTel GenAI Agent Spans](https://opentelemetry.io/docs/specs/semconv/gen-ai/gen-ai-agent-spans/)[OTel GenAI Attribute Registry](https://opentelemetry.io/docs/specs/semconv/registry/attributes/gen-ai/)`gen_ai.*`\n\nattribute list with types[MS Semantic Kernel Observability](https://learn.microsoft.com/en-us/semantic-kernel/concepts/enterprise-readiness/observability/)`gen_ai`\n\nattribute usage[MS Agent Framework Observability](https://learn.microsoft.com/en-us/semantic-kernel/frameworks/agent/agent-observability)`invoke_agent`\n\n, `chat`\n\n, `execute_tool`\n\n[Langfuse OTel Integration](https://langfuse.com/integrations/native/opentelemetry)[Traceloop OpenLLMetry](https://github.com/traceloop/openllmetry)[Email](mailto:info@immersivefusion.com) |\n[LinkedIn](https://www.linkedin.com/company/immersivefusion) |\n[Discord](https://discord.gg/zevywnQp6K) |\n[GitHub](https://github.com/immersivefusion) |\n[Bluesky](https://bsky.app/profile/immersivefusion.bsky.social) |\n[Twitter/X](https://twitter.com/immersivefusion) |\n[YouTube](https://www.youtube.com/@immersivefusion) |\n[Twitch](https://www.twitch.tv/immersivefusion)\n\n[Try IAPM](https://immersivefusion.com/landing/default) for your own projects.\n\nApache License 2.0 - see [LICENSE](/ImmersiveFusion/if-opentelemetry-tracegen/blob/main/LICENSE) for details.\n\nCopyright 2026 [ImmersiveFusion](https://immersivefusion.com)", "url": "https://wpnews.pro/news/show-hn-tracegen-realistic-opentelemetry-traces-incl-ai-agent-one-binary", "canonical_source": "https://github.com/ImmersiveFusion/if-opentelemetry-tracegen", "published_at": "2026-07-07 15:19:50+00:00", "updated_at": "2026-07-07 15:30:34.226680+00:00", "lang": "en", "topics": ["artificial-intelligence", "large-language-models", "ai-agents", "ai-tools", "developer-tools"], "entities": ["Immersive Fusion", "TraceGen", "OpenTelemetry", "IAPM", "Langfuse", "LangSmith", "Helicone", "Arize"], "alternates": {"html": "https://wpnews.pro/news/show-hn-tracegen-realistic-opentelemetry-traces-incl-ai-agent-one-binary", "markdown": "https://wpnews.pro/news/show-hn-tracegen-realistic-opentelemetry-traces-incl-ai-agent-one-binary.md", "text": "https://wpnews.pro/news/show-hn-tracegen-realistic-opentelemetry-traces-incl-ai-agent-one-binary.txt", "jsonld": "https://wpnews.pro/news/show-hn-tracegen-realistic-opentelemetry-traces-incl-ai-agent-one-binary.jsonld"}}