Show HN: Snowglobe: a whole distributed system's OpenTelemetry, from one binary

ImmersiveFusion released Snowglobe, an Apache-2.0 open-source tool that generates synthetic OpenTelemetry data—logs, traces, and metrics—from a single binary simulating up to 28 services, including AI agentic traces under OTel GenAI semantic conventions. The tool, available as a 6.4 MB container, aims to help developers test observability backends without complex setups, and is positioned as a maintained alternative to existing generators like telemetrygen.

A sealed world you can shake. One binary that generates a whole distributed system's telemetry: logs, traces and metrics from a topology of up to 28 services with the shapes real systems actually make. The default -complexity normal runs 20 traditional services; -complexity heavy adds the 8 AI services for 28. Diamond dependencies, scatter-gather fan-out, sagas that compensate, timeouts that cascade. Point it at anything that speaks OTLP and watch it arrive. No Docker Compose. No microservices to deploy. No 6GB stack to babysit. One executable, or a 6.4 MB container. docker run --rm immersivefusion/snowglobe -insecure -endpoint host.docker.internal:4317 A public good for the OpenTelemetry community. Apache-2.0, no account, no sign-up, no lock-in. The output is plain OTLP, so it works in whatever you already run: Jaeger, Tempo, Grafana, SigNoz, an OpenTelemetry Collector, or spatial tools such as DeepCube, which are one consumer among many. How this differs from telemetrygen. telemetrygen generates spans, and it is the official tool and good at that. Snowglobe generates a system : a topology with real dependency shapes, injectable failures, and AI agent spans under the OTel GenAI semantic conventions. If you need load, use telemetrygen . If you need something that looks like a production incident, that is what this is for.Most trace generators die. Their repos go quiet, the SDKs move, and the thing stops building. This one is maintained, and that is the point of it. It has a sibling. Snowglobe makes realistic telemetry out of nothing: synthetic OTel. sos-beacon https://github.com/ImmersiveFusion/sos-beacon is the organic counterpart, a real workload doing a real job that emits real telemetry as a byproduct. Between them you can fill a backend with either kind. Every existing trace generator falls into one of two categories: 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 And 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. This 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. The AI services and their GenAI spans are in the heavy tier, so run -complexity heavy to get them; the default emits traditional traffic only. Download the latest release or build from source go install github.com/ImmersiveFusion/snowglobe/cmd/snowglobe@latest Send to a local OTLP collector Jaeger, Tempo, etc. snowglobe -insecure Send to a remote endpoint with auth headers snowglobe -endpoint your-otlp-endpoint:443 -headers "api-key=YOUR KEY" Or set headers via the standard OTel environment variable export OTEL EXPORTER OTLP HEADERS="api-key=YOUR KEY" snowglobe -endpoint your-otlp-endpoint:443 Installed from an old path? Releases up to v0.7.7 were published as github.com/ImmersiveFusion/if-opentelemetry-tracegen , and releases up to v0.8.0 as github.com/ImmersiveFusion/opentelemetry-tracegen . Pinned installs of those versions keep working, but @latest on either old path stops resolving from the next release onward, so update your command to the path above. The installed binary is now named snowglobe rather than tracegen . See it in 3D- Send traces to DeepCube snowglobe -endpoint otlp.deepcube.ai:443 -headers "api-key=YOUR KEY" , how to get a key 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 Shoebox at shoebox.deepcube.ai - paste a diagram of a system, break a call in it, and fire one request through. Seven always-on demo grids stream live OpenTelemetry traces, logs and metrics into DeepCube'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.deepcube.ai:443 . Just want to look? The grids are streamed live on Twitch at twitch.tv/deepcubelive https://www.twitch.tv/deepcubelive , around the clock. No account, no install, nothing to sign up for. It is running whether or not you do anything, which is the easiest way to see what this generator's output actually looks like at the other end. See them in 3D: the full experience is DeepCube https://docs.deepcube.ai/DC/3D/ , the immersive 3D client: install it and open a grid to walk the live traces. On mobile or can't install right now? DeepCube Web https://docs.deepcube.ai/DC/Web/ runs the same grids in your browser at portal.deepcube.ai https://portal.deepcube.ai . Where else does Snowglobe run? : a community board of deployments. Add yours. | Service | Pods | Role | |---|---|---| | web-frontend | 2 | Browser client, SPA | | api-gateway | 3 | HTTP routing, auth | | order-service | 3 | Order lifecycle | | payment-service | 2 | Stripe integration | | inventory-service | 2 | Stock management | | notification-service | 2 | Event-driven notifications | | user-service | 2 | Auth, profiles | | cache-service | 3 | Redis cluster | | search-service | 2 | Elasticsearch queries | | scheduler-service | 1 | Cron jobs singleton | | auth-service | 3 | JWT, webhook verification | | recommendation-service | 2 | ML-based recommendations | | cart-service | 2 | Shopping cart | | product-service | 3 | Product catalog | | shipping-service | 2 | Rates, labels, tracking | | fraud-service | 2 | ML fraud scoring | | email-service | 2 | SMTP relay SendGrid | | tax-service | 1 | Tax calculation | | analytics-service | 3 | Event tracking Kafka | | config-service | 1 | Feature flags | | Service | Pods | Role | |---|---|---| | llm-gateway | 3 | OpenAI API routing, token tracking | | embedding-service | 2 | Text-to-vector operations | | vector-db-service | 2 | Qdrant similarity search | | ai-agent-service | 2 | Agent orchestration plan/act/reflect | | content-moderation-service | 2 | Safety classifiers, PII detection | | model-registry-service | 1 | Model versioning singleton | | feature-store-service | 2 | ML feature serving | | data-pipeline-service | 2 | Batch embedding, retraining | All 59 pods are distributed across 5 AKS VMSS nodes 2 node pools with realistic service.instance.id and host.name resource attributes. | Scenario | Graph Shape | Key Pattern | |---|---|---| Create Order | Long chain 8 services, 14+ spans | Producer/consumer with queue delays | Search & Browse | Linear with cache | Elasticsearch + Redis | User Login | Branching success/failure | Auth with session creation | Failed Payment | Error chain | Stripe 402 + error propagation | Bulk Notifications | Fan-out 3-5 parallel | Batch email processing | Health Check | Star topology 6 parallel | Concurrent health pings | Inventory Sync | Fan-out + reindex | Parallel cache warming | Scheduled Report | Headless chain no UI | Cron job entry point | Stripe Webhook | Headless chain no gateway | External callback entry | Recommendations | Scatter-gather / bowtie | Fan-out to 3, gather, cache | Add to Cart | Cross-service with feature flags | Config service + analytics | Full Checkout | Monster chain 15 services | Tax+shipping parallel, fraud ML | Shipping Update | Carrier webhook headless | External webhook + email relay | Saga Compensation | Forward chain + 4-way compensation fan-out | Payment retries + rollback | Timeout Cascade | Branching with circuit breaker | Stale cache fallback | User Registration | Linear with async branch | Email verification token, duplicate detection | Product Review | Write + async moderation | Optimistic write + background processing | Return/Refund | Parallel reverse flow 16-18 spans | Parallel refund + restock, reverse money flow | Wishlist + Price Alert | Write-through with async | Write-through cache, async price monitoring | Coupon Application | Validation chain | Cart recalculation, validation branch | Gift Card Purchase | Payment splitting | Balance check, payment splitting | Subscription Management | Webhook-driven lifecycle | Stripe subscription, renewal webhook | A/B Test Exposure | Feature flag branch | Variant assignment, sticky session | Rate Limiting | Early termination 4-6 spans | Redis sliding window, 429 response | Admin Product CRUD | Write-amplification fan-out | Cache + search reindex on write | Order History | Paginated read | Keyset pagination, cursor-based | Support Ticket | Cross-domain trace | SLA assignment, team routing | Multi-Currency Checkout | External API chain | FX rate API, cache hit ratio | | Scenario | Graph Shape | Key Pattern | |---|---|---| Semantic Search RAG | Linear with 2 LLM calls 14-16 spans | Embedding + vector search + LLM reranking | AI Chatbot with Tool Use | Double bowtie 18-22 spans | Plan - fan-out tool calls - synthesize | AI Content Moderation | Parallel classifiers + 3-way branch 12-16 spans | Safety/spam scoring, guardrail decisions | Multi-Step Agent | Iterative loop 28-40 spans | Plan - act - reflect cycle 3-5 iterations | AI Customer Support | Branching with escalation 16-20 spans | Sentiment classification, intent detection | AI Content Generation | Linear with safety filter 12-15 spans | Temperature-controlled generation, content safety | Embedding Pipeline | High fan-out batch 25-40 spans | Batch chunking, parallel embedding, vector upsert | Dynamic Pricing Agent | Headless agent 14-18 spans | Feature store lookup, autonomous price updates | Fraud with Explainability | Linear with LLM explanation 10-12 spans | SHAP-style feature attribution via LLM | Inventory Reorder Agent | Autonomous agent 16-20 spans | Demand forecast, autonomous purchase orders | Model Retraining Pipeline | Batch pipeline 14-18 spans | ML training spans, model registry, quality gate | Conversational Commerce | Multi-turn session 10-14 spans/turn | Growing context tokens, session continuity | Note:Failed Payment, Saga Compensation, Timeout Cascade, lost messages, and retry storms only activate when -errors 0 . AI error scenarios rate limits, hallucinated tool calls, token budget exceeded, content filter blocks also require -errors 0 . Every 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. 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 Disable with -no-logs to emit traces only. Every service emits OTel metrics via OTLP alongside traces and logs. They are derived from the spans the scenarios already produce , so metrics and traces agree by construction: the p99 in a histogram is the same span you can click into. Metrics aggregate in process and flush on an interval, so unlike traces their volume is decoupled from -level . A -level 10 run emits roughly 350 traces/second and the same metric load as -level 1 . | Metric | Type | What it shows | |---|---|---| http.server.request.duration | Histogram s | RED metrics per route, method and status. Semconv names, seconds, spec bucket boundaries | http.server.active requests | UpDownCounter | In-flight requests, rising and falling with real scenario concurrency | system.cpu.utilization | Gauge | Driven by each service's actual span volume, not a random walk | system.memory.usage | UpDownCounter | Resident memory, drifting with load. Carries system.memory.state | db.client.connection.count | UpDownCounter | Pool occupancy split used / idle , tracking real database spans | tracegen.messaging.queue.depth | Gauge | Published minus consumed, per destination | gen ai.client.token.usage | Histogram | Input and output tokens by model, system and operation | gen ai.client.operation.duration | Histogram s | LLM call latency | The queue depth is the one to watch. Producer spans increment it and consumer spans decrement it, so running with -no-consumers makes the backlog climb without bound while the trace graph still looks busy. That is a story traces alone can only imply. Naming. Where OpenTelemetry defines a metric, Snowglobe uses its name and conforms to its contract, including seconds as the unit. Everything else lives under a tracegen. prefix rather than extending a semconv namespace. That prefix keeps the old name on purpose: a metric name is a wire contract, and renaming it to snowglobe. would break every dashboard and alert already keyed on it. Do not sweep it. Cardinality. service.instance.id is deliberately off by default: it multiplies every series by the pod count up to 59 at -complexity heavy , and metric backends commonly bill per active series with no cardinality control on an OTLP push path. Turn it on with -metrics-instance-id when per-pod resolution is the thing you are demonstrating. Metric attributes are an explicit allowlist, never a copy of the span's attributes, so user, session and order ids never reach a metric. Temporality. -metrics-temporality delta switches from the default cumulative. Prometheus-backed stores are cumulative-native, while some OTLP ingest paths ask producers for delta, and which behavior a given endpoint implements is often undocumented. A producer that can emit either shape on demand is the cheapest way to settle that by experiment. Disable with -no-metrics to emit traces and logs only. All 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/agent-framework/agents/observability . Span types: | Span Name Pattern | SpanKind | Example | |---|---|---| chat {model} | CLIENT | chat gpt-4o | embedding {model} | CLIENT | embedding text-embedding-3-small | invoke agent {name} | CLIENT | invoke agent CustomerSupportAgent | execute tool {name} | INTERNAL | execute tool get order status | {operation} {collection} | CLIENT | query product-embeddings | Attributes on every LLM span: gen ai.provider.name - LLM provider e.g., openai gen ai.request.model / gen ai.response.model - model requested and used gen ai.usage.input tokens / gen ai.usage.output tokens - token consumption gen ai.response.finish reasons - completion reason stop , tool calls , length , content filter gen ai.response.id - unique response identifier gen ai.request.temperature , gen ai.request.max tokens - request parameters Agent-specific attributes: gen ai.agent.id / gen ai.agent.name / gen ai.agent.description - agent identity gen ai.conversation.id - session linking for multi-turn interactions gen ai.tool.name / gen ai.tool.type / gen ai.tool.call.id - tool call tracking gen ai.data source.id - RAG data source identifier gen ai.request.embedding.dimensions - embedding dimensions These attributes match what every LLM observability tool on the market tracks - enabling direct comparison of visualization capabilities. | Feature | Description | |---|---| Lost messages | 5% chance per queue hop that the consumer never fires - trace ends abruptly | Dead consumer mode | -no-consumers flag: producers fire, consumers never pick up | Retry storms | Payment retries 3x with exponential backoff before saga compensation | Timeout cascades | Search service times out, gateway returns 504, circuit breaker serves stale cache | Saga compensation | Payment fails after order+inventory committed - triggers 4-way parallel rollback | LLM rate limits | OpenAI 429 with token budget details, fallback to text search | Hallucinated tool calls | Agent requests non-existent tool, triggers error handling | Token budget exceeded | Agent exceeds iteration token limit, graceful degradation | Content filter blocks | Safety classifier blocks content, alternate flow triggered | Tunable error rate | -errors 0 none to -errors 10 chaos with realistic .NET stack traces | The generated traces simulate a .NET-based e-commerce platform with AI capabilities. Stack traces and library names reflect the .NET ecosystem by design. 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 snowglobe flags Flags: -endpoint string OTLP gRPC endpoint host:port default "localhost:4317" -headers string OTLP headers as key=value pairs, comma-separated or set OTEL EXPORTER OTLP HEADERS -complexity string Topology complexity: light, normal, heavy default "normal" -level int Aggressiveness 1-10 default 1 -errors int Error rate 0-10 default 0 -no-consumers Disable all async consumers -no-ai-backends Disable LLM/AI backends AI spans emit errors -ai-only Only run AI agentic scenarios -no-logs Disable OTel log record emission traces only -no-metrics Disable OTel metric emission -metrics-interval Metric export interval default 15s -metrics-temporality Metric aggregation temporality: cumulative or delta default cumulative -metrics-instance-id Add service.instance.id to metrics per-pod resolution; multiplies series count -metrics-verify Emit tracegen.spans.emitted, the emission oracle -insecure Use plaintext gRPC no TLS for local backends -log-level string Console verbosity: silent, error, info, debug default "info" -quiet Errors only alias for -log-level=error ; silences the periodic "traces sent" heartbeat Console verbosity. silent / error suppress 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 env var handy for containers . Precedence: -quiet -log-level TRACEGEN LOG LEVEL default info . The published container image defaults to TRACEGEN LOG LEVEL=error so it doesn't spam logs; the bare CLI stays info . Override with -e TRACEGEN LOG LEVEL=info or -log-level / -quiet . | Complexity | Services | Pods | Scenarios | Best for | |---|---|---|---|---| light | 10 core | ~20 min replicas | 6 | Clean demos, small graphs | normal | 20 traditional | ~40 | 16 | General testing, full e-commerce | heavy | 28 + AI | 59 | 20 of 40 defined | Full topology with AI agentic flows | 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 . Normal default adds all remaining traditional services and scenarios including chaos/failure modes. Heavy adds all 8 AI services and 4 of the 12 AI agentic scenarios RAG Search, AI Chatbot, Content Moderation, Multi-Step Agent . | Level | Label | Rate | |---|---|---| | 1 | whisper | ~2 traces/s | | 2 | gentle | ~3 traces/s | | 3 | calm | ~3 traces/s | | 4 | moderate | ~5 traces/s | | 5 | steady | ~7 traces/s | | 6 | brisk | ~15 traces/s | | 7 | aggressive | ~21 traces/s | | 8 | intense | ~40 traces/s | | 9 | firehose | ~83 traces/s | | 10 | SCREAM | ~350 traces/s | Send to a local Jaeger/Tempo/Collector default endpoint localhost:4317 snowglobe -insecure Clean demo with minimal services - great for presentations snowglobe -complexity light -level 1 -insecure Full e-commerce topology default snowglobe -level 1 -insecure Everything including AI agentic scenarios snowglobe -complexity heavy -level 3 -insecure Moderate load with normal error rates snowglobe -level 5 -errors 5 -insecure Simulate dead consumers messages pile up, consumers never fire snowglobe -level 3 -no-consumers -insecure AI scenarios only - great for LLM observability testing snowglobe -level 3 -ai-only -insecure Simulate AI backend outage LLM rate limits, timeouts snowglobe -level 5 -no-ai-backends -errors 5 -insecure Chaos mode - maximum load and errors snowglobe -level 10 -errors 10 -insecure Send to a remote endpoint with authentication snowglobe -endpoint otlp.example.com:443 -headers "api-key=YOUR KEY" Multiple headers via environment variable export OTEL EXPORTER OTLP HEADERS="api-key=SECRET,x-team=platform" snowglobe -endpoint otlp.example.com:443 Send to DeepCube 3D trace visualization snowglobe -endpoint otlp.deepcube.ai:443 -headers "API-Key=YOUR DEEPCUBE KEY" | Capability | Snowglobe | OTel telemetrygen | OTel Astronomy Shop | Jaeger HotROD | k6 + xk6-tracing | |---|---|---|---|---|---| | Single binary, zero infra | Yes | 1 binary | 15+ containers, ~6GB | 4 containers | k6 + extension | | Services | 28 | 1 | ~22 | 4 | User-defined | | Pod instances | 59 | 0 | 1/svc | 0 | 0 | | Scenario flows | 40 | 0 | ~10 | 1 | User-defined | | AI agentic scenarios | 12 | No | No | No | No | | OTel GenAI conventions | Yes | No | No | No | No | | Agent tool call traces | Yes | No | No | No | No | | RAG pipeline traces | Yes | No | No | No | No | | Diamond dependencies | Yes | No | Implicit | No | No | | Scatter-gather | Yes | No | No | No | No | | Lost messages | Yes | No | No | No | No | | Dead consumer mode | Yes | No | No | No | No | | Saga compensation | Yes | No | No | No | No | | Retry storms | Yes | No | No | No | No | | Timeout cascade | Yes | No | No | No | No | | LLM failure injection | Yes | No | No | No | No | | Tunable error rate | 0-10 | No | Fixed | No | No | | Tunable throughput | 2-350/s | Rate flag | Locust | Fixed | k6 VUs | | Headless flows webhook/cron | 3 | No | No | No | No | | Startup time | <1s | <1s | 3-5 min | 30s | <5s | Works with any OTLP gRPC-compatible backend: - Any OpenTelemetry Collector - Datadog with OTLP endpoint - DeepCube 3D visualization - Elastic APM - Grafana Tempo - Honeycomb - Jaeger - New Relic - Splunk Observability Listed alphabetically on purpose. DeepCube is one consumer of this tool among many, and the tool is not built to favour it. The AI agentic traces are also compatible with LLM-specialized observability tools that accept OTel input: - Langfuse OTel-native since SDK v3 - Arize Phoenix OTel instrumentation - Traceloop / OpenLLMetry built on OTel Part of a small family of single-binary, zero-infra OTel tools, all Apache-2.0 and all usable without any Immersive Fusion account: - The sos-beacon https://github.com/ImmersiveFusion/sos-beacon organic counterpart to this tool's synthetic output. A real workload doing a real job surfacing people asking for help in public forums, for a human to answer , OTel-instrumented, so it emits genuine production telemetry as a byproduct. Where Snowglobe invents a system, sos-beacon is one.- The world you build, where Snowglobe is the world pre-made. Paste a Mermaid diagram of a system, break something in it, fire one request, and read what comes out. A snowglobe is sealed; in a shoebox you can open it up. Shoebox https://github.com/ImmersiveFusion/shoebox See both in 3D https://shoebox.deepcube.ai . git clone https://github.com/ImmersiveFusion/snowglobe.git cd snowglobe go build -o snowglobe ./cmd/snowglobe Linux GOOS=linux GOARCH=amd64 go build -o snowglobe ./cmd/snowglobe macOS Apple Silicon GOOS=darwin GOARCH=arm64 go build -o snowglobe ./cmd/snowglobe Windows GOOS=windows GOARCH=amd64 go build -o snowglobe.exe ./cmd/snowglobe The 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. This 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. Microsoft'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} , chat {model} , and execute tool {function} . 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. The 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. | Source | Decision Informed | |---|---| | 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. attribute list with types MS Semantic Kernel Observability https://learn.microsoft.com/en-us/semantic-kernel/concepts/enterprise-readiness/observability/ gen ai attribute usage MS Agent Framework Observability https://learn.microsoft.com/en-us/agent-framework/agents/observability invoke agent , chat , execute tool Langfuse OTel Integration https://langfuse.com/integrations/native/opentelemetry Traceloop OpenLLMetry https://github.com/traceloop/openllmetry Email mailto:info@immersivefusion.com | LinkedIn https://www.linkedin.com/company/immersivefusion | Discord https://discord.gg/zevywnQp6K | GitHub https://github.com/immersivefusion | Bluesky https://bsky.app/profile/immersivefusion.bsky.social | Twitter/X https://twitter.com/immersivefusion | YouTube https://www.youtube.com/@immersivefusion | Twitch https://www.twitch.tv/immersivefusion Apache License 2.0 - see LICENSE /ImmersiveFusion/snowglobe/blob/main/LICENSE for details. Copyright 2026 ImmersiveFusion https://immersivefusion.com