{"id":50664988,"url":"https://github.com/towlabs/dashfrog","last_synced_at":"2026-06-08T05:31:23.845Z","repository":{"id":317409371,"uuid":"1066967608","full_name":"towlabs/dashfrog","owner":"towlabs","description":"A Grafana your support team can actually use.","archived":false,"fork":false,"pushed_at":"2025-12-10T17:02:37.000Z","size":7720,"stargazers_count":3,"open_issues_count":4,"forks_count":1,"subscribers_count":0,"default_branch":"main","last_synced_at":"2026-01-08T03:15:01.579Z","etag":null,"topics":["b2b","metrics","monitoring","observability","opentelemetry","operations","sla-tracking","status-page"],"latest_commit_sha":null,"homepage":"","language":"TypeScript","has_issues":true,"has_wiki":null,"has_pages":null,"mirror_url":null,"source_name":null,"license":"mit","status":null,"scm":"git","pull_requests_enabled":true,"icon_url":"https://github.com/towlabs.png","metadata":{"files":{"readme":"README.md","changelog":null,"contributing":null,"funding":null,"license":null,"code_of_conduct":null,"threat_model":null,"audit":null,"citation":null,"codeowners":null,"security":null,"support":null,"governance":null,"roadmap":null,"authors":null,"dei":null,"publiccode":null,"codemeta":null,"zenodo":null,"notice":null,"maintainers":null,"copyright":null,"agents":null,"dco":null,"cla":null}},"created_at":"2025-09-30T07:47:42.000Z","updated_at":"2025-12-11T15:43:14.000Z","dependencies_parsed_at":"2025-10-25T00:14:34.743Z","dependency_job_id":"1d22b0ce-1c07-4202-93d9-7bbbfff21faf","html_url":"https://github.com/towlabs/dashfrog","commit_stats":null,"previous_names":["towlabs/dashfrog"],"tags_count":9,"template":false,"template_full_name":null,"purl":"pkg:github/towlabs/dashfrog","repository_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/towlabs%2Fdashfrog","tags_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/towlabs%2Fdashfrog/tags","releases_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/towlabs%2Fdashfrog/releases","manifests_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/towlabs%2Fdashfrog/manifests","owner_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/owners/towlabs","download_url":"https://codeload.github.com/towlabs/dashfrog/tar.gz/refs/heads/main","sbom_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/towlabs%2Fdashfrog/sbom","scorecard":null,"host":{"name":"GitHub","url":"https://github.com","kind":"github","repositories_count":286080680,"owners_count":34050225,"icon_url":"https://github.com/github.png","version":null,"created_at":"2022-05-30T11:31:42.601Z","updated_at":"2026-05-26T15:22:16.424Z","status":"online","status_checked_at":"2026-06-08T02:00:07.615Z","response_time":111,"last_error":null,"robots_txt_status":"success","robots_txt_updated_at":"2025-07-24T06:49:26.215Z","robots_txt_url":"https://github.com/robots.txt","online":true,"can_crawl_api":true,"host_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub","repositories_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories","repository_names_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repository_names","owners_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/owners"}},"keywords":["b2b","metrics","monitoring","observability","opentelemetry","operations","sla-tracking","status-page"],"created_at":"2026-06-08T05:31:22.545Z","updated_at":"2026-06-08T05:31:23.829Z","avatar_url":"https://github.com/towlabs.png","language":"TypeScript","funding_links":[],"categories":[],"sub_categories":[],"readme":"# DashFrog\n\nA Grafana your support team can actually use.\n\n![DashFrog Status Page](docs/assets/main.png)\n\n## What is DashFrog?\n\nDashFrog is open-source observability built around your customers, not your infrastructure.\n\nIt sits on OpenTelemetry but abstracts away the complexity. Customer namespaces are auto-created as you push data. Anyone on your team can explore what's happening with a customer — no PromQL, no trace IDs.\n\n**Not a replacement for dev observability.** Keep using Datadog, Grafana, or whatever you use for infrastructure monitoring. DashFrog complements them by organizing telemetry per customer — making it easy for support, account managers, and customers themselves to understand what's happening.\n\n**Key Features:**\n- 🎯 **Customer-first** - Organize telemetry by customer, not infrastructure\n- ⚡ **Zero config** - Customer namespaces auto-created as data arrives\n- 🔍 **No query languages** - Explore without PromQL or trace IDs\n- 📊 **Shareable insights** - Give customers visibility into their own data\n\n## Try the Demo\n\nSee DashFrog in action with a 2-minute demo:\n\n```bash\ncurl -fsSL https://raw.githubusercontent.com/towlabs/dashfrog/main/bin/deploy | bash -s -- --with-demo\n```\n\nThis will:\n1. Install DashFrog with Docker Compose\n2. Start the demo generating sample data\n3. Create status page notebooks for 3 customers\n\nAccess the UI at **http://localhost:8000** (login: `admin` / `admin`)\n\n\u003e **For production:** See the [Deployment Guide](docs/deployment.md) for Kubernetes, custom configuration, and security hardening.\n\n## Key concepts\n\n### Flows\n\nFlows let you follow a distributed workflow as logical steps.\n\nYou define a flow in your code. DashFrog tracks it across services using OpenTelemetry. Your support team sees \"customer X's import is stuck at validation\" — not span IDs and service graphs.\n\n```python\nfrom dashfrog import flow, step\n\n# Start a flow for a customer\nwith flow.start(\n    name=\"customer_data_import\",  # flow name\n    tenant=\"acme-corp\",  # tenant name\n    env=\"prod\"  # optional labels\n):\n    # Each step is tracked\n    with step.start(\"validate_data\"):\n        # validation logic\n        validate_csv(file)\n\n    with step.start(\"transform_data\"):\n        # transformation logic\n        transform(data)\n\n    with step.start(\"load_to_database\"):\n        # database logic\n        db.insert(data)\n```\n\nFlow data is automatically available in notebooks, where you can query and visualize workflows per customer.\n\n→ See [Flows documentation](docs/flows.md) for distributed flows, error handling, and advanced usage.\n\n### Metrics\n\nMetrics use standard OTel under the hood. DashFrog presents them so you don't need to know what a gauge, counter, or histogram is.\n\n```python\nfrom fastapi import FastAPI\nfrom dashfrog import metrics\n\napp = FastAPI()\n\ncomputation_duration = metrics.Histogram(\n    \"computation_duration\", labels=[\"env\"], pretty_name=\"Computation Duration\", unit=\"s\"\n)\ncomputation_count = metrics.Counter(\"computation_count\", labels=[\"env\"], pretty_name=\"Computations\")\n\n@app.get(\"/heavy-computation/{customer_id}/{env}\")\nasync def heavy_computation(customer_id: str, env: str):\n    duration = sleep(3)\n    computation_duration.record(duration, tenant=customer_id, env=env)\n    computation_count.add(1, tenant=customer_id, env=env)\n```\n\nMetrics data is automatically available in notebooks for querying and visualization.\n\n→ See [Metrics documentation](docs/metrics.md) for histograms, percentiles, labels, and best practices.\n\n## Notebooks\n\nBuild customer-specific dashboards with a block-based editor. Combine metrics and flows to create views you can share publicly with customers or use internally for support.\n\n**Features:**\n- Drill-down into historical data by clicking any metric or flow\n- Share public notebooks via URL\n- Add time annotations for releases, incidents, and events\n\n→ See [Notebooks documentation](docs/notebooks.md) for details\n\n## Roadmap\n\nIdeas we're exploring:\n- External data sources(API, Prometheus, ...)\n- Helpdesk integrations (Zendesk, Intercom)\n- Alerting rules\n- Frontend SDK for embedding components in apps\n\n## License\n\nMIT License - see [LICENSE](LICENSE) for details.\n","project_url":"https://awesome.ecosyste.ms/api/v1/projects/github.com%2Ftowlabs%2Fdashfrog","html_url":"https://awesome.ecosyste.ms/projects/github.com%2Ftowlabs%2Fdashfrog","lists_url":"https://awesome.ecosyste.ms/api/v1/projects/github.com%2Ftowlabs%2Fdashfrog/lists"}