feat: scaffold initial core architecture for the OtelContext observability platform, including storage, API, UI, and real-time eventing.

Author: aksOps

.air.toml

@@ -2,8 +2,8 @@ root = "."
 tmp_dir = "tmp"
 
 [build]
-cmd = "go build -o ./tmp/argus.exe ."
-bin = "./tmp/argus.exe"
+cmd = "go build -o ./tmp/OtelContext.exe ."
+bin = "./tmp/OtelContext.exe"
 include_ext = ["go", "tpl", "tmpl", "html"]
 exclude_dir = ["assets", "tmp", "vendor", "testdata", "web", "test"]
 include_dir = []
@@ -23,3 +23,4 @@ runner = "green"
 
 [misc]
 clean_on_exit = true
+

.claude/settings.local.json

@@ -3,8 +3,9 @@
     "allow": [
       "mcp__plugin_context-mode_context-mode__ctx_batch_execute",
       "mcp__plugin_context-mode_context-mode__ctx_search",
-      "Read(///wsl.localhost/FedoraDevPod/home/dev/git/Argus/**)",
+      "Read(///wsl.localhost/FedoraDevPod/home/dev/git/OtelContext/**)",
       "Read(///wsl.localhost/FedoraDevPod/mnt/**)"
     ]
   }
 }
+

.env.example

@@ -6,8 +6,9 @@ GRPC_PORT=4317
 # Database Configuration
 # Options: mysql, sqlite, sqlserver
 DB_DRIVER=mysql
-DB_DSN=root:admin@tcp(127.0.0.1:3306)/argus?charset=utf8mb4&parseTime=True&loc=Local
+DB_DSN=root:admin@tcp(127.0.0.1:3306)/OtelContext?charset=utf8mb4&parseTime=True&loc=Local
 
 # For SQLite:
 # DB_DRIVER=sqlite
-# DB_DSN=argus.db
+# DB_DSN=OtelContext.db
+

.gitignore

@@ -111,9 +111,9 @@ go.work.sum
 
 .dev-pids
 
-*argus*.db*
-/argus
-/argus.exe
+*OtelContext*.db*
+/OtelContext
+/OtelContext.exe
 
 .agent/
 
@@ -122,4 +122,4 @@ go.work.sum
 *.stderr
 *scheduled_tasks*
 tmp/
-data/
+data/

CLAUDE.md

@@ -1,8 +1,8 @@
-# Argus — AI Agent Instructions
+# OtelContext — AI Agent Instructions
 
 ## Project Overview
 
-Argus is a self-hosted OTLP observability platform. Single Go binary with embedded React frontend.
+OtelContext is a self-hosted OTLP observability platform. Single Go binary with embedded React frontend.
 - **Backend:** Go 1.24, native `net/http` (no frameworks), GORM ORM, gRPC for OTLP ingestion
 - **Frontend:** React 19 + TypeScript + Mantine UI v8 + ECharts + ReactFlow
 - **Ports:** gRPC `:4317` (OTLP), HTTP `:8080` (API + WebSocket + UI)
@@ -78,3 +78,4 @@ web/            # React frontend (Vite + Mantine)
 test/           # Microservice simulation (7 services)
 docs/           # Specifications and plans
 ```
+

README.md

@@ -1,38 +1,38 @@
-# Project Argus
+# OtelContext
 
-[![Latest Release](https://img.shields.io/github/v/release/RandomCodeSpace/argus)](https://github.com/RandomCodeSpace/argus/releases)
-[![CodeQL](https://github.com/RandomCodeSpace/argus/actions/workflows/github-code-scanning/codeql/badge.svg)](https://github.com/RandomCodeSpace/argus/actions/workflows/github-code-scanning/codeql)
-![Go Version](https://img.shields.io/github/go-mod/go-version/RandomCodeSpace/argus)
+[![Latest Release](https://img.shields.io/github/v/release/RandomCodeSpace/otelcontext)](https://github.com/RandomCodeSpace/otelcontext/releases)
+[![CodeQL](https://github.com/RandomCodeSpace/otelcontext/actions/workflows/github-code-scanning/codeql/badge.svg)](https://github.com/RandomCodeSpace/otelcontext/actions/workflows/github-code-scanning/codeql)
+![Go Version](https://img.shields.io/github/go-mod/go-version/RandomCodeSpace/otelcontext)
 ![React](https://img.shields.io/badge/frontend-React%20v18-61dafb?logo=react)
 
-Project Argus is an integrated observability and AI analysis platform.
+OtelContext is an integrated observability and AI analysis platform.
 
 ## Getting Started
 
 ### Installation
 ```bash
-go install github.com/RandomCodeSpace/argus@latest
+go install github.com/RandomCodeSpace/otelcontext@latest
 ```
 
 ### Running
 Simply run the binary:
 ```bash
-argus
+otelcontext
 ```
-By default, Argus will use an embedded SQLite database (`argus.db`) in the current directory. No configuration is required.
+By default, OtelContext will use an embedded SQLite database (`otelcontext.db`) in the current directory. No configuration is required.
 
 ### Configuration (Optional)
 You can configure the database using environment variables or a `.env` file:
 
 - **MySQL**:
   ```bash
   DB_DRIVER=mysql
-  DB_DSN=root:password@tcp(localhost:3306)/argus?charset=utf8mb4&parseTime=True&loc=Local
+  DB_DSN=root:password@tcp(localhost:3306)/otelcontext?charset=utf8mb4&parseTime=True&loc=Local
   ```
 - **SQLite** (Default):
   ```bash
   DB_DRIVER=sqlite
-  DB_DSN=argus.db
+  DB_DSN=otelcontext.db
   ```
 
 ## Features
@@ -41,23 +41,25 @@ You can configure the database using environment variables or a `.env` file:
 - **Dashboard**: Real-time metrics and traffic analysis.
 
 ## OTLP Integration
-Argus acts as an OTLP Receiver (gRPC) on port `4317` by default.
+OtelContext acts as an OTLP Receiver (gRPC) on port `4317` by default.
 
 ### As an OTel Collector Target
-You can configure any OpenTelemetry Collector to export data to Argus.
+You can configure any OpenTelemetry Collector to export data to OtelContext.
 
 ```yaml
 exporters:
-  otlp/argus:
+  otlp/otelcontext:
     endpoint: "localhost:4317"
     tls:
       insecure: true
 
 service:
   pipelines:
     traces:
-      exporters: [otlp/argus]
+      exporters: [otlp/otelcontext]
     logs:
-      exporters: [otlp/argus]
+      exporters: [otlp/otelcontext]
 ```
 See `docs/otel-collector-example.yaml` for a full configuration example.
+
+

docs/IMPLEMENTATION_PLAN.md

@@ -1,15 +1,15 @@
-# Argus Observability & Performance Improvement Plan
+# OtelContext Observability & Performance Improvement Plan
 
 ## Context
 
-Argus is a self-hosted OTLP observability platform (Go backend + React frontend) that ingests traces, logs, and metrics via gRPC, stores them in a relational DB (SQLite/MySQL/PostgreSQL/MSSQL), and serves dashboards via HTTP/WebSocket. When connected to active services pushing continuous telemetry, the DB will grow unbounded and performance will degrade. The goal is to:
+OtelContext is a self-hosted OTLP observability platform (Go backend + React frontend) that ingests traces, logs, and metrics via gRPC, stores them in a relational DB (SQLite/MySQL/PostgreSQL/MSSQL), and serves dashboards via HTTP/WebSocket. When connected to active services pushing continuous telemetry, the DB will grow unbounded and performance will degrade. The goal is to:
 
 1. **Capture all metrics, traces, and logs** — comprehensively but smartly
 2. **Create an AI-consumable system graph API** — structured JSON that any AI agent can query
 3. **Maximize performance** — fix identified bottlenecks, use specialized in-memory DBs as accelerators
 4. **Hot/cold storage tiering** — keep 7 days hot, archive older data to compressed cold storage
 5. **Data compression** — prevent DB blowup from continuous ingestion
-6. **HTTP Streamable MCP Server** — universal AI agent access to Argus tools
+6. **HTTP Streamable MCP Server** — universal AI agent access to OtelContext tools
 
 ### Storage Architecture Principle
 
@@ -54,7 +54,7 @@ Specialized in-memory/embedded databases are used as **processing accelerators**
 
 ### 1.4 TSDB flush channel — visibility + throughput
 - **File:** `internal/tsdb/aggregator.go` — `flush()` (line 114)
-- **Fix:** Increase channel cap to 500, add `argus_tsdb_batches_dropped` counter, spawn multiple persistence workers (default 3)
+- **Fix:** Increase channel cap to 500, add `OtelContext_tsdb_batches_dropped` counter, spawn multiple persistence workers (default 3)
 
 ### 1.5 In-memory TTL cache for dashboard queries
 - **New file:** `internal/cache/ttl.go` — simple `sync.Map` with expiry
@@ -123,7 +123,7 @@ data/cold/
   - SQLite: `VACUUM`, `PRAGMA optimize`
   - PostgreSQL: `VACUUM ANALYZE`
   - MySQL: `OPTIMIZE TABLE`
-- Track and expose `argus_archive_last_run`, `argus_archive_records_moved`, `argus_hot_db_size_bytes`
+- Track and expose `OtelContext_archive_last_run`, `OtelContext_archive_records_moved`, `OtelContext_hot_db_size_bytes`
 
 ---
 
@@ -141,25 +141,25 @@ data/cold/
 
 | Metric | Type | Labels | Purpose |
 |--------|------|--------|---------|
-| `argus_grpc_requests_total` | CounterVec | method, status | gRPC call counts |
-| `argus_grpc_request_duration_seconds` | HistogramVec | method | gRPC latency |
-| `argus_grpc_batch_size` | Histogram | — | Spans/logs per Export call |
-| `argus_http_requests_total` | CounterVec | method, path, status | API call counts |
-| `argus_http_request_duration_seconds` | HistogramVec | method, path | API latency |
-| `argus_tsdb_ingest_total` | Counter | — | Raw metric points ingested |
-| `argus_tsdb_flush_duration_seconds` | Histogram | — | Flush window time |
-| `argus_tsdb_batches_dropped_total` | Counter | — | Dropped batches |
-| `argus_ws_messages_sent_total` | CounterVec | type | WS broadcast count |
-| `argus_ws_slow_clients_removed_total` | Counter | — | Dropped slow clients |
-| `argus_dlq_enqueued_total` | Counter | — | DLQ writes |
-| `argus_dlq_replay_success_total` | Counter | — | Successful replays |
-| `argus_dlq_replay_failure_total` | Counter | — | Failed replays |
-| `argus_dlq_disk_bytes` | Gauge | — | DLQ disk usage |
-| `argus_archive_records_moved` | Counter | type | Records archived |
-| `argus_hot_db_size_bytes` | Gauge | — | Hot DB file size |
-| `argus_cold_storage_bytes` | Gauge | — | Cold archive size |
-| `argus_go_goroutines` | Gauge | — | Active goroutines |
-| `argus_go_heap_alloc_bytes` | Gauge | — | Heap memory |
+| `OtelContext_grpc_requests_total` | CounterVec | method, status | gRPC call counts |
+| `OtelContext_grpc_request_duration_seconds` | HistogramVec | method | gRPC latency |
+| `OtelContext_grpc_batch_size` | Histogram | — | Spans/logs per Export call |
+| `OtelContext_http_requests_total` | CounterVec | method, path, status | API call counts |
+| `OtelContext_http_request_duration_seconds` | HistogramVec | method, path | API latency |
+| `OtelContext_tsdb_ingest_total` | Counter | — | Raw metric points ingested |
+| `OtelContext_tsdb_flush_duration_seconds` | Histogram | — | Flush window time |
+| `OtelContext_tsdb_batches_dropped_total` | Counter | — | Dropped batches |
+| `OtelContext_ws_messages_sent_total` | CounterVec | type | WS broadcast count |
+| `OtelContext_ws_slow_clients_removed_total` | Counter | — | Dropped slow clients |
+| `OtelContext_dlq_enqueued_total` | Counter | — | DLQ writes |
+| `OtelContext_dlq_replay_success_total` | Counter | — | Successful replays |
+| `OtelContext_dlq_replay_failure_total` | Counter | — | Failed replays |
+| `OtelContext_dlq_disk_bytes` | Gauge | — | DLQ disk usage |
+| `OtelContext_archive_records_moved` | Counter | type | Records archived |
+| `OtelContext_hot_db_size_bytes` | Gauge | — | Hot DB file size |
+| `OtelContext_cold_storage_bytes` | Gauge | — | Cold archive size |
+| `OtelContext_go_goroutines` | Gauge | — | Active goroutines |
+| `OtelContext_go_heap_alloc_bytes` | Gauge | — | Heap memory |
 
 ### 3.2 HTTP metrics middleware
 - **New file:** `internal/api/middleware.go`
@@ -168,7 +168,7 @@ data/cold/
 
 ### 3.3 gRPC interceptor
 - **File:** `main.go` — add `grpc.UnaryInterceptor()` to gRPC server
-- Records `argus_grpc_*` metrics per RPC
+- Records `OtelContext_grpc_*` metrics per RPC
 
 ### 3.4 Runtime metrics goroutine
 - **File:** `main.go` — background goroutine sampling `runtime.MemStats` every 15s
@@ -324,7 +324,7 @@ All internal DBs are **embedded** (compiled into the single binary, no external
 
 ## Phase 6: HTTP Streamable MCP Server
 
-Expose Argus as an MCP (Model Context Protocol) server over HTTP with SSE streaming, so any AI agent (Claude, GPT, Cursor, etc.) can discover and call Argus tools natively — no custom API integration needed.
+Expose OtelContext as an MCP (Model Context Protocol) server over HTTP with SSE streaming, so any AI agent (Claude, GPT, Cursor, etc.) can discover and call OtelContext tools natively — no custom API integration needed.
 
 ### 6.1 MCP server package
 - **New package:** `internal/mcp/`
@@ -371,7 +371,7 @@ Each tool returns structured content that AI agents can reason over:
     {
       "type": "resource",
       "resource": {
-        "uri": "argus://system/graph",
+        "uri": "OtelContext://system/graph",
         "mimeType": "application/json",
         "text": "{...system graph JSON...}"
       }
@@ -387,9 +387,9 @@ Each tool returns structured content that AI agents can reason over:
 - **Cache:** MCP tools use the same TTL cache from Phase 1.5
 
 ### 6.6 MCP Resources (read-only data subscriptions)
-- `argus://system/graph` — live system graph (subscribable)
-- `argus://services/{name}/health` — per-service health
-- `argus://metrics/prometheus` — current Prometheus metrics dump
+- `OtelContext://system/graph` — live system graph (subscribable)
+- `OtelContext://services/{name}/health` — per-service health
+- `OtelContext://metrics/prometheus` — current Prometheus metrics dump
 - AI agents can subscribe to resources for real-time updates via SSE
 
 ---
@@ -398,7 +398,7 @@ Each tool returns structured content that AI agents can reason over:
 
 **Changes delivered:**
 - `internal/ingest/sampler.go` *(new)* — per-service token bucket sampler; always keeps errors, slow traces, new services; configurable via `SAMPLING_RATE`, `SAMPLING_ALWAYS_ON_ERRORS`, `SAMPLING_LATENCY_THRESHOLD_MS`
-- `internal/tsdb/aggregator.go` — `SetCardinalityLimit(max, onOverflow)` routes excess metric series to overflow bucket; increments `argus_tsdb_cardinality_overflow_total`; wired via `METRIC_MAX_CARDINALITY`
+- `internal/tsdb/aggregator.go` — `SetCardinalityLimit(max, onOverflow)` routes excess metric series to overflow bucket; increments `OtelContext_tsdb_cardinality_overflow_total`; wired via `METRIC_MAX_CARDINALITY`
 - `internal/api/ratelimit.go` *(new)* — per-IP token bucket rate limiter; X-Forwarded-For aware; configurable via `API_RATE_LIMIT_RPS`; wraps HTTP handler in `main.go`
 
 ## Phase 7: Smart Observability
@@ -413,7 +413,7 @@ Each tool returns structured content that AI agents can reason over:
 - **File:** `internal/tsdb/aggregator.go`
 - Attribute allowlist: `METRIC_ATTRIBUTE_KEYS=service,method,status_code`
 - Max cardinality guard: if `len(buckets)` > 10,000, route to overflow bucket
-- Metric: `argus_tsdb_cardinality_overflow_total`
+- Metric: `OtelContext_tsdb_cardinality_overflow_total`
 
 ### 7.3 DLQ bounded disk + backoff
 - **File:** `internal/queue/dlq.go`
@@ -454,5 +454,6 @@ Each tool returns structured content that AI agents can reason over:
 2. **Metrics:** Verify all new Prometheus metrics appear at `GET /metrics`
 3. **Graph API:** Hit `GET /api/system/graph` during simulation, validate JSON schema
 4. **Archival:** Set `HOT_RETENTION_DAYS=0`, trigger archival, verify cold files created and hot DB shrunk
-5. **Sampling:** Enable sampling, verify reduced ingestion rate via `argus_ingestion_rate` while errors still captured
+5. **Sampling:** Enable sampling, verify reduced ingestion rate via `OtelContext_ingestion_rate` while errors still captured
 6. **MCP Server:** `POST /mcp` with `{"jsonrpc":"2.0","method":"initialize",...}` returns tool list; call `tools/call` with `get_system_graph` returns valid graph; connect from Claude Desktop or any MCP client
+