AI Infrastructure

Central infrastructure for AI tools, MCP servers, gateways, and platform services.

TODO

Create an ai-infrastructure skill (in ~/AI/skills/ or in this repo) that documents all services, ports, startup commands, and dependencies so AI agents know what infrastructure is available when setting up machines or debugging connectivity issues.

Architecture

@startuml ai-infrastructure
!theme plain
skinparam backgroundColor #FEFEFE
skinparam componentStyle rectangle
skinparam defaultFontName Consolas

title AI Infrastructure Architecture

' === AI Clients ===
package "AI Clients" as clients #E3F2FD {
  [VS Code Copilot] as vscode
  [Claude Desktop] as claude
  [Cline] as cline
  [Other Clients] as other
}

' === Gateway Layer ===
package "Gateway Layer" as gateway_layer #E8F5E9 {
  component "agentgateway\n:3847 (HTTP)\n:15001 (Admin UI)\n:15020 (Metrics)" as agentgateway #C8E6C9
  component "nginx-proxy\n:3443 (HTTPS)\n:9223 (CDP)" as nginx #A5D6A7
}

' === MCP Backends - Currently Running ===
package "Running MCPs" as running #FFF3E0 {
  component "stdio-proxy\n:7030 (SSE)\n:61822 (Kapture WS)" as stdio_proxy #FFCC80

  package "stdio MCPs" as stdio_mcps #FFE0B2 {
    [sequential-thinking\n(1 tool)] as seq_think
    [memory\n(8 tools)] as memory
    [kapture\n(15+ tools)] as kapture_mcp
  }
}

' === SSE-based MCPs - Running ===
package "SSE MCPs" as sse_mcps #FFF3E0 {
  [context7\n:7008] as context7 #FFCC80
  [playwright\n:7007] as playwright #FFCC80
  [browser-use\n:7011] as browser_use #FFCC80
  [hass-mcp\n:7010] as hass_mcp #FFCC80
  [qdrant-mcp\n:7020] as qdrant_mcp #FFCC80
}

' === Platform Services ===
package "Platform Services" as platform #F3E5F5 {
  [Langfuse\n:3100 (UI)\n(LLM Observability)] as langfuse #CE93D8
  [langfuse-mcp\n:7012] as langfuse_mcp #E1BEE7
}

' === Observability Stack ===
package "Observability" as observability #E1F5FE {
  component "OpenTelemetry\nCollector\n:4317/:4318 (internal)" as otel #81D4FA
  component "Jaeger\n:16686 (UI)" as jaeger #4FC3F7
  component "Prometheus\n:9090" as prometheus #29B6F6
  component "Grafana\n:3000" as grafana #03A9F4
}

' === Connections ===
' Clients to Gateway
vscode --> agentgateway : HTTP\nx-client-id
claude --> agentgateway : HTTP\nx-client-id
cline --> agentgateway : HTTP\nx-client-id
other --> nginx : HTTPS

' nginx to agentgateway
nginx --> agentgateway : proxy

' agentgateway to MCPs
agentgateway --> stdio_proxy : SSE
agentgateway --> context7 : SSE
agentgateway --> playwright : SSE
agentgateway --> browser_use : SSE
agentgateway --> hass_mcp : SSE
agentgateway --> qdrant_mcp : SSE
agentgateway --> langfuse_mcp : MCP

' stdio-proxy to stdio MCPs
stdio_proxy --> seq_think : stdio
stdio_proxy --> memory : stdio
stdio_proxy --> kapture_mcp : stdio

' Langfuse MCP to Langfuse platform
langfuse_mcp --> langfuse : API

' Browser to Langfuse
browser .up.> langfuse : UI

' Observability connections
agentgateway --> otel : OTLP traces
otel --> jaeger : traces
otel --> prometheus : span metrics
agentgateway --> prometheus : metrics scrape
grafana --> prometheus : query
grafana --> jaeger : query

' Legend
legend right
  |= Color |= Status |
  | <#C8E6C9> | Gateway |
  | <#FFCC80> | Running MCP |
  | <#81D4FA> | Observability |
  | <#CE93D8> | Platform Service |
  | <#E1BEE7> | Planned |
endlegend

@enduml

Current Status

Component	Status	Tools
agentgateway	✅ Running	-
sequential-thinking	✅ Running	1
memory	✅ Running	8
kapture	✅ Running	15+
context7	✅ Running	2
playwright	✅ Running	15+
browser-use	✅ Running	10+
hass-mcp	✅ Running	5+
qdrant-mcp	✅ Running	6
langfuse	✅ Available	-
Total		62+ tools

Network Architecture

@startuml network-ports
!theme plain
skinparam backgroundColor #FEFEFE
skinparam defaultFontName Consolas
skinparam rectangleBorderColor #666666
skinparam rectangleBackgroundColor #F5F5F5

title Host ↔ Docker Port Mappings

rectangle "HOST" as host {
  rectangle "AI Clients" as clients #E3F2FD
  rectangle "Chrome :9222" as chrome #FFF9C4
  rectangle "Browser" as browser #E1F5FE
}

rectangle "DOCKER (ai-infrastructure network)" as docker {
  rectangle ":3847 agentgateway" as gw #C8E6C9
  rectangle ":15001 Admin UI" as admin #C8E6C9
  rectangle ":3443/:9223 nginx" as nginx #A5D6A7
  rectangle ":7030/:61822 stdio-proxy" as stdio #FFCC80
  rectangle ":7020 qdrant-mcp" as qdrant #FFCC80
  rectangle ":3000 Grafana" as grafana #81D4FA
  rectangle ":9090 Prometheus" as prom #81D4FA
  rectangle ":16686 Jaeger" as jaeger #81D4FA
  rectangle ":3100 Langfuse" as langfuse #CE93D8
}

clients -down-> gw : "HTTP :3847"
clients -down-> admin : "HTTP :15001"
browser -down-> grafana : ":3000"
browser -down-> prom : ":9090"
browser -down-> jaeger : ":16686"
browser -down-> langfuse : ":3100"
nginx -up-> chrome : "CDP :9223→:9222"

@enduml

Internal Docker Network (ai-infrastructure):

From	To	Port	Purpose
agentgateway	stdio-proxy	7030	SSE to stdio MCPs
agentgateway	otel-collector	4317	OTLP traces
otel-collector	jaeger	14317	Trace export
otel-collector	(self)	8889	Span metrics
prometheus	agentgateway	15020	Metrics scrape
prometheus	otel-collector	8889	Span metrics scrape
grafana	prometheus	9090	Metrics queries
grafana	jaeger	16686	Trace queries

Directory Structure

ai-infrastructure/
├── clients/           # AI client configurations
│   ├── claude/        # Claude Desktop config
│   ├── cline/         # Cline config
│   └── copilot/       # VS Code Copilot config
├── gateways/          # MCP gateways
│   └── agentgateway/  # Linux Foundation MCP gateway
├── mcps/              # MCP servers
│   ├── browser-use/   # AI browser automation
│   ├── context7/      # Library documentation
│   ├── hass-mcp/      # Home Assistant
│   ├── kapture/       # Chrome extension MCP
│   ├── mcpx/          # MCPX gateway (alternative)
│   ├── memory/        # Memory/knowledge graph
│   ├── playwright/    # Playwright browser automation
│   ├── qdrant-mcp/    # Qdrant semantic search (mcp-proxy + mcp-server-qdrant)
│   ├── sequential-thinking/ # Chain of thought reasoning
│   └── stdio-proxy/   # stdio→SSE bridge
├── platform/          # Platform services
│   ├── context-lens/  # LLM context window inspector
│   ├── langfuse/      # LLM observability, prompts, evals
│   └── observability/ # Prometheus, Grafana, Jaeger
└── workflows/         # Custom workflow definitions

Components

Gateways

Gateway	Description	Status
agentgateway	Linux Foundation MCP gateway with auth, RBAC, rate limiting	✅ Running

MCP Servers

MCP	Description	Status	Docs
sequential-thinking	Chain of thought reasoning	✅ Running	→
memory	Knowledge graph & memory	✅ Running	→
stdio-proxy	stdio→SSE bridge (mcp-proxy)	✅ Running	→
kapture	Chrome extension MCP	✅ Running	→
playwright	Browser automation	✅ Running	→
qdrant-mcp	Qdrant semantic search (work, code)	✅ Running	→
browser-use	AI browser automation	✅ Running	→
context7	Context7 library docs	✅ Running	→
hass-mcp	Home Assistant	✅ Running	→

Platform Services

Service	Description	Status	Docs
Observability	Prometheus, Grafana, Jaeger	✅ Running	→
Langfuse	LLM observability, prompts, evals	✅ Available	→
Context Lens	LLM context window inspector (proxy + web UI)	✅ Running	→

Clients

See clients/readme.md for configuration.

Client	Config
VS Code Copilot	copilot/
Claude Desktop	claude/
Cline	cline/

Quick Start

1. Create Docker network

docker network create mcpx_ai-infrastructure

2. Start stdio-proxy (for stdio-based MCPs)

cd mcps/stdio-proxy
docker-compose up -d

3. Start agentgateway

cd gateways/agentgateway
docker-compose up -d

4. Start observability stack (optional)

cd platform/observability
docker-compose up -d

5. Start Langfuse (optional)

cd platform/langfuse
docker compose up -d

6. Access

MCP Endpoint: http://localhost:3847/mcp
Admin UI: http://localhost:15001/ui
Grafana: http://localhost:3000 (admin/admin)
Langfuse: http://localhost:3100 (create account on first visit)

8. Configure your AI client

See clients/ for configuration examples for each AI client.

Ports

Port	Service	Protocol	Notes
3847	agentgateway MCP	HTTP	Main MCP endpoint
15001	agentgateway Admin UI	HTTP	Playground & config
15020	agentgateway Metrics	Prometheus	Scraped by Prometheus
3443	nginx-proxy HTTPS	HTTPS	TLS termination
9223	nginx-proxy CDP	CDP	Proxies to host Chrome :9222
7030	stdio-proxy	SSE	Bridges stdio MCPs
61822	Kapture WebSocket	WebSocket	Chrome extension
16686	Jaeger UI	HTTP	Trace visualization
9090	Prometheus	HTTP	Metrics UI & API
3000	Grafana	HTTP	Dashboards (admin/admin)
3100	Langfuse	HTTP	LLM observability UI
6333	Qdrant HTTP API	HTTP	Vector DB REST API
6334	Qdrant gRPC API	gRPC	Vector DB gRPC API
7020	qdrant-mcp	SSE	Semantic search MCP (mcp-proxy)
9190	Langfuse MinIO	HTTP	S3-compatible storage
4040	Context Lens Proxy	HTTP	LLM API interception proxy
4041	Context Lens UI	HTTP	Context composition web UI
4317/4318	OTel Collector	gRPC/HTTP	Internal only (Docker network)
8889	OTel Collector Metrics	Prometheus	Span metrics (internal)

Observability

The observability stack provides metrics, tracing, and visualization:

Component	Port	Purpose
agentgateway Admin UI	:15001	Admin UI with playground
agentgateway Metrics	:15020	Prometheus metrics endpoint
Prometheus	:9090	Metrics storage and queries
Grafana	:3000	Dashboards (admin/admin)
Jaeger	:16686	Distributed tracing
Langfuse	:3100	LLM observability & prompts
OpenTelemetry Collector	:4317/:4318 (internal)	Trace processing & span metrics

Trace Flow:

agentgateway → OTel Collector → Jaeger (traces)
                             → Prometheus (span metrics)

Metrics include:

agentgateway_requests_total - HTTP requests by client, method, status
agentgateway_mcp_requests - MCP tool calls
tool_calls_total - Tool calls by server and tool name
list_calls_total - List operations
Span-derived metrics (latency histograms, call counts) from OTel Collector

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Repository files navigation

AI Infrastructure

TODO

Architecture

Current Status

Network Architecture

Directory Structure

Components

Gateways

MCP Servers

Platform Services

Clients

Quick Start

1. Create Docker network

2. Start stdio-proxy (for stdio-based MCPs)

3. Start agentgateway

4. Start observability stack (optional)

5. Start Langfuse (optional)

6. Access

8. Configure your AI client

Ports

Observability

TODO

Resources

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Name		Name	Last commit message	Last commit date
Latest commit History 48 Commits
.claude		.claude
clients		clients
docs		docs
gateways/agentgateway		gateways/agentgateway
mcps		mcps
platform		platform
.gitignore		.gitignore
README.md		README.md

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Repository files navigation

AI Infrastructure

TODO

Architecture

Current Status

Network Architecture

Directory Structure

Components

Gateways

MCP Servers

Platform Services

Clients

Quick Start

1. Create Docker network

2. Start stdio-proxy (for stdio-based MCPs)

3. Start agentgateway

4. Start observability stack (optional)

5. Start Langfuse (optional)

6. Access

8. Configure your AI client

Ports

Observability

TODO

Resources

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Resources

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