On-Premise Deployment

Run the full Context7 stack inside your own infrastructure, so code and documentation never leave your environment

Context7 On-Premise lets you run the full Context7 stack inside your own infrastructure. Your code, documentation, and embeddings never leave your environment.

What’s Included

• Full Context7 parsing and indexing pipeline
• Local vector storage (no external vector DB required)
• Built-in MCP server. Works with any MCP-compatible AI client
• Web UI for managing indexed libraries and configuration
• REST API compatible with the public Context7 API
• Private GitHub and GitLab repository ingestion

Setup

1. Request a trial

   Go to context7.com/plans and click On-Premise Trial. Fill out the request form. No credit card required. You’ll receive a 30-day full-featured license key via email once approved.

2. Deploy

   Follow the deployment guide for your platform:

   Docker

   Deploy with Docker Compose

   Kubernetes

   Deploy on Kubernetes with raw manifests

3. Complete the setup wizard

   Open http://localhost:3000 in your browser. On first launch, the setup wizard guides you through configuring:

   1. AI Provider - Choose OpenAI, Anthropic, Gemini, or a custom OpenAI-compatible endpoint. Enter your API key and model name.
   2. Embedding Provider - Use the same provider as your LLM, or configure a separate one for embeddings.
   3. Git Tokens - Add a GitHub and/or GitLab token for the platforms you use.

   All configuration is stored locally in the embedded database and can be updated later from the Settings page.

4. Ingest your first repository

   From the dashboard, click Add Repository and enter a GitHub or GitLab URL. Once ingestion completes, your private docs are ready to query.

   You can also add libraries via the REST API:

   curl -X POST http://localhost:3000/api/parse \
     -H "Content-Type: application/json" \
     -d '{"repoUrl": "https://github.com/your-org/your-repo"}'
   

Connecting Your AI Client

The Context7 CLI is the recommended way to connect an AI coding agent. Install it once on the machine that runs your client:

Public npm registry

npm install --global ctx7@latest

Internal npm registry

npm install --global ctx7 \
  --registry https://npm.internal.yourcompany.com

For restricted or air-gapped environments, mirror the ctx7 package and its dependencies in your approved internal npm registry. Once installed, on-premise setup does not contact npm, GitHub, or Context7 Cloud. It uses bundled rule and skill content and contacts only your deployment to discover its MCP authentication setting.

Pass the root URL of your deployment (without /mcp or /api) and select the client:

Claude Code

ctx7 setup --mcp \
  --base-url https://context7.internal.yourcompany.com \
  --claude

Cursor

ctx7 setup --mcp \
  --base-url https://context7.internal.yourcompany.com \
  --cursor

Codex

ctx7 setup --mcp \
  --base-url https://context7.internal.yourcompany.com \
  --codex

OpenCode

ctx7 setup --mcp \
  --base-url https://context7.internal.yourcompany.com \
  --opencode

Setup reads the deployment’s MCP authentication setting. When MCP authentication is disabled, no credential is written. When it is enabled, sign in to the on-premise dashboard and click Generate setup key in the Connect card. Run the command for your client and paste the one-time-visible key when the CLI requests it with hidden input.

For non-interactive automation, provide the personal key through the environment:

CONTEXT7_API_KEY=ctx7op-... ctx7 setup --mcp --yes \
  --base-url https://context7.internal.yourcompany.com \
  --claude

You can use --api-key ctx7op-... instead, but command-line arguments may be retained in shell history. Setup writes the MCP client configuration, a Context7 rule, and a bundled context7-mcp skill. The bundled files mean the deployment does not need to expose Context7 Cloud’s skill APIs.

Custom --base-url setup uses the deployment’s Streamable HTTP endpoint. Hosted OAuth, local stdio transport, and CLI-query mode are not available for on-premise targets.

Manual configuration

If your client is not supported by ctx7 setup, point it at <deployment-root>/mcp. When MCP authentication is enabled, send the personal key as a standard bearer token:

{
  "mcpServers": {
    "context7": {
      "url": "https://context7.internal.yourcompany.com/mcp",
      "headers": {
        "Authorization": "Bearer ctx7op-..."
      }
    }
  }
}

For other clients, see All Clients.

Configuration

Environment Variables

These are set in your docker-compose.yml or .env file before starting the container.

Variable	Required	Description
LICENSE_KEY	Yes	License key issued by Upstash
PORT	No	HTTP port (default: 3000)
DATA_DIR	No	Data directory inside the container (default: /data)

AI provider keys, model settings, and git tokens are not set via environment variables. They are configured through the setup wizard and can be updated anytime from the Settings page in the web UI.

AI Provider Settings

Configured via the Settings page in the web UI.

Setting	Description
LLM Provider	openai, anthropic, gemini, or custom
LLM API Key	API key for your chosen provider
LLM Model	Model name (e.g. gpt-4o, claude-sonnet-4-5, gemini-2.5-flash)
LLM Base URL	Custom OpenAI-compatible endpoint (for local models or proxies)

Examples

OpenRouter

Provider: custom
Base URL: https://openrouter.ai/api/v1
Model: openai/gpt-4o
API Key: sk-or-v1-...

Local Model (Ollama, vLLM)

Provider: custom
Base URL: http://host.docker.internal:11434/v1
Model: llama3.2
API Key: ollama

Embedding Settings

By default, Context7 uses the same provider as your LLM for generating embeddings. You can configure a separate embedding provider if needed.

Setting	Description
Embedding Provider	openai or gemini
Embedding API Key	Separate API key for embeddings (falls back to LLM API key)
Embedding Model	Embedding model name (e.g. text-embedding-3-small)
Embedding Base URL	Custom embedding endpoint

Git Access Tokens

Configured via the Settings page in the web UI.

Setting	Description
GitHub Token	GitHub Personal Access Token. Required for GitHub repositories
GitLab Token	GitLab token. Required for GitLab repositories

You only need tokens for the platforms you use. If you only parse GitLab repos, you don’t need a GitHub token, and vice versa. Create tokens with repo scope (GitHub) or read_repository scope (GitLab) for private repository access.

Access Control

Admin credentials are set during first login (default: admin / admin). Change these immediately after setup via Settings > Change Credentials.

The Settings page lets you control which operations are available without authentication, including MCP and API search access.

Permission	Default	Description
Allow anonymous parse	Off	Allow unauthenticated users to trigger parsing
Allow anonymous refresh	Off	Allow unauthenticated users to refresh libraries
Allow anonymous delete	Off	Allow unauthenticated users to delete libraries
Allow anonymous support bundle	Off	Allow unauthenticated support bundle downloads
Require MCP authentication	Off	Require a personal API key for /mcp and external /api/v2 search requests

When MCP authentication is enabled, each user creates personal keys from Personal Settings > API Keys. Clients send the key as Authorization: Bearer <key>.

Policies

Policies let you control which public documentation from the Context7 cloud is accessible to your on-premise instance. They do not affect locally parsed on-premise content.

Access Policies from Settings > Policies tab. Requires admin login and a valid LICENSE_KEY.

For details on source type toggles and library filters, see Customizing What Is Retrieved.

Web UI

Open your deployment URL in a browser to access the dashboard. From here you can:

• Add and remove libraries
• Trigger re-indexing
• Monitor parsing status and logs
• Update AI provider settings, git tokens, and permissions
• Configure policies for public cloud documentation access
• Test MCP connectivity
• Change admin credentials

Operations

For updating, health checks, and other operational tasks, see the deployment guide for your platform:

• Docker Operations
• Kubernetes Operations

Support

For license issues, upgrade requests, or deployment questions, contact [e-mail].