🛒 Continue's open source AI programming assistant in-depth application solution for developers covers core capabilities such as multi-LLM backend switching, custom prompt words, local model reasoning, code base context understanding, Slash command customization, Rules configuration, etc., to build a flexible and controllable AI programming environment.

Continue open source AI programming assistant in-depth solution

Solution overview

This program is intended for software R&D teams and individual developers to systematically sort out the full-link configuration and in-depth application methods of Continue open source AI programming assistant. Continue is currently one of the open source AI programming plug-ins with the highest GitHub stars. Its core advantage is that it is not locked to a single model or bound to a specific manufacturer - developers can freely switch to any LLM such as OpenAI API, Claude, Ollama and other local models. backend, and get a consistent AI-assisted experience across both VS Code and JetBrains platforms.

Unlike closed source solutions such as GitHub Copilot or Cursor, Continue completely returns configuration rights and data sovereignty to developers - prompt word templates, model selection, and contextual policies are all managed in the form of local files, which can be incorporated into version control, team sharing, and even private deployment.

Target users: Front-line software R&D developers, technical team leaders, DevOps engineers, and enterprise R&D teams who are concerned about data privacy or need to connect to private models.

Prerequisites:

• Have basic experience using VS Code or JetBrains IDE
• Ability to install VS Code extensions or JetBrains plugins (no admin rights required)
• If you need to access cloud LLM (such as OpenAI, Claude), you need the API Key of the corresponding platform
• To run local models (such as through Ollama), hardware with at least 8GB of VRAM or 16GB of RAM is required

Toolchain list

Cost Note: Continue itself is completely free. If you only use local models (Ollama/LM Studio), there is no cost except hardware; if you connect to the cloud API, the cost depends on the selected model and usage. Compared to GitHub Copilot ($19/month·person) or Cursor Pro ($20/month·person), Continue can significantly reduce marginal costs when the team size is larger.

Preparation

Environmental Checklist

• [ ] VS Code 1.80+ or JetBrains 2023.2+ IDE
• [ ] Node.js 18+ (required for some advanced configuration)
• [ ] Git installed and configured (for configuration synchronization)
• [ ] For native model: NVIDIA GPU driver or Apple Silicon Mac (M1+)

Account and Credentials

• [ ] For cloud model: OpenAI / Anthropic / other API Key
• [ ] For local model: Ollama installs and pulls the target model

Knowledge preparation

• [ ] Understand Continue’s three interaction modes: code completion (Autocomplete), conversation (Chat), and inline editing (Inline Edit)
• [ ] Understand the basic syntax of YAML/JSON (for configuration file modification)

Step-by-step guide

Step 1: Plug-in installation and basic verification

⏱ Estimated time: 10-15 minutes 🎯 Goal: Complete the Continue plug-in installation and verify the basic interaction process ⚠️ Prerequisites: VS Code or JetBrains IDE installed

Operation instructions

Continue provides two installation methods: VS Code extension and JetBrains plug-in. After installation, you will get basic conversational programming capabilities.

Specific operations

1. VS Code Installation: Search for "Continue" in the extension market or visit continue.dev to get the installation link, and click to install.
2. JetBrains installation: Open Settings → Plugins → Marketplace, search for "Continue" to install.
3. Initial configuration: After installation, Continue will add an icon to the IDE sidebar. Click to open to start the conversation.
4. Basic verification: Enter "Explain the current file" in the dialog box to confirm that Continue can correctly read the content of the file in the current editor and give a reply.

🔑Expert View: After installation, Continue comes with a free proxy model by default (accessed through the continue.dev proxy), and developers can immediately experience the complete conversation function without any API Key. This lowers the threshold for getting started - it is recommended to use the default proxy to verify plug-in availability before entering custom model configuration.

Acceptance Criteria

• [ ] Sidebar Continue icon is visible and the conversation panel can be opened
• [ ] The "Edit" or "Ask" floating button appears after selecting the code
• [ ] Conversations can be sent normally and replies received

Step 2: Multiple LLM backend configuration and switching

⏱ Estimated time: 30-60 minutes 🎯 Goal: Configure at least 2 LLM backends (cloud + local) and verify model switching ⚠️ Prerequisite: Plug-in installation verification passed

Operation instructions

The core design concept of Continue is "model independent". Through the config.json configuration file, developers can register multiple LLM backends at the same time and flexibly switch according to scenarios.

Specific operations

1. Open the configuration file: Click the gear icon at the bottom of the Continue panel → open ~/.continue/config.json.
2. Add OpenAI backend:

   {
    "models": [
      {
        "title": "GPT-4o",
        "provider": "openai",
        "model": "gpt-4o",
        "apiKey": "sk-..."
      },
      {
        "title": "Claude 3.5 Sonnet",
        "provider": "anthropic",
        "model": "claude-3-5-sonnet-20241022",
        "apiKey": "sk-ant-..."
      }
    ]
   }

3. Add Ollama local backend (if required):

   {
    "title": "Local Llama 3",
    "provider": "ollama",
    "model": "llama3"
   }

4. Model switching: Switch the currently used model in the drop-down menu at the top of the Continue dialogue panel, enter the same question to compare the answer quality of different models.
5. Multi-model concurrency: Configure "completionOptions": {} to set parameters such as temperature and maxTokens separately for each model.

🔑 Expert view: Continue's model configuration adopts a "provider + model" two-tier architecture, and the same provider can register multiple model versions. It is recommended to allocate models by task type: lightweight models (such as GPT-4o mini or local 7B models) for daily coding completion, and flagship models (GPT-4o, Claude Sonnet) for complex reconstruction and analysis. This "hierarchical model" strategy is more economical than a single model solution, and can be included in version control through config.json.

Acceptance Criteria

• [ ] Configure at least one cloud model and one local model
• [ ] can switch between models and get responses normally
• [ ] There are obvious differences in the reply quality of different models (confirm that the switch takes effect)

Step 3: Customize prompt words and Slash command

⏱ Estimated time: 1-2 hours 🎯 Goal: Write a custom prompt word file and register the Slash command ⚠️ Precondition: Model configuration has been completed

Operation instructions

Continue supports customizing Slash commands through .prompt file extensions. This is its most significant differentiating capability from closed-source AI programming assistants - teams can accumulate exclusive workflow prompts for code review, architecture review, unit test generation, etc.

Specific operations

1. Create Custom Prompts directory: Create the .continue/prompts/ folder in the project root directory.

2. Write code review command: Create file .continue/prompts/code-review.prompt:

   You are a senior code review expert. Please review the selected code changes according to the following dimensions:
   
   1. **Security**: Are there risks such as SQL injection, XSS, permission bypass, etc.
   2. **Performance**: Are there optimizable loops and unnecessary memory allocations?
   3. **Maintainability**: Is the naming clear and whether there is duplicate code?
   4. **Boundary Conditions**: Is there any unhandled null value or abnormal input?
   
   Please give the severity level (Critical/Major/Minor) and attach suggestions for repair.

3. Write unit test generation command: Create file .continue/prompts/gen-test.prompt:

   You are a testing expert. Please generate pytest unit tests for the selected functions/classes:
   
   - Use pytest framework
   - Covers normal paths, boundary conditions, and abnormal paths
   - Use mocks to isolate external dependencies
   - Test naming follows the given_when_then pattern

4. Register Slash command in config.json:

   {
    "slashCommands": [
      {
        "name": "review",
        "description": "code review",
        "promptPath": ".continue/prompts/code-review.prompt"
      },
      {
        "name": "test",
        "description": "Generate unit tests",
        "promptPath": ".continue/prompts/gen-test.prompt"
      }
    ]
   }

5. Verification: Enter /review or /test in the Continue dialog panel to confirm that the custom command takes effect.

🔑 Expert opinion: The Slash command is the most valuable function for team collaboration in the Continue solution. Compared with Cursor's Rules or GitHub Copilot's Chat Participants, Continue's .prompt file is essentially a plain text template and naturally supports Git version management. The team can create an independent prompts warehouse and distribute it to various projects through git submodule or symbolic links to achieve "write once and reuse globally". It is recommended that the team only maintain 3-5 core Slash commands in the early stage to avoid excessive selection costs.

Acceptance Criteria

• [ ] Create at least 2 Slash commands and register them in config.json
• [ ] /review command can review code according to specified dimensions
• [ ] /test command can generate test code that conforms to the project style

Step 4: Rules and code base context configuration

⏱ Estimated time: 1-2 hours 🎯 Goal: Configure @ context reference and Rules rules to improve AI’s understanding of project structure ⚠️ Prerequisite: Slash command configuration completed

Operation instructions

Continue's "context" mechanism determines how much project information the AI can "see". Properly configured context sources can significantly improve the quality of code completion and dialogue, especially in large projects.

Specific operations

1. Configure @file reference: Enter @file in the dialog and select the current file, confirm that Continue can read the file content.
2. Configure @folder reference: Enter @folder to select the project directory so that AI can read the directory structure and file list.
3. Configuration @codebase: Configure the code base index in config.json:

   {
    "contextProviders": [
      {
        "name": "codebase",
        "params": {
          "useRag": true,
          "maxResults": 20
        }
      },
      {
        "name": "file",
        "params": {}
      },
      {
        "name": "folder",
        "params": {}
      }
    ]
   }

4. Customized Rules: Create the "rules" field in ~/.continue/config.json or use the project-level .continuerules file:

   {
    "rules": [
      "Always use TypeScript strict mode",
      "Follow the existing code style in the project",
      "Use React hooks for state management",
      "Write JSDoc comments for all exported functions"
    ]
   }

5. Configure @docs index: Add project documents or third-party library documents to the index so that AI can retrieve the document content:

   {
    "contextProviders": [
      {
        "name": "docs",
        "params": {
          "docs": [
            {
              "title": "Project API Documentation",
              "url": "https://your-project-docs-url"
            }
          ]
        }
      }
    ]
   }

🔑 Expert view: Context configuration is a key watershed for the advanced use of Continue. In the default configuration, Continue only reads the currently opened file, and answers often lack project-level vision. After turning on the RAG (Retrieval Enhanced Generation) mode of codebase, Continue will automatically build a vector index for the project and retrieve relevant code snippets during the conversation. For large projects with more than 100,000 rows, it is recommended to set maxResults to 15-20 and combine it with @folder to manually limit the search range to avoid index noise.

Acceptance Criteria

• [ ] @codebase references can correctly retrieve relevant code snippets in the project
• [ ] Rules Rules take effect in the conversation (such as the requirement "Use TypeScript strict mode" is complied with)
• [ ] Conversation responses reflect item-level contextual understanding

Step 5: Local model inference integration

⏱ Estimated time: 2-4 hours (including model download) 🎯 Goal: Run local open source models through Ollama or LM Studio to achieve complete offline AI programming assistance ⚠️ Prerequisites: Local GPU or Apple Silicon hardware

Operation instructions

The on-premises model is one of Continue’s core advantages over cloud-only solutions. Through Ollama or LM Studio, developers can run open source models such as Llama 3, CodeLlama, Mistral, and DeepSeek Coder locally to achieve a completely offline, zero-latency, and zero-data leakage AI programming experience.

Specific operations

Route A: Using Ollama

1. Install Ollama: Visit ollama.com to download and install the version corresponding to the platform.

2. Pull programming-specific models:

   # Code completion recommendations
   ollama pull codellama:7b-instruct
   
   # General programming conversation recommendations
   ollama pull deepseek-coder:6.7b-instruct
   
   #Lightweight options
   ollama pull qwen2.5-coder:7b-instruct

3. Add Ollama model configuration in Continue config.json:

   {
    "models": [
      {
        "title": "DeepSeek Coder Local",
        "provider": "ollama",
        "model": "deepseek-coder:6.7b-instruct"
      }
    ]
   }

Route B: Using LM Studio

1. Download and install LM Studio.
2. Search and download the target model (such as Qwen2.5-Coder-7B-Instruct-GGUF) in LM Studio.
3. Start the local inference server (Local Inference Server) and record the port number.
4. Configure the LM Studio backend in Continue:

   {
    "models": [
      {
        "title": "LM Studio Local",
        "provider": "openai",
        "model": "qwen2.5-coder-7b-instruct",
        "apiBase": "http://localhost:1234/v1",
        "apiKey": "not-needed"
      }
    ]
   }

🔑 Expert perspective: The local model and the cloud model are not substitutes, but complementary. The recommended layering strategy is: use the local 7B model (response speed < 1 second) for daily code completion and simple conversations, and use the cloud flagship model for complex architecture reviews or deep refactorings. Continue supports switching models in the same session without restarting the IDE. In terms of hardware, Apple Silicon Mac (M2+ 16GB) can run the 7B model smoothly, and NVidia RTX 3060+ can run the 13B-34B model. If the hardware is limited, Qwen2.5-Coder or the 1.5B-7B quantized version of the DeepSeek Coder series is preferred.

Acceptance Criteria

• [ ] The local model can start normally and accept requests
• [ ] Continue can successfully connect to the local model backend
• [ ] You can still use Continue to conduct code conversations in an offline environment (disconnected from the network)

Step 6: Team configuration synchronization and private deployment

⏱ Estimated time: 2-4 hours 🎯 Goal: Establish a team-level Continue configuration management process to achieve multi-member configuration consistency ⚠️ Prerequisites: Personal configuration completed and verified

Operation instructions

When multiple people on a team use Continue, fragmented configuration file management can quickly become a bottleneck. This step binds Continue's configuration to the project warehouse to implement Configuration as Code.

Specific operations

1. Project-level configuration: Create a .continue/ directory in the project root directory and place the project-specific config.json:

   {
    "models": [...],
    "slashCommands": [...],
    "rules": [...],
    "contextProviders": [...]
   }

   Continue will automatically merge user-level (~/.continue/config.json) and project-level (.continue/config.json) configurations, with project-level configuration having higher priority.

2. Configure into version control: Submit the .continue/ directory to Git:

   git add .continue/
   git commit -m "chore: add Continue team config"

3. Desensitization of sensitive information: Use environment variables instead of API Key:

   {
    "models": [
      {
        "title": "GPT-4o",
        "provider": "openai",
        "model": "gpt-4o",
        "apiKey": "${OPENAI_API_KEY}"
      }
    ]
   }

   Exclude .env files in .gitignore to distribute environment variables via CI/CD or team documentation.

4. Private deployment agent (enterprise scenario): If you need unified model routing and usage auditing, you can deploy Continue Hub (community version) as the model agent layer to uniformly manage API Keys and request logs.

🔑 Expert opinion: Continue’s “user-level + project-level” two-tier configuration system is the essence of its team collaboration design. The recommended hierarchical strategy is: user-level configuration stores personal API Keys and personalized model preferences, and project-level configuration stores the team's unified Slash commands, Rules, and context policies. This separation ensures that while individuals can switch models freely, the team's core workflow cues remain consistent. Compared with GitHub Copilot, which only supports personal-level configuration mode, Continue's team configuration management is more suitable for medium and large R&D teams that require unified coding standards.

Acceptance Criteria

• [ ] Project-level .continue/config.json has been included in Git management
• [ ] Another team member can use the configuration directly after pulling the project (after supplementing the personal API Key)
• [ ] $ environment variable references are correctly replaced at runtime

Expected results

Acceptance criteria

• [ ] Developers can freely switch between more than 3 models
• [ ] At least 3 custom Slash commands can be shared within the team
• [ ] Local models can complete code completion and dialogue in an offline environment
• [ ] Team configuration is distributed through Git, and new members can complete the environment setup in 15 minutes

Frequently Asked Questions and Troubleshooting

Q: What is the core difference between Continue and GitHub Copilot/Cursor? A: Continue is an open source plug-in rather than a standalone IDE. It does not lock in any model vendor and allows free configuration to call any LLM backend (including locally running open source models). GitHub Copilot is bound to Azure OpenAI. Although Cursor can use a variety of models, it is not open source. Continue's core values ​​are configuration sovereignty and data privacy.

Q: Can the performance of the local model be compared with GPT-4o? A: The current performance of local open source models at levels 7B-14B is close to the GPT-4 level in code completion and simple question and answer (HumanEval score is about 60-75% vs. GPT-4 about 85%), but there is still a gap in complex architecture reasoning, multi-file reconstruction and deep bug analysis. The recommended strategy is for the local model to handle daily coding and the cloud model to handle complex tasks.

Q: What infrastructure is required for team privatization deployment Continue? A: The basic plan only requires a Git repository distribution configuration file (zero server cost). If you need centralized model routing and usage management, you can deploy the Continue Hub Community Edition (requires a Linux server or Kubernetes cluster), and uniformly configure API Key rotation policies and request audit logs.

Q: How to solve the problem of slow inference speed of local model? A: The hardware determines the lower speed limit. Apple Silicon Macs use the MLX backend (Ollama automatically enabled) for optimal inference efficiency; NVidia GPUs recommend CUDA + quantized models (Q4_K_M). If the hardware is limited, give priority to a quantitative model with <= 7B parameters, and enable Continue's "streaming response" mode (enabled by default). You can start reading replies after the first token delay.

Q: Will sensitive code in the project be uploaded to the cloud? A: When using the local model (Ollama/LM Studio), all code processing is completely completed locally, and zero data is sent out. When using the cloud API, the code is sent to the corresponding model provider via HTTPS. Continue itself does not store or review code content. For enterprise compliance scenarios, it is recommended to configure the local model as the default backend and use the cloud API only for non-sensitive modules.

Advancement and Expansion

Multi-IDE synchronization

Continue's user-level configuration is stored in the ~/.continue/ directory. VS Code and JetBrains share the same configuration directory. After installing the Continue plug-in in JetBrains, existing configurations will be automatically read to achieve seamless switching across IDEs.

Custom context Provider

Continue supports writing custom Context Providers through TypeScript to inject external data sources such as internal API documents, Confluence knowledge base, internal NPM package documents, etc. into the AI conversation context. For detailed development guidelines, see the "Custom Context Provider" chapter of the Continue official documentation.

CI/CD integration

Through the Continue CLI tool, you can call Continue in the CI pipeline to perform automated code review or code documentation generation, extending AI-assisted capabilities from the IDE to DevOps workflows.

Model routing strategy

Enterprise teams can configure model routing strategies through Continue Hub: automatically route to different models by code base path, file suffix, or keyword. For example: src/**/*.ts files automatically call GPT-4o, docs/** files call Claude, and test files call local models.

Period and result

Note: The above cycle is based on the reference value of the first implementation by a 3-5 person R&D team. Individual developers can compress their cycles by 40-50%.

Advantages and Disadvantages of the Solution

Advantages

• Model freedom: Not bound to any model manufacturer, you can switch or use multiple models in parallel at any time
• Data sovereignty: The local model solution achieves zero code outsourcing and meets enterprise data compliance requirements
• Controllable Cost: Open source and free + local model, the larger the team size, the more significant the savings compared to commercial solutions
• Configuration as code: Slash commands and Rules are managed in the form of files, naturally supporting Git version control and team distribution
• Cross-platform consistency: VS Code and JetBrains share the same configuration, developers can seamlessly switch between different IDEs

Limitations

• Local model effect upper limit: The current open source model still lags behind flagship models such as GPT-4o/Claude in complex inference tasks
• Configuration Complexity: Compared with "out-of-the-box" commercial solutions, Continue requires developers to invest initial configuration time
• Hardware Threshold: Certain hardware conditions (GPU or Apple Silicon) are required for local models to run smoothly
• Community-driven: Some function iterations rely on community contributions, and the release time of functions is uncontrollable.
• Lack of unified market: There are multiple plug-ins with the same name/similarity in the VS Code extension market, which need to be installed from the official source (continue.dev) to avoid confusion.

Tool summary

Applicable to crowd diversion

Optimal scenario

• R&D teams with high requirements for model selection and data privacy (finance, medical, government and other strictly regulated industries)
• Medium and large development teams that want to unify team coding standards and workflow prompts
• Developers who need to program in offline or restricted network environments
• Individual developers or start-up teams with limited budget who want to obtain AI programming assistance at low cost
• Technology enthusiasts who want to deeply understand the underlying interaction mechanism of AI programming

Not suitable for the scene

• Teams that pursue "out-of-the-box" and don't want to spend time configuring models (it is recommended to choose Cursor or GitHub Copilot)
• The IDE technology stack of team members is highly inconsistent (editors such as Eclipse and Vim are not supported)
• Scenarios where the need for AI programming assistance is only "occasional simple code completion" and no custom workflow is required
• Teams whose hardware conditions cannot meet the local model running requirements and are unwilling to use the cloud