Evolve AI — Context-Aware AI Coding Assistant for VS Code

Evolve AI brings powerful AI code assistance directly into your editor. It works with Ollama (local/offline), Gemma 4 (Google's multimodal open model), GLM / CodeGeeX (local coding models), Anthropic Claude, OpenAI-compatible APIs, Google Gemini, GLM (Z.ai), and Hugging Face — so you choose where your code goes.

Why Evolve AI?

• Free & private — runs fully offline with Ollama or Gemma 4. Your code never leaves your machine.
• Auto-detecting stack plugins — 17 plugins that activate automatically based on your project: Databricks, Terraform, Docker, Kubernetes, Django, FastAPI, dbt, Airflow, PyTorch, Data Analysis & Reporting, and more.
• Any AI provider — bring your own model or API key. Switch between local and cloud in one click.
• Deep context — understands your active file, related files, diagnostics, git state, and cloud platform connections.
• Connect to GitHub or Bitbucket in one click (v2.0) — wizard handles git install, identity, init/clone, auth (PAT / SSH / VS Code GitHub auth / gh CLI), and verifies the connection. → docs/GIT_CONNECT.md
• Author CI/CD pipelines with AI (v2.1) — auto-detecting plugin for GitHub Actions / GitLab CI / Jenkins / CircleCI / Azure / Bitbucket, plus a setup wizard that generates a stack-tailored starter pipeline (pinned actions, OIDC, caching, concurrency built in). Includes a "Fix failing run" command — paste a CI log, AI diagnoses against your active pipeline file. → docs/CICD.md

Also works in Cursor, VSCodium, and other VS Code forks.

Built for Data Engineers

If you live in dbt + Airflow + Databricks/BigQuery files, Evolve AI is purpose-built for your day:

• Lineage-aware AI prompts (v1.5) — open a dbt model or PySpark notebook and the AI's suggestions use your real column names. Walks {{ ref() }} / {{ source() }} / spark.table(...) and pulls schemas from target/manifest.json, schema.yml, or Unity Catalog. No more hallucinated columns. → docs/LINEAGE.md
• Query cost preview (v1.7) — Ctrl+Alt+Q shows bytes scanned, estimated USD cost, warnings (SELECT *, missing partition filter, cross join, large-scan) before you run. Databricks EXPLAIN COST and BigQuery dry-run. Free on BigQuery's side. → docs/QUERY_ANALYSIS.md
• dbt impact analysis (v1.8) — Ctrl+Alt+I opens an Impact panel showing every model + exposure that depends on the one you're editing. Refactor with AI (impact-aware) button injects the blast radius into the prompt. → docs/DBT_MANIFEST.md
• Airflow DAG simulator (v1.9) — Ctrl+Alt+D does static analysis on DAG files: cycles, broken >> edges, duplicate task_ids, sensor poke-starvation, missing catchup=False, invalid cron, TaskFlow () mistakes — inline diagnostics + a Fix all with AI button. No Python interpreter, no Airflow install required. → docs/AIRFLOW_SIMULATOR.md

All four work on cloud LLMs (Anthropic / OpenAI / Gemini / HF) and on local providers (Ollama / Gemma 4) — your data stays where you decide.

Get Started in 60 Seconds

# 1. Install Ollama (free, local AI)
# Download from https://ollama.com — or on Linux:
curl -fsSL https://ollama.ai/install.sh | sh

# 2. Pull a model (pick one)
ollama pull gemma4:e4b        # Google Gemma 4 — multimodal, 128K context (recommended)
ollama pull qwen2.5-coder:7b  # Qwen — optimized for code

# 3. Install the extension and start coding
# Ctrl+Shift+A to open chat — Evolve AI detects Ollama automatically

No API key. No account. No data leaving your machine. That's it.

Connecting your repo to GitHub or Bitbucket (new in v2.0)

Open the command palette and run Evolve AI: Connect Git Remote (Wizard), or click the · not connected hint in the status bar. The wizard:

1. Installs Git if missing (Win / mac / linux instructions)
2. Sets your user.name / user.email if not already configured
3. Initialises / clones / links a repo
4. Authenticates: VS Code GitHub (built-in, recommended for github.com) · PAT · SSH ed25519 · gh auth login — pick what fits you
5. Optionally creates a new repo on GitHub or Bitbucket via API
6. Verifies the connection with git ls-remote origin

Tokens are stored in vscode.SecretStorage — they never touch settings.json. Existing credential.helper is never overwritten. Full guide: docs/GIT_CONNECT.md.

Setting up CI/CD (new in v2.1)

Open the command palette and run Evolve AI: CI/CD Setup Wizard, or click Optimize Pipeline on a CodeLens above any existing pipeline. The wizard:

1. Detects your stack (language, package manager, test framework, git host)
2. Asks: which CI platform · what kind of pipeline (test only / + deploy / + container build) · which deploy target (npm / PyPI / Docker / AWS ECS / GCP Cloud Run / Azure / k8s)
3. Generates a starter pipeline tailored to your stack — pinned actions, OIDC where the platform supports it, dependency caching by lockfile hash, concurrency control, timeouts, least-privilege permissions
4. Writes it to the right path and opens it for review

For existing pipelines, the CI/CD plugin auto-activates on detection of .github/workflows/*.yml, .gitlab-ci.yml, Jenkinsfile, .circleci/config.yml, azure-pipelines.yml, or bitbucket-pipelines.yml. It contributes platform-aware best practices into every AI prompt and adds CodeLens / lightbulb actions like Pin actions to commit SHA, Replace long-lived secrets with OIDC, Convert to matrix strategy. Full guide: docs/CICD.md.

How Does It Compare?

Features

Multi-Provider AI Support

AI Chat — Sidebar or Editor Tab

• Two ways to chat. Open in the sidebar (Ctrl+Shift+A), or click the Evolve AI icon in any file's editor title bar to open the chat as a tab to the right of your code — Claude Code-style. Both views share the same conversation in real time.
• Inline mode pill — pick Chat (ask), Edit (modify the active file), or Create (generate new files) directly above the input box.
• Inline model pill — switch models within the active provider (e.g., between your installed Ollama models, or between claude-sonnet-4-6 and claude-opus-4-7) without leaving the chat. A More providers… item handles cross-provider changes.
• Streaming responses with full project context.
• Understands your active file, related files, diagnostics, and git state.
• Context budget system ensures efficient token usage.

Smart Code Actions

• CodeLens hints above every function: Explain | Tests | Refactor
• Lightbulb actions: "Fix with AI" on any diagnostic
• Right-click menu: Explain, refactor, fix, document, generate tests
• Keyboard shortcuts: Quick access to common actions

18 Core Commands

• Open AI Chat — sidebar (Ctrl+Shift+A)
• Open AI Chat — editor tab (top-right icon in any file)
• Generate Code from Description (Ctrl+Alt+G)
• Fix Current Errors (Ctrl+Alt+F)
• Explain Selected Code (Ctrl+Alt+E)
• Generate Commit Message (Ctrl+Alt+M)
• Refactor Selection, Add Documentation, Generate Tests, Apply Folder Transforms
• Explain Changes, Generate PR Description, Build Framework, Run & Auto-Fix
• Switch Provider, Setup Ollama, Gemma 4 Info & Tips, What's New

17 Auto-Detecting Plugins

Plugins activate automatically based on your workspace files. No configuration required.

Data Analysis & Reporting

Give Evolve AI a data file and an instruction — get insights and a report, PowerBI-style, without leaving your editor.

• Right-click a data file (.csv / .tsv / .json / .xlsx / .parquet) → Analyze Data & Report, or use the command palette.
• Deliverables: Insights in chat (Gemini-style narrative analysis inline, with follow-up questions), a self-contained HTML report (KPI tiles, charts, AI insights), a reproducible pandas/plotly notebook or script, or a profiling summary.
• Not just local files — Analyze Data from Database or Cloud Source pulls a sample from BigQuery, Databricks SQL, Cosmos DB, Azure Log Analytics, DynamoDB, or S3 / GCS / Azure Blob objects, reusing your existing connected-plugin credentials. For Postgres / MySQL / SQLite / Snowflake / SQL Server, it generates a pandas.read_sql script you run with your own connection string (DB_URL env var — no passwords stored).
• Size-adaptive: small files are analysed directly by the AI; for large files it generates a script that reads the full dataset locally — your data never leaves your machine. If a sample would go to a cloud provider, you're asked first.
• Output lands next to your data (sales.csv → sales-report.html). Excel/PDF export is produced by the generated script.
• Declarative pipelines — define a repeatable analysis once in evolve-data-pipeline.json (steps = source + analysis) and run them all with Run Data Pipeline. A versioned, backend-free "workflow" you own in your repo. Full guide: docs/DATA_ANALYSIS.md

Cloud Platform Integration

The Databricks, AWS, Google Cloud, and Azure plugins go beyond code assistance. They connect to your actual cloud accounts to:

• Manage resources — list and inspect Lambda functions, Cloud Run services, Azure Functions, Databricks clusters
• Execute queries — run SQL on BigQuery, Cosmos DB, Databricks SQL warehouses
• Browse storage — navigate S3 buckets, GCS objects, Azure Blob containers, Unity Catalog
• Trigger and monitor jobs — run Glue jobs, Databricks workflows, Step Functions
• AI-powered diagnostics — analyze failed job runs with AI explanations and fix suggestions
• Deploy from VS Code — deploy notebooks, upload to S3/GCS/Azure Storage, manage DLT pipelines

Secure by Design

• API keys stored in VS Code's encrypted SecretStorage — never in plaintext settings
• Cloud credentials use standard provider SDKs and authentication flows
• All file edits go through VS Code's undo stack
• Diff preview before applying AI-generated changes
• Context budget caps prevent excessive token usage
• Workspace Trust enforced — in untrusted workspaces, workspace-level overrides of provider host URLs (ollamaHost, openaiBaseUrl, huggingfaceBaseUrl) are ignored so a malicious .vscode/settings.json can't redirect your chat to an attacker-controlled server
• Remote-host warning — if a provider URL isn't loopback/private, a one-time toast tells you your code is leaving your machine
• Image uploads validated (10 MB cap, PNG/JPEG/WEBP/GIF only)
• Ollama minimum 0.12.4 — the smart-setup wizard prompts for upgrades to close known Ollama CVEs

Quick Start

1. Install the extension from the VS Code Marketplace
2. Choose your AI provider:
   • For local/private: Install Ollama, pull a model (ollama pull qwen2.5-coder:7b), and you're ready
   • For cloud AI: Run Evolve AI: Switch AI Provider from the command palette, select your provider, and enter your API key when prompted
3. Start coding: Open the AI Chat sidebar (Ctrl+Shift+A) or use any command from the command palette
4. Cloud plugins activate automatically when they detect relevant files in your workspace

AI Providers

Ollama (local, recommended)

Run AI completely on your machine — no API key, no cost, no data leaving your network.

# Install Ollama: https://ollama.ai
ollama pull qwen2.5-coder:7b

Set aiForge.provider to ollama (or leave on auto — it detects Ollama automatically).

Also compatible with LM Studio, llama.cpp, and Jan — point aiForge.ollamaHost at your server.

Gemma 4 (local, multimodal)

Google's latest open model with text, image, and audio understanding. Runs locally and privately via Ollama. Apache 2.0 licensed.

One-click setup — run Switch AI Provider → select Gemma 4. Evolve AI:

1. Asks consent to inspect your system (RAM, GPU, disk, Ollama version) — no data leaves your machine
2. Recommends the variant that fits your hardware
3. Shows a single "Install Everything" button that handles Ollama install/upgrade + model download + config
4. Reports live download progress (MB/total) right in the notification

If your system can't run any variant, you get actionable alternatives instead of a dead end (cloud providers or offline mode).

Or set up manually:

# Install Ollama: https://ollama.com
ollama pull gemma4:e4b    # Recommended for most users (~9.6GB)

Choose your variant in aiForge.gemma4Model:

Anthropic Claude

1. Get an API key from console.anthropic.com
2. Run command: Switch AI Provider -> select Anthropic
3. Enter your API key when prompted (stored in VS Code SecretStorage)

OpenAI / Compatible

Works with OpenAI, Groq, Mistral, Together AI, LiteLLM, and any OpenAI-compatible endpoint.

1. Set aiForge.openaiBaseUrl to your endpoint (default: https://api.openai.com/v1)
2. Set aiForge.openaiModel to your model name
3. Run Switch AI Provider -> select OpenAI -> enter API key

Google Gemini

Use Google's Gemini models via the official OpenAI-compatible endpoint.

1. Get an API key from aistudio.google.com/apikey
2. Set aiForge.geminiModel (default: gemini-2.5-flash; also gemini-2.5-pro, gemini-2.0-flash)
3. Run Switch AI Provider -> select Google Gemini -> enter API key

GLM (local, offline)

Run a GLM / CodeGeeX coding model fully offline via Ollama — no API key, no data leaves your machine. Default is codegeex4-all-9b (a coding model built on GLM-4-9B, ~5.5GB, 128K context).

1. Install Ollama
2. Run Switch AI Provider -> select GLM (local) -> pick a model (codegeex4-all-9b, glm4:9b, or glm4) -> it offers to download it
3. Runs locally from then on

Note: the 355B+ GLM-4.5 / GLM-4.6 flagships are too large to run on a normal machine — those are cloud-only (see below).

GLM (Z.ai, cloud)

Use Zhipu / Z.ai's flagship GLM models (glm-4.6, glm-4.5) via their OpenAI-compatible cloud API.

1. Get an API key from z.ai
2. Run Switch AI Provider -> select GLM (Z.ai) -> enter API key -> pick a model
3. Set aiForge.zaiModel (default: glm-4.6)

HuggingFace Inference API

Access thousands of open models via the HuggingFace Inference API.

1. Get a token from huggingface.co/settings/tokens
2. Set aiForge.huggingfaceModel (default: Qwen/Qwen2.5-Coder-32B-Instruct)
3. Run Switch AI Provider -> select HuggingFace -> enter token

Built-in Offline AI

Pattern-based code analysis — works instantly with no setup, no network, no LLM.

Settings

Keyboard Shortcuts

How Context Works

Every AI call automatically includes:

1. Active file — full content of your current file (priority budget allocation)
2. Related files — imported/importing files (remaining budget, capped at maxContextFiles)
3. Diagnostics — current errors and warnings (if includeErrorsInContext is enabled)
4. Git diff — unstaged changes (if includeGitDiffInContext is enabled)
5. Plugin context — domain-specific data from active plugins (e.g., dbt manifest, Terraform state, Databricks cluster info)

Total characters capped by contextBudgetChars (default 24,000). Increase for larger models; decrease for faster/cheaper ones.

Cloud Plugin Setup Guides

Databricks Connected

Connect to your Databricks workspace for live cluster management, job monitoring, notebook deployment, Unity Catalog browsing, and SQL execution.

What you need: A Databricks workspace URL and a Personal Access Token (PAT).

Setup:

1. Open the command palette (Ctrl+Shift+P)
2. Run Evolve AI: Databricks: Connect to Workspace
3. Enter your workspace URL (e.g., https://adb-1234567890.12.azuredatabricks.net)
4. Enter your Personal Access Token
   • Generate one at: Workspace > User Settings > Developer > Access Tokens > Generate New Token
5. The status bar will show a green dot with your workspace name when connected

Available commands after connecting:

AWS Connected

Connect to your AWS account for Lambda management, Glue job monitoring, S3 browsing, CloudFormation analysis, Step Functions design, and DynamoDB exploration.

What you need: An IAM user or role with programmatic access (Access Key ID + Secret Access Key).

Recommended IAM permissions: ReadOnlyAccess for browsing, plus lambda:InvokeFunction, glue:StartJobRun, s3:PutObject, states:StartExecution for execution commands.

Setup:

1. Open the command palette (Ctrl+Shift+P)
2. Run Evolve AI: AWS: Connect to Account
3. Enter your AWS Access Key ID
4. Enter your AWS Secret Access Key
5. Enter your AWS Region (e.g., us-east-1, eu-west-1)
6. The extension tests the connection with STS GetCallerIdentity

Environment variable alternative: Set AWS_ACCESS_KEY_ID, AWS_SECRET_ACCESS_KEY, and AWS_DEFAULT_REGION — the plugin picks these up automatically.

Available commands after connecting:

Google Cloud Connected

Connect to your GCP project for Cloud Functions management, Cloud Run monitoring, BigQuery analysis, GCS browsing, Pub/Sub messaging, and Firestore exploration.

What you need: A GCP service account JSON key file and your project ID.

Setup:

1. Open the command palette (Ctrl+Shift+P)
2. Run Evolve AI: Google Cloud: Connect to Project
3. Select your service account JSON key file (file picker dialog)
   • Create one at: GCP Console > IAM & Admin > Service Accounts > Keys > Add Key > JSON
4. Enter your GCP project ID
5. The extension tests the connection by fetching project info

Recommended roles: Viewer for browsing, plus Cloud Functions Invoker, BigQuery User, Storage Object Admin for execution commands.

Available commands after connecting:

Azure Connected

Connect to your Azure subscription for Functions management, Logic Apps monitoring, Cosmos DB querying, Storage browsing, DevOps pipeline analysis, and Log Analytics.

What you need: An Azure service principal (App Registration) with Tenant ID, Client ID, Client Secret, and Subscription ID.

Setup:

1. Open the command palette (Ctrl+Shift+P)
2. Run Evolve AI: Azure: Connect to Subscription
3. Enter your Tenant ID
4. Enter your Application (Client) ID
5. Enter your Client Secret
6. Enter your Subscription ID
7. The extension tests the connection by fetching subscription info

Creating a service principal:

# Using Azure CLI
az ad sp create-for-rbac --name "Evolve-AI" --role "Reader" \
  --scopes /subscriptions/<your-subscription-id>

This outputs appId (Client ID), password (Client Secret), and tenant (Tenant ID).

Available commands after connecting:

Troubleshooting

Chat shows OFFLINE / No response

Ollama not detected:

1. Verify Ollama is running: open http://localhost:11434 in your browser — it should say "Ollama is running"
2. If using Windows and localhost doesn't work, try setting aiForge.ollamaHost to http://127.0.0.1:11434
3. Make sure you have a model pulled: ollama list should show at least one model
4. Check the model name matches aiForge.ollamaModel (default: qwen2.5-coder:7b)

Cloud provider not responding:

1. Check your API key is set: run Evolve AI: Switch AI Provider and re-enter your key
2. Verify network connectivity to the provider's API endpoint
3. Check VS Code's Developer Tools console (Help > Toggle Developer Tools) for error messages

Chat input not responding / buttons don't work

1. Reload the window: Ctrl+Shift+P > "Developer: Reload Window"
2. If the issue persists, close and reopen the chat panel
3. Check VS Code's Developer Tools console for JavaScript errors in the webview

Plugin not activating

Plugins activate automatically based on workspace files. If a plugin isn't showing:

1. Make sure the workspace contains the expected marker files (see the plugin table above)
2. Check aiForge.disabledPlugins in settings — make sure the plugin ID isn't listed
3. Reload the window to trigger re-detection

Cloud plugin shows "not connected"

1. Run the connect command for your provider (e.g., AWS: Connect to Account)
2. Verify your credentials are correct — the connect command tests the connection
3. Check that your credentials have sufficient permissions (see setup guides above)
4. For AWS: ensure your region is correct and your IAM user/role is active
5. For GCP: ensure the service account JSON key is valid and not expired
6. For Azure: ensure the client secret hasn't expired
7. For Databricks: ensure the PAT hasn't expired and your workspace URL is correct

Commands show "command not found"

This happens when a cloud plugin command is triggered but the plugin isn't active. Cloud plugin commands only register when:

1. The plugin detects matching files in your workspace (e.g., serverless.yml for AWS)
2. The plugin has activated (connected to the cloud provider)

Fix: Open a workspace that contains files for that cloud platform, then run the connect command.

Slow responses

1. Ollama: Use a smaller model (e.g., qwen2.5-coder:3b instead of 7b)
2. Context too large: Reduce aiForge.contextBudgetChars (try 12000) or aiForge.maxContextFiles (try 3)
3. Cloud context: Connected plugins add live data to context — this adds a small delay on each request

Gemma 4 setup wizard issues

"aiForge.gemma4Model is not a registered configuration" error

• This happens on v1.4.0 only, when the extension is installed or upgraded into a running VS Code window. VS Code's Configuration Registry hasn't picked up the new settings schema yet.
• Fix: Reload the window (Ctrl+Shift+P → "Developer: Reload Window"), then run Switch AI Provider → Gemma 4 again. Setup will complete normally.
• Fixed in v1.4.1+: The wizard now detects this and shows a one-click Reload Window button automatically.

"System cannot run Gemma 4" modal appears

• Your RAM or free disk space is below the minimum for any variant (8GB RAM, 8GB disk)
• The modal lists the specific blockers and three alternatives (cloud, offline, free up resources)
• If you know you have plenty of disk, the check looks at the Ollama models directory (~/.ollama/models on Linux/macOS, %USERPROFILE%\.ollama\models on Windows). Run df -h ~/.ollama (or check disk in Explorer) to confirm

Setup hangs at "Downloading… 0%"

• Verify Ollama is running: open http://localhost:11434 in your browser
• Verify internet connectivity: ping ollama.ai
• If stuck more than 5 minutes, click Cancel in the progress notification and retry

Hardware detection shows "No GPU detected" but you have one

• NVIDIA: ensure nvidia-smi is on your PATH (nvidia-smi --version in terminal)
• AMD: ensure rocm-smi is installed (Linux only)
• Apple Silicon: detection requires system_profiler (built-in on macOS)
• Intel integrated GPUs are not detected — Gemma 4 won't use them anyway
• You can manually pick a variant via the "Choose Different Variant" button in the wizard

Ollama upgrade fails during setup

• The wizard auto-upgrades Ollama when it's older than 0.3.10 (required for Gemma 4)
• If the upgrade fails, manually download from ollama.com and run the installer
• Then re-run Evolve AI: Switch AI Provider → Gemma 4

"Could not find gemma4 variant" after setup completes

• Ollama may still be pulling the model in the background — wait 5-10 minutes
• Verify with ollama list in your terminal — should show your gemma4:* tag
• If missing, run ollama pull gemma4:e4b manually and try again

How to disconnect / change credentials

Run the disconnect command for your provider:

• Evolve AI: AWS: Disconnect
• Evolve AI: Google Cloud: Disconnect
• Evolve AI: Azure: Disconnect
• Evolve AI: Databricks: Disconnect

Then run the connect command again with new credentials.

FAQ

General

Q: Is my code sent to the cloud? A: It depends on your provider. With Ollama, everything stays on your machine — no data leaves your network. With cloud providers (Anthropic, OpenAI, Google Gemini, HuggingFace), your code context is sent to their API. Choose based on your privacy requirements.

Q: Which AI provider should I use? A: For privacy and cost: Gemma 4 or Ollama (free, local, your code never leaves your machine). For best quality: Anthropic Claude, OpenAI GPT-4o, or Google Gemini 2.5 Pro. For speed on a budget: Groq (via OpenAI-compatible endpoint) or Gemini 2.0 Flash. For no setup: the built-in offline mode (limited to pattern-based analysis).

Q: What is Gemma 4 and why should I use it? A: Gemma 4 is Google's latest open-weight AI model (Apache 2.0 license). It runs locally via Ollama with no API key, no cost, and no data leaving your machine. It supports text, image, and audio input with 128K-256K context windows. The E4B variant (~9.6GB) is recommended for most users. Select Gemma 4 in the provider switcher for a guided setup wizard.

Q: How does the Gemma 4 setup wizard pick the right variant for me? A: When you select Gemma 4, Evolve AI asks one-time consent to inspect your system: RAM (os.totalmem()), GPU (NVIDIA via nvidia-smi, AMD via rocm-smi, Apple Silicon via system_profiler), free disk space, and your Ollama version. It scores each variant against your hardware and recommends one — typically E2B for 8GB RAM, E4B for 16GB, 26B MoE for 32GB+, 31B Dense for 32GB+ with a GPU. No data leaves your machine — detection is purely local.

Q: Will the wizard auto-install Ollama or download models without asking? A: No. Each step asks explicit consent before running:

• "Install Ollama?" — opens the official installer download (only if Ollama isn't installed)
• "Upgrade Ollama?" — only if your version is older than 0.3.10 (required for Gemma 4)
• "Download ?" — confirms before pulling the model You see a setup plan listing every step before clicking "Install Everything", and the whole process is cancellable mid-way.

Q: What if my system can't run Gemma 4? A: The wizard shows a modal explaining exactly why (e.g. "only 4GB RAM detected — needs at least 8GB") and offers three actionable alternatives:

• Switch to a cloud provider (Anthropic Claude, OpenAI, Google Gemini, GLM/Z.ai, HuggingFace) — runs in the cloud, only needs an API key
• Use Offline mode — pattern-based AI, no LLM required, works instantly
• Free up resources — disk-space tips if that's the blocker You're never left at a dead end.

Q: Can I disable hardware detection? A: Yes. Set aiForge.allowHardwareDetection to false in settings. The wizard then falls back to showing all 4 variants without inspection — you pick manually. Or decline the one-time consent dialog when it first appears.

Q: Can I use multiple providers? A: You can switch providers at any time via Evolve AI: Switch AI Provider. The extension uses one provider at a time.

Q: What models work with Ollama? A: Any model Ollama supports. Recommended: gemma4:e4b (Google Gemma 4, multimodal, strong coding), qwen2.5-coder:7b (code-optimized), codellama:13b (larger, better quality), deepseek-coder:6.7b. Run ollama list to see installed models.

Q: Does Evolve AI work with LM Studio / llama.cpp / Jan? A: Yes. Set aiForge.ollamaHost to your server's URL (e.g., http://localhost:1234/v1 for LM Studio). These servers implement the same API as Ollama.

Plugins

Q: How do plugins activate? A: Automatically. When you open a workspace, Evolve AI scans for marker files (e.g., Dockerfile for Docker, manage.py for Django). Matching plugins activate silently and start injecting domain knowledge into every AI interaction. The status bar shows active plugins.

Q: Can I disable a plugin? A: Yes. Add the plugin ID to aiForge.disabledPlugins in settings. Example: ["databricks", "docker"]. Plugin IDs: databricks, databricks-connected, aws, aws-connected, gcp, gcp-connected, azure, azure-connected, dbt, airflow, pytest, fastapi, django, terraform, kubernetes, docker, jupyter, pytorch, security, git.

Q: What's the difference between the base and connected versions of cloud plugins? A: The base plugin (e.g., AWS) activates on file detection and injects best-practice knowledge into AI responses — no credentials needed. The connected plugin (e.g., AWS Connected) adds live API access — browse resources, run queries, analyze failures, deploy code. Both can be active simultaneously.

Q: Do cloud plugins cost anything? A: The plugins themselves are free. But they call your cloud provider's APIs, which may incur costs depending on your plan. Read-only operations (listing resources, reading logs) are typically free or low-cost. Execution operations (invoking Lambda, running BigQuery queries) may have associated costs.

Cloud Credentials

Q: Where are my credentials stored? A: In VS Code's encrypted SecretStorage — the same mechanism VS Code uses for its own authentication. Credentials are never written to settings files, .env files, or any plaintext location.

Q: Can I use temporary/session credentials? A: For AWS, yes — you can provide a session token along with your access key and secret key. For Azure, the client secret has an expiry set in Azure AD. For GCP, service account keys don't expire but can be rotated. For Databricks, PATs have configurable expiry.

Q: What permissions do I need? A: At minimum, read-only access to list and inspect resources. For execution features (invoking functions, running jobs, deploying files), you need the corresponding write permissions. See each cloud plugin's setup guide above for specific IAM recommendations.

Q: Is it safe to use in production? A: The extension only performs the actions you explicitly trigger via commands. It never modifies cloud resources automatically. Execution commands (run job, invoke function, deploy) always require your manual action.

Contributing

Contributions are welcome! See CONTRIBUTING.md for the full guide.

Quick start for contributors:

1. Fork the repo and clone it
2. npm install && npm run watch
3. Press F5 to launch the Extension Development Host
4. Make your changes and test them

Easiest way to contribute — add a new stack plugin:

1. Read docs/PLUGIN_GUIDE.md for the step-by-step template
2. Create src/plugins/<name>.ts implementing the IPlugin interface
3. Register it in src/plugins/index.ts
4. Add commands to package.json under contributes.commands

Plugin ideas (community contributions welcome):

• Next.js — App Router, Server Components, API routes
• Rust — ownership, lifetimes, async patterns
• Go — goroutines, interfaces, error handling
• GraphQL — schema, resolvers, queries
• React Native — Expo, Metro, native modules
• Spring Boot — Java/Kotlin, dependency injection, JPA

Requirements

• VS Code 1.85.0 or later
• For local AI: Ollama with a pulled model
• For cloud AI: An API key from your chosen provider
• For cloud plugins: Appropriate credentials (see setup guides above)

License

MIT