vide-code
vide-code is an AI engineering workspace inside VS Code: you can ask, plan, execute, review, and automate in a single flow.
It is not just "chat for code". It is a product built to turn real tasks into real delivery with quality, traceability, and speed.
Why use it
- Faster delivery: from quick bug fixes to full features with clear progress.
- Less context switching: chat, terminal, review, and automation in one place.
- Quality-first workflow: AI-assisted code review, validations, and outcome-driven iteration.
- Scalable execution: combine modes, agents, and external tools for complex tasks.
Modes that match your moment
- Ask: understand code, unblock decisions, and move fast on technical questions.
- Agent: execute real changes across files, commands, and project workflows.
- Planner: break larger goals into clear, actionable steps before implementation.
- Dag: orchestrate dependent and parallel steps for predictable execution flows.
Core product capabilities
- Multi-tab chat: keep isolated contexts per stream (feature, hotfix, refactor, review).
- vide-code CLI: the same chat experience in the terminal, in a faster advanced mode for high-speed execution.
- AI-assisted Code Review: surface risks, regressions, and maintainability improvements.
- MCP: connect tools, APIs, and external services to expand what the assistant can do.
- Multiagent: split complex work across specialized agents to increase throughput.
- Custom Agents: define agents with custom instructions, style, and scope.
- Skills: plug in reusable capabilities to standardize recurring team workflows.
AI models and providers
vide-code is provider-flexible so you can optimize for quality, speed, privacy, or cost by task:
- OpenAI: strong quality for coding, planning, review, and automation.
- Gemini: robust option for broad-context analysis and engineering work.
- Grok: fast alternative for exploration and technical iteration.
- Ollama: run local models for stronger privacy and infrastructure control.
- Bedrock: access models through AWS with enterprise-ready ecosystem integration.
- OpenAI Compatibility: connect providers that support OpenAI-compatible APIs.
Beyond chat models, you can also configure embedding models compatible with OpenAIEmbeddings, enabling semantic search, RAG workflows, and richer project context retrieval.
Recommended quick start
- Install the extension in VS Code.
- Configure your provider (OpenAI, Gemini, Grok, Ollama, Bedrock, or OpenAI Compatibility).
- Open two chat tabs: one for implementation and one for review.
- Use
Planner to structure, Agent to execute, and Code Review to validate.
vide-code: less friction to decide, more capacity to build.
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