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Getting Started
This guide gets a working Eva installation from zero to a first useful task. Start with the standalone desktop app unless you are developing Eva itself.
| Goal | What you need |
|---|---|
| Eva with cloud-backed AIG | Linux, Node.js 24+, Python 3.12+, and GitHub Copilot CLI signed in locally |
| Fully local chat | LM Studio with a local OpenAI-compatible model; no cloud model key required |
| Direct OpenAI, GitHub Models, or Gemini | An account and key for the selected provider only |
| Coding Workspaces and ACP tools | GitHub Copilot CLI authenticated with copilot auth login
|
| Browser, desktop, camera, Signal, or Local Voices | The optional prerequisite for that feature; configure it only when needed |
Eva's standalone runtime keeps mutable state under
~/.config/eva-standalone by default. Set EVA_CONFIG_DIR before launching if
you want a different local state location. The rebuildable installed source is
kept separately under ~/.eva on Linux.
curl -fsSL https://appatalks.github.io/eva-agent/get-eva.sh | bash
evaThe installer adds Eva to your application menu as well. Use the app menu or
the eva command for subsequent launches.
- Open Settings.
- In Models, select Eva (AIG) for Eva's integrated memory, persona, retrieval, and optional review pipeline.
- In Auth, configure the one provider route you want to begin with: Copilot ACP, OpenAI, GitHub Models, Gemini, or none for LM Studio.
- Return to chat and ask for a bounded, observable task, such as: "Summarize this plan and identify the next three decisions."
- When Eva requests an approval, read what will happen and choose Allow once only when it matches your intent.
Eva starts a fresh chat on each launch. Previous sessions remain available in the Session Explorer, so a clean first screen does not delete your saved work.
Use Eva (AIG) when you want Eva's full orchestration: structured memory context, relevant data retrieval, persistent goals and skills, emotion tracking, and optional adaptive review. Its selected backend can use Copilot ACP, OpenAI, GitHub Models, or LM Studio. See Models and Providers.
- Start an OpenAI-compatible model server in LM Studio.
- Confirm it is listening on the configured local endpoint, normally
http://localhost:1234/v1/chat/completions. - Choose an LM Studio model and set data retrieval mode to Local.
- Send a simple prompt, then add local MCP tools only when you need them.
Eva's local SQLite memory remains available in this route. Details are in Local Mode and MCP.
For Copilot-backed chat, tools, retrieval, subagents, and coding workspaces, install and authenticate the CLI on the same computer:
copilot auth loginThen choose a Copilot ACP backend in Settings. Eva speaks to the CLI through a loopback bridge; it does not need you to paste the CLI credential into the UI.
| Setting area | Typical first configuration |
|---|---|
| General | Theme, speech engine/voice, auto-speak, local/cloud retrieval mode |
| Models | Eva backend, provider model, reasoning effort, adaptive review |
| Auth | A provider key, GitHub authentication, or optional Signal details |
| Prompts | A personality preset or a carefully scoped system prompt |
| MCP | Optional Kusto, GitHub, Azure, or custom tool configuration |
Credentials are local configuration. Do not paste API keys, personal access tokens, or private data into chat, Skills, issue reports, or screenshots.
Open Settings > General and confirm the app reports the expected data mode. Then ask Eva for a non-sensitive task. If you selected a provider requiring authentication, a clear provider error is preferable to a silent fallback; use Troubleshooting to diagnose it.
For source builds, AppImage details, and Windows packaging, see Developer Guide.
- Using Eva for chat, sessions, Voice View, and settings habits.
- Memory, Learning, and Privacy before enabling persistent or autonomous workflows.
- Abilities and Automation when you want Eva to act outside chat.
Eva AI Assistant is MIT licensed. This wiki documents the current standalone application and its optional integrations. Configuration values, credentials, workspace content, and generated artifacts remain local unless you choose to send them to a configured provider or service.
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