Your project.AI inside the work.

Leera AI is the assistant inside your project workspace. Use project context to answer questions, create or update issues with enabled tools, plan sprints, and build charts and reports. Review suggested subtasks and test cases, or refine selected text in documents and comments. Scheduled Leera AI Agents and external tools connected over MCP are separate ways to work with AI.

Your workspace needs an available AI provider with an enabled model. If none is configured, the app shows a setup notice. Self-hosted instance administrators configure providers in Instance Settings. Leera Cloud providers are managed by Leera through the cloud admin console.

Choose Fast for quick answers, standups, and status updates. Choose Thinking for more complex questions such as sprint planning, retrospectives, or trade-offs. Both modes are selected in the chat composer alongside the project.

Chat uses the selected project and your conversation; use issue or team-member mentions to make a request specific. Comment AI Assist includes the issue details, existing comments, recent activity, and your draft. Editor actions work from the selected passage. Available project data and tools depend on the workflow and access granted.

In Suggest subtasks, edit and select suggestions, then use the Create button, which shows the number of selected subtasks. Comment AI Assist inserts a draft into the composer; you still post it with Comment. In the editor, Accept replaces the selection and Add Below keeps the original. Chat and agents can use enabled tools to create or update records; those tool actions can make changes directly, so review the enabled tools and the resulting records.

Yes. Ask in chat for a chart, naming the project, period, and metric. Chart results can include interactive values and a written explanation. Follow up in the conversation to refine the question, or reopen generated charts from the Charts view.

Chat is an interactive conversation. For recurring work, configure a Leera AI Agent with an objective, tools, and schedule, then inspect its execution history and output. Execution depends on the deployment, agent setup, permissions, and provider. Coordination is a separate workflow for gathering updates from participants.

Leera AI is the assistant inside the workspace. MCP gives an external AI client access to supported Leera tools under OAuth consent or a scoped personal access token. The external client runs its own conversation or task; Leera provides project access within the granted permissions.

In-product chat and scheduled agents do not have document creation or update tools. Use the document editor’s AI writing actions to review a selected passage. External AI clients connected over MCP can read, create, and update documents when their connection has the required document scopes and permissions.

Yes. Project creation can propose an issue hierarchy from the project description; select the issues to add. Create New Sprint includes Suggest Tickets based on a Sprint Goal; remove unwanted suggestions before creating the sprint. In an issue’s test cases, Generate test cases proposes steps and expected results for you to select and save to the QA repository. These entry points depend on the modules and AI configuration available in your workspace.

OpenAI, Anthropic, Google Gemini, and Vertex AI — plus any OpenAI-compatible endpoint. On self-hosted instances you plug in your own API keys and test the connection from settings.

AI requests are processed by the provider configured for your deployment. Review the provider’s applicable data processing and retention terms, your account settings, and any requirements for sensitive project information.

No. These interactive examples use sample project data to demonstrate the controls and review steps. They do not call an AI model, create real issues, post comments, or configure agents.