LazyMind

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Make AI reliably complete real tasks using your knowledge, standards, and preferences.

LazyMind is an AI Skill Runtime for knowledge-intensive work. It connects reusable knowledge, executable Skills, observable workflows, editable artifacts, and evaluation-driven improvement in one workspace.

Instead of repeatedly uploading context, tuning prompts, and supervising every agent step, you choose the knowledge and workflow once. LazyMind then plans, executes, exposes intermediate results, and carries accepted feedback into the next run. Use it locally in Desktop Mode or deploy it as a shared enterprise service.

Quick start · Product architecture · Build a workflow · Desktop mode

What can you ship with it?

How LazyMind works

flowchart LR
    K["Knowledge<br/>local files · cloud docs · object storage"] --> R["Retrieve & reason<br/>parse · OCR · hybrid search · rerank"]
    S["Skills & workflows<br/>instructions · tools · state machines"] --> X["Observable execution<br/>steps · approvals · retry · rewind"]
    R --> X
    X --> A["Editable artifacts<br/>citations · revisions · delivery"]
    A --> F["Feedback & evaluation<br/>preferences · bad cases · A/B tests"]
    F --> K
    F --> S

This loop is built from three connected systems:

Core highlights

1. Deliver outcomes, not chat messages

Choose knowledge and a Skill; LazyMind continues from source organization through planning, generation, review, and delivery. Workflows define steps, tools, inputs, outputs, and transitions as state machines, while artifacts preserve editable results and revision history.

For long-running work, each step remains visible. Users can approve checkpoints, edit an artifact, or rerun from the failed step instead of restarting the whole task.

Inspect and edit the Artifact before continuing

Review a version diff and restore the result you need

2. Ground every run in reusable knowledge

Local directories, object storage, Feishu, Notion, and other sources feed a unified knowledge base. PDFReader, MinerU, or PaddleOCR-VL parses documents; multi-embedding retrieval, hybrid search, and reranking keep results grounded in relevant evidence.

Organize documents and track parsing status in one knowledge base

Ground answers with inline citations and traceable references

3. Package expert practice into reusable workflows

Research methods, writing processes, and domain standards can be managed as Skills and converted into executable Workflows. Teams can diagnose, repair, publish, version, and roll them back instead of rebuilding the same setup from prompts and scripts. See the Workflow format specification.

Select a Skill as the source of a new workflow

Inspect, refine, publish, and version the generated workflow

4. Improve only after evidence

Knowledge Ops captures what the user wants—preferences, terminology, experience, and Skills. evo tests how the system should improve by turning bad cases into evaluation samples and running baseline evaluation, diagnosis, repair, and A/B testing.

Knowledge Ops reviews Skills, preferences, terminology, and experience

Algorithm evolution validates improvements before rollout

5. Start local, scale when collaboration requires it

Desktop Mode uses native processes, SQLite, and Milvus Lite with platform-standard data paths. Shared deployments add Kong, JWT/RBAC, Core ACL, external Milvus/OpenSearch, and on-premises OCR. Your workflow stays recognizable across both modes.

Quick start

Run locally

Prerequisites: Go, Python 3, uv, pnpm, and Node.js.

make local-up

On native Windows PowerShell:

make local-win-up

After startup:

• LazyMind: http://localhost:8090
• API docs: http://localhost:8090/docs.html
• Default credentials: admin / admin

After login, open Settings in the frontend:

• Add provider credentials and API keys under Model Providers, then select the default LLM, embedding, and reranker under System Defaults. Multimodal embedding, VLM, speech, image, video, and evolution models are optional.
• Configure service credentials under Tools when needed, including MinerU or PaddleOCR for document parsing, web and academic search engines, and other integrations. No environment variable is required for a hosted MinerU API key.

Select the default models for each system capability

Configure document parsing, search, and integration credentials

Stop the local runtime with:

make local-down

Use make local-win-down on Windows. See the Quick Start guide for complete configuration.

Build the desktop application

Deploy with containers

make up

This starts both the Docker services and the host-side Assistant Bridge. Open Settings → Assistants to connect Codex, Cursor, WorkBuddy, TRAE Work, or DeepSeek Harness without running separate MCP configuration commands. If Docker is installed but Go is not, the bridge is cross-compiled for the host automatically inside Docker.

Startup Command Reference

See the Colima setup guide or the complete Quick Start guide. The Architecture guide covers service dependencies, environment variables, and the authentication chain.

Available today

This table describes capabilities implemented in the repository today, not a future roadmap. See docs for module design and implementation details.

Roadmap

LazyMind's next phase is not about adding more isolated features. The goal is to make knowledge bases, Skills, Workflows, and self-evolution work together in complete, real-world task loops.

Near term: flagship workflows people can try immediately

• Knowledge to deliverable: complete workflows for customer solutions, product manuals, and product research—from retrieval and planning to drafting, review, and delivery.
• Better local revision: selection-based rewriting, knowledge-grounded expansion, diffs, accept/reject controls, and partial reruns from affected steps.
• Result delivery: stronger Markdown, DOCX, and PDF export, shareable result pages, and initial publishing targets such as Feishu and Notion.
• Ready-to-run demos: sample knowledge packs, task templates, and completed outputs so new users can experience an end-to-end workflow without preparing private data first.
• Desktop experience: simpler installation, model setup, data import, and local-runtime diagnostics.

Mid term: a distribution network for knowledge and capabilities

• Knowledge and Skill/Workflow marketplace: curated discovery, one-click installation, updates, dependency checks, and trusted-source information.
• Reusable scenario packages: combine workflows, knowledge packs, review rules, and output formats into installable industry solutions.
• External agent access: expose LazyMind knowledge and workflows to Codex, Cursor, Hermes Agent, OpenClaw, and others through MCP, CLI, OpenAPI, and SDKs.
• More connectors: progressively connect collaboration, email, calendar, code, and task systems for weekly reports, research, and content workflows.
• Team collaboration: improve workflow sharing, approvals, permissions, run history, and organization-level template governance.

Long term: from executable workflows to a self-evolving work system

• Detect workflow and knowledge gaps from user edits, reruns, citations, and final acceptance signals.
• Continuously evaluate and A/B test retrieval strategies, prompts, models, tools, and Workflow revisions.
• Turn successful execution patterns into reusable Skills, templates, and organizational memory with full provenance and version history.
• Expand across industries through horizontal task templates plus vertical knowledge packs instead of rebuilding the product for every industry.

The roadmap will evolve based on real workflow completion rates, output quality, human interventions, latency, and cost. Repository issues, milestones, and release notes remain the source of truth for specific releases.

Project layout

LazyMind/
├── frontend/                   # Web UI and desktop frontend
├── backend/
│   ├── auth-service/           # Authentication, OAuth, and users
│   ├── core/                   # Data, tasks, retrieval, Workflows, and ACL
│   └── scan-control-plane/     # Source scanning and synchronization
├── algorithm/
│   └── lazymind/               # Chat, parsing, retrieval, and agent runtime
├── workflows/                    # Built-in Workflows
├── skills/                     # Built-in and curated Skills
├── evo/                        # Self-evolution and evaluation loop
├── desktop/                    # Electron desktop application and packaging
├── local/                      # Host-local runtime management
├── api/                        # OpenAPI specifications
├── docs/                       # Architecture, usage, and design docs
└── tests/                      # Cross-service tests

Development and testing

make lint              # Python, Go, docs, and other static checks
make lint-only-diff    # Check changed files only
make test              # Test with host-provided runtimes
make test-hermetic     # Test the same scope in project-managed runtimes

• Python 3.11+
• Go 1.24.0
• Node.js 20
• OpenAPI specifications are maintained under api/

License

See LICENSE.