LazyMind AI Agent

LazyMind

中文 | English

<img width="1672" height="600" alt="LazyMind logo" src="docs/assets/lazymind-banner.png" />

LazyMind: Keep AI-Agent under your control.

LazyMind is an AI Skill Runtime for knowledge-intensive work: an environment that turns knowledge, expert practice, and tools into executable, recoverable tasks. 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, deploy it as a shared enterprise service, or provide knowledge, Skills, and Workflows to external agents such as Codex, Cursor, and WorkBuddy.

• Desktop Mode is designed for technically capable professionals whose primary job is not programming, including solution architects, product managers, operators, testers, and content creators.
• Team and enterprise deployments serve secondary development, project delivery, proposals, FDE work, and organization-wide knowledge collaboration with shared permissions, deployment, and evaluation capabilities.

Quick start · Showcase · Example directory · Product architecture · Build a workflow · Desktop mode

---

Ways to use LazyMind

Mode	Best for	How it works
Use LazyMind directly	Local knowledge work or shared team workflows	Run the desktop app for a local-first experience, or deploy the service for your team. Create tasks, use knowledge bases, and run Skills and Workflows in the LazyMind workspace.
Use LazyMind as an agent plugin	Extending the agent environment you already use	Connect LazyMind from Settings → Assistants. Codex and DeepSeek Harness can then use LazyMind knowledge, Skills, and Workflows in their native sessions, including workflow progress, review checkpoints, and artifacts.

See the DeepSeek Harness integration for implementation details.

---

What can you ship with it?

Scenario	LazyMind runs	You receive
Research and review	Search sources → retrieve evidence → compare → synthesize → review	A traceable report grounded in your documents and external sources
AI Writer	Organize sources → outline → draft sections → revise → final review	An editable, versioned document rather than a one-shot answer
Presentations	Clarify intent → gather sources → outline → create and revise slides → export	A PDF or PPTX with speaker notes and locally editable slides
AI Image	Interpret intent → collect references → refine prompt → generate/edit	Images and animated stickers with the process retained
Knowledge assistant	Connect sources → parse/OCR → hybrid retrieve → rerank → answer	Answers linked back to reusable organizational knowledge
External agent augmentation	Connect projects and sessions → call knowledge, Skills, or Workflows → track execution	LazyMind capabilities and artifacts inside the agent you already use
Quality improvement	Capture a bad case → evaluate → diagnose → A/B test → deploy	A verified strategy improvement, not an unchecked prompt change

For an end-to-end product video, see the Chinese README.

Showcase

See LazyMind in action through published demos. All videos and articles below are in Chinese; links open on their original platforms.

Demo	What it shows	Length	Case details
DeepSeek Harness × LazyMind: from a request to a slide deck	Connect the workflow, clarify requirements, preview backgrounds, review slide content, and export the result	1:29	Case notes
From a product idea to a proposal, PRD, and prototype	Clarify the product direction through questions, then connect competitor analysis, product planning, requirements, and prototype previews	0:55	Case notes
Codex × LazyMind: create and refine a presentation	Start a task in Codex, preview and revise intermediate content, and adjust individual elements in the generated slides	1:23	Case notes

More demos

• Refining AI presentation layouts and content (Bilibili) · Case notes
• Creating your own animated stickers (WeChat Channels) · Case notes
• Bringing AI agents into your workflow (WeChat Channels) · Case notes

Articles

• LazyMind v0.2: from answers to deliverables (2026-08-04, release introduction)
• Why are you still working overtime after AI finishes? (2026-09-01, product introduction)

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:

System	Responsibility	Product behavior
Knowledge Foundation	Give AI the right context	Multi-source ingestion, OCR, hybrid retrieval, reranking, and source traceability
State Brain	Keep long tasks on course	Visible steps, approvals, editable artifacts, retries, rewinds, and version history
AI Growth Engine	Improve future runs safely	Reviewable preferences and terminology plus evaluation, diagnosis, A/B tests, and rollback

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.

<table> <tr> <td width="50%" align="center"> <a href="docs/assets/artifact-workspace-en.png"><img src="docs/assets/artifact-workspace-en.png" alt="A real Artifact remains editable at an approval checkpoint" width="100%" /></a> <br />_{Inspect and edit the Artifact before continuing} </td> <td width="50%" align="center"> <a href="docs/assets/artifact-version-diff-en.png"><img src="docs/assets/artifact-version-diff-en.png" alt="Inspect the execution trail and approve the next step without restarting" width="100%" /></a> <br />_{Review a version diff and restore the result you need} </td> </tr> </table>

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.

<table> <tr> <td width="50%" align="center"> <a href="docs/assets/knowledge-library-en.jpg"><img src="docs/assets/knowledge-library-en.jpg" alt="Manage parsed documents in a reusable knowledge base" width="100%" /></a> <br />_{Organize documents and track parsing status in one knowledge base} </td> <td width="50%" align="center"> <a href="docs/assets/knowledge-cited-answer-en.jpg"><img src="docs/assets/knowledge-cited-answer-en.jpg" alt="Answer with inline citations and an automatically generated reference list" width="100%" /></a> <br />_{Ground answers with inline citations and traceable references} </td> </tr> </table>

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.

<table> <tr> <td width="50%" align="center"> <a href="docs/assets/skill-to-workflow-entry-en.png"><img src="docs/assets/skill-to-workflow-entry-en.png" alt="Create an executable workflow from an existing Skill" width="100%" /></a> <br />_{Select a Skill as the source of a new workflow} </td> <td width="50%" align="center"> <a href="docs/assets/skill-to-workflow-editor-en.png"><img src="docs/assets/skill-to-workflow-editor-en.png" alt="Manage converted and custom workflows after generation" width="100%" /></a> <br />_{Inspect, refine, publish, and version the generated workflow} </td> </tr> </table>

4. From an idea to an editable presentation

The PPT Workflow carries a presentation request through research, outline approval, slide generation, review, and export. It can use the user's prompt, uploaded files, LazyMind knowledge bases, web search results, and AI-generated images as source material, and it produces speaker notes for each slide.

Intermediate results, including collected materials and the outline, remain visible and editable throughout generation. After generation, users can drag the left thumbnails to reorder slides, remove slides and select text or elements for natural-language content and style changes. Results can be exported as PDF, image-based PPTX, or editable PPTX containing text, shapes, images, and charts.

<table> <tr> <td width="50%" align="center" valign="top"> <a href="docs/assets/ppt-workflow-overview-en.png"><img src="docs/assets/ppt-workflow-overview-en.png" alt="Gather material from the web, generate slides, and drag the left thumbnails to reorder pages" width="100%" /></a> <br />_{Gather material from the web, generate slides, and drag the left thumbnails to reorder pages} </td> <td width="50%" align="center" valign="top"> <a href="docs/assets/ppt-workflow-outline-en.png"><img src="docs/assets/ppt-workflow-outline-en.png" alt="Inspect and edit intermediate results, including the outline, throughout generation" width="100%" /></a> <br />_{Inspect and edit intermediate results, including the outline, throughout generation} </td> </tr> <tr> <td width="50%" align="center" valign="top"> <a href="docs/assets/ppt-workflow-edit-en.png"><img src="docs/assets/ppt-workflow-edit-en.png" alt="Select slide content and describe changes in natural language" width="100%" /></a> <br />_{Select slide content and describe changes in natural language} </td> <td width="50%" align="center" valign="top"> <a href="docs/assets/ppt-workflow-export-en.png"><img src="docs/assets/ppt-workflow-export-en.png" alt="Export as PDF, image-based PPTX, or editable PPTX" width="100%" /></a> <br />_{Export as PDF, image-based PPTX, or editable PPTX} </td> </tr> </table>

5. Discover ready-to-run scenarios in the Capability Center

The Capability Center brings quick chat, complex tasks, and curated examples into one entry point. Users can filter by scenario, capability type, and technical tags; inspect the description, workflow, example input, and interactive result; then launch the capability in Chat or Work. Curated entries are backed by runnable Skills and installed on demand when first used.

<table> <tr> <td width="50%" align="center" valign="top"> <a href="docs/assets/capability-center-browse-en.png"><img src="docs/assets/capability-center-browse-en.png" alt="Search and filter ready-to-run capabilities by scenario and goal" width="100%" /></a> <br />_{Search and filter ready-to-run capabilities by scenario and goal} </td> <td width="50%" align="center" valign="top"> <a href="docs/assets/capability-center-example-en.png"><img src="docs/assets/capability-center-example-en.png" alt="Explore an example’s execution steps and output, then select Try it" width="100%" /></a> <br />_{Explore an example’s execution steps and output, then select Try it} </td> </tr> <tr> <td colspan="2" align="center" valign="top"> <a href="docs/assets/capability-center-template-en.png"><img src="docs/assets/capability-center-template-en.png" alt="Load the example’s task template, add your own requirements and references, and start" width="100%" /></a> <br />_{Load the example’s task template, add your own requirements and references, and start} </td> </tr> <tr> <td colspan="2" align="center" valign="top"> <a href="docs/assets/capability-center-result-en.png"><img src="docs/assets/capability-center-result-en.png" alt="Follow execution and compare the reference image with generated stickers and downloadable outputs" width="100%" /></a> <br />_{Follow execution and compare the reference image with generated stickers and downloadable outputs} </td> </tr> </table>

6. Read and ask questions directly inside documents

The document preview supports conversations scoped to the current document and can cite PDF selections, knowledge chunks, or partial chunk content. Temporary document conversations stay out of normal chat history unless the user chooses to promote and save them.

<table> <tr> <td width="50%" align="center"> <a href="docs/assets/document-chat-selection-en.png"><img src="docs/assets/document-chat-selection-en.png" alt="Select text in a PDF and ask a question in the document conversation" width="100%" /></a> <br />_{Select text in a PDF and ask a question in the document conversation} </td> <td width="50%" align="center"> <a href="docs/assets/document-chat-answer-en.png"><img src="docs/assets/document-chat-answer-en.png" alt="Read the answer alongside the PDF and save the conversation to Chat when needed" width="100%" /></a> <br />_{Read the answer alongside the PDF and save the conversation to Chat when needed} </td> </tr> </table>

7. Give external agents the same knowledge and capabilities

LazyMind can discover local projects and native task histories from Codex, Cursor, WorkBuddy, TRAE Work, DeepSeek Harness, and other agents, then create, continue, and inspect their work in one workspace. Through MCP, external agents can also use LazyMind Workflows, Skills, knowledge bases, and cloud documents without rebuilding the same task context.

<table> <tr> <td width="50%" align="center" valign="top"> <a href="docs/assets/external-agent-codex-control-en.png"><img src="docs/assets/external-agent-codex-control-en.png" alt="Codex controls LazyMind to generate images and displays the workflow result" width="100%" /></a> <br />_{Codex controls LazyMind to generate images and displays the workflow result} </td> <td width="50%" align="center" valign="top"> <a href="docs/assets/external-agent-codex-workflow-en.png"><img src="docs/assets/external-agent-codex-workflow-en.png" alt="Codex follows a LazyMind Workflow to generate and return an image artifact" width="100%" /></a> <br />_{Codex follows a LazyMind Workflow to generate and return an image artifact} </td> </tr> <tr> <td width="50%" align="center" valign="top"> <a href="docs/assets/external-agent-codex-embedded-en.png"><img src="docs/assets/external-agent-codex-embedded-en.png" alt="Open an embedded LazyMind Workflow in Codex to inspect and review intermediate results" width="100%" /></a> <br />_{Open an embedded LazyMind Workflow in Codex to inspect and review intermediate results} </td> <td width="50%" align="center" valign="top"> <a href="docs/assets/external-agent-dsh-embedded-en.png"><img src="docs/assets/external-agent-dsh-embedded-en.png" alt="Display a LazyMind Workflow in DeepSeek Harness to review slides and speaker notes" width="100%" /></a> <br />_{Display a LazyMind Workflow in DeepSeek Harness to review slides and speaker notes} </td> </tr> </table>

8. 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.

<table> <tr> <td width="50%" align="center"> <a href="docs/assets/skill-review-en.jpg"><img src="docs/assets/skill-review-en.jpg" alt="Knowledge Ops reviews and improves reusable Skills" width="100%" /></a> <br />_{Knowledge Ops reviews Skills, preferences, terminology, and experience} </td> <td width="50%" align="center"> <a href="docs/assets/evo-pipeline-en.jpg"><img src="docs/assets/evo-pipeline-en.jpg" alt="The evo workspace turns failures into an evaluated improvement pipeline" width="100%" /></a> <br />_{Algorithm evolution validates improvements before rollout} </td> </tr> </table>

9. 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.

<table> <tr> <td width="50%" align="center" valign="top"> <a href="docs/assets/deployment-local-vscode.png"><img src="docs/assets/deployment-local-vscode.png" alt="Local runtime: SQLite databases and state storage, with services bound to localhost" width="100%" /></a> <br />_{Local runtime: SQLite databases and state storage, with services bound to localhost} </td> <td width="50%" align="center" valign="top"> <a href="docs/assets/deployment-compose-vscode.png"><img src="docs/assets/deployment-compose-vscode.png" alt="Container deployment: Kong with RBAC, an authentication service, and persistent PostgreSQL storage" width="100%" /></a> <br />_{Container deployment: Kong with RBAC, an authentication service, and persistent PostgreSQL storage} </td> </tr> </table>

---

Quick start

Install the desktop app (recommended for local use)

Download the latest installer from GitHub Releases:

• macOS (Apple silicon): download the .dmg, open it, and drag LazyMind into Applications.
• Windows x64: download and run the installer .exe.

The installers include the local runtime needed for Desktop Mode. For normal local use, install the .dmg or .exe instead of building and packaging LazyMind from source.

Run locally from source

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.

<table> <tr> <td width="50%" align="center"> <a href="docs/assets/settings-models-en.jpg"><img src="docs/assets/settings-models-en.jpg" alt="Select default models in frontend settings" width="100%" /></a> <br />_{Select the default models for each system capability} </td> <td width="50%" align="center"> <a href="docs/assets/settings-tools-en.jpg"><img src="docs/assets/settings-tools-en.jpg" alt="Configure document parsing and search providers in frontend settings" width="100%" /></a> <br />_{Configure document parsing, search, and integration credentials} </td> </tr> </table>

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 from source

The commands below are intended for development and release packaging. Local users should normally use the installers above.

Platform	Command	Output
macOS arm64	make desktop-darwin-arm64	macOS desktop application
Windows x64	make desktop-windows-x64	Portable ZIP
Windows x64	make desktop-windows-x64-installer	Installer

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, Raccoon, 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.

On Windows, run this command from Git Bash with make available. Do not replace it with bare docker compose up: containers cannot inspect or launch programs installed on the Windows host. On macOS and Windows, the host bridge discovers CLIs and desktop applications through the operating system; Settings → Assistants also accepts a custom or portable host path when automatic discovery cannot resolve it.

Startup Command Reference

Scenario	Command
Build images and start	make up-build
Deploy MinerU OCR on-premises	make up LAZYMIND_DEPLOY_MINERU=1
Deploy PaddleOCR on-premises	make up LAZYMIND_DEPLOY_PADDLEOCR=1
Use external Milvus/OpenSearch	make up LAZYMIND_MILVUS_URI=http://your-milvus:19530 LAZYMIND_OPENSEARCH_URI=https://your-opensearch:9200

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

Area	Current capabilities
Knowledge base	Multiple sources, OCR, vectorization, hybrid retrieval, reranking, sync management
Agents	RAG chat, tool calls, subtask timeline, artifacts, task center, long-context compression
Content creation	AI Writer, PPT Workflow, AI Image, multi-format export
Workflows	State machines, dynamic routing, automatic review, retry/rewind, visual execution, versioned artifacts
Skills	Installation, organization, review, revisions, rollback, Skill → Workflow
Capability Center	Curated Skills, scenario categories, interactive demos, on-demand installation
Document reading	Current-document chat, PDF selection citations, source navigation, temporary conversations
External agents	Local project and session discovery, task continuation, MCP access to knowledge and capabilities
Tasks and conversations	Terminal states, archives, recycle bin, recovery, synchronized task and conversation status
Self-evolution	Eval-set generation, evaluation, bad-case analysis, repair, deployment, A/B testing
Local experience	macOS/Windows local runtime, desktop builds, platform-standard data paths
Enterprise	Kong, JWT/RBAC, ACL, OAuth sources, optional external storage

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 strengthen the Skill Runtime so knowledge, Skills, Workflows, and self-evolution form complete task loops both inside LazyMind and across other agents.

Near term: complete the core v0.4 capabilities

Skill Runtime and Desktop

• Popular Skill compatibility and evaluation: measure installability, safety, core-flow completion, and output quality so verified Skills run without source changes and failures explain the actual cause.
• Curated Skill scale-up: expand verified official Skills and reproducible demos across representative industries and tasks, using quality results as a recommendation signal.
• Skill → Workflow experience: add preflight checks, progress and failure diagnosis, task recovery, and explicit association with the original Skill; let users choose either execution path.
• On-demand Skill and Tool retrieval: retrieve only the capabilities relevant to a task and load full definitions after a match to reduce context and token use.
• Trusted local workspace: let Desktop Work users select and authorize local folders, read or write within explicit boundaries, and review or revoke access.
• Browser context: use a Chrome extension, with user authorization, to obtain the current page title, body, links, and basic metadata for complex tasks.
• Onboarding and task preflight: improve model setup, dependency checks, permission guidance, failure recovery, and local-runtime diagnostics for non-developers.

External agent ecosystem

• Complete Workflow invocation: let Codex, Cursor, WorkBuddy, TRAE Work, DeepSeek Harness, and other supported agents submit Skills and tasks, then receive status, result files, and LazyMind task links.
• Unified document access: expose local files, Feishu, Notion, and Google Drive within existing authorization boundaries and return consistent content and source metadata.
• Shared models and tools: proxy authorized models and tools through LazyMind so multiple agents can reuse one configuration without receiving raw API keys.
• Permissions and audit: centrally manage external-agent grants, call status, usage, and execution history.

Task and resource infrastructure

• Unified File Resource: cover attachments, knowledge bases, web results, MCP Resources, and every Artifact type, with on-demand reading for large content.
• Conversation organization: support conversation forks, groups, and retained sub-conversations so exploratory branches remain organized and resumable.
• Artifact delivery: give documents, sheets, images, code, and archives explicit completion, preview, and download states.
• Preference consolidation: merge duplicate or conflicting preferences and remove low-value entries so important preferences continue to reach task context.

Mid term: connect knowledge, creation, and distribution

Knowledge sources, editing, and publishing

• Long-form sources: connect Obsidian, GitHub Docs/Wiki, and Yuque while improving Notion synchronization, attachments, and write-back.
• Unified content representation: strengthen round-trip conversion among Markdown, Writer IR, and platform rich text, previewing format degradation without silently dropping images, code, or body content.
• Result delivery: improve high-fidelity DOCX and other exports, shareable result pages, and publishing to Feishu, Notion, and WeChat Official Account drafts.
• Multi-channel notification and email: deliver scheduled-task results through Feishu, WeCom, WeChat, and other channels; support email reading, task assistance, and draft generation.
• Scenario packages and team governance: package Workflows, Skills, knowledge, review rules, and output formats together, with dependency, security, version, and organization controls.

Flagship scenario: evidence-driven paper research and technical writing

• Translate, explain, question, collect vocabulary, and save evidence from PDF selections, with navigation back to the original text.
• Expand from one paper to related-paper search, import, multi-paper comparison, and literature review while distinguishing full text, abstracts, and model inference.
• Introduce Evidence between papers and the final document, bind evidence to sections, draft incrementally, and check whether key claims are supported.
• Reuse AI Writer for material analysis, outlining, section drafting, local evidence repair, citation verification, and export with a complete paper list.

Image placeholder: paper reading to technical-guide workflow. Use a horizontal flow or four-panel composition showing PDF selection reading, the paper Evidence list, multi-paper comparison, and citation-grounded writing in AI Writer. Every stage should visibly preserve source information or navigation back to the original text.

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, vertical knowledge packs, and installable scenario packages 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.