README
LingTai
The self-evolving Digital Scientist — a lifelong agent that grows with you and your work.
Digital Scientist · lifelong agent · self-growing memory · durable knowledge & skills · local-first · multi-agent networks
English · 中文 · 文言 · Website · Tutorial · Releases
Most agent tools give you a better one-shot worker: a chat window that forgets, or a coding agent that closes with the terminal. LingTai is different — it is a Digital Scientist that lives in your project and gets better over time. It holds a question or a codebase for weeks, works with evidence and tools, records what it learns as durable knowledge and reusable skills, forms its own operating style, and delegates deep sub-problems to specialists it spawns. The work you do together becomes state the next session starts from.
It is filesystem-native, not a chat window. Every agent has a home under .lingtai/; its durable state — mail, memory, knowledge, skills, logs, heartbeats — lives in local files and directories you can inspect with standard tools, your editor, or another coding agent. Close the terminal and the scientist persists: it can be inspected, restarted, taught, and recovered.
A day (and a month) with a Digital Scientist
You
"Hold this research question for me: does our solar-wind classifier
drift across instruments? Read the literature and our data, run
experiments, and keep me posted."
LingTai
reads the literature with web search and research tools
→ inspects the datasets and the classifier code in the repo
→ runs experiments, verifies every claim against evidence
→ records findings in its durable knowledge library
→ spawns a specialist avatar to go deep on one instrument's calibration
→ over weeks, refines its own operating style and reusable skills
→ sends you a brief through Desktop / TUI / Telegram / email with the artifacts
Nothing above is a one-off. The literature notes, the verified findings, the calibration specialist, the working style it settled on — all of it is durable. When you come back next week, the scientist resumes from that accumulated state instead of starting cold. The same loop serves engineering just as well: hold a codebase, reproduce a bug with evidence, patch it, and remember why.
Why a lifelong, self-evolving scientist?
A good scientist is defined not only by results, but by the practice that produces them: evidence over assumption, tools mastered deliberately, experiments recorded, findings reviewed and iterated. LingTai turns that practice into a growth loop, backed by real files on disk:
- Work produces experience. Tasks use real tools when action is needed — shell, file I/O, web search, vision, coding-agent hands — and every assertion is expected to rest on evidence, not guesswork.
- Experience is distilled into durable state. When the context window fills, the agent molts (凝蜕 — "crystallize the essence, shed the chaff"): it saves what matters and resets the window. Across molts, that experience accumulates as four inspectable forms of growth —
- Knowledge — its private library of accumulated research, findings, and notes.
- Skills — reusable procedures it can invoke on demand and share with peers.
- Character — its evolving operating style, expertise, and goals.
- Avatars — persistent specialist agents it spawned to master one sub-problem, recorded in an append-only ledger.
- Future work starts from that state. The next session reloads character, knowledge, and skills — so the scientist is a little sharper each time, in a direction you can inspect and steer.
This is growth you can read and audit, not a black box. The loop is explicit, inspectable, and steerable; you stay in charge of direction, and external side effects (sending mail, filing issues) are treated as real actions that respect your authorization.
Capabilities, as outcomes
- Keeps a long-running question or project — durable memory and goals survive sessions, restarts, and closing the terminal.
- Works like a scientist — evidence-first tool use, experiments, verified findings, and durable records you can review.
- Grows its own toolkit — distills what it learns into reusable skills and a private knowledge library.
- Scales beyond one mind — spawns persistent specialist avatars for deep sub-problems and lightweight daemons for temporary parallel work.
- Reaches you where you are — you talk to the same scientist through Desktop, the TUI, and external channels like Telegram, Feishu, WeChat, WhatsApp, and email, while the portal shows the network and history.
- Stays inspectable and recoverable — durable project state lives locally under
.lingtai/as inspectable files, rather than trapped in a hosted chat transcript.
Quick start
curl -fsSL https://lingtai.ai/install.sh | bash
mkdir my-project && cd my-project
lingtai-tui
Development installer — current main of both the TUI and kernel
For the explicit development installer (current main of both the TUI and kernel), use:
curl -fsSL https://lingtai.ai/install.sh | bash -s -- --latest
It prints and records the exact full commit SHA for each repository. This mode is separate from the default stable installer and cannot be combined with --version, --ref, --update, --source, or --skip-python.
The installer supports macOS, Linux, and WSL and installs lingtai-tui and lingtai-portal; on macOS it also provides lingtai-desktop.
Native Windows/PowerShell is also available:
irm https://lingtai.ai/install.ps1 | iex
This resolves the latest tagged release, verifies the Windows binary archive and the pinned kernel release against their published checksums, and installs both lingtai-tui/lingtai-portal and the Python runtime venv. Pass -SkipVenv to install the TUI/portal binaries only. See RELEASING.md for the exact contract.
Native Windows current-main debugging install — install.ps1 -Latest
For a native Windows current-main debugging install, use .\install.ps1 -Latest. It is amd64-only; on ARM64, use WSL2 with install.sh --latest.
It checks Git, Go, Node.js/npm (Node 20.19+, 22.12+, or a newer major; Node 21 and Node 22.<12 are unsupported), and supported 64-bit CPython 3.11–3.13 in one pass, then uses winget install --id <ID> --exact --source winget --accept-source-agreements --accept-package-agreements --disable-interactivity --silent for only the missing or unsupported prerequisite packages (Git.Git, GoLang.Go, OpenJS.NodeJS.LTS, and/or Python.Python.3.13). Successful prerequisite installs are external winget changes and are not rolled back if a later package or checkout fails; LingTai destination writes still wait until validation/build succeeds. The installer refreshes this process PATH and revalidates before pinning full main SHAs, building both binaries, and installing the checked-out kernel source as a non-editable local build into %USERPROFILE%\.lingtai-tui\runtime\venv. If winget or package policy/elevation blocks the repair, it fails with exact remediation commands.
-Latest -DryRun reports the exact repair plan without invoking winget or writing destinations, PATH, or config. -Latest cannot be combined with -Version, -ArchivePath, or -SkipVenv. The separate website repository still needs a matching install-flow note.
[!TIP] New here? Follow the step-by-step tutorial at lingtai.ai — install, first task, channels, memory, and lifecycle, walked through end to end.
[!NOTE] Homebrew (
brew install lingtai-ai/lingtai/lingtai-tui) still works for existing users, but the one-line installer is the recommended path for new installs. ThelingtaiPyPI package is the Python runtime the TUI manages for you — reach forpiponly when developing or diagnosing the kernel itself.
For deeper TUI/portal update operations, install-method detection, Homebrew, and mainland-China build routing, see the bundled lingtai-update skill.
Ways to work with it
Desktop — lingtai-desktop (macOS) is the native app for LingTai: see your projects and scientists, chat and exchange mail, adjust setup and presets, and manage their work in one place.
TUI — lingtai-tui brings LingTai to the terminal: set up projects and models, chat and read mail, check scientist status, and open /knowledge, /skills, /system, /daemons, or /goal when you need a deeper view. Type /help for the complete slash-command reference (the canonical catalog is the bundled lingtai-tui-help skill; this README does not duplicate it). Run lingtai-tui doctor if anything looks broken after an upgrade.
Portal — lingtai-portal is the visualization server. It reads project state to show the live agent network, mail edges, and history — useful once a project has more than one agent or when you want to see how the work evolved.
External channels bridge the same scientist to the platforms you already use — memory, tools, and history are shared across them, and they are doors into one assistant, not separate bots. Setup follows the current MCP/curated-addon documentation and requires explicit authorization; the TUI's /mcp panel is read-only and only inspects configured bridges and their status. Credentials live in local .secrets/ files (never in Git); external side effects are treated as real actions, and channel addons support sender allowlists — the shipped example configs enable them by default, so open access must be opted into explicitly.
| Addon | Use it for |
|---|---|
telegram | Talk to your scientist from Telegram (DMs, optional allowlist, voice/file passthrough). |
feishu | Feishu/Lark — WebSocket long connection, no public IP or webhook required. |
wechat | WeChat through an iLink/gewechat-style bridge. |
whatsapp | WhatsApp through the curated LingTai bridge. |
imap | Real email through IMAP/SMTP — multi-account, with optional sender allowlist. |
Coding agents as hands. Coding CLIs are capable hands for precise implementation, and LingTai is the mind around those hands — it owns the long-running plan, memory, and coordination. Supported coding CLIs (such as Claude Code and Codex) can run as daemon backends for focused implementation jobs; other agents can collaborate as peers through the shared .lingtai/human/ mailbox protocol.
- Claude Code —
claude plugin add Lingtai-AI/claude-code-plugin - OpenAI Codex CLI —
git clone https://github.com/Lingtai-AI/codex-plugin.git && cd codex-plugin && ./install.sh - Other agents (OpenCode, OpenClaw, Hermes, …) — vendor the
lingtai-skillprotocol skill under your tool's skills directory.
Inspectable architecture
LingTai is split across three product repositories.
| Repository | Product role |
|---|---|
Lingtai-AI/lingtai (this one) | Terminal app, visual portal, and installer. |
Lingtai-AI/lingtai-kernel | Keeps scientists running and handles their tools, memory, and conversations. |
Lingtai-AI/lingtai-desktop | Native macOS app for working with your projects and scientists. |
Desktop and the TUI are two interfaces to the same LingTai project and the same scientists. The kernel keeps those scientists running and listening even when you close either interface. The project stays local and inspectable, so your editor and other tools can work with it too.
For the source-grounded repo map, start at ANATOMY.md, then descend into tui/ANATOMY.md or portal/ANATOMY.md. For what each layer's interfaces and expected agent behavior promise, read CONTRACT.md. To navigate by knowledge graph, see docs/graphify.md.
Development & contributing
Build the TUI with cd tui && make build; build the portal with cd portal && make build. You need Go 1.26+, make, and (for the portal) Node.js/npm.
Contributions are source-grounded and workflow-aware. Before any development work, find and read this repository's local dev guide — the repository-root dev-guide-skill; it routes each task through the baseline, the distributed ANATOMY.md and CONTRACT.md systems, validation, and the PR gate without duplicating them.
- Read the relevant anatomy first — root
ANATOMY.md, thentui/ANATOMY.mdorportal/ANATOMY.md— and the pairedCONTRACT.mdwhen changing an interface or expected behavior. - Work in a branch or worktree off
origin/main; keep the change scoped. - Run the relevant validation. Update
ANATOMY.mdfor structural/navigation changes; updateCONTRACT.mdand its conformance tests for interface or expected-behavior changes; update both only when both change. - Open a PR that says what changed, why, and how you validated it.
# TUI changes
cd tui && go test ./... && go vet ./... && go build -o bin/lingtai-tui .
# Portal changes
cd portal/web && npm ci && npm run build && cd .. && go test ./... && go build -o bin/lingtai-portal .
# Docs-only
git diff --check && git status --short
See RELEASING.md for the release process. Areas that often need help: TUI usability and accessibility, portal visualization, MCP/addon onboarding, cross-platform install polish, docs, runtime diagnostics, and reusable skills.
Community
- Website, tutorial, and release notes: https://lingtai.ai
- Main repo: https://github.com/Lingtai-AI/lingtai · Kernel: https://github.com/Lingtai-AI/lingtai-kernel
- Discord: https://discord.gg/pfc7z2TRq
- Issues: https://github.com/Lingtai-AI/lingtai/issues · Discussions: https://github.com/Lingtai-AI/lingtai/discussions
For Chinese-language discussion and early testing, scan the WeChat QR below. Add the author on WeChat with the note lingtai; if the QR has expired, open an issue and we will refresh it.
License
Apache-2.0 — see LICENSE.
Contributors
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README
LingTai
The self-evolving Digital Scientist — a lifelong agent that grows with you and your work.
Digital Scientist · lifelong agent · self-growing memory · durable knowledge & skills · local-first · multi-agent networks
English · 中文 · 文言 · Website · Tutorial · Releases
Most agent tools give you a better one-shot worker: a chat window that forgets, or a coding agent that closes with the terminal. LingTai is different — it is a Digital Scientist that lives in your project and gets better over time. It holds a question or a codebase for weeks, works with evidence and tools, records what it learns as durable knowledge and reusable skills, forms its own operating style, and delegates deep sub-problems to specialists it spawns. The work you do together becomes state the next session starts from.
It is filesystem-native, not a chat window. Every agent has a home under .lingtai/; its durable state — mail, memory, knowledge, skills, logs, heartbeats — lives in local files and directories you can inspect with standard tools, your editor, or another coding agent. Close the terminal and the scientist persists: it can be inspected, restarted, taught, and recovered.
A day (and a month) with a Digital Scientist
You
"Hold this research question for me: does our solar-wind classifier
drift across instruments? Read the literature and our data, run
experiments, and keep me posted."
LingTai
reads the literature with web search and research tools
→ inspects the datasets and the classifier code in the repo
→ runs experiments, verifies every claim against evidence
→ records findings in its durable knowledge library
→ spawns a specialist avatar to go deep on one instrument's calibration
→ over weeks, refines its own operating style and reusable skills
→ sends you a brief through Desktop / TUI / Telegram / email with the artifacts
Nothing above is a one-off. The literature notes, the verified findings, the calibration specialist, the working style it settled on — all of it is durable. When you come back next week, the scientist resumes from that accumulated state instead of starting cold. The same loop serves engineering just as well: hold a codebase, reproduce a bug with evidence, patch it, and remember why.
Why a lifelong, self-evolving scientist?
A good scientist is defined not only by results, but by the practice that produces them: evidence over assumption, tools mastered deliberately, experiments recorded, findings reviewed and iterated. LingTai turns that practice into a growth loop, backed by real files on disk:
- Work produces experience. Tasks use real tools when action is needed — shell, file I/O, web search, vision, coding-agent hands — and every assertion is expected to rest on evidence, not guesswork.
- Experience is distilled into durable state. When the context window fills, the agent molts (凝蜕 — "crystallize the essence, shed the chaff"): it saves what matters and resets the window. Across molts, that experience accumulates as four inspectable forms of growth —
- Knowledge — its private library of accumulated research, findings, and notes.
- Skills — reusable procedures it can invoke on demand and share with peers.
- Character — its evolving operating style, expertise, and goals.
- Avatars — persistent specialist agents it spawned to master one sub-problem, recorded in an append-only ledger.
- Future work starts from that state. The next session reloads character, knowledge, and skills — so the scientist is a little sharper each time, in a direction you can inspect and steer.
This is growth you can read and audit, not a black box. The loop is explicit, inspectable, and steerable; you stay in charge of direction, and external side effects (sending mail, filing issues) are treated as real actions that respect your authorization.
Capabilities, as outcomes
- Keeps a long-running question or project — durable memory and goals survive sessions, restarts, and closing the terminal.
- Works like a scientist — evidence-first tool use, experiments, verified findings, and durable records you can review.
- Grows its own toolkit — distills what it learns into reusable skills and a private knowledge library.
- Scales beyond one mind — spawns persistent specialist avatars for deep sub-problems and lightweight daemons for temporary parallel work.
- Reaches you where you are — you talk to the same scientist through Desktop, the TUI, and external channels like Telegram, Feishu, WeChat, WhatsApp, and email, while the portal shows the network and history.
- Stays inspectable and recoverable — durable project state lives locally under
.lingtai/as inspectable files, rather than trapped in a hosted chat transcript.
Quick start
curl -fsSL https://lingtai.ai/install.sh | bash
mkdir my-project && cd my-project
lingtai-tui
Development installer — current main of both the TUI and kernel
For the explicit development installer (current main of both the TUI and kernel), use:
curl -fsSL https://lingtai.ai/install.sh | bash -s -- --latest
It prints and records the exact full commit SHA for each repository. This mode is separate from the default stable installer and cannot be combined with --version, --ref, --update, --source, or --skip-python.
The installer supports macOS, Linux, and WSL and installs lingtai-tui and lingtai-portal; on macOS it also provides lingtai-desktop.
Native Windows/PowerShell is also available:
irm https://lingtai.ai/install.ps1 | iex
This resolves the latest tagged release, verifies the Windows binary archive and the pinned kernel release against their published checksums, and installs both lingtai-tui/lingtai-portal and the Python runtime venv. Pass -SkipVenv to install the TUI/portal binaries only. See RELEASING.md for the exact contract.
Native Windows current-main debugging install — install.ps1 -Latest
For a native Windows current-main debugging install, use .\install.ps1 -Latest. It is amd64-only; on ARM64, use WSL2 with install.sh --latest.
It checks Git, Go, Node.js/npm (Node 20.19+, 22.12+, or a newer major; Node 21 and Node 22.<12 are unsupported), and supported 64-bit CPython 3.11–3.13 in one pass, then uses winget install --id <ID> --exact --source winget --accept-source-agreements --accept-package-agreements --disable-interactivity --silent for only the missing or unsupported prerequisite packages (Git.Git, GoLang.Go, OpenJS.NodeJS.LTS, and/or Python.Python.3.13). Successful prerequisite installs are external winget changes and are not rolled back if a later package or checkout fails; LingTai destination writes still wait until validation/build succeeds. The installer refreshes this process PATH and revalidates before pinning full main SHAs, building both binaries, and installing the checked-out kernel source as a non-editable local build into %USERPROFILE%\.lingtai-tui\runtime\venv. If winget or package policy/elevation blocks the repair, it fails with exact remediation commands.
-Latest -DryRun reports the exact repair plan without invoking winget or writing destinations, PATH, or config. -Latest cannot be combined with -Version, -ArchivePath, or -SkipVenv. The separate website repository still needs a matching install-flow note.
[!TIP] New here? Follow the step-by-step tutorial at lingtai.ai — install, first task, channels, memory, and lifecycle, walked through end to end.
[!NOTE] Homebrew (
brew install lingtai-ai/lingtai/lingtai-tui) still works for existing users, but the one-line installer is the recommended path for new installs. ThelingtaiPyPI package is the Python runtime the TUI manages for you — reach forpiponly when developing or diagnosing the kernel itself.
For deeper TUI/portal update operations, install-method detection, Homebrew, and mainland-China build routing, see the bundled lingtai-update skill.
Ways to work with it
Desktop — lingtai-desktop (macOS) is the native app for LingTai: see your projects and scientists, chat and exchange mail, adjust setup and presets, and manage their work in one place.
TUI — lingtai-tui brings LingTai to the terminal: set up projects and models, chat and read mail, check scientist status, and open /knowledge, /skills, /system, /daemons, or /goal when you need a deeper view. Type /help for the complete slash-command reference (the canonical catalog is the bundled lingtai-tui-help skill; this README does not duplicate it). Run lingtai-tui doctor if anything looks broken after an upgrade.
Portal — lingtai-portal is the visualization server. It reads project state to show the live agent network, mail edges, and history — useful once a project has more than one agent or when you want to see how the work evolved.
External channels bridge the same scientist to the platforms you already use — memory, tools, and history are shared across them, and they are doors into one assistant, not separate bots. Setup follows the current MCP/curated-addon documentation and requires explicit authorization; the TUI's /mcp panel is read-only and only inspects configured bridges and their status. Credentials live in local .secrets/ files (never in Git); external side effects are treated as real actions, and channel addons support sender allowlists — the shipped example configs enable them by default, so open access must be opted into explicitly.
| Addon | Use it for |
|---|---|
telegram | Talk to your scientist from Telegram (DMs, optional allowlist, voice/file passthrough). |
feishu | Feishu/Lark — WebSocket long connection, no public IP or webhook required. |
wechat | WeChat through an iLink/gewechat-style bridge. |
whatsapp | WhatsApp through the curated LingTai bridge. |
imap | Real email through IMAP/SMTP — multi-account, with optional sender allowlist. |
Coding agents as hands. Coding CLIs are capable hands for precise implementation, and LingTai is the mind around those hands — it owns the long-running plan, memory, and coordination. Supported coding CLIs (such as Claude Code and Codex) can run as daemon backends for focused implementation jobs; other agents can collaborate as peers through the shared .lingtai/human/ mailbox protocol.
- Claude Code —
claude plugin add Lingtai-AI/claude-code-plugin - OpenAI Codex CLI —
git clone https://github.com/Lingtai-AI/codex-plugin.git && cd codex-plugin && ./install.sh - Other agents (OpenCode, OpenClaw, Hermes, …) — vendor the
lingtai-skillprotocol skill under your tool's skills directory.
Inspectable architecture
LingTai is split across three product repositories.
| Repository | Product role |
|---|---|
Lingtai-AI/lingtai (this one) | Terminal app, visual portal, and installer. |
Lingtai-AI/lingtai-kernel | Keeps scientists running and handles their tools, memory, and conversations. |
Lingtai-AI/lingtai-desktop | Native macOS app for working with your projects and scientists. |
Desktop and the TUI are two interfaces to the same LingTai project and the same scientists. The kernel keeps those scientists running and listening even when you close either interface. The project stays local and inspectable, so your editor and other tools can work with it too.
For the source-grounded repo map, start at ANATOMY.md, then descend into tui/ANATOMY.md or portal/ANATOMY.md. For what each layer's interfaces and expected agent behavior promise, read CONTRACT.md. To navigate by knowledge graph, see docs/graphify.md.
Development & contributing
Build the TUI with cd tui && make build; build the portal with cd portal && make build. You need Go 1.26+, make, and (for the portal) Node.js/npm.
Contributions are source-grounded and workflow-aware. Before any development work, find and read this repository's local dev guide — the repository-root dev-guide-skill; it routes each task through the baseline, the distributed ANATOMY.md and CONTRACT.md systems, validation, and the PR gate without duplicating them.
- Read the relevant anatomy first — root
ANATOMY.md, thentui/ANATOMY.mdorportal/ANATOMY.md— and the pairedCONTRACT.mdwhen changing an interface or expected behavior. - Work in a branch or worktree off
origin/main; keep the change scoped. - Run the relevant validation. Update
ANATOMY.mdfor structural/navigation changes; updateCONTRACT.mdand its conformance tests for interface or expected-behavior changes; update both only when both change. - Open a PR that says what changed, why, and how you validated it.
# TUI changes
cd tui && go test ./... && go vet ./... && go build -o bin/lingtai-tui .
# Portal changes
cd portal/web && npm ci && npm run build && cd .. && go test ./... && go build -o bin/lingtai-portal .
# Docs-only
git diff --check && git status --short
See RELEASING.md for the release process. Areas that often need help: TUI usability and accessibility, portal visualization, MCP/addon onboarding, cross-platform install polish, docs, runtime diagnostics, and reusable skills.
Community
- Website, tutorial, and release notes: https://lingtai.ai
- Main repo: https://github.com/Lingtai-AI/lingtai · Kernel: https://github.com/Lingtai-AI/lingtai-kernel
- Discord: https://discord.gg/pfc7z2TRq
- Issues: https://github.com/Lingtai-AI/lingtai/issues · Discussions: https://github.com/Lingtai-AI/lingtai/discussions
For Chinese-language discussion and early testing, scan the WeChat QR below. Add the author on WeChat with the note lingtai; if the QR has expired, open an issue and we will refresh it.
License
Apache-2.0 — see LICENSE.
Similar repos
Contributors
Languages
Go
82.1%
Shell
6.3%
PowerShell
5.9%
Python
3.3%
TypeScript
1.3%