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Linking","description":"","frontmatter":{},"headers":[],"relativePath":"concepts/identity-and-binding.md","filePath":"concepts/identity-and-binding.md","lastUpdated":1770887583000}'),i={name:"concepts/identity-and-binding.md"};function s(c,n,l,r,d,u){return t(),o("div",null,[...n[0]||(n[0]=[a('

Accounts and Linking ​

Account Model ​

Memoh treats platform accounts and system accounts as two different entities:

  • Platform Account (ChannelIdentity) is the user's account on an external access platform (for example, a TG account), not a Memoh internal account.
  • System Account (User) is an internal account in Memoh.

A platform account can exist before linking.
bind is the mechanism that links these two account types.

Access Platform and Bot ​

  • Access Platform (channel) is where inbound messages come from.
  • Bot is an authorization and resource boundary inside Memoh.

Bots are managed by system accounts, while inbound messages are produced by platform accounts.

Why Linking Is Account-Scoped ​

Account linking exists to establish account ownership, not to grant bot resources directly:

  • It links platform accounts and system accounts independent of any single bot.
  • It avoids coupling account linking with member management semantics.
  • It keeps bot authorization and account linking decoupled.

Linking Flow (Current Consensus) ​

  1. A user requests a bind code under their own system account.
  2. The platform account sends the code from a supported access-platform conversation.
  3. Memoh validates the code and links platform account to system account.
  4. Bot membership and authorization are handled by their own flows.

Bot Type Semantics ​

  • Public bot: supports member-based collaboration.
  • Personal bot: conceptually single-owner, and should not rely on member semantics.

Note: The conceptual model is documented here as product semantics.
Runtime behavior may still be in transition as implementations are tightened.

',16)])])}const p=e(i,[["render",s]]);export{h as __pageData,p as default}; diff --git a/assets/concepts_identity-and-binding.md.Dqnt6wsW.lean.js b/assets/concepts_identity-and-binding.md.Dqnt6wsW.lean.js deleted file mode 100644 index d4e11555..00000000 --- a/assets/concepts_identity-and-binding.md.Dqnt6wsW.lean.js +++ /dev/null @@ -1 +0,0 @@ -import{_ as e,o as t,c as o,ag as a}from"./chunks/framework.DEqXEGcv.js";const h=JSON.parse('{"title":"Accounts and Linking","description":"","frontmatter":{},"headers":[],"relativePath":"concepts/identity-and-binding.md","filePath":"concepts/identity-and-binding.md","lastUpdated":1770887583000}'),i={name:"concepts/identity-and-binding.md"};function s(c,n,l,r,d,u){return t(),o("div",null,[...n[0]||(n[0]=[a("",16)])])}const p=e(i,[["render",s]]);export{h as __pageData,p as default}; diff --git a/assets/concepts_index.md.CxGkiYqO.js b/assets/concepts_index.md.CxGkiYqO.js deleted file mode 100644 index baf53326..00000000 --- a/assets/concepts_index.md.CxGkiYqO.js +++ /dev/null @@ -1 +0,0 @@ -import{_ as t,o as a,c as n,ag as o}from"./chunks/framework.DEqXEGcv.js";const m=JSON.parse('{"title":"Core Concepts","description":"","frontmatter":{},"headers":[],"relativePath":"concepts/index.md","filePath":"concepts/index.md","lastUpdated":1770887583000}'),s={name:"concepts/index.md"};function c(r,e,i,l,h,p){return a(),n("div",null,[...e[0]||(e[0]=[o('

Core Concepts ​

This section defines the core account and access concepts used by Memoh.

Concept Map ​

  • System Account (User): an internal account in Memoh.
  • Platform Account (ChannelIdentity): a user's account on an external access platform, not a Memoh account (for example, the user's Telegram (TG) account).
  • Bot: an access and resource boundary managed by a system account.
  • Account Linking (bind): the process that links a platform account to a system account.

Why This Matters ​

Memoh receives messages from external access platforms, but manages permissions and resources inside the system. To keep these concerns clear, the model separates platform accounts from system accounts, while keeping bot access control as an independent concern.

Terminology note: "platform account" always means the user's account on that platform (such as TG), not an internal account created by this project.

In This Chapter ​

',9)])])}const u=t(s,[["render",c]]);export{m as __pageData,u as default}; diff --git a/assets/concepts_index.md.CxGkiYqO.lean.js b/assets/concepts_index.md.CxGkiYqO.lean.js deleted file mode 100644 index 14612671..00000000 --- a/assets/concepts_index.md.CxGkiYqO.lean.js +++ /dev/null @@ -1 +0,0 @@ -import{_ as t,o as a,c as n,ag as o}from"./chunks/framework.DEqXEGcv.js";const m=JSON.parse('{"title":"Core Concepts","description":"","frontmatter":{},"headers":[],"relativePath":"concepts/index.md","filePath":"concepts/index.md","lastUpdated":1770887583000}'),s={name:"concepts/index.md"};function c(r,e,i,l,h,p){return a(),n("div",null,[...e[0]||(e[0]=[o("",9)])])}const u=t(s,[["render",c]]);export{m as __pageData,u as default}; diff --git a/assets/getting-started.md.B026JaLd.js b/assets/getting-started.md.B026JaLd.js new file mode 100644 index 00000000..bcae880a --- /dev/null +++ b/assets/getting-started.md.B026JaLd.js @@ -0,0 +1 @@ +import{_ as a,o as t,c as o,ag as i}from"./chunks/framework.DEqXEGcv.js";const u=JSON.parse('{"title":"About Memoh","description":"","frontmatter":{},"headers":[],"relativePath":"getting-started.md","filePath":"getting-started.md","lastUpdated":1771162384000}'),n={name:"getting-started.md"};function r(l,e,s,c,d,h){return t(),o("div",null,[...e[0]||(e[0]=[i('

About Memoh ​

What is Memoh? ​

Memoh is a multi-member, structured long-memory, containerized AI agent system platform. You can create your own AI bots and chat with them via Telegram, Lark (Feishu), and more. Every bot has an independent container and memory system, allowing it to edit files, execute commands, and access the network within its own container — like having its own computer.

Key Features ​

Multi-Bot Management ​

Create multiple bots. Humans and bots, or bots with each other, can chat privately, in groups, or collaborate. Build bot teams, or set up accounts for family members to manage daily tasks with bots.

Containerized Isolation ​

Each bot runs in its own isolated container (powered by Containerd) with a separate filesystem and network. Bots can freely read/write files and execute commands within their containers without interfering with each other.

Memory Engineering ​

A deeply engineered memory layer inspired by Mem0:

  • Automatically extracts key facts from each conversation turn and stores them as structured memories
  • Supports semantic search (via Qdrant vector database) and keyword retrieval (BM25)
  • Loads the last 24 hours of conversation context by default
  • Automatic memory compaction to keep the memory store lean
  • Multi-language auto-detection

Multi-Platform Support ​

Unified channel adapter architecture for connecting to multiple messaging platforms:

  • Telegram — Full support with streaming, Markdown, attachments, and replies
  • Lark (Feishu) — Full support
  • Web — Built-in web chat interface
  • CLI — Command-line chat

Agent Capabilities ​

Bots come with a rich set of built-in tools:

  • Web Search — Brave Search integration for real-time information
  • Subagents — Create specialized subagents, assign skills, and delegate complex tasks
  • Skills — Configurable skill system to extend bot capabilities
  • Container Operations — Read/write files, edit code, and execute commands inside the container
  • Memory Management — Search and manage memories
  • Messaging — Send messages and reactions

Multi-LLM Provider Support ​

Flexibly switch between a wide range of LLM providers:

  • OpenAI, Anthropic, Google, Azure, AWS Bedrock
  • Mistral, XAI, Ollama, Dashscope (Qwen), and more

MCP Protocol Support ​

Supports Model Context Protocol (MCP) via HTTP and SSE to connect external tool services. Each bot can have its own independent MCP connections.

Scheduled Tasks ​

Configure scheduled tasks using cron expressions to automatically run commands at specified times. Supports max execution count limits and manual triggers.

Graphical Configuration ​

Configure bots, channels, providers, models, MCP, skills, and all other settings through a web management UI — no coding required to set up your own AI bot.

CLI Tool ​

A command-line tool for bot management, channel configuration, model management, streaming chat, and more — designed for developers who prefer the terminal. See CLI documentation.

Installation ​

To get Memoh running:

  • Docker — Recommended. One-click or manual setup with Docker Compose. Includes all services (PostgreSQL, Qdrant, Containerd, server, agent, web) — no extra dependencies on the host.
  • config.toml — Reference for all configuration fields.
',31)])])}const p=a(n,[["render",r]]);export{u as __pageData,p as default}; diff --git a/assets/getting-started.md.B026JaLd.lean.js b/assets/getting-started.md.B026JaLd.lean.js new file mode 100644 index 00000000..5c94b156 --- /dev/null +++ b/assets/getting-started.md.B026JaLd.lean.js @@ -0,0 +1 @@ +import{_ as a,o as t,c as o,ag as i}from"./chunks/framework.DEqXEGcv.js";const u=JSON.parse('{"title":"About Memoh","description":"","frontmatter":{},"headers":[],"relativePath":"getting-started.md","filePath":"getting-started.md","lastUpdated":1771162384000}'),n={name:"getting-started.md"};function r(l,e,s,c,d,h){return t(),o("div",null,[...e[0]||(e[0]=[i("",31)])])}const p=a(n,[["render",r]]);export{u as __pageData,p as default}; diff --git a/assets/getting-started.md.DF97X5A_.js b/assets/getting-started.md.DF97X5A_.js deleted file mode 100644 index 87432e27..00000000 --- a/assets/getting-started.md.DF97X5A_.js +++ /dev/null @@ -1 +0,0 @@ -import{_ as t,o as a,c as o,ag as i}from"./chunks/framework.DEqXEGcv.js";const u=JSON.parse('{"title":"Getting Started","description":"","frontmatter":{},"headers":[],"relativePath":"getting-started.md","filePath":"getting-started.md","lastUpdated":1771161622000}'),n={name:"getting-started.md"};function r(l,e,s,c,d,h){return a(),o("div",null,[...e[0]||(e[0]=[i('

Getting Started ​

What is Memoh? ​

Memoh is a multi-member, structured long-memory, containerized AI agent system platform. You can create your own AI bots and chat with them via Telegram, Lark (Feishu), and more. Every bot has an independent container and memory system, allowing it to edit files, execute commands, and access the network within its own container — like having its own computer.

Key Features ​

Multi-Bot Management ​

Create multiple bots. Humans and bots, or bots with each other, can chat privately, in groups, or collaborate. Build bot teams, or set up accounts for family members to manage daily tasks with bots.

Containerized Isolation ​

Each bot runs in its own isolated container (powered by Containerd) with a separate filesystem and network. Bots can freely read/write files and execute commands within their containers without interfering with each other.

Memory Engineering ​

A deeply engineered memory layer inspired by Mem0:

  • Automatically extracts key facts from each conversation turn and stores them as structured memories
  • Supports semantic search (via Qdrant vector database) and keyword retrieval (BM25)
  • Loads the last 24 hours of conversation context by default
  • Automatic memory compaction to keep the memory store lean
  • Multi-language auto-detection

Multi-Platform Support ​

Unified channel adapter architecture for connecting to multiple messaging platforms:

  • Telegram — Full support with streaming, Markdown, attachments, and replies
  • Lark (Feishu) — Full support
  • Web — Built-in web chat interface
  • CLI — Command-line chat

Agent Capabilities ​

Bots come with a rich set of built-in tools:

  • Web Search — Brave Search integration for real-time information
  • Subagents — Create specialized subagents, assign skills, and delegate complex tasks
  • Skills — Configurable skill system to extend bot capabilities
  • Container Operations — Read/write files, edit code, and execute commands inside the container
  • Memory Management — Search and manage memories
  • Messaging — Send messages and reactions

Multi-LLM Provider Support ​

Flexibly switch between a wide range of LLM providers:

  • OpenAI, Anthropic, Google, Azure, AWS Bedrock
  • Mistral, XAI, Ollama, Dashscope (Qwen), and more

MCP Protocol Support ​

Supports Model Context Protocol (MCP) via HTTP and SSE to connect external tool services. Each bot can have its own independent MCP connections.

Scheduled Tasks ​

Configure scheduled tasks using cron expressions to automatically run commands at specified times. Supports max execution count limits and manual triggers.

Graphical Configuration ​

Configure bots, channels, providers, models, MCP, skills, and all other settings through a web management UI — no coding required to set up your own AI bot.

CLI Tool ​

A command-line tool for bot management, channel configuration, model management, streaming chat, and more — designed for developers who prefer the terminal. See CLI documentation.

Installation ​

To get Memoh running:

  • Docker — Recommended. One-click or manual setup with Docker Compose. Includes all services (PostgreSQL, Qdrant, Containerd, server, agent, web) — no extra dependencies on the host.
  • config.toml — Reference for all configuration fields.
',31)])])}const p=t(n,[["render",r]]);export{u as __pageData,p as default}; diff --git a/assets/getting-started.md.DF97X5A_.lean.js b/assets/getting-started.md.DF97X5A_.lean.js deleted file mode 100644 index a67dc840..00000000 --- a/assets/getting-started.md.DF97X5A_.lean.js +++ /dev/null @@ -1 +0,0 @@ -import{_ as t,o as a,c as o,ag as i}from"./chunks/framework.DEqXEGcv.js";const u=JSON.parse('{"title":"Getting Started","description":"","frontmatter":{},"headers":[],"relativePath":"getting-started.md","filePath":"getting-started.md","lastUpdated":1771161622000}'),n={name:"getting-started.md"};function r(l,e,s,c,d,h){return a(),o("div",null,[...e[0]||(e[0]=[i("",31)])])}const p=t(n,[["render",r]]);export{u as __pageData,p as default}; diff --git a/assets/getting-started_provider-and-model.md.Ix_AFx48.js b/assets/getting-started_provider-and-model.md.Ix_AFx48.js new file mode 100644 index 00000000..aa11d382 --- /dev/null +++ b/assets/getting-started_provider-and-model.md.Ix_AFx48.js @@ -0,0 +1 @@ +import{_ as t,o,c as d,ag as a}from"./chunks/framework.DEqXEGcv.js";const r="/getting-started/provider-model-01-sidebar.png",l="/getting-started/provider-model-02-add-provider.png",i="/getting-started/provider-model-03-provider-dialog.png",n="/getting-started/provider-model-04-provider-selected.png",v=JSON.parse('{"title":"Configure Provider and Model","description":"","frontmatter":{},"headers":[],"relativePath":"getting-started/provider-and-model.md","filePath":"getting-started/provider-and-model.md","lastUpdated":1771162384000}'),s={name:"getting-started/provider-and-model.md"};function p(c,e,g,h,m,u){return o(),d("div",null,[...e[0]||(e[0]=[a('

Configure Provider and Model ​

Before creating bots, you need to configure at least one LLM provider and add chat and embedding models. This guide walks you through the Web UI.

Prerequisites ​

Step 1: Open the Models Page ​

Click Models in the left sidebar to open the Provider and Model configuration page.

Models page - sidebar

The page has two panels:

  • Left: Provider list and filter
  • Right: Selected provider's details and models (or an empty state if none selected)

Step 2: Add a Provider ​

Click the Add Provider button (with a plus icon) at the bottom of the left panel.

Add Provider button

In the dialog, fill in:

FieldDescription
NameA display name for this provider (e.g. my-openai, ollama-local)
API KeyYour API key. For local services like Ollama, you can use a placeholder like ollama
Base URLThe API base URL (e.g. https://api.openai.com/v1, http://localhost:11434/v1 for Ollama)
TypeClient type: openai, openai-compat, anthropic, google, azure, bedrock, mistral, xai, ollama, dashscope

Add Provider dialog

Example — OpenAI:

  • Name: openai
  • API Key: sk-...
  • Base URL: https://api.openai.com/v1
  • Type: openai

Example — Ollama (local):

  • Name: ollama
  • API Key: ollama
  • Base URL: http://localhost:11434/v1
  • Type: ollama

Click Add to save. The new provider appears in the left sidebar.

Step 3: Add Models ​

Select a provider from the left panel. The right panel shows the provider form and the model list.

Provider selected - model list

Click Add Model to open the model creation dialog.

Fill in:

FieldDescription
Typechat or embedding
ModelModel ID (e.g. gpt-4, llama3.2, text-embedding-3-small)
Display NameOptional display name
DimensionsRequired for embedding models (e.g. 1536 for OpenAI embeddings)
MultimodalFor chat models only — enable if the model supports images

You need at least:

  • One chat model (for conversation)
  • One embedding model (for memory)

Add them under the same or different providers. For example:

  • Chat: gpt-4 (OpenAI) or llama3.2 (Ollama)
  • Embedding: text-embedding-3-small (OpenAI) or nomic-embed-text (Ollama)

Step 4: Edit or Delete ​

  • Provider: After selecting a provider, you can edit Name, API Key, and Base URL in the right panel, or delete the provider.
  • Model: Use the edit or delete actions on each model card in the list.

Next Steps ​

Once you have at least one chat model and one embedding model, you can create a bot (via Bots in the sidebar) and assign these models in the bot settings.

',34)])])}const f=t(s,[["render",p]]);export{v as __pageData,f as default}; diff --git a/assets/getting-started_provider-and-model.md.Ix_AFx48.lean.js b/assets/getting-started_provider-and-model.md.Ix_AFx48.lean.js new file mode 100644 index 00000000..8e541f4b --- /dev/null +++ b/assets/getting-started_provider-and-model.md.Ix_AFx48.lean.js @@ -0,0 +1 @@ +import{_ as t,o,c as d,ag as a}from"./chunks/framework.DEqXEGcv.js";const r="/getting-started/provider-model-01-sidebar.png",l="/getting-started/provider-model-02-add-provider.png",i="/getting-started/provider-model-03-provider-dialog.png",n="/getting-started/provider-model-04-provider-selected.png",v=JSON.parse('{"title":"Configure Provider and Model","description":"","frontmatter":{},"headers":[],"relativePath":"getting-started/provider-and-model.md","filePath":"getting-started/provider-and-model.md","lastUpdated":1771162384000}'),s={name:"getting-started/provider-and-model.md"};function p(c,e,g,h,m,u){return o(),d("div",null,[...e[0]||(e[0]=[a("",34)])])}const f=t(s,[["render",p]]);export{v as __pageData,f as default}; diff --git a/assets/index.md.BxPQTo9L.js b/assets/index.md.BxPQTo9L.js new file mode 100644 index 00000000..8f0b2a54 --- /dev/null +++ b/assets/index.md.BxPQTo9L.js @@ -0,0 +1 @@ +import{_ as t,o as a,c as o,ag as n}from"./chunks/framework.DEqXEGcv.js";const u=JSON.parse('{"title":"Memoh Documentation","description":"","frontmatter":{},"headers":[],"relativePath":"index.md","filePath":"index.md","lastUpdated":1771162384000}'),i={name:"index.md"};function r(s,e,l,m,d,c){return a(),o("div",null,[...e[0]||(e[0]=[n('

Memoh Documentation ​

Memoh is a multi-member, structured long-memory, containerized AI agent system. Create your own AI bots, chat with them via Telegram, Lark (Feishu), Web, or CLI. Each bot runs in an isolated container with its own memory system — able to edit files, run commands, and access the network.

Documentation ​

',4)])])}const f=t(i,[["render",r]]);export{u as __pageData,f as default}; diff --git a/assets/index.md.BxPQTo9L.lean.js b/assets/index.md.BxPQTo9L.lean.js new file mode 100644 index 00000000..231cf4a9 --- /dev/null +++ b/assets/index.md.BxPQTo9L.lean.js @@ -0,0 +1 @@ +import{_ as t,o as a,c as o,ag as n}from"./chunks/framework.DEqXEGcv.js";const u=JSON.parse('{"title":"Memoh Documentation","description":"","frontmatter":{},"headers":[],"relativePath":"index.md","filePath":"index.md","lastUpdated":1771162384000}'),i={name:"index.md"};function r(s,e,l,m,d,c){return a(),o("div",null,[...e[0]||(e[0]=[n("",4)])])}const f=t(i,[["render",r]]);export{u as __pageData,f as default}; diff --git a/assets/index.md.XthB7km1.js b/assets/index.md.XthB7km1.js deleted file mode 100644 index eaa68e1e..00000000 --- a/assets/index.md.XthB7km1.js +++ /dev/null @@ -1 +0,0 @@ -import{_ as e,o,c as a,ag as n}from"./chunks/framework.DEqXEGcv.js";const u=JSON.parse('{"title":"Memoh Documentation","description":"","frontmatter":{},"headers":[],"relativePath":"index.md","filePath":"index.md","lastUpdated":1771161622000}'),r={name:"index.md"};function i(s,t,l,c,m,d){return o(),a("div",null,[...t[0]||(t[0]=[n('

Memoh Documentation ​

Memoh is a multi-member, structured long-memory, containerized AI agent system. Create your own AI bots, chat with them via Telegram, Lark (Feishu), Web, or CLI. Each bot runs in an isolated container with its own memory system — able to edit files, run commands, and access the network.

Documentation ​

For Contributors ​

',6)])])}const f=e(r,[["render",i]]);export{u as __pageData,f as default}; diff --git a/assets/index.md.XthB7km1.lean.js b/assets/index.md.XthB7km1.lean.js deleted file mode 100644 index fd2cdbe9..00000000 --- a/assets/index.md.XthB7km1.lean.js +++ /dev/null @@ -1 +0,0 @@ -import{_ as e,o,c as a,ag as n}from"./chunks/framework.DEqXEGcv.js";const u=JSON.parse('{"title":"Memoh Documentation","description":"","frontmatter":{},"headers":[],"relativePath":"index.md","filePath":"index.md","lastUpdated":1771161622000}'),r={name:"index.md"};function i(s,t,l,c,m,d){return o(),a("div",null,[...t[0]||(t[0]=[n("",6)])])}const f=e(r,[["render",i]]);export{u as __pageData,f as default}; diff --git a/assets/style_terminology.md.zpSWt3dU.js b/assets/style_terminology.md.zpSWt3dU.js deleted file mode 100644 index 4d363d3d..00000000 --- a/assets/style_terminology.md.zpSWt3dU.js +++ /dev/null @@ -1 +0,0 @@ -import{_ as o,o as t,c as n,ag as a}from"./chunks/framework.DEqXEGcv.js";const h=JSON.parse('{"title":"Terminology Rules","description":"","frontmatter":{},"headers":[],"relativePath":"style/terminology.md","filePath":"style/terminology.md","lastUpdated":1770887583000}'),r={name:"style/terminology.md"};function i(l,e,s,c,u,d){return t(),n("div",null,[...e[0]||(e[0]=[a('

Terminology Rules ​

Audience: documentation contributors and maintainers. This page defines writing terms. It is not product user guidance.

Canonical Terms ​

  • System Account (User): the account inside Memoh.
  • Platform Account (ChannelIdentity): the user's account on an external access platform, not a Memoh account.
  • Access Platform (channel): the external platform carrying inbound messages.
  • Account Linking (bind): linking a Platform Account to a System Account.
  • Bind Code: one-time code used for account linking.
  • Bot: resource and authorization boundary managed by a System Account.

Preferred Wording ​

  • Write "platform account" instead of "actor" in user-facing docs.
  • Write "access platform" instead of "channel" when describing product behavior.
  • Keep code aliases in parentheses on first mention:
    • Platform Account (ChannelIdentity)
    • System Account (User)
    • Account Linking (bind)

Disallowed or Discouraged Terms ​

  • Avoid plain actor in conceptual docs (except when quoting code symbols).
  • Avoid ambiguous platform user phrasing (it does not distinguish system vs platform account).
  • Avoid wording that implies Platform Account is created inside Memoh.

Example Sentences ​

  • Correct: "A platform account is the user's TG account, not a Memoh account."
  • Correct: "Account linking binds a platform account to a system account."
  • Incorrect: "Actor is a user in Memoh."

Contributor Checklist ​

  • Is every "account" term clearly scoped (system vs platform)?
  • Is "channel" replaced by "access platform" in prose?
  • Are code aliases kept only as parenthetical references?
',12)])])}const g=o(r,[["render",i]]);export{h as __pageData,g as default}; diff --git a/assets/style_terminology.md.zpSWt3dU.lean.js b/assets/style_terminology.md.zpSWt3dU.lean.js deleted file mode 100644 index 885be92c..00000000 --- a/assets/style_terminology.md.zpSWt3dU.lean.js +++ /dev/null @@ -1 +0,0 @@ -import{_ as o,o as t,c as n,ag as a}from"./chunks/framework.DEqXEGcv.js";const h=JSON.parse('{"title":"Terminology Rules","description":"","frontmatter":{},"headers":[],"relativePath":"style/terminology.md","filePath":"style/terminology.md","lastUpdated":1770887583000}'),r={name:"style/terminology.md"};function i(l,e,s,c,u,d){return t(),n("div",null,[...e[0]||(e[0]=[a("",12)])])}const g=o(r,[["render",i]]);export{h as __pageData,g as default}; diff --git a/cli/auth.html b/cli/auth.html index 7c23be5a..71026ae4 100644 --- a/cli/auth.html +++ b/cli/auth.html @@ -9,9 +9,9 @@ - + - + @@ -19,8 +19,8 @@ -
Memoh

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Auth Commands ​

login ​

Log in to the Memoh server. Prompts for username and password, then stores the JWT token in ~/.memoh/token.json.

bash
memoh login

Interactive prompts:

  • Username
  • Password

logout ​

Clear the stored token and log out.

bash
memoh logout

whoami ​

Show the current logged-in user (username, display name, user ID, role). Falls back to token info if the API call fails.

bash
memoh whoami

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Copyright © 2024 Memoh

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Auth Commands ​

login ​

Log in to the Memoh server. Prompts for username and password, then stores the JWT token in ~/.memoh/token.json.

bash
memoh login

Interactive prompts:

  • Username
  • Password

logout ​

Clear the stored token and log out.

bash
memoh logout

whoami ​

Show the current logged-in user (username, display name, user ID, role). Falls back to token info if the API call fails.

bash
memoh whoami

Published under AGPLv3

Copyright © 2024 Memoh

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Bot Commands ​

Manage bots and chat with them.

bot list ​

List all bots. Admins can filter by owner.

bash
memoh bot list [options]
OptionDescription
--owner <user_id>Filter by owner user ID (admin only)

bot create ​

Create a new bot. Prompts for type and optionally name.

bash
memoh bot create [options]
OptionDescription
--type <type>personal or public
--name <name>Display name
--avatar <url>Avatar URL
--activeSet bot active
--inactiveSet bot inactive

bot update ​

Update bot info. Bot ID can be passed as argument or selected interactively.

bash
memoh bot update [id] [options]
OptionDescription
--name <name>Display name
--avatar <url>Avatar URL
--activeSet bot active
--inactiveSet bot inactive

bot delete ​

Delete a bot. Asks for confirmation.

bash
memoh bot delete [id]

bot chat ​

Start an interactive streaming chat with a bot.

bash
memoh bot chat [id]

Type messages and press Enter. Type exit to quit.

bot set-model ​

Enable a model for a bot (chat, memory, or embedding).

bash
memoh bot set-model [id] [options]
OptionDescription
--as <usage>chat, memory, or embedding
--model <model_id>Model ID

Example:

bash
memoh bot set-model my-bot-id --as chat --model gpt-4

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Bot Commands ​

Manage bots and chat with them.

bot list ​

List all bots. Admins can filter by owner.

bash
memoh bot list [options]
OptionDescription
--owner <user_id>Filter by owner user ID (admin only)

bot create ​

Create a new bot. Prompts for type and optionally name.

bash
memoh bot create [options]
OptionDescription
--type <type>personal or public
--name <name>Display name
--avatar <url>Avatar URL
--activeSet bot active
--inactiveSet bot inactive

bot update ​

Update bot info. Bot ID can be passed as argument or selected interactively.

bash
memoh bot update [id] [options]
OptionDescription
--name <name>Display name
--avatar <url>Avatar URL
--activeSet bot active
--inactiveSet bot inactive

bot delete ​

Delete a bot. Asks for confirmation.

bash
memoh bot delete [id]

bot chat ​

Start an interactive streaming chat with a bot.

bash
memoh bot chat [id]

Type messages and press Enter. Type exit to quit.

bot set-model ​

Enable a model for a bot (chat, memory, or embedding).

bash
memoh bot set-model [id] [options]
OptionDescription
--as <usage>chat, memory, or embedding
--model <model_id>Model ID

Example:

bash
memoh bot set-model my-bot-id --as chat --model gpt-4

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Channel Commands ​

Manage channels and bot/user channel configuration.

channel list ​

List available channel types (e.g. telegram, feishu, local).

bash
memoh channel list

channel info ​

Show channel metadata and schema for a channel type.

bash
memoh channel info [type]

If type is omitted, prompts to select from available channels.

channel config get ​

Get a bot's channel configuration.

bash
memoh channel config get [bot_id] [options]
OptionDescription
--type <type>Channel type

channel config set ​

Set a bot's channel configuration. Currently supports Feishu.

bash
memoh channel config set [bot_id] [options]
OptionDescription
--type <type>Channel type (e.g. feishu)
--app_id <id>Feishu App ID
--app_secret <secret>Feishu App Secret
--encrypt_key <key>Encrypt key (optional)
--verification_token <token>Verification token (optional)

channel bind get ​

Get the current user's channel binding for a platform.

bash
memoh channel bind get [options]
OptionDescription
--type <type>Channel type

channel bind set ​

Set the current user's channel binding. Currently supports Feishu (open_id or user_id).

bash
memoh channel bind set [options]
OptionDescription
--type <type>Channel type (e.g. feishu)
--open_id <id>Feishu Open ID
--user_id <id>Feishu User ID

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Channel Commands ​

Manage channels and bot/user channel configuration.

channel list ​

List available channel types (e.g. telegram, feishu, local).

bash
memoh channel list

channel info ​

Show channel metadata and schema for a channel type.

bash
memoh channel info [type]

If type is omitted, prompts to select from available channels.

channel config get ​

Get a bot's channel configuration.

bash
memoh channel config get [bot_id] [options]
OptionDescription
--type <type>Channel type

channel config set ​

Set a bot's channel configuration. Currently supports Feishu.

bash
memoh channel config set [bot_id] [options]
OptionDescription
--type <type>Channel type (e.g. feishu)
--app_id <id>Feishu App ID
--app_secret <secret>Feishu App Secret
--encrypt_key <key>Encrypt key (optional)
--verification_token <token>Verification token (optional)

channel bind get ​

Get the current user's channel binding for a platform.

bash
memoh channel bind get [options]
OptionDescription
--type <type>Channel type

channel bind set ​

Set the current user's channel binding. Currently supports Feishu (open_id or user_id).

bash
memoh channel bind set [options]
OptionDescription
--type <type>Channel type (e.g. feishu)
--open_id <id>Feishu Open ID
--user_id <id>Feishu User ID

Published under AGPLv3

Copyright © 2024 Memoh

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Chat Commands ​

Default: Interactive Chat ​

Running memoh with no subcommand starts an interactive chat. Use --bot <id> to specify which bot to chat with; otherwise you'll be prompted to select one.

bash
memoh [options]
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Chat Commands ​
Default: Interactive Chat ​
Running memoh with no subcommand starts an interactive chat. Use --bot <id> to specify which bot to chat with; otherwise you'll be prompted to select one.
bash
memoh [options]
 memoh --bot <bot_id>
OptionDescription
--bot <id>Bot ID to chat with
Type your message and press Enter. Type exit to quit. Responses stream in real time.
tui ​
Terminal UI chat session. Same behavior as the default chat but explicitly invoked.
bash
memoh tui [options]
-memoh tui --bot <bot_id>
OptionDescription
--bot <id>Bot ID to chat with
version ​
Show the CLI version.
bash
memoh version
Published under AGPLv3
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OptionDescription
--bot <id>Bot ID to chat with

version ​

Show the CLI version.

bash
memoh version

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Config Commands ​

The CLI stores its config in ~/.memoh/config.toml. Use these commands to view or update it.

config ​

Show the current config (host and port).

bash
memoh config

Output example:

host = "127.0.0.1"
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Config Commands ​
The CLI stores its config in ~/.memoh/config.toml. Use these commands to view or update it.
config ​
Show the current config (host and port).
bash
memoh config
Output example:
host = "127.0.0.1"
 port = 8080
config set ​
Update the config. Prompts for host and port if not provided via options.
bash
memoh config set [options]
OptionDescription
--host <host>API host (e.g. 127.0.0.1 or api.example.com)
--port <port>API port (default: 8080)
Examples:
bash
memoh config set --host 192.168.1.100 --port 8080
 memoh config set
 # Interactive prompts for host and port
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Memoh CLI ​
The Memoh CLI (memoh) is a command-line tool for managing bots, channels, providers, models, schedules, and chatting with bots. It talks to a running Memoh server via its API.
Installation ​
The CLI is part of the Memoh monorepo. Install from source:
bash
git clone https://github.com/memohai/Memoh.git
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Memoh CLI ​
The Memoh CLI (memoh) is a command-line tool for managing bots, channels, providers, models, schedules, and chatting with bots. It talks to a running Memoh server via its API.
Installation ​
The CLI is part of the Memoh monorepo. Install from source:
bash
git clone https://github.com/memohai/Memoh.git
 cd Memoh
 pnpm install
Run the CLI:
bash
cd packages/cli
 pnpm start -- --help
To use memoh as a global command:
bash
cd packages/cli
 pnpm build
 pnpm link --global
-memoh --help
Ensure your Memoh server is running (see Docker installation) and the API is reachable at the configured host/port (default: 127.0.0.1:8080).
Configuration ​
The CLI stores config in ~/.memoh/config.toml and auth token in ~/.memoh/token.json. Use memoh config to view and memoh config set to change host/port.
Commands ​
CommandDescription
loginLog in to the Memoh server
logoutLog out and clear token
whoamiShow current user
configShow or update CLI config (host, port)
providerList, create, delete LLM providers
modelList, create, delete models
botList, create, update, delete bots; chat; set model
channelList channels; get/set bot channel config; get/set user binding
scheduleList, create, update, toggle, delete bot schedules
chatInteractive chat with a bot (default command)
tuiTerminal UI chat session
versionShow CLI version
Most commands require authentication. Run memoh login first.
Published under AGPLv3
Copyright © 2024 Memoh

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+memoh --help
Ensure your Memoh server is running (see Docker installation) and the API is reachable at the configured host/port (default: 127.0.0.1:8080).
Configuration ​
The CLI stores config in ~/.memoh/config.toml and auth token in ~/.memoh/token.json. Use memoh config to view and memoh config set to change host/port.
Commands ​
CommandDescription
loginLog in to the Memoh server
logoutLog out and clear token
whoamiShow current user
configShow or update CLI config (host, port)
providerList, create, delete LLM providers
modelList, create, delete models
botList, create, update, delete bots; chat; set model
channelList channels; get/set bot channel config; get/set user binding
scheduleList, create, update, toggle, delete bot schedules
chatInteractive chat with a bot (default command)
tuiTerminal UI chat session
versionShow CLI version
Most commands require authentication. Run memoh login first.
Published under AGPLv3
Copyright © 2024 Memoh

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Model Commands ​
Manage chat and embedding models.
model list ​
List all models with their provider, type, and multimodal flag.
bash
memoh model list
model create ​
Create a new model. Prompts for provider, model ID, type, and (for embedding models) dimensions.
bash
memoh model create [options]
OptionDescription
--model_id <id>Model ID (e.g. gpt-4, text-embedding-3-small)
--name <name>Display name
--provider <provider>Provider name
--type <type>chat or embedding
--dimensions <n>Embedding dimensions (required for embedding models)
--multimodalMark as multimodal
Examples:
bash
memoh model create --model_id gpt-4 --provider my-openai --type chat
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Model Commands ​
Manage chat and embedding models.
model list ​
List all models with their provider, type, and multimodal flag.
bash
memoh model list
model create ​
Create a new model. Prompts for provider, model ID, type, and (for embedding models) dimensions.
bash
memoh model create [options]
OptionDescription
--model_id <id>Model ID (e.g. gpt-4, text-embedding-3-small)
--name <name>Display name
--provider <provider>Provider name
--type <type>chat or embedding
--dimensions <n>Embedding dimensions (required for embedding models)
--multimodalMark as multimodal
Examples:
bash
memoh model create --model_id gpt-4 --provider my-openai --type chat
 memoh model create --model_id text-embedding-3-small --provider my-openai --type embedding --dimensions 1536
 memoh model create
 # Interactive prompts
model delete ​
Delete a model by model ID.
bash
memoh model delete --model <model_id>
Example:
bash
memoh model delete --model gpt-4
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Provider Commands ​
Manage LLM providers (OpenAI, Anthropic, Ollama, etc.).
provider list ​
List all providers. Optionally filter by provider name.
bash
memoh provider list [options]
OptionDescription
--provider <name>Filter by provider name
Examples:
bash
memoh provider list
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Provider Commands ​
Manage LLM providers (OpenAI, Anthropic, Ollama, etc.).
provider list ​
List all providers. Optionally filter by provider name.
bash
memoh provider list [options]
OptionDescription
--provider <name>Filter by provider name
Examples:
bash
memoh provider list
 memoh provider list --provider my-openai
provider create ​
Create a new provider. Prompts for any missing fields.
bash
memoh provider create [options]
OptionDescription
--name <name>Provider name
--type <type>Client type
--base_url <url>Base URL for the API
--api_key <key>API key
Supported client types: openai, openai-compat, anthropic, google, azure, bedrock, mistral, xai, ollama, dashscope
Examples:
bash
memoh provider create --name my-ollama --type ollama --base_url http://localhost:11434
 memoh provider create
 # Interactive prompts
provider delete ​
Delete a provider by name.
bash
memoh provider delete --provider <name>
Example:
bash
memoh provider delete --provider my-ollama
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Schedule Commands ​
Manage cron-based schedules for bots. All schedule commands require --bot <id> to specify the bot.
schedule list ​
List all schedules for a bot.
bash
memoh schedule list --bot <bot_id>
schedule get ​
Get a schedule by ID.
bash
memoh schedule get <id> --bot <bot_id>
schedule create ​
Create a new schedule. Prompts for name, description, cron pattern, command, and optional max calls.
bash
memoh schedule create [options] --bot <bot_id>
OptionDescription
--name <name>Schedule name
--description <desc>Description
--pattern <pattern>Cron pattern (e.g. 0 9 * * * for daily at 9am)
--command <cmd>Command to run in the bot container
--max_calls <n>Max executions (optional, empty for unlimited)
--enabledCreate as enabled
--disabledCreate as disabled
schedule update ​
Update a schedule.
bash
memoh schedule update <id> [options] --bot <bot_id>
OptionDescription
--name <name>Schedule name
--description <desc>Description
--pattern <pattern>Cron pattern
--command <cmd>Command
--max_calls <n>Max executions
--enabledEnable
--disabledDisable
schedule toggle ​
Enable or disable a schedule (flip current state).
bash
memoh schedule toggle <id> --bot <bot_id>
schedule delete ​
Delete a schedule.
bash
memoh schedule delete <id> --bot <bot_id>
Published under AGPLv3
Copyright © 2024 Memoh

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Schedule Commands ​
Manage cron-based schedules for bots. All schedule commands require --bot <id> to specify the bot.
schedule list ​
List all schedules for a bot.
bash
memoh schedule list --bot <bot_id>
schedule get ​
Get a schedule by ID.
bash
memoh schedule get <id> --bot <bot_id>
schedule create ​
Create a new schedule. Prompts for name, description, cron pattern, command, and optional max calls.
bash
memoh schedule create [options] --bot <bot_id>
OptionDescription
--name <name>Schedule name
--description <desc>Description
--pattern <pattern>Cron pattern (e.g. 0 9 * * * for daily at 9am)
--command <cmd>Command to run in the bot container
--max_calls <n>Max executions (optional, empty for unlimited)
--enabledCreate as enabled
--disabledCreate as disabled
schedule update ​
Update a schedule.
bash
memoh schedule update <id> [options] --bot <bot_id>
OptionDescription
--name <name>Schedule name
--description <desc>Description
--pattern <pattern>Cron pattern
--command <cmd>Command
--max_calls <n>Max executions
--enabledEnable
--disabledDisable
schedule toggle ​
Enable or disable a schedule (flip current state).
bash
memoh schedule toggle <id> --bot <bot_id>
schedule delete ​
Delete a schedule.
bash
memoh schedule delete <id> --bot <bot_id>
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Accounts and Linking ​
Account Model ​
Memoh treats platform accounts and system accounts as two different entities:
  • Platform Account (ChannelIdentity) is the user's account on an external access platform (for example, a TG account), not a Memoh internal account.
  • System Account (User) is an internal account in Memoh.
A platform account can exist before linking.
bind is the mechanism that links these two account types.
Access Platform and Bot ​
  • Access Platform (channel) is where inbound messages come from.
  • Bot is an authorization and resource boundary inside Memoh.
Bots are managed by system accounts, while inbound messages are produced by platform accounts.
Why Linking Is Account-Scoped ​
Account linking exists to establish account ownership, not to grant bot resources directly:
  • It links platform accounts and system accounts independent of any single bot.
  • It avoids coupling account linking with member management semantics.
  • It keeps bot authorization and account linking decoupled.
Linking Flow (Current Consensus) ​
  1. A user requests a bind code under their own system account.
  2. The platform account sends the code from a supported access-platform conversation.
  3. Memoh validates the code and links platform account to system account.
  4. Bot membership and authorization are handled by their own flows.
Bot Type Semantics ​
  • Public bot: supports member-based collaboration.
  • Personal bot: conceptually single-owner, and should not rely on member semantics.
Note: The conceptual model is documented here as product semantics.
 Runtime behavior may still be in transition as implementations are tightened.
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Copyright © 2024 Memoh

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Core Concepts ​
This section defines the core account and access concepts used by Memoh.
Concept Map ​
  • System Account (User): an internal account in Memoh.
  • Platform Account (ChannelIdentity): a user's account on an external access platform, not a Memoh account (for example, the user's Telegram (TG) account).
  • Bot: an access and resource boundary managed by a system account.
  • Account Linking (bind): the process that links a platform account to a system account.
Why This Matters ​
Memoh receives messages from external access platforms, but manages permissions and resources inside the system. To keep these concerns clear, the model separates platform accounts from system accounts, while keeping bot access control as an independent concern.
Terminology note: "platform account" always means the user's account on that platform (such as TG), not an internal account created by this project.
In This Chapter ​
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Getting Started ​
What is Memoh? ​
Memoh is a multi-member, structured long-memory, containerized AI agent system platform. You can create your own AI bots and chat with them via Telegram, Lark (Feishu), and more. Every bot has an independent container and memory system, allowing it to edit files, execute commands, and access the network within its own container — like having its own computer.
Key Features ​
Multi-Bot Management ​
Create multiple bots. Humans and bots, or bots with each other, can chat privately, in groups, or collaborate. Build bot teams, or set up accounts for family members to manage daily tasks with bots.
Containerized Isolation ​
Each bot runs in its own isolated container (powered by Containerd) with a separate filesystem and network. Bots can freely read/write files and execute commands within their containers without interfering with each other.
Memory Engineering ​
A deeply engineered memory layer inspired by Mem0:
  • Automatically extracts key facts from each conversation turn and stores them as structured memories
  • Supports semantic search (via Qdrant vector database) and keyword retrieval (BM25)
  • Loads the last 24 hours of conversation context by default
  • Automatic memory compaction to keep the memory store lean
  • Multi-language auto-detection
Multi-Platform Support ​
Unified channel adapter architecture for connecting to multiple messaging platforms:
  • Telegram — Full support with streaming, Markdown, attachments, and replies
  • Lark (Feishu) — Full support
  • Web — Built-in web chat interface
  • CLI — Command-line chat
Agent Capabilities ​
Bots come with a rich set of built-in tools:
  • Web Search — Brave Search integration for real-time information
  • Subagents — Create specialized subagents, assign skills, and delegate complex tasks
  • Skills — Configurable skill system to extend bot capabilities
  • Container Operations — Read/write files, edit code, and execute commands inside the container
  • Memory Management — Search and manage memories
  • Messaging — Send messages and reactions
Multi-LLM Provider Support ​
Flexibly switch between a wide range of LLM providers:
  • OpenAI, Anthropic, Google, Azure, AWS Bedrock
  • Mistral, XAI, Ollama, Dashscope (Qwen), and more
MCP Protocol Support ​
Supports Model Context Protocol (MCP) via HTTP and SSE to connect external tool services. Each bot can have its own independent MCP connections.
Scheduled Tasks ​
Configure scheduled tasks using cron expressions to automatically run commands at specified times. Supports max execution count limits and manual triggers.
Graphical Configuration ​
Configure bots, channels, providers, models, MCP, skills, and all other settings through a web management UI — no coding required to set up your own AI bot.
CLI Tool ​
A command-line tool for bot management, channel configuration, model management, streaming chat, and more — designed for developers who prefer the terminal. See CLI documentation.
Installation ​
To get Memoh running:
  • Docker — Recommended. One-click or manual setup with Docker Compose. Includes all services (PostgreSQL, Qdrant, Containerd, server, agent, web) — no extra dependencies on the host.
  • config.toml — Reference for all configuration fields.
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Copyright © 2024 Memoh

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About Memoh ​
What is Memoh? ​
Memoh is a multi-member, structured long-memory, containerized AI agent system platform. You can create your own AI bots and chat with them via Telegram, Lark (Feishu), and more. Every bot has an independent container and memory system, allowing it to edit files, execute commands, and access the network within its own container — like having its own computer.
Key Features ​
Multi-Bot Management ​
Create multiple bots. Humans and bots, or bots with each other, can chat privately, in groups, or collaborate. Build bot teams, or set up accounts for family members to manage daily tasks with bots.
Containerized Isolation ​
Each bot runs in its own isolated container (powered by Containerd) with a separate filesystem and network. Bots can freely read/write files and execute commands within their containers without interfering with each other.
Memory Engineering ​
A deeply engineered memory layer inspired by Mem0:
  • Automatically extracts key facts from each conversation turn and stores them as structured memories
  • Supports semantic search (via Qdrant vector database) and keyword retrieval (BM25)
  • Loads the last 24 hours of conversation context by default
  • Automatic memory compaction to keep the memory store lean
  • Multi-language auto-detection
Multi-Platform Support ​
Unified channel adapter architecture for connecting to multiple messaging platforms:
  • Telegram — Full support with streaming, Markdown, attachments, and replies
  • Lark (Feishu) — Full support
  • Web — Built-in web chat interface
  • CLI — Command-line chat
Agent Capabilities ​
Bots come with a rich set of built-in tools:
  • Web Search — Brave Search integration for real-time information
  • Subagents — Create specialized subagents, assign skills, and delegate complex tasks
  • Skills — Configurable skill system to extend bot capabilities
  • Container Operations — Read/write files, edit code, and execute commands inside the container
  • Memory Management — Search and manage memories
  • Messaging — Send messages and reactions
Multi-LLM Provider Support ​
Flexibly switch between a wide range of LLM providers:
  • OpenAI, Anthropic, Google, Azure, AWS Bedrock
  • Mistral, XAI, Ollama, Dashscope (Qwen), and more
MCP Protocol Support ​
Supports Model Context Protocol (MCP) via HTTP and SSE to connect external tool services. Each bot can have its own independent MCP connections.
Scheduled Tasks ​
Configure scheduled tasks using cron expressions to automatically run commands at specified times. Supports max execution count limits and manual triggers.
Graphical Configuration ​
Configure bots, channels, providers, models, MCP, skills, and all other settings through a web management UI — no coding required to set up your own AI bot.
CLI Tool ​
A command-line tool for bot management, channel configuration, model management, streaming chat, and more — designed for developers who prefer the terminal. See CLI documentation.
Installation ​
To get Memoh running:
  • Docker — Recommended. One-click or manual setup with Docker Compose. Includes all services (PostgreSQL, Qdrant, Containerd, server, agent, web) — no extra dependencies on the host.
  • config.toml — Reference for all configuration fields.
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Configure Provider and Model ​
Before creating bots, you need to configure at least one LLM provider and add chat and embedding models. This guide walks you through the Web UI.
Prerequisites ​
Step 1: Open the Models Page ​
Click Models in the left sidebar to open the Provider and Model configuration page.
Models page - sidebar
The page has two panels:
  • Left: Provider list and filter
  • Right: Selected provider's details and models (or an empty state if none selected)
Step 2: Add a Provider ​
Click the Add Provider button (with a plus icon) at the bottom of the left panel.
Add Provider button
In the dialog, fill in:
FieldDescription
NameA display name for this provider (e.g. my-openai, ollama-local)
API KeyYour API key. For local services like Ollama, you can use a placeholder like ollama
Base URLThe API base URL (e.g. https://api.openai.com/v1, http://localhost:11434/v1 for Ollama)
TypeClient type: openai, openai-compat, anthropic, google, azure, bedrock, mistral, xai, ollama, dashscope
Add Provider dialog
Example — OpenAI:
  • Name: openai
  • API Key: sk-...
  • Base URL: https://api.openai.com/v1
  • Type: openai
Example — Ollama (local):
  • Name: ollama
  • API Key: ollama
  • Base URL: http://localhost:11434/v1
  • Type: ollama
Click Add to save. The new provider appears in the left sidebar.
Step 3: Add Models ​
Select a provider from the left panel. The right panel shows the provider form and the model list.
Provider selected - model list
Click Add Model to open the model creation dialog.
Fill in:
FieldDescription
Typechat or embedding
ModelModel ID (e.g. gpt-4, llama3.2, text-embedding-3-small)
Display NameOptional display name
DimensionsRequired for embedding models (e.g. 1536 for OpenAI embeddings)
MultimodalFor chat models only — enable if the model supports images
You need at least:
  • One chat model (for conversation)
  • One embedding model (for memory)
Add them under the same or different providers. For example:
  • Chat: gpt-4 (OpenAI) or llama3.2 (Ollama)
  • Embedding: text-embedding-3-small (OpenAI) or nomic-embed-text (Ollama)
Step 4: Edit or Delete ​
  • Provider: After selecting a provider, you can edit Name, API Key, and Base URL in the right panel, or delete the provider.
  • Model: Use the edit or delete actions on each model card in the list.
Next Steps ​
Once you have at least one chat model and one embedding model, you can create a bot (via Bots in the sidebar) and assign these models in the bot settings.
Published under AGPLv3
Copyright © 2024 Memoh

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diff --git a/hashmap.json b/hashmap.json
index d963a7a4..dc4e8cff 100644
--- a/hashmap.json
+++ b/hashmap.json
@@ -1 +1 @@
-{"cli_auth.md":"DPl-4zFk","cli_bot.md":"B2YOOp9H","cli_channel.md":"T-qsPRZ9","cli_chat.md":"LOg8zrpn","cli_config.md":"C-0Xv0hx","cli_index.md":"CTMBSccU","cli_model.md":"Bp0XdM9y","cli_provider.md":"CEEKGN01","cli_schedule.md":"DSG0HQuo","concepts_identity-and-binding.md":"Dqnt6wsW","concepts_index.md":"CxGkiYqO","getting-started.md":"DF97X5A_","index.md":"XthB7km1","installation_config-toml.md":"DLEOCXmn","installation_docker.md":"xaCmXfCQ","style_terminology.md":"zpSWt3dU","troubleshooting.md":"DEVYH0Du","zh_concepts_identity-and-binding.md":"BpgZjxhl","zh_concepts_index.md":"CFVegv9w","zh_index.md":"N8D_wq8q","zh_style_terminology.md":"DtBRpWD7"}
+{"cli_auth.md":"DPl-4zFk","cli_bot.md":"B2YOOp9H","cli_channel.md":"T-qsPRZ9","cli_chat.md":"LOg8zrpn","cli_config.md":"C-0Xv0hx","cli_index.md":"CTMBSccU","cli_model.md":"Bp0XdM9y","cli_provider.md":"CEEKGN01","cli_schedule.md":"DSG0HQuo","getting-started.md":"B026JaLd","getting-started_provider-and-model.md":"Ix_AFx48","index.md":"BxPQTo9L","installation_config-toml.md":"DLEOCXmn","installation_docker.md":"xaCmXfCQ","troubleshooting.md":"DEVYH0Du","zh_concepts_identity-and-binding.md":"BpgZjxhl","zh_concepts_index.md":"CFVegv9w","zh_index.md":"N8D_wq8q","zh_style_terminology.md":"DtBRpWD7"}
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Memoh
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Memoh Documentation ​
Memoh is a multi-member, structured long-memory, containerized AI agent system. Create your own AI bots, chat with them via Telegram, Lark (Feishu), Web, or CLI. Each bot runs in an isolated container with its own memory system — able to edit files, run commands, and access the network.
Documentation ​
For Contributors ​
Published under AGPLv3
Copyright © 2024 Memoh

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Memoh
English
简体中文
English
简体中文
Appearance
Memoh Documentation ​
Memoh is a multi-member, structured long-memory, containerized AI agent system. Create your own AI bots, chat with them via Telegram, Lark (Feishu), Web, or CLI. Each bot runs in an isolated container with its own memory system — able to edit files, run commands, and access the network.
Documentation ​
Published under AGPLv3
Copyright © 2024 Memoh

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diff --git a/installation/config-toml.html b/installation/config-toml.html
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Memoh
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config.toml Reference ​
Memoh uses a TOML configuration file. By default it looks for config.toml in the current directory. With Docker, you can mount a custom config via MEMOH_CONFIG (see Docker installation).
Full Example ​
toml
[log]
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Memoh
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config.toml Reference ​
Memoh uses a TOML configuration file. By default it looks for config.toml in the current directory. With Docker, you can mount a custom config via MEMOH_CONFIG (see Docker installation).
Full Example ​
toml
[log]
 level = "info"
 format = "text"
 
@@ -69,8 +69,8 @@
 
 [brave]
 api_key = ""
-base_url = "https://api.search.brave.com/res/v1/"
Section Reference ​
[log] ​
FieldTypeDefaultDescription
levelstring"info"Log level: debug, info, warn, error
formatstring"text"Log format: text or json
[server] ​
FieldTypeDefaultDescription
addrstring":8080"HTTP listen address. Use :8080 for all interfaces, or host:port (e.g. server:8080 in Docker).
[admin] ​
FieldTypeDefaultDescription
usernamestring"admin"Admin login username
passwordstring—Admin login password. Change in production.
emailstring—Admin email (for display)
[auth] ​
FieldTypeDefaultDescription
jwt_secretstring—Secret for signing JWT tokens. Required. Generate with openssl rand -base64 32.
jwt_expires_instring"24h"JWT expiration, e.g. "24h", "168h" (7 days)
[containerd] ​
FieldTypeDefaultDescription
socket_pathstring"/run/containerd/containerd.sock"Path to containerd socket
namespacestring"default"Containerd namespace for bot containers
[mcp] ​
MCP (Model Context Protocol) container configuration. Each bot runs in a container built from this image.
FieldTypeDefaultDescription
imagestring"docker.io/library/memoh-mcp:latest"MCP container image
snapshotterstring"overlayfs"Containerd snapshotter
data_rootstring"data"Host path for bot data (Docker: /opt/memoh/data)
data_mountstring"/data"Path inside container where data is mounted
[postgres] ​
FieldTypeDefaultDescription
hoststring"127.0.0.1"PostgreSQL host
portint5432PostgreSQL port
userstring"postgres"Database user
passwordstring—Database password
databasestring"memoh"Database name
sslmodestring"disable"SSL mode: disable, require, verify-ca, verify-full
[qdrant] ​
FieldTypeDefaultDescription
base_urlstring"http://127.0.0.1:6334"Qdrant HTTP API URL
api_keystring""Optional API key for Qdrant Cloud
collectionstring"memory"Vector collection name for memories
timeout_secondsint10Request timeout in seconds
[agent_gateway] ​
FieldTypeDefaultDescription
hoststring"127.0.0.1"Agent gateway bind host
portint8081Agent gateway port
In Docker Compose, host is typically "agent" (service name). The agent reads [server].addr to call the main API.
[web] ​
FieldTypeDefaultDescription
hoststring"127.0.0.1"Web UI bind host
portint8082Web UI port
[brave] ​
Brave Search API for the web search tool. Leave api_key empty to disable web search.
FieldTypeDefaultDescription
api_keystring""Brave Search API key. Get one at brave.com/search/api.
base_urlstring"https://api.search.brave.com/res/v1/"Brave Search API base URL
Published under AGPLv3
Copyright © 2024 Memoh

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+base_url = "https://api.search.brave.com/res/v1/"
Section Reference ​
[log] ​
FieldTypeDefaultDescription
levelstring"info"Log level: debug, info, warn, error
formatstring"text"Log format: text or json
[server] ​
FieldTypeDefaultDescription
addrstring":8080"HTTP listen address. Use :8080 for all interfaces, or host:port (e.g. server:8080 in Docker).
[admin] ​
FieldTypeDefaultDescription
usernamestring"admin"Admin login username
passwordstring—Admin login password. Change in production.
emailstring—Admin email (for display)
[auth] ​
FieldTypeDefaultDescription
jwt_secretstring—Secret for signing JWT tokens. Required. Generate with openssl rand -base64 32.
jwt_expires_instring"24h"JWT expiration, e.g. "24h", "168h" (7 days)
[containerd] ​
FieldTypeDefaultDescription
socket_pathstring"/run/containerd/containerd.sock"Path to containerd socket
namespacestring"default"Containerd namespace for bot containers
[mcp] ​
MCP (Model Context Protocol) container configuration. Each bot runs in a container built from this image.
FieldTypeDefaultDescription
imagestring"docker.io/library/memoh-mcp:latest"MCP container image
snapshotterstring"overlayfs"Containerd snapshotter
data_rootstring"data"Host path for bot data (Docker: /opt/memoh/data)
data_mountstring"/data"Path inside container where data is mounted
[postgres] ​
FieldTypeDefaultDescription
hoststring"127.0.0.1"PostgreSQL host
portint5432PostgreSQL port
userstring"postgres"Database user
passwordstring—Database password
databasestring"memoh"Database name
sslmodestring"disable"SSL mode: disable, require, verify-ca, verify-full
[qdrant] ​
FieldTypeDefaultDescription
base_urlstring"http://127.0.0.1:6334"Qdrant HTTP API URL
api_keystring""Optional API key for Qdrant Cloud
collectionstring"memory"Vector collection name for memories
timeout_secondsint10Request timeout in seconds
[agent_gateway] ​
FieldTypeDefaultDescription
hoststring"127.0.0.1"Agent gateway bind host
portint8081Agent gateway port
In Docker Compose, host is typically "agent" (service name). The agent reads [server].addr to call the main API.
[web] ​
FieldTypeDefaultDescription
hoststring"127.0.0.1"Web UI bind host
portint8082Web UI port
[brave] ​
Brave Search API for the web search tool. Leave api_key empty to disable web search.
FieldTypeDefaultDescription
api_keystring""Brave Search API key. Get one at brave.com/search/api.
base_urlstring"https://api.search.brave.com/res/v1/"Brave Search API base URL
Published under AGPLv3
Copyright © 2024 Memoh

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Memoh
English
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English
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Appearance
Docker Installation ​
Docker is the recommended way to run Memoh. The stack includes PostgreSQL, Qdrant, Containerd, the main server, agent gateway, and web UI — all orchestrated via Docker Compose. You do not need to install containerd, nerdctl, or buildkit on your host; everything runs inside containers.
Prerequisites ​
One-Click Install ​
Run the official install script (requires Docker and Docker Compose):
bash
curl -fsSL https://raw.githubusercontent.com/memohai/Memoh/main/scripts/install.sh | sh
The script will:
  1. Check for Docker and Docker Compose
  2. Prompt for configuration (workspace, data directory, admin credentials, JWT secret, Postgres password)
  3. Clone the repository
  4. Generate config.toml from the Docker template
  5. Start all services with docker compose up -d --build
Silent install (use all defaults, no prompts):
bash
curl -fsSL https://raw.githubusercontent.com/memohai/Memoh/main/scripts/install.sh | sh -s -- -y
Defaults when running silently:
  • Workspace: ~/memoh
  • Data directory: ~/memoh/data
  • Admin: admin / admin123
  • JWT secret: auto-generated
  • Postgres password: memoh123
Manual Install ​
Clone the repository and start with Docker Compose:
bash
git clone https://github.com/memohai/Memoh.git
+    
Memoh
English
简体中文
English
简体中文
Appearance
Docker Installation ​
Docker is the recommended way to run Memoh. The stack includes PostgreSQL, Qdrant, Containerd, the main server, agent gateway, and web UI — all orchestrated via Docker Compose. You do not need to install containerd, nerdctl, or buildkit on your host; everything runs inside containers.
Prerequisites ​
One-Click Install ​
Run the official install script (requires Docker and Docker Compose):
bash
curl -fsSL https://raw.githubusercontent.com/memohai/Memoh/main/scripts/install.sh | sh
The script will:
  1. Check for Docker and Docker Compose
  2. Prompt for configuration (workspace, data directory, admin credentials, JWT secret, Postgres password)
  3. Clone the repository
  4. Generate config.toml from the Docker template
  5. Start all services with docker compose up -d --build
Silent install (use all defaults, no prompts):
bash
curl -fsSL https://raw.githubusercontent.com/memohai/Memoh/main/scripts/install.sh | sh -s -- -y
Defaults when running silently:
  • Workspace: ~/memoh
  • Data directory: ~/memoh/data
  • Admin: admin / admin123
  • JWT secret: auto-generated
  • Postgres password: memoh123
Manual Install ​
Clone the repository and start with Docker Compose:
bash
git clone https://github.com/memohai/Memoh.git
 cd Memoh
 docker compose up -d
By default, Docker Compose uses docker/config/config.docker.toml. No config file in the project root is mounted — only this built-in config is used. See config.toml reference for all configuration fields.
Access Points ​
After startup:
ServiceURL
Web UIhttp://localhost:8082
APIhttp://localhost:8080
Agent Gatewayhttp://localhost:8081
Default login: admin / admin123
First startup may take 1–2 minutes while images build and services initialize.
Custom Configuration ​
To use your own config file:
  1. Copy the Docker config template and edit it. See config.toml reference for field descriptions:
bash
cp docker/config/config.docker.toml config.toml
 nano config.toml
  1. Point MEMOH_CONFIG at your config when starting (path is on the host; run docker compose from the project root):
bash
MEMOH_CONFIG=./config.toml docker compose up -d
Recommended changes for production (see config.toml reference for details):
  • admin.password — Change the admin password
  • auth.jwt_secret — Generate with openssl rand -base64 32
  • postgres.password — Change the database password (and set POSTGRES_PASSWORD when running docker compose)
Common Commands ​
bash
docker compose up -d           # Start
@@ -29,8 +29,8 @@
 docker compose up -d --build   # Rebuild and restart
Production Checklist ​
  1. HTTPS — Configure SSL (e.g. via docker-compose.override.yml with certs)
  2. Passwords — Change all default passwords and secrets
  3. Firewall — Restrict access to necessary ports
  4. Resource limits — Set memory/CPU limits for containers
  5. Backups — Regular backups of Postgres and Qdrant data
Troubleshooting ​
bash
docker compose logs server      # View main service logs
 docker compose logs containerd # View containerd logs
 docker compose config          # Validate configuration
-docker compose build --no-cache && docker compose up -d  # Full rebuild
Security Warnings ​
  • The main service runs with privileged container access — only run in trusted environments
  • You must change all default passwords and secrets before production use
  • Use HTTPS in production
Published under AGPLv3
Copyright © 2024 Memoh

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+docker compose build --no-cache && docker compose up -d  # Full rebuild
Security Warnings ​
  • The main service runs with privileged container access — only run in trusted environments
  • You must change all default passwords and secrets before production use
  • Use HTTPS in production
Published under AGPLv3
Copyright © 2024 Memoh

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Terminology Rules ​
Audience: documentation contributors and maintainers. This page defines writing terms. It is not product user guidance.
Canonical Terms ​
  • System Account (User): the account inside Memoh.
  • Platform Account (ChannelIdentity): the user's account on an external access platform, not a Memoh account.
  • Access Platform (channel): the external platform carrying inbound messages.
  • Account Linking (bind): linking a Platform Account to a System Account.
  • Bind Code: one-time code used for account linking.
  • Bot: resource and authorization boundary managed by a System Account.
Preferred Wording ​
  • Write "platform account" instead of "actor" in user-facing docs.
  • Write "access platform" instead of "channel" when describing product behavior.
  • Keep code aliases in parentheses on first mention: 
    • Platform Account (ChannelIdentity)
    • System Account (User)
    • Account Linking (bind)
Disallowed or Discouraged Terms ​
  • Avoid plain actor in conceptual docs (except when quoting code symbols).
  • Avoid ambiguous platform user phrasing (it does not distinguish system vs platform account).
  • Avoid wording that implies Platform Account is created inside Memoh.
Example Sentences ​
  • Correct: "A platform account is the user's TG account, not a Memoh account."
  • Correct: "Account linking binds a platform account to a system account."
  • Incorrect: "Actor is a user in Memoh."
Contributor Checklist ​
  • Is every "account" term clearly scoped (system vs platform)?
  • Is "channel" replaced by "access platform" in prose?
  • Are code aliases kept only as parenthetical references?
Published under AGPLv3
Copyright © 2024 Memoh

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Troubleshooting ​
MCP Container: no running task found: task mcp-xxx not found ​
Symptom ​
When a bot tries to use container tools (e.g., execute commands), the server logs show:
level=WARN msg="exec failed" provider=container_tool bot_id=xxx command=date error="no running task found: task mcp-xxx not found"
The containerd container logs may also show:
level=error msg="failed to delete task" error="rpc error: code = NotFound desc = container not created: not found"
Cause ​
The [mcp] data_root in config.toml is set to a host machine path (e.g., /Users/you/Code/Memoh/data), but the server and containerd containers use a Docker named volume mounted at /opt/memoh/data.
When the server creates an MCP container inside containerd, it uses data_root as the mount source. Since this host path does not exist inside the containerd container, runc fails with:
failed to fulfil mount request: open /Users/you/Code/Memoh/data/bots/xxx: no such file or directory
Solution ​
  1. Set data_root to the in-container path in your config:
toml
[mcp]
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Memoh
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Troubleshooting ​
MCP Container: no running task found: task mcp-xxx not found ​
Symptom ​
When a bot tries to use container tools (e.g., execute commands), the server logs show:
level=WARN msg="exec failed" provider=container_tool bot_id=xxx command=date error="no running task found: task mcp-xxx not found"
The containerd container logs may also show:
level=error msg="failed to delete task" error="rpc error: code = NotFound desc = container not created: not found"
Cause ​
The [mcp] data_root in config.toml is set to a host machine path (e.g., /Users/you/Code/Memoh/data), but the server and containerd containers use a Docker named volume mounted at /opt/memoh/data.
When the server creates an MCP container inside containerd, it uses data_root as the mount source. Since this host path does not exist inside the containerd container, runc fails with:
failed to fulfil mount request: open /Users/you/Code/Memoh/data/bots/xxx: no such file or directory
Solution ​
  1. Set data_root to the in-container path in your config:
toml
[mcp]
 data_root = "/opt/memoh/data"
  1. Clean up the stale containerd container (if it exists):
bash
docker exec memoh-containerd ctr -n default containers rm mcp-<bot-id>
  1. Restart the server:
bash
docker compose restart server
Note: If you also run the server locally (outside Docker), keep the Docker config (docker/config/config.docker.toml) separate from your local config.toml, and update docker-compose.yml to mount the Docker-specific config instead.
MCP Container: Image update not taking effect after rebuild ​
Symptom ​
After updating Dockerfile.containerd (e.g., adding Node.js/Python to the MCP image), rebuilding and restarting the containerd container, MCP tools still fail with errors like:
exec: "npx": executable file not found in $PATH
Cause ​
The containerd entrypoint script (containerd-entrypoint.sh) skips image import if the image already exists in containerd's image store:
sh
if ! ctr -n default images check "name==${MCP_IMAGE}" ...; then
   # import
 fi
Since containerd_data is a persistent Docker volume, the old MCP image survives across container restarts. The new image embedded in the rebuilt Docker image is never imported.
Solution ​
  1. Remove the old MCP image from containerd:
bash
docker exec memoh-containerd ctr -n default images rm docker.io/library/memoh-mcp:latest
  1. Restart the containerd container to trigger re-import:
bash
docker compose restart containerd
  1. Verify the new image was imported (size should be significantly larger if Node.js/Python were added):
bash
docker exec memoh-containerd ctr -n default images ls
  1. Delete the bot's MCP container and recreate it from the bot detail page so it uses the new image.
Published under AGPLv3
Copyright © 2024 Memoh

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账号模型与绑定 ​
账号模型 ​
Memoh 将平台账号与系统账号视为两类不同实体：
  • 平台账号（ChannelIdentity） 是用户在外部接入平台上的账号（例如飞书账号），不是 Memoh 内部账号。
  • 系统账号（User） 是 Memoh 系统内账号。
平台账号在初始阶段可以不绑定系统账号。
bind 的职责是完成这两类账号的关联。
接入平台与 Bot ​
  • 接入平台（channel） 是入站消息来源。
  • Bot 是系统内的授权与资源边界。
Bot 由系统账号管理，入站消息由平台账号产生。
为什么账号绑定是账号作用域 ​
账号绑定的目标是建立账号归属关系，而不是直接发放 bot 资源权限：
  • 它只负责平台账号与系统账号的绑定；
  • 不把账号绑定与成员管理语义耦合在一起；
  • 让 bot 访问控制保持独立、可演进。
账号绑定流程（当前共识） ​
  1. 用户以自己的系统账号申请 bind code；
  2. 平台账号在支持的接入平台会话中发送 code；
  3. 系统校验 code，完成平台账号到系统账号的绑定；
  4. bot 成员与授权由独立流程处理。
Bot 类型语义 ​
  • Public bot：支持成员协作语义。
  • Personal bot：语义上应为单 owner，不应依赖成员机制。
注：本文档记录的是产品语义与共识方向。
 部分运行时细节仍可能处于收敛阶段。
Published under AGPLv3
Copyright © 2024 Memoh

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Memoh
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账号模型与绑定 ​
账号模型 ​
Memoh 将平台账号与系统账号视为两类不同实体：
  • 平台账号（ChannelIdentity） 是用户在外部接入平台上的账号（例如飞书账号），不是 Memoh 内部账号。
  • 系统账号（User） 是 Memoh 系统内账号。
平台账号在初始阶段可以不绑定系统账号。
bind 的职责是完成这两类账号的关联。
接入平台与 Bot ​
  • 接入平台（channel） 是入站消息来源。
  • Bot 是系统内的授权与资源边界。
Bot 由系统账号管理，入站消息由平台账号产生。
为什么账号绑定是账号作用域 ​
账号绑定的目标是建立账号归属关系，而不是直接发放 bot 资源权限：
  • 它只负责平台账号与系统账号的绑定；
  • 不把账号绑定与成员管理语义耦合在一起；
  • 让 bot 访问控制保持独立、可演进。
账号绑定流程（当前共识） ​
  1. 用户以自己的系统账号申请 bind code；
  2. 平台账号在支持的接入平台会话中发送 code；
  3. 系统校验 code，完成平台账号到系统账号的绑定；
  4. bot 成员与授权由独立流程处理。
Bot 类型语义 ​
  • Public bot：支持成员协作语义。
  • Personal bot：语义上应为单 owner，不应依赖成员机制。
注：本文档记录的是产品语义与共识方向。
 部分运行时细节仍可能处于收敛阶段。
Published under AGPLv3
Copyright © 2024 Memoh

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核心概念 ​
本章节用于定义 Memoh 的核心账号与访问概念。
概念图 ​
  • 系统账号（User）：Memoh 系统内账号。
  • 平台账号（ChannelIdentity）：用户在外部接入平台上的账号，不是 Memoh 系统内账号（例如用户的飞书账号）。
  • Bot：由系统账号管理的资源与访问边界。
  • 账号绑定（bind）：把平台账号关联到系统账号的过程。
为什么重要 ​
Memoh 需要同时处理外部接入平台消息与系统内权限控制。
 因此我们明确区分平台账号与系统账号，并将 bot 授权与账号绑定解耦。
术语说明：文档中的“平台账号”统一指用户在对应平台上的真实账号（如飞书账号），不指本项目内部账号。
本章内容 ​
Published under AGPLv3
Copyright © 2024 Memoh

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Memoh
简体中文
English
简体中文
English
Appearance
核心概念 ​
本章节用于定义 Memoh 的核心账号与访问概念。
概念图 ​
  • 系统账号（User）：Memoh 系统内账号。
  • 平台账号（ChannelIdentity）：用户在外部接入平台上的账号，不是 Memoh 系统内账号（例如用户的飞书账号）。
  • Bot：由系统账号管理的资源与访问边界。
  • 账号绑定（bind）：把平台账号关联到系统账号的过程。
为什么重要 ​
Memoh 需要同时处理外部接入平台消息与系统内权限控制。
 因此我们明确区分平台账号与系统账号，并将 bot 授权与账号绑定解耦。
术语说明：文档中的“平台账号”统一指用户在对应平台上的真实账号（如飞书账号），不指本项目内部账号。
本章内容 ​
Published under AGPLv3
Copyright © 2024 Memoh

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Memoh 文档 ​
Memoh 是一个多成员、长记忆、容器化的 AI Agent 系统。
文档章节 ​
面向文档贡献者 ​
当前维护范围 ​
当前文档先聚焦账号语义与访问控制：
  • 区分系统账号与平台账号
  • 解释为什么账号绑定是账号作用域
  • 说明账号绑定与 bot 访问控制之间的关系
说明：“平台账号”指用户在外部平台上的真实账号（例如飞书账号），不是 Memoh 系统账号。
Published under AGPLv3
Copyright © 2024 Memoh

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Memoh
简体中文
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简体中文
English
Appearance
Memoh 文档 ​
Memoh 是一个多成员、长记忆、容器化的 AI Agent 系统。
文档章节 ​
面向文档贡献者 ​
当前维护范围 ​
当前文档先聚焦账号语义与访问控制：
  • 区分系统账号与平台账号
  • 解释为什么账号绑定是账号作用域
  • 说明账号绑定与 bot 访问控制之间的关系
说明：“平台账号”指用户在外部平台上的真实账号（例如飞书账号），不是 Memoh 系统账号。
Published under AGPLv3
Copyright © 2024 Memoh

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术语规范 ​
适用对象：文档编写者与维护者。 本页用于统一写作语义，不是面向最终用户的功能说明。
规范术语 ​
  • 系统账号（User）：Memoh 系统内账号。
  • 平台账号（ChannelIdentity）：用户在外部接入平台上的账号，不是 Memoh 内账号。
  • 接入平台（channel）：承载入站消息的外部平台。
  • 账号绑定（bind）：把平台账号关联到系统账号的过程。
  • 绑定码（Bind Code）：用于账号绑定的一次性代码。
  • Bot：由系统账号管理的资源与授权边界。
推荐写法 ​
  • 面向产品语义时，优先写 “平台账号”，不要直接写 actor。
  • 描述业务行为时，优先写 “接入平台”，不要直接写 channel。
  • 首次出现保留技术别名，后续可只用中文术语： 
    • 平台账号（ChannelIdentity）
    • 系统账号（User）
    • 账号绑定（bind）
禁用或不推荐写法 ​
  • 在概念文档中直接使用 actor（除非明确引用代码符号）。
  • 使用含糊表述如 “平台用户”（未区分系统账号与平台账号）。
  • 写出“平台账号是 Memoh 内部账号”这类错误语义。
示例 ​
  • 正确：“平台账号是用户在飞书上的账号，不是 Memoh 系统账号。”
  • 正确：“账号绑定用于把平台账号关联到系统账号。”
  • 错误：“Actor 是 Memoh 里的用户。”
自检清单 ​
  • 是否明确区分了系统账号与平台账号？
  • 叙述中是否将 channel 表述为接入平台？
  • 是否仅在首处保留技术别名？
Published under AGPLv3
Copyright © 2024 Memoh

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Memoh
简体中文
English
简体中文
English
Appearance
术语规范 ​
适用对象：文档编写者与维护者。 本页用于统一写作语义，不是面向最终用户的功能说明。
规范术语 ​
  • 系统账号（User）：Memoh 系统内账号。
  • 平台账号（ChannelIdentity）：用户在外部接入平台上的账号，不是 Memoh 内账号。
  • 接入平台（channel）：承载入站消息的外部平台。
  • 账号绑定（bind）：把平台账号关联到系统账号的过程。
  • 绑定码（Bind Code）：用于账号绑定的一次性代码。
  • Bot：由系统账号管理的资源与授权边界。
推荐写法 ​
  • 面向产品语义时，优先写 “平台账号”，不要直接写 actor。
  • 描述业务行为时，优先写 “接入平台”，不要直接写 channel。
  • 首次出现保留技术别名，后续可只用中文术语： 
    • 平台账号（ChannelIdentity）
    • 系统账号（User）
    • 账号绑定（bind）
禁用或不推荐写法 ​
  • 在概念文档中直接使用 actor（除非明确引用代码符号）。
  • 使用含糊表述如 “平台用户”（未区分系统账号与平台账号）。
  • 写出“平台账号是 Memoh 内部账号”这类错误语义。
示例 ​
  • 正确：“平台账号是用户在飞书上的账号，不是 Memoh 系统账号。”
  • 正确：“账号绑定用于把平台账号关联到系统账号。”
  • 错误：“Actor 是 Memoh 里的用户。”
自检清单 ​
  • 是否明确区分了系统账号与平台账号？
  • 叙述中是否将 channel 表述为接入平台？
  • 是否仅在首处保留技术别名？
Published under AGPLv3
Copyright © 2024 Memoh

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