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Introduction to Memoh - The Case for an Always-On, Containerized Home Agent ​

Overview ​

We enter 2026 with a familiar tension: models get smarter every quarter, but the “agent experience” still breaks on context, latency, privacy, and real-world workflows. Over the past year, We kept circling three questions:

Memoh is our attempt to turn those questions into something buildable—not a manifesto, but a system that can survive contact with reality.

Story Time ​

Time travels fast. Somewhere between “I’ll remember this” and “wait, why did we decide that?”, a year disappears.

That’s the annoying part of building: most progress doesn’t feel like progress while it’s happening. It’s just a stream of small choices, half-finished threads, late-night fixes, and the occasional moment that actually clicks. The kind of moment where you sit back and think: okay—this is real.

Around the same time, I noticed something else: the internet started to feel smoother—and worse.

Text got cleaner, longer, more polite, more… empty. You could smell when something was generated: low information density, too many metaphors, too much agreement, not enough stakes.

I caught myself doing it too.

So I started forcing a constraint: say it plainly. Keep the density. Don’t inflate. Don’t hide behind style. If something mattered, anchor it to a real moment, a real trade-off, a real cost paid.

Because the thing LLMs can’t give you is not “intelligence.” It’s weight. The feeling that a human actually stood somewhere in time and wrote from that position.

That’s when I realized what I wanted wasn’t “an AI that can talk.” I wanted an AI that can live with you—quietly, continuously, accumulating context without turning your life into content sludge.

Phones were out first instinct—it's personal, powerful, always there. But mobile OS is closed: without OEM privileges you can build an app, not ambient infrastructure.

So We looked for the always-on node every home already has: the router (conceptually). Then the economics clash—router-class hardware can’t carry memory, RAG, tools, and multi-user agents. The device evolves: more RAM/storage, a screen, mic/speaker, tiny battery for take out, portable form.

Eventually it stops being a router. It becomes a new category: a home agent base layer.

What ​

Memoh is a containerized home/studio AI base layer: cloud-grade model capability paired with local-first memory (knowledge base, RAG/search, conversation history) that stays under your control.

Why ​

Long-context models raise the ceiling for agents—but they also make “fully local” expensive and “fully cloud” uncomfortable. People don’t want to re-brief AI every day, and they don’t want their durable context trapped in someone else’s feed. Containerization makes Memoh portable, reproducible, and safe to run as always-on infrastructure—so continuity becomes cheap, private, and dependable.

How ​

We run Memoh as a containerized stack: isolated services for storage (files/DB/vector index), retrieval, tool/runtime execution, and the control plane. Inference calls cloud APIs when you need frontier capability; durable memory and indexing stay local. The device acts as an always-on node (router-like, not a router) serving multiple users with strict boundaries: sharing is explicit, private context remains private, and everything is deployable/upgradable as versioned containers.

Features ​

Compare to OpenClaw ​

We Shared core belief: both Memoh and OpenClaw treat the agent as more than a chatbox—we give the LLM a playground: a real environment where it can remember, use tools, and iterate.

Where Memoh differs:

Conclusion ​

Memoh is built for one thing: always-on continuity—an AI that stays online, and a memory that stays yours.

We keep frontier inference in the cloud, keep durable context local, and run everything as a containerized, always-on stack. If you want an agent that feels less like an app and more like home infrastructure, that’s the bet Memoh is making.

Furthermore, we will continue to operate and permanently open source it, permanently open-source Memoh, making it a product with long impact.

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Introduction to Memoh - The Case for an Always-On, Containerized Home Agent ​

Overview ​

We enter 2026 with a familiar tension: models get smarter every quarter, but the “agent experience” still breaks on context, latency, privacy, and real-world workflows. Over the past year, We kept circling three questions:

  • Where does the capability boundary of agents actually sit?
  • What’s the real value of long context?
  • What hardware form factor makes “always-on, personal AI” feel natural?

Memoh is our attempt to turn those questions into something buildable—not a manifesto, but a system that can survive contact with reality.

Story Time ​

Time travels fast. Somewhere between “I’ll remember this” and “wait, why did we decide that?”, a year disappears.

That’s the annoying part of building: most progress doesn’t feel like progress while it’s happening. It’s just a stream of small choices, half-finished threads, late-night fixes, and the occasional moment that actually clicks. The kind of moment where you sit back and think: okay—this is real.

Around the same time, I noticed something else: the internet started to feel smoother—and worse.

Text got cleaner, longer, more polite, more… empty. You could smell when something was generated: low information density, too many metaphors, too much agreement, not enough stakes.

I caught myself doing it too.

So I started forcing a constraint: say it plainly. Keep the density. Don’t inflate. Don’t hide behind style. If something mattered, anchor it to a real moment, a real trade-off, a real cost paid.

Because the thing LLMs can’t give you is not “intelligence.” It’s weight. The feeling that a human actually stood somewhere in time and wrote from that position.

That’s when I realized what I wanted wasn’t “an AI that can talk.” I wanted an AI that can live with you—quietly, continuously, accumulating context without turning your life into content sludge.

Phones were out first instinct—it's personal, powerful, always there. But mobile OS is closed: without OEM privileges you can build an app, not ambient infrastructure.

So We looked for the always-on node every home already has: the router (conceptually). Then the economics clash—router-class hardware can’t carry memory, RAG, tools, and multi-user agents. The device evolves: more RAM/storage, a screen, mic/speaker, tiny battery for take out, portable form.

Eventually it stops being a router. It becomes a new category: a home agent base layer.

What ​

Memoh is a containerized home/studio AI base layer: cloud-grade model capability paired with local-first memory (knowledge base, RAG/search, conversation history) that stays under your control.

Why ​

Long-context models raise the ceiling for agents—but they also make “fully local” expensive and “fully cloud” uncomfortable. People don’t want to re-brief AI every day, and they don’t want their durable context trapped in someone else’s feed. Containerization makes Memoh portable, reproducible, and safe to run as always-on infrastructure—so continuity becomes cheap, private, and dependable.

How ​

We run Memoh as a containerized stack: isolated services for storage (files/DB/vector index), retrieval, tool/runtime execution, and the control plane. Inference calls cloud APIs when you need frontier capability; durable memory and indexing stay local. The device acts as an always-on node (router-like, not a router) serving multiple users with strict boundaries: sharing is explicit, private context remains private, and everything is deployable/upgradable as versioned containers.

Features ​

  • Multi-bot Management: Create multiple bots; humans and bots, or bots with each other, can chat privately, in groups, or collaborate. Multi-bot Management

  • Containerized: Each bot runs in its own isolated container. Bots can freely execute commands, edit files, and access the network within their containers—like having their own computer. Containerized

  • Memory Engineering: Every chat is stored in the database, with the last 24 hours of context loaded by default. Each conversation turn is stored as memory and can be retrieved by bots through semantic search. Memory Engineering

  • Various Platforms: Supports Telegram, Lark (Feishu), and more.

  • Simple and Easy to Use: Configure bots and settings for Provider, Model, Memory, Channel, MCP, and Skills through a graphical interface—no coding required to set up your own AI bot.

  • Scheduled Tasks: Schedule tasks with cron expressions to run commands at specified times.

  • More...

Compare to OpenClaw ​

We Shared core belief: both Memoh and OpenClaw treat the agent as more than a chatbox—we give the LLM a playground: a real environment where it can remember, use tools, and iterate.

Where Memoh differs:

  • Lighter and Faster: built as home/studio infrastructure, can be held in the edge device
  • Containerized by default: each bot gets an isolated container (files/commands/network/jobs).
  • Hybrid split: cloud inference, local-first memory + indexing.
  • Multi-user first: explicit sharing and privacy boundaries, support a2a (Agent2Agent).
  • Sustainable: have an experienced team and confidence to push forward and build it.

Conclusion ​

Memoh is built for one thing: always-on continuity—an AI that stays online, and a memory that stays yours.

We keep frontier inference in the cloud, keep durable context local, and run everything as a containerized, always-on stack. If you want an agent that feels less like an app and more like home infrastructure, that’s the bet Memoh is making.

Furthermore, we will continue to operate and permanently open source it, permanently open-source Memoh, making it a product with long impact.

Published under AGPLv3

Copyright © 2024 Memoh

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z5I}039FKmYc>Za!a9Isw03Bz~W_JI6c&;v#4Pp%$*a9{G-%sq-Z~m1Nbp68x#GIpq0G_QfbBkD)hGXAo-`X6H{BYOY< literal 0 HcmV?d00001 diff --git a/blogs/index.html b/blogs/index.html new file mode 100644 index 00000000..05b3f400 --- /dev/null +++ b/blogs/index.html @@ -0,0 +1,26 @@ + + + + + + Blogs | Memoh Documentation + + + + + + + + + + + + + + + +

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Blogs ​

This section contains the latest blogs about Memoh.

Published under AGPLv3

Copyright © 2024 Memoh

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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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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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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

Published under AGPLv3

Copyright © 2024 Memoh

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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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Copyright © 2024 Memoh

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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
Copyright © 2024 Memoh

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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
Published under AGPLv3
Copyright © 2024 Memoh

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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
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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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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
Published under AGPLv3
Copyright © 2024 Memoh

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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
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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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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Bot ​
A bot is the primary runtime entity in Memoh.
Each bot has its own:
  • Configuration
  • Container lifecycle
  • Memory scope
  • Channel bindings
  • Model assignments
Key Settings ​
  • max-load-time (max_context_load_time): how many minutes of recent conversation context are loaded into prompts
  • language: preferred language for interaction (default is auto)
  • chat model / memory model / embedding model: model IDs used by this bot
Why It Matters ​
The bot abstraction allows Memoh to isolate behavior and resources per agent, while keeping management centralized in one Web UI.
Web UI Path ​
  • Bots > Select a bot > Settings
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Bot ​
A bot is the primary runtime entity in Memoh.
Each bot has its own:
  • Configuration
  • Container lifecycle
  • Memory scope
  • Channel bindings
  • Model assignments
Key Settings ​
  • max-load-time (max_context_load_time): how many minutes of recent conversation context are loaded into prompts
  • language: preferred language for interaction (default is auto)
  • chat model / memory model / embedding model: model IDs used by this bot
Why It Matters ​
The bot abstraction allows Memoh to isolate behavior and resources per agent, while keeping management centralized in one Web UI.
Web UI Path ​
  • Bots > Select a bot > Settings
Published under AGPLv3
Copyright © 2024 Memoh

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Channel ​
A channel connects a bot to an external communication platform.
Examples include:
  • Telegram
  • Feishu (Lark)
  • Web chat
What a Channel Configuration Defines ​
  • Platform type and credentials
  • Message routing from external users to a target bot
  • Reply delivery back to the source platform
Why It Matters ​
Channels decouple bot logic from transport, so one bot can serve users across multiple platforms.
Web UI Path ​
  • Bots > Select a bot > Channels
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Channel ​
A channel connects a bot to an external communication platform.
Examples include:
  • Telegram
  • Feishu (Lark)
  • Web chat
What a Channel Configuration Defines ​
  • Platform type and credentials
  • Message routing from external users to a target bot
  • Reply delivery back to the source platform
Why It Matters ​
Channels decouple bot logic from transport, so one bot can serve users across multiple platforms.
Web UI Path ​
  • Bots > Select a bot > Channels
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Container ​
Each bot runs in its own isolated container.
What Isolation Gives You ​
  • Separate filesystem per bot
  • Separate runtime process space
  • Controlled lifecycle (create/start/stop/delete)
This prevents one bot from interfering with another bot's execution environment.
Why It Matters ​
Container isolation is the foundation that allows bots to run tools, commands, and file operations safely in parallel.
Web UI Path ​
  • Bots > Select a bot > Container
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Container ​
Each bot runs in its own isolated container.
What Isolation Gives You ​
  • Separate filesystem per bot
  • Separate runtime process space
  • Controlled lifecycle (create/start/stop/delete)
This prevents one bot from interfering with another bot's execution environment.
Why It Matters ​
Container isolation is the foundation that allows bots to run tools, commands, and file operations safely in parallel.
Web UI Path ​
  • Bots > Select a bot > Container
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Conversation and History ​
Memoh organizes interactions as conversations with preserved history.
What This Includes ​
  • Ongoing chat context between users and bots
  • Message-level timeline and traceability
  • Source context for memory extraction and debugging
Why It Matters ​
Conversation history powers:
  • Better response continuity
  • Auditing and troubleshooting
  • Post-hoc analysis of bot behavior
Web UI Path ​
  • Chat > Select conversation
  • Bots > Select a bot > History
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Conversation and History ​
Memoh organizes interactions as conversations with preserved history.
What This Includes ​
  • Ongoing chat context between users and bots
  • Message-level timeline and traceability
  • Source context for memory extraction and debugging
Why It Matters ​
Conversation history powers:
  • Better response continuity
  • Auditing and troubleshooting
  • Post-hoc analysis of bot behavior
Web UI Path ​
  • Chat > Select conversation
  • Bots > Select a bot > History
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Concepts Overview ​
This section explains the core design concepts behind Memoh.
Use these pages when you want to understand how Memoh is designed, why features exist, and how different parts work together.
Concept Map ​
  • Bot: the core runtime unit
  • Provider and Model: how LLM capability is plugged in
  • Memory: how long-term knowledge is stored and retrieved
  • Channel: how external platforms connect to bots
  • Schedule: how tasks are triggered automatically
  • Container: isolated execution environment per bot
  • MCP: external tool and service integration protocol
  • Subagents: specialized delegated agents
  • Skills: reusable capability prompts/instructions
  • Conversation and History: chat context and traceability
  1. Bot
  2. Provider and Model
  3. Memory
  4. Channel
  5. Container
  6. Schedule
  7. MCP
  8. Subagents
  9. Skills
  10. Conversation and History
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Concepts Overview ​
This section explains the core design concepts behind Memoh.
Use these pages when you want to understand how Memoh is designed, why features exist, and how different parts work together.
Concept Map ​
  • Bot: the core runtime unit
  • Provider and Model: how LLM capability is plugged in
  • Memory: how long-term knowledge is stored and retrieved
  • Channel: how external platforms connect to bots
  • Schedule: how tasks are triggered automatically
  • Container: isolated execution environment per bot
  • MCP: external tool and service integration protocol
  • Subagents: specialized delegated agents
  • Skills: reusable capability prompts/instructions
  • Conversation and History: chat context and traceability
  1. Bot
  2. Provider and Model
  3. Memory
  4. Channel
  5. Container
  6. Schedule
  7. MCP
  8. Subagents
  9. Skills
  10. Conversation and History
Published under AGPLv3
Copyright © 2024 Memoh

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MCP ​
MCP (Model Context Protocol) connects Memoh bots to external tool services.
What MCP Adds ​
  • Standardized tool discovery and invocation
  • Remote capability extension without changing core bot code
  • Per-bot integration flexibility
Memoh supports MCP connections so bots can access external systems through a unified protocol.
Why It Matters ​
MCP makes capability extension modular. You can add or swap tool backends with minimal coupling.
Web UI Path ​
  • Bots > Select a bot > MCP
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MCP ​
MCP (Model Context Protocol) connects Memoh bots to external tool services.
What MCP Adds ​
  • Standardized tool discovery and invocation
  • Remote capability extension without changing core bot code
  • Per-bot integration flexibility
Memoh supports MCP connections so bots can access external systems through a unified protocol.
Why It Matters ​
MCP makes capability extension modular. You can add or swap tool backends with minimal coupling.
Web UI Path ​
  • Bots > Select a bot > MCP
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Memory ​
Memory is Memoh's long-term knowledge system for each bot.
How It Works ​
  • Important facts are extracted from conversations
  • Memories are stored and indexed
  • Relevant memories are retrieved by semantic similarity when needed
Memoh combines vector retrieval with keyword-style retrieval for better recall and precision.
Why It Matters ​
Memory enables continuity across long timelines, so bots can maintain context beyond short prompt windows.
Web UI Path ​
  • Bots > Select a bot > Memory
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Memory ​
Memory is Memoh's long-term knowledge system for each bot.
How It Works ​
  • Important facts are extracted from conversations
  • Memories are stored and indexed
  • Relevant memories are retrieved by semantic similarity when needed
Memoh combines vector retrieval with keyword-style retrieval for better recall and precision.
Why It Matters ​
Memory enables continuity across long timelines, so bots can maintain context beyond short prompt windows.
Web UI Path ​
  • Bots > Select a bot > Memory
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Provider and Model ​
In Memoh, provider and model are separate but connected concepts:
  • A provider is the LLM service configuration (API endpoint, key, client type)
  • A model is the concrete chat or embedding model under that provider
Typical Setup ​
At minimum, a production-ready bot usually needs:
  • One chat model for dialog generation
  • One embedding model for memory indexing and retrieval
Model Assignment to Bot ​
Bots reference model IDs in settings:
  • chat_model_id
  • memory_model_id
  • embedding_model_id
This enables per-bot customization (for quality, latency, or cost).
Web UI Path ​
  • Models > Add Provider > Select Provider > Add Model
  • Bots > Select a bot > Settings > Choose chat/memory/embedding models
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Provider and Model ​
In Memoh, provider and model are separate but connected concepts:
  • A provider is the LLM service configuration (API endpoint, key, client type)
  • A model is the concrete chat or embedding model under that provider
Typical Setup ​
At minimum, a production-ready bot usually needs:
  • One chat model for dialog generation
  • One embedding model for memory indexing and retrieval
Model Assignment to Bot ​
Bots reference model IDs in settings:
  • chat_model_id
  • memory_model_id
  • embedding_model_id
This enables per-bot customization (for quality, latency, or cost).
Web UI Path ​
  • Models > Add Provider > Select Provider > Add Model
  • Bots > Select a bot > Settings > Choose chat/memory/embedding models
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Schedule ​
A schedule is a cron-based automation rule that runs bot commands at defined times.
Core Fields ​
  • pattern: cron expression
  • command: command or task to execute
  • enabled: whether the schedule is active
  • max_calls: optional execution cap
  • current_calls: current run count
Why It Matters ​
Schedules make bots proactive, not only reactive.
 You can automate recurring operations like summaries, reminders, checks, and maintenance tasks.
Web UI Path ​
  • Not available in current Web UI
Schedule is supported by backend and CLI/API, but there is currently no dedicated schedule page in the Web UI.
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Schedule ​
A schedule is a cron-based automation rule that runs bot commands at defined times.
Core Fields ​
  • pattern: cron expression
  • command: command or task to execute
  • enabled: whether the schedule is active
  • max_calls: optional execution cap
  • current_calls: current run count
Why It Matters ​
Schedules make bots proactive, not only reactive.
 You can automate recurring operations like summaries, reminders, checks, and maintenance tasks.
Web UI Path ​
  • Not available in current Web UI
Schedule is supported by backend and CLI/API, but there is currently no dedicated schedule page in the Web UI.
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Skills ​
Skills are reusable capability instructions that guide how a bot (or subagent) solves specific classes of tasks.
Typical Skill Content ​
  • Scope and purpose
  • Input expectations
  • Execution steps
  • Constraints and output format
Why It Matters ​
Skills help keep behavior consistent, reduce prompt duplication, and make complex workflows easier to maintain.
Web UI Path ​
  • Bots > Select a bot > Subagents
Skills are currently primarily managed through agent/workspace configuration and runtime conventions, while subagent-related behavior is visible from the Subagents area.
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Skills ​
Skills are reusable capability instructions that guide how a bot (or subagent) solves specific classes of tasks.
Typical Skill Content ​
  • Scope and purpose
  • Input expectations
  • Execution steps
  • Constraints and output format
Why It Matters ​
Skills help keep behavior consistent, reduce prompt duplication, and make complex workflows easier to maintain.
Web UI Path ​
  • Bots > Select a bot > Subagents
Skills are currently primarily managed through agent/workspace configuration and runtime conventions, while subagent-related behavior is visible from the Subagents area.
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Subagents ​
Subagents are specialized helper agents that a bot can delegate tasks to.
Core Idea ​
  • The main bot acts as coordinator
  • Subagents focus on narrower tasks
  • Results are combined back into the main workflow
This supports decomposition of complex requests into smaller, more reliable execution units.
Why It Matters ​
Subagent architecture improves scalability for multi-step tasks and helps separate responsibilities by role.
Web UI Path ​
  • Bots > Select a bot > Subagents
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Subagents ​
Subagents are specialized helper agents that a bot can delegate tasks to.
Core Idea ​
  • The main bot acts as coordinator
  • Subagents focus on narrower tasks
  • Results are combined back into the main workflow
This supports decomposition of complex requests into smaller, more reliable execution units.
Why It Matters ​
Subagent architecture improves scalability for multi-step tasks and helps separate responsibilities by role.
Web UI Path ​
  • Bots > Select a bot > Subagents
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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.
Published under AGPLv3
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.
Published under AGPLv3
Copyright © 2024 Memoh

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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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Memoh
English
简体中文
English
简体中文
Appearance
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.
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-{"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_bot.md":"1aZ6YcT_","concepts_channel.md":"kmh0thdg","concepts_container.md":"CGr24tv7","concepts_conversation-and-history.md":"CrsSw6px","concepts_index.md":"C-_J0Dnw","concepts_mcp.md":"Bi2x7eIb","concepts_memory.md":"C_8mQlYI","concepts_provider-and-model.md":"D9DoYLAT","concepts_schedule.md":"DZlMw6iP","concepts_skills.md":"F6zpo0BV","concepts_subagents.md":"B1gVZCBU","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"}
+{"blogs_2026-02-16.md":"ZcmuKUmd","blogs_index.md":"liWx4LKT","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_bot.md":"1aZ6YcT_","concepts_channel.md":"kmh0thdg","concepts_container.md":"CGr24tv7","concepts_conversation-and-history.md":"CrsSw6px","concepts_index.md":"C-_J0Dnw","concepts_mcp.md":"Bi2x7eIb","concepts_memory.md":"C_8mQlYI","concepts_provider-and-model.md":"D9DoYLAT","concepts_schedule.md":"DZlMw6iP","concepts_skills.md":"F6zpo0BV","concepts_subagents.md":"B1gVZCBU","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 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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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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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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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"
 
@@ -70,7 +70,7 @@
 [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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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
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Memoh
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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
 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
@@ -30,7 +30,7 @@
 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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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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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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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
English
简体中文
English
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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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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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