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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, Discord, Lark (Feishu), Email, Web, or CLI. 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 and brain.

Key Features ​

Multi-Bot Management ​

Create multiple bots. Humans and bots, or bots with each other, can chat privately, in groups, or collaborate. Supports role-based access control (owner / admin / member) with ownership transfer. Build bot teams, or set up accounts for family members to manage daily tasks with bots.

Multi-User & Identity Recognition ​

Bots can distinguish individual users in group chats, remember each person's context separately, and send direct messages to specific users. Cross-platform identity binding unifies the same person across Telegram, Discord, Lark, and Web.

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. Supports container snapshots for save/restore.

Memory Engineering ​

A deeply engineered, multi-provider memory layer:

  • Multi-provider architecture: Built-in (with off/sparse/dense modes), Mem0 (SaaS), and OpenViking (self-hosted or SaaS)
  • Automatically extracts key facts from each conversation turn and stores them as structured memories
  • Hybrid retrieval: semantic search (via Qdrant vector database) + keyword retrieval (BM25) + neural sparse vectors
  • Loads the last 24 hours of conversation context by default
  • Automatic memory compaction and rebuild capabilities
  • 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
  • Discord — Full support
  • Lark (Feishu) — Full support
  • Email — Inbound webhook + outbound providers (Mailgun, generic SMTP)
  • Web — Built-in web chat interface with streaming
  • CLI — Command-line chat

Agent Capabilities ​

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

  • Web Search — Configurable search providers (Brave, Bing, Google, Tavily, SearXNG, DuckDuckGo, and more) for real-time information
  • Browser Automation — Use Playwright-powered browser tools for navigation, clicking, form filling, screenshots, PDF export, and rendered page inspection
  • Subagents — Create specialized subagents with independent context, assign skills, and delegate complex tasks
  • Skills — Define bot personality via IDENTITY.md, SOUL.md, and modular skill files that bots can enable/disable at runtime
  • Container Operations — Read/write files, edit code, and execute commands inside the container
  • Memory Management — Search and manage memories
  • Text-to-Speech — Synthesize speech via configurable TTS providers (Edge TTS with 256+ voices)
  • Messaging — Send messages and reactions to specific users or channels

Multi-LLM Provider Support ​

Flexibly switch between a wide range of LLM providers via four client types:

  • OpenAI Responses API, OpenAI Chat Completions API (including compatible services)
  • Anthropic Messages API, Google Generative AI API

Per-bot model assignment for chat, memory, and embedding.

MCP Protocol Support ​

Full MCP (Model Context Protocol) support via HTTP, SSE, and Stdio to connect external tool services. Built-in tools for container operations, memory search, web search, scheduling, messaging, and more. 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.

Inbox ​

Cross-channel inbox — messages from other channels are queued and surfaced in the system prompt so the bot never misses context.

Graphical Configuration ​

Modern web UI (Vue 3 + Tailwind CSS) with real-time streaming chat, tool call visualization, container filesystem browser, and visual configuration for bots, channels, providers, models, MCP, skills, and all other settings. Dark/light theme, i18n. 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.

Published under AGPLv3

Copyright © 2024 Memoh

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t}from"./chunks/framework.BZohXCq9.js";const k=JSON.parse('{"title":"Docker Installation","description":"","frontmatter":{},"headers":[],"relativePath":"installation/docker.md","filePath":"installation/docker.md","lastUpdated":1773501089000}'),n={name:"installation/docker.md"};function o(l,s,r,h,d,p){return e(),a("div",null,[...s[0]||(s[0]=[t(`

Docker Installation ​

Docker is the recommended way to run Memoh. The stack includes PostgreSQL, Qdrant, the main server (with embedded Containerd), 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.

Service Architecture ​

The Docker Compose stack consists of multiple services. Some are always started, others are optional and enabled via --profile:

ServiceProfileDescription
server(core)Main Memoh server with embedded Containerd
agent(core)Agent gateway for bot execution
web(core)Web UI (Vue 3)
postgres(core)PostgreSQL database
qdrantqdrantQdrant vector database for memory search (sparse and dense modes)
browserbrowserPlaywright-based browser gateway for bot web automation
sparsesparseNeural sparse encoding service for memory retrieval (see below)

Sparse Service ​

The sparse container provides neural sparse vector encoding for memory retrieval. It runs a lightweight Python (Flask) service on port 8085 that uses the opensearch-neural-sparse-encoding-multilingual-v1 model from OpenSearch.

What it does:

  • Converts document text into sparse vectors (a compact list of token indices + importance weights) using a masked language model
  • Encodes queries using IDF-weighted term lookup for fast, efficient retrieval
  • Works with Qdrant to enable semantic memory search without requiring an external embedding API

Why use it:

  • No embedding API costs — The model runs locally inside the container, so you don't need an OpenAI/Cohere/etc. embedding API key
  • Multilingual — The underlying model supports multiple languages out of the box
  • Good retrieval quality — Neural sparse encoding provides significantly better results than keyword-only search (BM25), while being lighter than dense embedding models

When to enable it:

Enable the sparse profile (--profile sparse) if you plan to use the built-in memory provider in sparse mode. The model is pre-downloaded during the Docker image build, so the container starts quickly without needing to fetch weights at runtime.

bash
docker compose --profile qdrant --profile sparse --profile browser up -d

For more details on memory modes, see Built-in Memory Provider.

Prerequisites ​

Run the official install script (requires Docker and Docker Compose):

bash
curl -fsSL https://memoh.sh | sudo sh

The script will:

  1. Check for Docker and Docker Compose
  2. Prompt for configuration (workspace, data directory, admin credentials, JWT secret, Postgres password, sparse service toggle, browser core selection)
  3. Fetch the latest release tag from GitHub and clone the repository
  4. Generate config.toml from the Docker template with your settings
  5. Pin Docker image versions to the release
  6. Build the browser image with selected cores and start all services

Silent install (use all defaults, no prompts):

bash
curl -fsSL https://memoh.sh | sudo sh -s -- -y

Defaults when running silently:

  • Workspace: ~/memoh
  • Data directory: ~/memoh/data
  • Admin: admin / admin123
  • JWT secret: auto-generated
  • Postgres password: memoh123

Install a specific version:

bash
curl -fsSL https://memoh.sh | sudo sh -s -- --version v0.5.0

Or using the environment variable:

bash
curl -fsSL https://memoh.sh | sudo MEMOH_VERSION=v0.5.0 sh

Use China mainland mirror (for slow image pulls):

bash
curl -fsSL https://memoh.sh | sudo USE_CN_MIRROR=true sh

Environment variables can be combined, e.g. curl -fsSL https://memoh.sh | sudo MEMOH_VERSION=v1.0.0 USE_CN_MIRROR=true sh

Manual Install ​

bash
git clone https://github.com/memohai/Memoh.git
+import{_ as i,o as e,c as a,ag as t}from"./chunks/framework.BZohXCq9.js";const c=JSON.parse('{"title":"Docker Installation","description":"","frontmatter":{},"headers":[],"relativePath":"installation/docker.md","filePath":"installation/docker.md","lastUpdated":1773818349000}'),n={name:"installation/docker.md"};function o(l,s,r,h,d,p){return e(),a("div",null,[...s[0]||(s[0]=[t(`
Docker Installation ​
Docker is the recommended way to run Memoh. The stack includes PostgreSQL, Qdrant, the main server (with embedded Containerd), 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.
Service Architecture ​
The Docker Compose stack consists of multiple services. Some are always started, others are optional and enabled via --profile:
ServiceProfileDescription
server(core)Main Memoh server with embedded Containerd
agent(core)Agent gateway for bot execution
web(core)Web UI (Vue 3)
postgres(core)PostgreSQL database
qdrantqdrantQdrant vector database for memory search (sparse and dense modes)
browserbrowserPlaywright-based browser gateway for bot web automation
sparsesparseNeural sparse encoding service for memory retrieval (see below)
Sparse Service ​
The sparse container provides neural sparse vector encoding for memory retrieval. It runs a lightweight Python (Flask) service on port 8085 that uses the opensearch-neural-sparse-encoding-multilingual-v1 model from OpenSearch.
What it does:
  • Converts document text into sparse vectors (a compact list of token indices + importance weights) using a masked language model
  • Encodes queries using IDF-weighted term lookup for fast, efficient retrieval
  • Works with Qdrant to enable semantic memory search without requiring an external embedding API
Why use it:
  • No embedding API costs — The model runs locally inside the container, so you don't need an OpenAI/Cohere/etc. embedding API key
  • Multilingual — The underlying model supports multiple languages out of the box
  • Good retrieval quality — Neural sparse encoding provides significantly better results than keyword-only search (BM25), while being lighter than dense embedding models
When to enable it:
Enable the sparse profile (--profile sparse) if you plan to use the built-in memory provider in sparse mode. The model is pre-downloaded during the Docker image build, so the container starts quickly without needing to fetch weights at runtime.
bash
docker compose --profile qdrant --profile sparse --profile browser up -d
For more details on memory modes, see Built-in Memory Provider.
Prerequisites ​
Run the official install script (requires Docker and Docker Compose):
bash
curl -fsSL https://memoh.sh | sudo sh
The script will:
  1. Check for Docker and Docker Compose
  2. Prompt for configuration (workspace, data directory, admin credentials, JWT secret, Postgres password, sparse service toggle, browser core selection)
  3. Fetch the latest release tag from GitHub and clone the repository
  4. Generate config.toml from the Docker template with your settings
  5. Pin Docker image versions to the release
  6. Build the browser image with selected cores and start all services
Silent install (use all defaults, no prompts):
bash
curl -fsSL https://memoh.sh | sudo sh -s -- -y
Defaults when running silently:
  • Workspace: ~/memoh
  • Data directory: ~/memoh/data
  • Admin: admin / admin123
  • JWT secret: auto-generated
  • Postgres password: memoh123
Install a specific version:
bash
curl -fsSL https://memoh.sh | sudo sh -s -- --version v0.5.0
Or using the environment variable:
bash
curl -fsSL https://memoh.sh | sudo MEMOH_VERSION=v0.5.0 sh
Use China mainland mirror (for slow image pulls):
bash
curl -fsSL https://memoh.sh | sudo USE_CN_MIRROR=true sh
Environment variables can be combined, e.g. curl -fsSL https://memoh.sh | sudo MEMOH_VERSION=v1.0.0 USE_CN_MIRROR=true sh
Manual Install ​
bash
git clone https://github.com/memohai/Memoh.git
 cd Memoh
-cp conf/app.docker.toml config.toml
Edit config.toml — at minimum change:
  • admin.password — Admin password
  • auth.jwt_secret — Generate with openssl rand -base64 32
  • postgres.password — Database password (also set POSTGRES_PASSWORD env var to match)
Then start (recommended — with Qdrant, Browser, and Sparse):
bash
sudo POSTGRES_PASSWORD=your-db-password docker compose --profile qdrant --profile browser --profile sparse up -d
Or start core services only (no vector DB or browser automation):
bash
sudo POSTGRES_PASSWORD=your-db-password docker compose up -d
On macOS or if your user is in the docker group, sudo is not required.
Important: docker-compose.yml mounts ./config.toml by default. You must create this file before starting — running without it will fail.
China Mainland Mirror ​
For users in mainland China who cannot access Docker Hub directly, uncomment the registry line in config.toml:
toml
[mcp]
+cp conf/app.docker.toml config.toml
Edit config.toml — at minimum change:
  • admin.password — Admin password
  • auth.jwt_secret — Generate with openssl rand -base64 32
  • postgres.password — Database password (also set POSTGRES_PASSWORD env var to match)
Then start (recommended — with Qdrant, Browser, and Sparse):
bash
sudo POSTGRES_PASSWORD=your-db-password docker compose --profile qdrant --profile browser --profile sparse up -d
Or start core services only (no vector DB or browser automation):
bash
sudo POSTGRES_PASSWORD=your-db-password docker compose up -d
On macOS or if your user is in the docker group, sudo is not required.
Important: docker-compose.yml mounts ./config.toml by default. You must create this file before starting — running without it will fail.
China Mainland Mirror ​
For users in mainland China who cannot access Docker Hub directly, uncomment the registry line in config.toml:
toml
[workspace]
 registry = "memoh.cn"
And add the China mirror compose overlay:
bash
sudo docker compose -f docker-compose.yml -f docker/docker-compose.cn.yml \\
   --profile qdrant --profile browser up -d
The install script handles this automatically when you set USE_CN_MIRROR=true.
Access Points ​
After startup:
ServiceURL
Web UIhttp://localhost:8082
APIhttp://localhost:8080
Agent Gatewayhttp://localhost:8081
Browser Gatewayhttp://localhost:8083
Default login: admin / admin123 (change this in config.toml).
First startup may take 1–2 minutes while images are pulled and services initialize.
Common Commands ​
Prefix with sudo on Linux if your user is not in the docker group.
bash
docker compose up -d           # Start
 docker compose down            # Stop
@@ -8,4 +8,4 @@ import{_ as i,o as e,c as a,ag as t}from"./chunks/framework.BZohXCq9.js";const k
 docker compose ps              # Status
 docker compose pull && docker compose up -d  # Update to latest images
Environment Variables ​
VariableDefaultDescription
POSTGRES_PASSWORDmemoh123PostgreSQL password (must match postgres.password in config.toml)
MEMOH_CONFIG./config.tomlPath to the configuration file
MEMOH_VERSION(latest release)Git tag to install (e.g. v1.0.0). Also pins Docker image versions.
USE_CN_MIRRORfalseSet to true to use China mainland mirror for Docker images
BROWSER_CORESchromium,firefoxBrowser engines to include in the browser image (chromium, firefox, or chromium,firefox)
USE_SPARSEfalseSet to true to enable the sparse memory service (--profile sparse)
Production Checklist ​
  1. Passwords — Change all default passwords and secrets in config.toml
  2. HTTPS — Configure SSL (e.g. via docker-compose.override.yml with certs or a reverse proxy)
  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 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
`,65)])])}const g=i(n,[["render",o]]);export{k as __pageData,g as default};
+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
`,65)])])}const g=i(n,[["render",o]]);export{c as __pageData,g as default}; diff --git a/assets/installation_docker.md.BYtBZBwq.lean.js b/assets/installation_docker.md.IuAKlQcI.lean.js similarity index 57% rename from assets/installation_docker.md.BYtBZBwq.lean.js rename to assets/installation_docker.md.IuAKlQcI.lean.js index fa7fee0e..89c8af81 100644 --- a/assets/installation_docker.md.BYtBZBwq.lean.js +++ b/assets/installation_docker.md.IuAKlQcI.lean.js @@ -1 +1 @@ -import{_ as i,o as e,c as a,ag as t}from"./chunks/framework.BZohXCq9.js";const k=JSON.parse('{"title":"Docker Installation","description":"","frontmatter":{},"headers":[],"relativePath":"installation/docker.md","filePath":"installation/docker.md","lastUpdated":1773501089000}'),n={name:"installation/docker.md"};function o(l,s,r,h,d,p){return e(),a("div",null,[...s[0]||(s[0]=[t("",65)])])}const g=i(n,[["render",o]]);export{k as __pageData,g as default}; +import{_ as i,o as e,c as a,ag as t}from"./chunks/framework.BZohXCq9.js";const c=JSON.parse('{"title":"Docker Installation","description":"","frontmatter":{},"headers":[],"relativePath":"installation/docker.md","filePath":"installation/docker.md","lastUpdated":1773818349000}'),n={name:"installation/docker.md"};function o(l,s,r,h,d,p){return e(),a("div",null,[...s[0]||(s[0]=[t("",65)])])}const g=i(n,[["render",o]]);export{c as __pageData,g as default}; diff --git a/blogs/2026-02-16.html b/blogs/2026-02-16.html index ea940827..95452ea6 100644 --- a/blogs/2026-02-16.html +++ b/blogs/2026-02-16.html @@ -9,9 +9,9 @@ - + - + @@ -20,7 +20,7 @@
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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 our 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 share a 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 Memoh, making it a product with long impact.

Published under AGPLv3

Copyright © 2024 Memoh

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Blogs ​

This section contains the latest blogs about Memoh.

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Discord Channel Configuration ​

Connecting your Memoh Bot to Discord allows it to join servers and interact with community members.

Step 1: Create a Discord Application ​

  1. Go to the Discord Developer Portal.
  2. Click New Application and give it a name.
  3. In the left sidebar, go to Bot.
  4. Click Reset Token to generate a Bot Token. Copy this token and store it securely.

Step 2: Configure Bot Settings ​

  1. Scroll down to the Privileged Gateway Intents section.
  2. Enable Message Content Intent (required for the bot to read user messages).
  3. Save changes.

Step 3: Invite the Bot to Your Server ​

  1. Go to OAuth2 > URL Generator.
  2. Select scopes: bot, applications.commands.
  3. Select permissions: Send Messages, Read Message History, Embed Links, Attach Files.
  4. Copy the generated URL and open it in your browser.
  5. Select the server you want to add the bot to and authorize it.

Official Guide: Discord Developer Portal - Bots

Step 4: Configure Memoh ​

  1. Go to your Bot's Channels tab in the Memoh Web UI.
  2. Click Add Channel and select Discord.
  3. Paste your Bot Token.
  4. Click Save and Enable.

Features Supported ​

  • Message Content: Full access to chat text.
  • Attachments: Support for images and files.
  • Commands: Integration with Discord's slash commands (if configured via MCP).

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Feishu (Lark) Channel Configuration ​

Memoh supports integrating with Feishu (Lark) via the Open Platform. This allows your enterprise bots to interact with users in groups or private chats.

Step 1: Create a Feishu App ​

  1. Go to the Feishu Open Platform and log in.
  2. Click Create Custom App.
  3. Enter a name and description, then click Create.
  4. In the left sidebar, go to App Settings > Credentials & Basic Info.
  5. Copy your App ID and App Secret.

Step 2: Enable Bot Capability ​

  1. In the Feishu Developer Console, go to Capabilities > Bot.
  2. Click Enable Bot.

Step 3: Configure Permissions ​

  1. Go to Capabilities > Permissions.
  2. Search for and enable the following permissions (at minimum):
    • im:message (Receive and send messages)
    • im:chat (Access group chat information)
  3. Click Apply for Permissions if required by your organization.

Step 4: Configure Events (Webhook Mode) ​

  1. In Memoh, go to your Bot's Channels tab and add a Feishu channel.
  2. Choose Inbound Mode: webhook.
  3. Fill in your App ID and App Secret.
  4. Click Save. Memoh will generate a Webhook Callback URL.
  5. Copy this URL.
  6. In the Feishu Console, go to App Settings > Event Subscriptions.
  7. Paste the URL into the Verification URL field and save.
  8. Add events like Receive Message (im.message.receive_v1).

Official Guide: Feishu Custom Bot Guide

Step 5: Publish Your App ​

  1. In the Feishu Console, go to App Release > Version Management & Release.
  2. Click Create a Version, fill in the details, and submit for approval.
  3. Once approved and published, the bot is ready to use.

Published under AGPLv3

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Channels Overview ​

Channels are the gateways that connect your Memoh Bots to the outside world. By configuring channels, you can interact with your bots via your favorite messaging platforms.

Memoh currently supports the following channels:

  • Telegram: The most feature-rich integration with streaming and attachment support.
  • Feishu (Lark): Enterprise-ready integration for business workflows.
  • Discord: Community-focused integration for servers and direct messages.
  • QQ: Quick setup for personal DM bots via the dedicated AI bot registration portal.
  • Email: Connect via standard SMTP and IMAP (configured through Email Providers).
  • Web: Built-in chat interface for immediate access.

General Setup Flow ​

  1. Create an external app/bot: Register your bot on the target platform (e.g., via BotFather on Telegram).
  2. Obtain credentials: Fetch API tokens, App IDs, or secrets.
  3. Configure in Memoh: Add the channel to your Bot's settings and paste the credentials.
  4. Enable: Activate the channel to start receiving and sending messages.

Choose a channel from the sidebar to see detailed configuration guides for each platform.

Published under AGPLv3

Copyright © 2024 Memoh

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QQ Channel Configuration ​

Connecting your Memoh Bot to QQ allows it to interact with users through QQ's official Bot platform.

Step 1: Create a QQ Bot ​

  1. Go to the QQ Bot Open Platform and log in with your QQ account.
  2. Click Create Bot (创建机器人) - no approval required. Each account can create up to 5 bots.
  3. Copy the AppID and AppSecret displayed on the page.

Important: AppSecret is only shown once. Save it securely - viewing it again will force a reset.

Step 2: Configure Memoh ​

  1. Go to your Bot's Channels tab in the Memoh Web UI.
  2. Click Add Channel and select QQ.
  3. Paste your AppID and AppSecret.
  4. Configure optional settings:
    • Markdown Support: Enable Markdown formatting (default: enabled).
    • Enable Input Hint: Show typing indicators (default: enabled).
  5. Click Save and Enable.

Step 3: Bind Your Identity (Optional) ​

If your bot is a personal type bot, bind your QQ identity to chat with it directly:

  1. In Memoh Web UI, go to Settings > Bind Code.
  2. Select QQ and click Generate.
  3. Copy the code and send it to your bot in a QQ direct message.

Features Supported ​

  • Message Content: Full support for text messages.
  • Markdown: Rich formatting with bold, italic, code blocks, and links.
  • Attachments: Support for images and files.
  • Typing Indicators: Visual feedback while generating responses.
  • Target Types: C2C (direct message), group, and channel.

Official Resources ​

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Telegram Channel Configuration ​

This guide walks you through connecting your Memoh Bot to Telegram. Telegram is one of the most supported platforms in Memoh, featuring streaming responses, Markdown formatting, and attachment support.

Step 1: Create a Telegram Bot ​

You need to create a bot on Telegram to get an API token.

  1. Open Telegram and search for the official @BotFather bot.
  2. Send the /newbot command.
  3. Follow the prompts:
    • Name: Choose a display name for your bot (e.g., My Memoh Bot).
    • Username: Choose a unique username ending in bot (e.g., my_memoh_bot).
  4. BotFather will provide you with an API Token (e.g., 123456789:ABCdefGHIjklMNOpqrsTUVwxyz). Keep this token secret.

Official Guide: Telegram Bot Tutorial

Step 2: Configure Memoh ​

  1. Go to your Bot's Detail Page in the Memoh Web UI.
  2. Select the Channels tab.
  3. Click Add Channel and select Telegram.
  4. Paste your API Token into the credentials field.
  5. Click Save and Enable.

Step 3: Bind Your Identity (Optional) ​

If your bot is a personal type bot, you need to bind your Telegram identity to your Memoh account to chat with it directly.

  1. In Memoh Web UI, go to Settings > Bind Code.
  2. Select Telegram and click Generate.
  3. Copy the code.
  4. Open a chat with your new bot on Telegram and send the bind code.
  5. The bot should respond that your identity is successfully linked.

Features Supported ​

  • Streaming: Responses appear as the bot "thinks."
  • Markdown: Support for bold, italic, code blocks, and links.
  • Attachments: Send images or files to the bot; the bot can also send files back.
  • Replies: The bot understands context from message replies.

Published under AGPLv3

Copyright © 2024 Memoh

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Bot Access Control ​

Memoh uses an ACL (Access Control List) system to control who can interact with your bot. You can configure guest access, whitelist specific users or channel identities, and blacklist others — all from the bot's Access tab.

Concepts ​

Authorization Layers ​

Bot access is enforced at two levels:

  1. Management Access: Only the bot owner and system admins can edit bot settings, manage ACL rules, and configure the bot. This is not configurable — it is based on ownership.
  2. Chat Trigger Access: Controls who can send messages to the bot and trigger a response. This is what the ACL system manages.

Subject Types ​

ACL rules can target three kinds of subjects:

SubjectDescription
Guest (all)A global toggle — when enabled, anyone can chat with the bot without being explicitly listed.
UserA specific Memoh user account.
Channel IdentityA specific identity on an external channel (e.g. a Telegram user, a Discord member). Useful when the person doesn't have a Memoh account.

Evaluation Order ​

When an incoming message arrives, the bot evaluates access in this order:

  1. Bot owner or system admin → Allow
  2. User or channel identity has a deny rule → Deny
  3. User or channel identity has an allow rule → Allow
  4. Guest access is enabled → Allow
  5. None of the above → Deny

Blacklist (deny) rules are always checked before whitelist (allow) rules. This means a blacklisted user cannot bypass the block even if guest access is enabled.

Managing Access ​

Open a bot's Access tab to configure its access control.

Guest Access ​

Toggle Allow Guest Access to let anyone chat with the bot without an explicit whitelist entry. This is useful for public-facing bots.

When guest access is disabled, only the bot owner, admins, and explicitly whitelisted users/identities can trigger the bot.

Whitelist ​

The whitelist grants specific users or channel identities permission to chat with the bot.

  1. Click Add in the Whitelist section.
  2. Select a subject type:
    • User: Search and select a Memoh user.
    • Channel Identity: Search and select a channel identity (e.g. a Telegram user the bot has seen before).
  3. Optionally set source scope to restrict the rule to a specific context:
    • Channel: Only applies when the message comes from a specific channel (e.g. your Telegram bot channel).
    • Conversation Type: private, group, or thread.
    • Conversation ID: A specific chat/group ID.
    • Thread ID: A specific thread within a conversation (requires Conversation ID).
  4. Click Save.

Without source scope, the rule applies globally — the subject can chat with the bot from any channel.

Blacklist ​

The blacklist denies specific users or channel identities from chatting with the bot. The setup process is the same as the whitelist.

Blacklist rules take priority over whitelist rules and guest access. Use this to block specific users while keeping the bot open to others.

Source Scope ​

Source scope lets you create fine-grained rules. For example:

  • Allow a user to chat only via Telegram, but not Discord
  • Block a channel identity only in group conversations
  • Restrict access to a specific thread in a specific group

Scope fields form a hierarchy: Channel → Conversation Type → Conversation ID → Thread ID. Each level is optional, but a Thread ID requires a Conversation ID, and a Conversation ID requires a Channel.

Examples ​

Public Bot (Anyone Can Chat) ​

  1. Open the bot's Access tab.
  2. Enable Allow Guest Access.
  3. Done — anyone on any connected channel can now message the bot.

Private Bot with Selected Users ​

  1. Disable Allow Guest Access.
  2. Add each authorized user or channel identity to the Whitelist.
  3. Only listed subjects (plus the bot owner and admins) can trigger the bot.

Public Bot with Blocked Users ​

  1. Enable Allow Guest Access.
  2. Add problematic users/identities to the Blacklist.
  3. Everyone except blacklisted subjects can chat with the bot.

Channel-Scoped Access ​

  1. Add a whitelist rule for a user.
  2. Set the Channel source scope to your Telegram channel.
  3. The user can only chat with the bot via Telegram — messages from other channels are denied.

Published under AGPLv3

Copyright © 2024 Memoh

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Bot Management ​

A Bot is an independent AI agent that comes with its own isolated container, persistent memory, and configurable personality. Bots can chat via various messaging platforms (Channels) and perform complex tasks using specialized tools.

Creating a Bot ​

  1. Navigate to the Bots page from the sidebar.
  2. Click the Create Bot button.
  3. Fill in the basic info:
    • Display Name: The name users will see in group chats.
    • Avatar: A URL for the bot's profile picture.
    • Type: Choose personal (private to owner) or public (accessible to guests).
  4. Click Create.

Bot Detail Page ​

Once created, clicking on a bot card takes you to its Detail Page, where you can manage its entire lifecycle through several specialized tabs.

Overview Tab ​

The Overview tab provides a quick health check of the bot's services. It monitors:

  • Container status (running/stopped)
  • Database connectivity
  • Channel configurations
  • Memory system health

If any check shows a warning or error, follow the provided details to troubleshoot.

Configuring the Bot's Core Settings ​

After creating a bot, the most important step is configuring its runtime settings. These define how the bot talks, remembers, searches, and uses browser automation.

  1. Navigate to your bot's Detail Page.
  2. Go to the Settings tab.
  3. Configure the core fields:
    • Chat Model: Used for standard conversations with users.
    • Memory Provider: Select the memory backend the bot should use.
    • Search Provider: Select the search engine provider for web search.
    • Browser Context: Select the browser profile the bot should use for browser automation.
  4. Click Save at the bottom of the form.

If you have not created these resources yet, set them up first:

Settings Tab Reference ​

The Settings tab contains all the core parameters that define your bot's behavior and runtime configuration.

FieldDescription
Chat ModelThe main LLM used for generating chat responses.
Memory ProviderThe memory backend assigned to the bot. The built-in provider can optionally define its own memory and embedding models.
Search ProviderThe search engine used for web browsing capabilities.
Browser ContextThe browser environment used for web automation, such as viewport, locale, and mobile behavior.
Max Context Load TimeTime limit (seconds) for loading context before generation.
Max Context TokensToken limit for the loaded conversation history.
LanguageThe bot's primary communication language.
Reasoning EnabledIf the selected model supports reasoning (like OpenAI o1), enable this to use its deep thinking capabilities.
Reasoning EffortSet the level of reasoning effort (low, medium, high).
Allow Guest(Public bots only) If enabled, non-registered users can interact with the bot.

Deleting a Bot ​

To permanently remove a bot and all its associated data (including container files and memory):

  1. Navigate to the Settings tab in the Bot Detail page.
  2. Scroll to the Danger Zone at the bottom.
  3. Click Delete Bot and confirm the action.

Warning: This action is irreversible. All persistent data for this bot will be lost.

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Browser Contexts ​

Memoh can give a bot access to a headless browser through the Browser Gateway. A Browser Context stores the browser environment a bot should use, such as viewport size, locale, timezone, and mobile behavior.

Once a browser context is assigned to a bot, the bot can use browser tools to open pages, click elements, fill forms, capture screenshots, and inspect page content.

Concept: Browser Gateway ​

The Browser Gateway is powered by Playwright and provides browser automation for bots. In practice, a browser context acts like a reusable browser profile configuration for one or more bots.

Typical use cases include:

  • Navigating websites
  • Clicking buttons and links
  • Filling and submitting forms
  • Reading rendered page content
  • Capturing screenshots or PDFs

Creating a Browser Context ​

Manage contexts from the Browser Contexts page in the sidebar.

  1. Navigate to the Browser Contexts page.
  2. Click Add Browser Context.
  3. Fill in the following field:
    • Name: A display name for this browser context.
  4. Click Create.

Configuring a Browser Context ​

After creating a context, select it from the sidebar and update its settings.

FieldDescription
NameThe display name shown in the UI.
CoreBrowser engine: chromium (default) or firefox.
Viewport WidthBrowser viewport width in pixels.
Viewport HeightBrowser viewport height in pixels.
User AgentOptional custom browser user agent string.
Device Scale FactorOptional device pixel ratio.
LocaleOptional locale such as en-US or zh-CN.
Timezone IDOptional timezone such as UTC or Asia/Shanghai.
Is MobileEnables mobile-style browser behavior.
Ignore HTTPS ErrorsAllows navigation to sites with invalid HTTPS certificates.

Managing Contexts ​

  • Edit: Select a context and update its configuration.
  • Delete: Remove a context you no longer use.

Assigning a Browser Context to a Bot ​

  1. Navigate to the Bots page and open your bot.
  2. Go to the Settings tab.
  3. Find the Browser Context dropdown.
  4. Select the context you created.
  5. Click Save.

After saving, the bot can use that browser context when browser tools are invoked.

Bot Interaction ​

When a browser context is configured, the bot can use built-in browser tools such as:

  • browser_action: perform actions like navigation, click, fill, select, scroll, tab management, screenshot, or PDF export
  • browser_observe: inspect the current page and gather information for the model

This lets the bot interact with real websites instead of relying only on static HTML or search results.

Browser Core Selection ​

Memoh's browser image can include Chromium, Firefox, or both. The available cores are determined at build time by the BROWSER_CORES build argument.

The install script prompts for browser core selection during setup. To rebuild manually with specific cores:

bash
BROWSER_CORES=chromium docker compose --profile browser build browser

Valid values for BROWSER_CORES: chromium, firefox, chromium,firefox (default).

Next Steps ​

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Bot Channels ​

Channels connect your Bot to various messaging platforms, allowing you to interact with it using your favorite chat applications.

Concept: Unified Communication ​

Memoh acts as a hub that bridges different messaging services. You can configure multiple channels for a single bot, enabling it to chat on Telegram, Discord, and more simultaneously.

Supported Channels ​

Configure your bot's connections from the Channels tab in the Bot Detail page.

For detailed step-by-step guides on how to create and configure bots for each platform, see:

Configuration Flow ​

1. Adding a Channel ​

  1. Click Add Channel.
  2. Select the platform from the list.
  3. Fill in the required credentials and configuration. The fields are dynamic and change based on the selected channel.

2. Common Fields ​

FieldDescription
CredentialsAPI tokens, secrets, or bot keys provided by the platform.
DisabledQuickly enable or disable a channel without removing its configuration.
RoutingConfigure how messages are mapped between the platform and Memoh.

3. Special Case: Feishu Webhook ​

If using Feishu in webhook inbound mode:

  1. Memoh will generate a Webhook Callback URL.
  2. Copy this URL and paste it into your Feishu App's event configuration.
  3. This allows Feishu to send messages directly to Memoh.

Operations ​

  • Save: Update the configuration.
  • Save and Enable: Update and immediately activate the channel.
  • Enable/Disable Toggle: Switch the channel's active status.
  • Delete: Permanently remove a channel's configuration.

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Bot Container Management ​

Every Bot in Memoh operates within its own isolated container environment. This isolation ensures security, provides a dedicated filesystem, and allows the bot to execute code or commands without affecting other bots or the host system.

Concept: The Isolated Workspace ​

The container acts as the bot's private "computer." Within it, the bot can:

  • Store and modify files
  • Install software via package managers
  • Execute scripts
  • Maintain state across multiple sessions

Operations ​

Manage the lifecycle of your bot's environment from the Container tab in the Bot Detail page.

Lifecycle Actions ​

  • Create: Initialize the container if it doesn't exist (using the configured image).
  • Start: Launch the container. The bot must have a running container to perform many operations like file editing or executing tools.
  • Stop: Gracefully shut down the container to save resources.
  • Delete: Remove the container instance. This will delete the temporary state but preserve the data in persistent volumes.

Container Information ​

The Container tab displays real-time data about the bot's runtime:

  • Container ID: Unique identifier for the instance.
  • Status: Whether it's currently running, stopped, or creating.
  • Image: The Docker/Containerd image used as the base.
  • Paths: Host and container paths for data persistence.
  • Tasks: Number of active background tasks running in the container.

Snapshots ​

Snapshots allow you to capture the current state of the bot's container and restore it later. This is useful for:

  • Saving a known good configuration
  • Versioning the bot's environment
  • Testing complex changes safely

Creating a Snapshot ​

  1. Ensure the container is stopped or in a stable state.
  2. Click Create Snapshot.
  3. Provide a name for the snapshot.

Restoring a Snapshot ​

  • Find the desired snapshot in the list and click Restore. This will reset the container to the captured state.

Managing Snapshots ​

  • View a list of existing snapshots with their creation timestamps and parent relationships.
  • Use the Delete button next to a snapshot to remove it.

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Email Providers and Bindings ​

Memoh allows your Bot to send and receive emails, providing another powerful channel for communication and task management.

Concept: Email as a Bot Channel ​

Connecting email to your Bot involves two steps:

  1. Email Provider: Configure a connection to an email service (like Mailgun or a generic SMTP server).
  2. Email Binding: Link a specific email address from a provider to a Bot.

Email Provider ​

Manage your email service connections from the Email Provider page in the sidebar.

Creating a Provider ​

  1. Navigate to the Email Provider page from the sidebar.
  2. Click Add Email Provider.
  3. Choose the provider type:
    • Mailgun: For high-volume delivery.
    • Generic SMTP: For traditional email servers.
  4. Fill in the required fields (e.g., domain, api_key, host, port, username, password).
  5. Click Create.

Bot Email Bindings ​

Configure your bot's email capabilities from the Email tab in the Bot Detail page.

Adding a Binding ​

  1. Click Add Binding.
  2. Select the Email Provider you created.
  3. Provide the Email Address to be associated with the bot.
  4. Set the bot's permissions:
    • Can Read: If enabled, the bot can check and process incoming emails.
    • Can Write: If enabled, the bot can compose and send outgoing emails.
    • Can Delete: If enabled, the bot can manage its own email inbox.
  5. Click Create.

Outbox and History ​

The Email tab also provides an Outbox showing an audit log of all emails sent by the bot:

  • To: The recipient's email address.
  • Subject: The email's subject line.
  • Status: Whether the email was sent successfully.
  • Sent At: The timestamp of the email delivery.

Bot Interaction ​

  • The bot can use its email permissions to send reports, respond to user inquiries, or trigger actions based on incoming mail.
  • Outgoing emails are tracked in the outbox for monitoring and troubleshooting.
  • The bot handles email in a structured way, allowing it to "converse" via email just as it does via chat.

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Bot Files Management ​

Every Memoh Bot has its own dedicated filesystem inside its isolated container. You can manage this filesystem directly from the Files tab in the Bot Detail page.

Operations ​

The FileManager component provides a familiar file-browsing experience with a toolbar, directory tree, and integrated editor.

Browsing and Navigation ​

  • Breadcrumb Navigation: Quickly move between parent directories.
  • Refresh: Reload the file list to see the latest changes (e.g., files created by the bot).
  • New Folder: Create a directory within the current path.

Managing Files ​

  • Upload: Select files from your local computer to transfer them into the bot's container.
  • Rename: Click on a file or folder and use the rename action to update its identifier.
  • Delete: Remove files or folders (with recursive support for directories).
  • Download: Retrieve a file from the bot's container back to your local machine.

Viewing and Editing ​

The integrated FileViewer allows you to interact with the bot's files without leaving the web UI.

Text Files ​

  • Read/Edit: Click a text file (e.g., .md, .js, .py, .toml) to open it in the built-in Monaco Editor.
  • Syntax Highlighting: Supports common programming languages and configuration formats.
  • Save: Modify the file's content and click Save to apply the changes to the bot's filesystem.

Images ​

  • Preview: Click an image file (e.g., .png, .jpg, .webp) to see a visual preview directly in the file manager.

Bot Interaction with Files ​

Remember that the bot itself can also perform these operations:

  • Use its Skills or MCP tools to read, write, and manage its own files.
  • The Files tab is your portal to monitor and manually intervene in the bot's workspace.

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Bot Heartbeat ​

The Heartbeat feature allows you to schedule periodic tasks for your Bot, enabling it to perform autonomous actions even when you aren't chatting with it.

Concept: Scheduled Autonomy ​

A Heartbeat is a recurring trigger that prompts the bot to "think" and execute its skills or tools at a set interval. This is useful for:

  • Periodic status checks
  • Automatic data collection
  • Cleaning up the filesystem
  • Sending scheduled notifications

Configuration ​

Configure the heartbeat from the Heartbeat tab in the Bot Detail page.

FieldDescription
EnabledToggle the heartbeat on or off.
IntervalHow often (in minutes) the heartbeat should trigger. The default is 30 minutes.
ModelThe LLM used to execute the heartbeat task. This can be different from the main chat model.

Logs and Monitoring ​

The Heartbeat tab provides a detailed audit log of every execution:

  • Status: Whether the heartbeat completed successfully (ok), encountered an issue (alert), or failed (error).
  • Time: When the heartbeat was triggered.
  • Duration: How long the bot took to process the task.
  • Result: A summary of the bot's action or response during that heartbeat.

Managing Logs ​

  • Filter by Status: Quickly find errors or alerts.
  • Refresh: Load the latest log entries.
  • Clear Logs: Remove old heartbeat records to keep the interface clean.
  • Load More: View older history.

Bot Interaction ​

  • During a heartbeat, the bot receives a special system prompt that it should perform its "routine" tasks.
  • The bot can use any of its assigned Skills or MCP tools during a heartbeat.
  • Heartbeat logs provide the "memory" of the bot's autonomous activities.

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MCP Connections ​

Memoh fully supports the Model Context Protocol (MCP), allowing you to connect your Bot to external tool services and context providers.

Concept: Extending Bot Capabilities ​

MCP provides a standardized way for bots to access external data sources and tools. Each Bot can have its own independent set of MCP connections.

Connection Types ​

Configure MCP connections from the MCP tab in the Bot Detail page.

1. Stdio (Local Process) ​

This type of connection executes a local command on the server and communicates via standard input/output.

FieldDescription
CommandThe executable to run (e.g., npx, python3).
ArgumentsA list of command-line arguments (e.g., -y, @modelcontextprotocol/server-filesystem, /opt/memoh/data).
EnvKey-value pairs for environment variables.
CWDWorking directory for the process.

2. Remote (HTTP/SSE) ​

This type of connection connects to a remote MCP server over the network.

FieldDescription
URLThe endpoint of the remote MCP server.
HeadersCustom HTTP headers (e.g., for authentication).
TransportChoose between http or sse (Server-Sent Events).

Operations ​

  • Add Connection: Click Add, choose the type, and fill in the configuration.
  • Import JSON: Use the Import button to quickly add multiple connections from a standard mcpServers JSON config file.
  • Toggle Active: Enable or disable specific connections without deleting them.
  • Search: Quickly find a connection by name or ID.
  • Export: Export a connection configuration to a JSON file.

Bot Interaction ​

Once an MCP connection is active, the bot will automatically:

  • Discover the tools and resources exposed by the MCP server.
  • Use these tools to perform tasks requested by the user.
  • Include the context provided by the MCP server in its reasoning process.

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Bot Memory Management ​

Memoh's structured long-term memory system allows bots to remember information across multiple conversations, providing contextually relevant and personalized interactions.

Prerequisites ​

Before using the Memory tab, make sure your bot already has a Memory Provider configured.

  1. Create a provider from one of the Memory Providers (Built-in, Mem0, or OpenViking).
  2. Open your bot's Settings tab.
  3. Select the provider in the Memory Provider field.
  4. Click Save.

Without a memory provider, the bot will not have an active memory backend configuration.

Concept: Memory Retrieval ​

Memories are stored and retrieved through the assigned memory provider. Depending on the provider type and mode, retrieval may use file-based indexing, sparse vectors, dense embeddings, or an external API. When a user sends a message, Memoh finds the most relevant memories and includes them in the bot's runtime context.

Operations ​

Manage your bot's memories from the Memory tab in the Bot Detail page.

1. Creating Memories ​

  • New Memory: Manually enter a memory's content in the provided textarea.
  • From Conversation: Select specific messages from the bot's conversation history to extract into memory.

2. Searching and Managing ​

  • Search: Filter memories by ID or text content.
  • Edit: Modify existing memory entries directly in the list.
  • Delete: Remove memories that are no longer needed.

Memory Compression (Compact) ​

Over time, memories can accumulate and become redundant. The Compact feature helps optimize the memory pool.

  • Ratio: Set the compression ratio (for example 0.8, 0.5, or 0.3) to determine how much information is retained.
  • Decay Days: Optionally specify a time window to compact only memories older than a certain number of days.

Visualization: Vector Manifold ​

The Memory tab includes visual tools to help you understand how the memory system is performing:

Top-K Bucket Chart ​

Shows the distribution of relevant memories retrieved for the most recent queries.

CDF Curve (Cumulative Distribution Function) ​

Visualizes the scoring threshold of retrieved memories, helping you fine-tune how much relevant information the bot should consider.

Bot Interaction ​

  • The bot automatically searches and retrieves memories during chat.
  • The assigned Memory Provider controls the memory backend used by the bot.
  • Provider-specific settings (such as memory mode, embedding model, or API keys) are configured in the provider itself — see Memory Providers.
  • Memories provide the long-term knowledge that makes each bot unique to its owner.

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LLM Provider and Model ​

To use Memoh, you first need to configure at least one LLM Provider and at least one Model.

LLM Provider ​

An LLM Provider represents a connection to an AI service (like OpenAI, Anthropic, or a self-hosted compatible API). It stores the base URL and authentication credentials.

Creating a Provider ​

  1. Navigate to the Models page from the sidebar.
  2. Click the Add Provider button at the bottom of the sidebar.
  3. Fill in the following fields:
    • Name: A display name for this provider (e.g., "OpenAI").
    • Base URL: The root URL of the API (e.g., https://api.openai.com/v1).
    • API Key: Your authentication token for the service.
  4. Click Create.

Managing Providers ​

  • Edit: Select a provider from the list and use the form on the right to update its name, URL, or API key.
  • Delete: Use the Delete Provider button in the provider settings form.

Model ​

A Model is a specific AI instance (like gpt-4o or text-embedding-3-small) that belongs to a Provider. Memoh distinguishes between Chat models (for conversation) and Embedding models (for memory search).

Adding a Model ​

  1. Select a Provider from the list on the Models page.
  2. Click Add Model in the model list section.
  3. Configure the following fields:
FieldRequiredDescription
TypeYeschat for conversation, embedding for vector search.
Model IDYesThe exact identifier used by the provider (e.g., gpt-4o).
NameNoA friendly display name (defaults to Model ID).
Client TypeYes (Chat)The API protocol: openai-responses, openai-completions, anthropic-messages, or google-generative-ai.
Input ModalitiesYes (Chat)Capabilities supported: text (default), image, audio, video, file.
Supports ReasoningNoEnable if the model supports internal reasoning steps (e.g., OpenAI o1).
DimensionsYes (Embed)The vector size for embedding models (e.g., 1536).
  1. Click Create.

Managing Models ​

  • Edit: Click the edit icon next to a model in the list.
  • Delete: Click the trash icon next to a model to remove it.

Next Steps ​

Now that you have configured your models, you can proceed to Create and Configure a Bot.

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Execution Flow ​

When a schedule fires:

  1. The cron service triggers the schedule.
  2. The current_calls counter is incremented.
  3. If max_calls is set and reached, the schedule is automatically disabled.
  4. The agent receives the command along with the schedule context.
  5. The agent executes the command using its tools (e.g., web search, file read, send message).
  6. Results can be delivered to any connected channel.

Schedule vs. Heartbeat ​

FeatureScheduleHeartbeat
TriggerCron expression (flexible timing)Fixed interval (minutes)
CommandCustom natural-language instructionGeneric "routine check" prompt
Max CallsOptional execution limitUnlimited
Use CaseSpecific recurring tasksPeriodic autonomous thinking

Both features complement each other: use Heartbeat for general-purpose periodic activity and Schedule for precise, task-oriented automation.

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Search Providers ​

Memoh allows you to connect your Bot to various search engines, enabling it to browse the web for real-time information and external knowledge.

Concept: Real-Time Knowledge ​

A Search Provider is a connection to a specific search API (like Brave, Google, or Bing). Once configured, it becomes a tool that your Bot can use during conversation.

Supported Search Engines ​

Configure your search engines from the Search Provider page in the sidebar.

Supported EnginesNotes
BraveHighly recommended for privacy and performance.
BingPowerful global search capability.
GoogleComprehensive web search.
TavilyDesigned specifically for AI-powered research.
SearxNGConnect to your own self-hosted search aggregator.
DuckDuckGoPrivacy-focused search.
Other ProvidersSogou, Serper, Jina, Exa, Bocha, Yandex.

Configuration Flow ​

1. Adding a Search Provider ​

  1. Navigate to the Search Provider page from the sidebar.
  2. Click Add Search Provider.
  3. Fill in the following:
    • Name: A friendly name (e.g., "My Brave Search").
    • API Key: Your credentials for the specific search engine API.
    • Other Parameters: Depending on the engine, you may need to specify additional settings (e.g., base_url for SearxNG).

2. Managing Providers ​

  • Edit: Select a provider from the sidebar and update its configuration in the form on the right.
  • Delete: Use the Delete Provider button in the provider settings form.

Assigning a Search Provider to a Bot ​

  1. Navigate to the Bots page and select your bot.
  2. Go to the Settings tab.
  3. Find the Search Provider dropdown and select the provider you created.
  4. Click Save.

Bot Interaction ​

  • When a user's question requires real-time information, the bot will automatically call its assigned Search Provider.
  • The bot will process the search results to provide a grounded and accurate response.
  • Multiple search results can be synthesized to provide a comprehensive answer.

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Managing Skills ​

Manage your bot's skill set from the Skills tab in the Bot Detail page.

Adding a Skill ​

  1. Click Add Skill.
  2. A dialog with a basic template will open in the Monaco Editor.
  3. Fill in the name, description, and content.
  4. Click Save.

Editing and Deleting ​

  • Edit: Click the pencil icon next to a skill card to modify its content or frontmatter.
  • Delete: Click the trash icon to remove a skill from the bot's container.

How Bots Use Skills ​

  • Skills are injected into the bot's system prompt during conversation.
  • The YAML frontmatter helps the system categorize and manage the skills as tools.
  • Modular skills allow you to easily "swap" behaviors or capabilities without rewriting the entire bot.

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Bot Subagents ​

Subagents are specialized AI entities with their own independent conversation context. They are managed by the main Bot to delegate complex tasks or focus on specific domains.

Concept: Task Specialization ​

A Subagent is like a specialized teammate for your Bot. While the main Bot handles general conversation, it can spin up and communicate with a Subagent to perform deep analysis, research, or execution.

Fields ​

Configure Subagents from the Subagents tab in the Bot Detail page.

FieldDescription
NameThe identifier for the subagent (e.g., "Research Assistant").
DescriptionA brief explanation of the subagent's purpose and role.
SkillsA list of specific Skills assigned from the bot's container.
MessagesThe conversation history and context specific to this subagent.
UsageStatistics on token consumption and activity.

Operations ​

  • Add Subagent: Create a new entity by providing a name and description.
  • Edit: Update the name or description of an existing subagent.
  • Delete: Permanently remove a subagent and its independent context.
  • View Context: Open a dialog to inspect the subagent's conversation history and usage metrics.

Bot Interaction ​

  • The main Bot uses the Subagent Tool to create, communicate with, and receive results from subagents.
  • Subagents inherit the main bot's container permissions but operate with their own "mental workspace."
  • This modular approach allows for building multi-agent systems within a single Bot's scope.

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Memoh Documentation ​

Memoh(/ˈmemoʊ/) 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 ​

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Docker Installation ​

Docker is the recommended way to run Memoh. The stack includes PostgreSQL, Qdrant, the main server (with embedded Containerd), 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.

Service Architecture ​

The Docker Compose stack consists of multiple services. Some are always started, others are optional and enabled via --profile:

ServiceProfileDescription
server(core)Main Memoh server with embedded Containerd
agent(core)Agent gateway for bot execution
web(core)Web UI (Vue 3)
postgres(core)PostgreSQL database
qdrantqdrantQdrant vector database for memory search (sparse and dense modes)
browserbrowserPlaywright-based browser gateway for bot web automation
sparsesparseNeural sparse encoding service for memory retrieval (see below)

Sparse Service ​

The sparse container provides neural sparse vector encoding for memory retrieval. It runs a lightweight Python (Flask) service on port 8085 that uses the opensearch-neural-sparse-encoding-multilingual-v1 model from OpenSearch.

What it does:

  • Converts document text into sparse vectors (a compact list of token indices + importance weights) using a masked language model
  • Encodes queries using IDF-weighted term lookup for fast, efficient retrieval
  • Works with Qdrant to enable semantic memory search without requiring an external embedding API

Why use it:

  • No embedding API costs — The model runs locally inside the container, so you don't need an OpenAI/Cohere/etc. embedding API key
  • Multilingual — The underlying model supports multiple languages out of the box
  • Good retrieval quality — Neural sparse encoding provides significantly better results than keyword-only search (BM25), while being lighter than dense embedding models

When to enable it:

Enable the sparse profile (--profile sparse) if you plan to use the built-in memory provider in sparse mode. The model is pre-downloaded during the Docker image build, so the container starts quickly without needing to fetch weights at runtime.

bash
docker compose --profile qdrant --profile sparse --profile browser up -d

For more details on memory modes, see Built-in Memory Provider.

Prerequisites ​

Run the official install script (requires Docker and Docker Compose):

bash
curl -fsSL https://memoh.sh | sudo sh

The script will:

  1. Check for Docker and Docker Compose
  2. Prompt for configuration (workspace, data directory, admin credentials, JWT secret, Postgres password, sparse service toggle, browser core selection)
  3. Fetch the latest release tag from GitHub and clone the repository
  4. Generate config.toml from the Docker template with your settings
  5. Pin Docker image versions to the release
  6. Build the browser image with selected cores and start all services

Silent install (use all defaults, no prompts):

bash
curl -fsSL https://memoh.sh | sudo sh -s -- -y

Defaults when running silently:

  • Workspace: ~/memoh
  • Data directory: ~/memoh/data
  • Admin: admin / admin123
  • JWT secret: auto-generated
  • Postgres password: memoh123

Install a specific version:

bash
curl -fsSL https://memoh.sh | sudo sh -s -- --version v0.5.0

Or using the environment variable:

bash
curl -fsSL https://memoh.sh | sudo MEMOH_VERSION=v0.5.0 sh

Use China mainland mirror (for slow image pulls):

bash
curl -fsSL https://memoh.sh | sudo USE_CN_MIRROR=true sh

Environment variables can be combined, e.g. curl -fsSL https://memoh.sh | sudo MEMOH_VERSION=v1.0.0 USE_CN_MIRROR=true sh

Manual Install ​

bash
git clone https://github.com/memohai/Memoh.git
 cd Memoh
-cp conf/app.docker.toml config.toml

Edit config.toml — at minimum change:

  • admin.password — Admin password
  • auth.jwt_secret — Generate with openssl rand -base64 32
  • postgres.password — Database password (also set POSTGRES_PASSWORD env var to match)

Then start (recommended — with Qdrant, Browser, and Sparse):

bash
sudo POSTGRES_PASSWORD=your-db-password docker compose --profile qdrant --profile browser --profile sparse up -d

Or start core services only (no vector DB or browser automation):

bash
sudo POSTGRES_PASSWORD=your-db-password docker compose up -d

On macOS or if your user is in the docker group, sudo is not required.

Important: docker-compose.yml mounts ./config.toml by default. You must create this file before starting — running without it will fail.

China Mainland Mirror ​

For users in mainland China who cannot access Docker Hub directly, uncomment the registry line in config.toml:

toml
[mcp]
+cp conf/app.docker.toml config.toml

Edit config.toml — at minimum change:

  • admin.password — Admin password
  • auth.jwt_secret — Generate with openssl rand -base64 32
  • postgres.password — Database password (also set POSTGRES_PASSWORD env var to match)

Then start (recommended — with Qdrant, Browser, and Sparse):

bash
sudo POSTGRES_PASSWORD=your-db-password docker compose --profile qdrant --profile browser --profile sparse up -d

Or start core services only (no vector DB or browser automation):

bash
sudo POSTGRES_PASSWORD=your-db-password docker compose up -d

On macOS or if your user is in the docker group, sudo is not required.

Important: docker-compose.yml mounts ./config.toml by default. You must create this file before starting — running without it will fail.

China Mainland Mirror ​

For users in mainland China who cannot access Docker Hub directly, uncomment the registry line in config.toml:

toml
[workspace]
 registry = "memoh.cn"

And add the China mirror compose overlay:

bash
sudo docker compose -f docker-compose.yml -f docker/docker-compose.cn.yml \
   --profile qdrant --profile browser up -d

The install script handles this automatically when you set USE_CN_MIRROR=true.

Access Points ​

After startup:

ServiceURL
Web UIhttp://localhost:8082
APIhttp://localhost:8080
Agent Gatewayhttp://localhost:8081
Browser Gatewayhttp://localhost:8083

Default login: admin / admin123 (change this in config.toml).

First startup may take 1–2 minutes while images are pulled and services initialize.

Common Commands ​

Prefix with sudo on Linux if your user is not in the docker group.

bash
docker compose up -d           # Start
 docker compose down            # Stop
@@ -29,8 +29,8 @@
 docker compose ps              # Status
 docker compose pull && docker compose up -d  # Update to latest images

Environment Variables ​

VariableDefaultDescription
POSTGRES_PASSWORDmemoh123PostgreSQL password (must match postgres.password in config.toml)
MEMOH_CONFIG./config.tomlPath to the configuration file
MEMOH_VERSION(latest release)Git tag to install (e.g. v1.0.0). Also pins Docker image versions.
USE_CN_MIRRORfalseSet to true to use China mainland mirror for Docker images
BROWSER_CORESchromium,firefoxBrowser engines to include in the browser image (chromium, firefox, or chromium,firefox)
USE_SPARSEfalseSet to true to enable the sparse memory service (--profile sparse)

Production Checklist ​

  1. Passwords — Change all default passwords and secrets in config.toml
  2. HTTPS — Configure SSL (e.g. via docker-compose.override.yml with certs or a reverse proxy)
  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 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

- +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

+ \ No newline at end of file diff --git a/memory-providers/builtin.html b/memory-providers/builtin.html index 3138b112..a39dec12 100644 --- a/memory-providers/builtin.html +++ b/memory-providers/builtin.html @@ -9,9 +9,9 @@ - + - + @@ -25,7 +25,7 @@ [sparse] base_url = "http://sparse:8085"

Dense Mode ​

Requires an embedding model (configured in the provider settings) and Qdrant:

bash
docker compose --profile qdrant up -d

The Qdrant section must be present in config.toml:

toml
[qdrant]
 base_url = "http://qdrant:6334"

Assigning a Memory Provider to a Bot ​

  1. Navigate to the Bots page and open your bot.
  2. Go to the Settings tab.
  3. Find the Memory Provider dropdown.
  4. Select the provider you created.
  5. Click Save.

If no memory provider is selected, the bot will not use that provider configuration in its runtime settings.

Using Memory After Setup ​

Once a memory provider is assigned to the bot, you can manage actual memories from the bot's Memory tab:

  • Create memories manually
  • Extract memories from conversations
  • Search, edit, and delete memories
  • Compact or rebuild the memory store

For day-to-day memory operations, continue with Bot Memory Management.

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Memory Providers ​

Memoh uses a Memory Provider to define how a bot stores, retrieves, and manages long-term memory. A bot can bind one memory provider in its Settings tab, and that provider becomes the backend for memory extraction and memory search.

Available Providers ​

Memoh supports the following memory providers:

  • Built-in: The default memory system included with Memoh. Supports three modes — off (file-based), sparse (neural sparse vectors), and dense (embedding-based semantic search). Fully self-hosted.
  • Mem0: SaaS memory provider via the Mem0 API. Requires an API key.
  • OpenViking: Self-hosted or SaaS memory provider with its own API.

Basic Flow ​

  1. Open the Memory Providers page from the sidebar.
  2. Create a provider instance using one of the supported provider types.
  3. Configure the provider settings.
  4. Open a bot's Settings tab and assign that provider in Memory Provider.
  5. Manage actual memories from the bot's Memory tab.

Next Steps ​

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Mem0 Memory Provider ​

Mem0 is a SaaS memory provider that connects your bot to the Mem0 platform. Instead of managing memory infrastructure yourself, Mem0 handles storage, retrieval, and indexing through its cloud API.

Creating a Mem0 Provider ​

  1. Navigate to the Memory Providers page.
  2. Click Add Memory Provider.
  3. Fill in the following fields:
    • Name: A display name for this provider.
    • Provider Type: Select mem0.
  4. Click Create.

Configuring a Mem0 Provider ​

After creating a provider, select it from the list and configure its settings.

FieldRequiredDescription
Base URLNoMem0 API base URL. Defaults to https://api.mem0.ai when empty.
API KeyYesAPI key for Mem0 authentication (stored as a secret).
Organization IDNoOrganization ID for workspace scoping.
Project IDNoProject ID for workspace scoping.

Assigning a Mem0 Provider to a Bot ​

  1. Navigate to the Bots page and open your bot.
  2. Go to the Settings tab.
  3. Find the Memory Provider dropdown.
  4. Select the Mem0 provider you created.
  5. Click Save.

Usage ​

Once assigned, the bot will use Mem0 as its memory backend. Memory extraction, search, and management operations are routed through the Mem0 API.

For day-to-day memory operations, see Bot Memory Management.

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- + \ No newline at end of file diff --git a/memory-providers/openviking.html b/memory-providers/openviking.html index 210cb80d..56bc8dde 100644 --- a/memory-providers/openviking.html +++ b/memory-providers/openviking.html @@ -9,9 +9,9 @@ - + - + @@ -20,7 +20,7 @@
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OpenViking Memory Provider ​

OpenViking is a memory provider that can be self-hosted or used as a SaaS service. It provides an alternative memory backend for bots that need a dedicated memory API.

Creating an OpenViking Provider ​

  1. Navigate to the Memory Providers page.
  2. Click Add Memory Provider.
  3. Fill in the following fields:
    • Name: A display name for this provider.
    • Provider Type: Select openviking.
  4. Click Create.

Configuring an OpenViking Provider ​

After creating a provider, select it from the list and configure its settings.

FieldRequiredDescription
Base URLYesOpenViking API endpoint (e.g. http://openviking:8088).
API KeyNoAPI key for authentication (stored as a secret).

Assigning an OpenViking Provider to a Bot ​

  1. Navigate to the Bots page and open your bot.
  2. Go to the Settings tab.
  3. Find the Memory Provider dropdown.
  4. Select the OpenViking provider you created.
  5. Click Save.

Usage ​

Once assigned, the bot will use OpenViking as its memory backend. Memory extraction, search, and management operations are routed through the OpenViking API.

For day-to-day memory operations, see Bot Memory Management.

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Edge TTS ​

Edge TTS uses Microsoft Edge's public read-aloud API for speech synthesis. It is free, requires no API key, and supports 256+ voices across 50+ languages.

Creating an Edge TTS Provider ​

  1. Navigate to the TTS Providers page.
  2. Click Add.
  3. Select edge as the provider type.
  4. Click Create.

The default model edge-read-aloud is automatically imported when the provider is created.

Configuring the Model ​

Click the edge-read-aloud model to configure its settings.

FieldDescription
VoiceLanguage + voice ID. Default: en-US-EmmaMultilingualNeural. Over 256 voices available across 50+ languages.
FormatAudio output format. Options: audio-24khz-48kbitrate-mono-mp3 (default), audio-24khz-96kbitrate-mono-mp3, webm-24khz-16bit-mono-opus.
SpeedPlayback speed. Options: 0.5, 1.0 (default), 2.0, 3.0.
PitchVoice pitch adjustment. Range: -100 to +100, default 0.

Assigning to a Bot ​

  1. Open a bot's Settings tab.
  2. Find the TTS Model dropdown.
  3. Select the configured Edge TTS model.
  4. Click Save.

The bot can now synthesize speech using Edge TTS.

Testing ​

Use the built-in synthesis test button on the model configuration page to preview how the selected voice, format, speed, and pitch sound before assigning to a bot.

Published under AGPLv3

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TTS Providers ​

Memoh supports Text-to-Speech (TTS) so bots can synthesize spoken audio from text. The TTS system is organized into three layers:

  • TTS Provider: A service type (e.g. Edge TTS). You create named provider instances from the TTS Providers page.
  • TTS Model: A specific voice/model under a provider (e.g. edge-read-aloud). Models have configurable voice, format, speed, and pitch settings.
  • Bot Assignment: In Bot Settings, select a TTS Model. The bot can then synthesize speech in conversations.

Basic Flow ​

  1. Navigate to the TTS Providers page from the sidebar.
  2. Click Add and select a provider type (e.g. edge).
  3. Click Create — the provider's default model is auto-imported.
  4. Click the model to configure voice, format, speed, and pitch.
  5. Test synthesis with the built-in test button.
  6. Open a bot's Settings tab and select the TTS Model.
  7. Save — the bot can now synthesize speech.

Available Providers ​

ProviderDescription
Edge TTSFree, uses Microsoft Edge's public read-aloud API. 256+ voices across 50+ languages. No API key required.

Next Steps ​

  • To set up the currently available provider, continue with Edge TTS.

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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 ​

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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

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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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