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- + \ No newline at end of file diff --git a/about.html b/about.html index 029eb596..4404949d 100644 --- a/about.html +++ b/about.html @@ -9,9 +9,9 @@ - + - + @@ -20,7 +20,7 @@
Memoh

English

简体中文

English

简体中文

Appearance

About Memoh ​

What Is Memoh? ​

Memoh is a multi-member, structured long-memory, containerized AI agent platform. You can create multiple AI bots, give each bot its own isolated workspace and long-term memory, and interact with them through Telegram, Discord, Lark (Feishu), QQ, Matrix, Misskey, DingTalk, WeCom, WeChat, WeChat Official Account, Email, or the built-in Web UI.

Every bot has its own execution environment, tools, memory configuration, and channel integrations. In practice, that means each bot behaves more like its own computer-backed agent than a shared chat preset.

What Makes Memoh Different ​

Multi-Bot And Multi-User ​

Memoh is built for real sharing and real separation at the same time:

  • create multiple bots for different roles or people
  • let humans and bots interact in private chats, groups, or delegated workflows
  • distinguish individual users in shared conversations
  • bind identities across channels so the same person can be recognized consistently

Containerized Workspaces ​

Each bot runs in its own isolated container workspace with a separate filesystem and network boundary. Bots can read and write files, run commands, and use tools inside that workspace without interfering with other bots.

Long-Term Memory And Context Management ​

Memoh separates two different problems:

  • Long-term memory stores durable facts and recalls them across conversations through memory providers
  • Session context compaction reduces the prompt size of an active session when the current conversation gets too large

This distinction is important: context compaction changes the active session window, while memory compaction rewrites stored memory entries.

Sessions And Discuss Mode ​

Each bot maintains independent sessions that preserve context. Memoh currently uses five session types:

  • Chat — regular user-facing conversations
  • Discuss — deliberative sessions where the bot can think through work and decide what to send outward
  • Heartbeat — periodic autonomous sessions
  • Schedule — cron-triggered task sessions
  • Subagent — delegated task sessions

You can start or route sessions with slash commands such as /new, and the Web UI exposes a session status panel with metrics like context usage, cache hit rate, and used skills.

Broad Channel Coverage ​

Memoh uses a unified channel adapter system so one bot can be reachable from many places at once.

Current user-facing integrations include:

  • Telegram
  • Discord
  • Lark (Feishu)
  • QQ
  • Matrix
  • Misskey
  • DingTalk
  • WeCom
  • WeChat
  • WeChat Official Account
  • Email
  • Web

Memoh also distinguishes between the personal WeChat QR-login integration and the webhook-based WeChat Official Account integration.

Tools, Skills, MCP, And Supermarket ​

Bots can use a rich set of built-in capabilities, including:

  • web search and web fetch
  • browser automation
  • file editing and command execution inside the bot workspace
  • memory search and management
  • messaging, email, and TTS
  • subagents for delegated work
  • skills for reusable behavior modules
  • MCP connections for external tool servers
  • Supermarket for curated skill and MCP template installation

Providers And Models ​

Memoh supports multiple provider client types, including:

  • OpenAI-compatible chat completions
  • OpenAI Responses API
  • Anthropic Messages
  • Google Generative AI
  • OpenAI Codex
  • GitHub Copilot
  • Edge Speech / TTS

Models are also separated by role:

  • chat models for normal interaction
  • embedding models for vector memory and search
  • speech models for TTS

Image generation is configured through compatible chat/image models rather than a separate image-provider system.

Operations And UI ​

The Web UI is designed so you can manage the whole system without editing config files by hand every day. It includes:

  • bot configuration tabs for general settings, access, channels, heartbeat, compaction, and more
  • provider and model management with OAuth flows where supported
  • session-side controls such as immediate compaction and status inspection
  • skill management with effective / shadowed / disabled visibility
  • slash-command driven control from channels

Where To Start ​

Published under AGPLv3

Copyright © 2024 Memoh

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a}from"./chunks/framework.CAXxHpAX.js";const k=JSON.parse('{"title":"Docker Installation","description":"","frontmatter":{},"headers":[],"relativePath":"installation/docker.md","filePath":"installation/docker.md","lastUpdated":1774787421000}'),n={name:"installation/docker.md"};function o(r,s,d,l,h,p){return i(),t("div",null,[...s[0]||(s[0]=[a(`

Docker Installation ​

Docker is the recommended way to run Memoh. The stack includes PostgreSQL, the main server (with embedded Containerd and in-process AI agent), and the 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 and in-process AI agent
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 v0.6.0

Or using the environment variable:

bash
curl -fsSL https://memoh.sh | sudo MEMOH_VERSION=v0.6.0 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=v0.6.0 USE_CN_MIRROR=true sh

Manual Install ​

bash
git clone https://github.com/memohai/Memoh.git
+import{_ as e,o as i,c as t,ag as a}from"./chunks/framework.CAXxHpAX.js";const k=JSON.parse('{"title":"Docker Installation","description":"","frontmatter":{},"headers":[],"relativePath":"installation/docker.md","filePath":"installation/docker.md","lastUpdated":1777195218000}'),n={name:"installation/docker.md"};function o(r,s,l,d,h,p){return i(),t("div",null,[...s[0]||(s[0]=[a(`
Docker Installation ​
Docker is the recommended way to run Memoh. The stack includes PostgreSQL, the main server (with embedded Containerd and in-process AI agent), and the 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 and in-process AI agent
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. Detect whether this is a first-time install, an upgrade, or a reinstall
  3. Prompt for configuration (workspace, data directory, admin credentials, JWT secret, Postgres password, sparse service toggle, browser core selection)
  4. Reuse the existing config.toml automatically during upgrades so database credentials stay aligned with the persisted PostgreSQL volume
  5. Offer a clean reinstall mode that removes Memoh Docker containers, volumes, and network before starting again
  6. Fetch the latest release tag from GitHub and clone the repository
  7. Generate config.toml from the Docker template with your settings when needed
  8. Pin Docker image versions to the release
  9. Select and pull the prebuilt browser image for the chosen cores and start all services
  10. Print recent postgres and migrate logs automatically if startup fails
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
If the script detects an existing Memoh installation in silent mode, it defaults to upgrade and reuses the previous config.toml. If Docker state exists but no reusable config.toml can be found, the script exits and asks you to choose an explicit reinstall.
Force a clean reinstall (removes Memoh Docker data before starting again):
bash
curl -fsSL https://memoh.sh | sudo MEMOH_INSTALL_MODE=reinstall sh
You can also pass the install mode as an argument:
bash
curl -fsSL https://memoh.sh | sudo sh -s -- --install-mode reinstall
Install a specific version:
bash
curl -fsSL https://memoh.sh | sudo sh -s -- --version v0.6.0
Or using the environment variable:
bash
curl -fsSL https://memoh.sh | sudo MEMOH_VERSION=v0.6.0 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=v0.6.0 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
[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
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.
Configuration Reference ​
The config.toml file controls all server behavior. Here is a summary of the available sections:
SectionDescription
[log]Logging level and format (info, debug; text, json)
[server]HTTP listen address (default :8080)
[admin]Admin account credentials (username, password, email)
[auth]JWT secret and token expiration
timezoneServer timezone (default UTC)
[containerd]Containerd socket path and namespace
[workspace]Container image, snapshotter, data paths, CNI config, optional registry mirror
[postgres]PostgreSQL connection (host, port, user, password, database, sslmode)
[qdrant]Qdrant vector database connection (base_url, api_key, timeout)
[sparse]Sparse encoding service URL
[registry]Provider definitions directory
[browser_gateway]Browser Gateway host, port, and server address
[web]Web frontend host and port
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
+docker compose down -v         # Stop and remove Memoh Docker data
 docker compose logs -f         # View logs
 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. v0.6.0). Also pins Docker image versions.
USE_CN_MIRRORfalseSet to true to use China mainland image mirrors
BROWSER_CORESchromium,firefoxBrowser engines to include in the browser image
BROWSER_TAGlatestDocker tag for the browser image
`,62)])])}const g=e(n,[["render",o]]);export{k as __pageData,g as default};
+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. v0.6.0). Also pins Docker image versions.
MEMOH_INSTALL_MODEautoInstall mode: auto, fresh, upgrade, or reinstall
USE_CN_MIRRORfalseSet to true to use China mainland image mirrors
BROWSER_CORESchromium,firefoxBrowser engines to include in the browser image
BROWSER_TAGlatestDocker tag for the browser image
`,67)])])}const g=e(n,[["render",o]]);export{k as __pageData,g as default}; diff --git a/assets/installation_docker.md.BgArxak6.lean.js b/assets/installation_docker.md.Dv63A02F.lean.js similarity index 71% rename from assets/installation_docker.md.BgArxak6.lean.js rename to assets/installation_docker.md.Dv63A02F.lean.js index a6619f04..84ac5e18 100644 --- a/assets/installation_docker.md.BgArxak6.lean.js +++ b/assets/installation_docker.md.Dv63A02F.lean.js @@ -1 +1 @@ -import{_ as e,o as i,c as t,ag as a}from"./chunks/framework.CAXxHpAX.js";const k=JSON.parse('{"title":"Docker Installation","description":"","frontmatter":{},"headers":[],"relativePath":"installation/docker.md","filePath":"installation/docker.md","lastUpdated":1774787421000}'),n={name:"installation/docker.md"};function o(r,s,d,l,h,p){return i(),t("div",null,[...s[0]||(s[0]=[a("",62)])])}const g=e(n,[["render",o]]);export{k as __pageData,g as default}; +import{_ as e,o as i,c as t,ag as a}from"./chunks/framework.CAXxHpAX.js";const k=JSON.parse('{"title":"Docker Installation","description":"","frontmatter":{},"headers":[],"relativePath":"installation/docker.md","filePath":"installation/docker.md","lastUpdated":1777195218000}'),n={name:"installation/docker.md"};function o(r,s,l,d,h,p){return i(),t("div",null,[...s[0]||(s[0]=[a("",67)])])}const g=e(n,[["render",o]]);export{k as __pageData,g as default}; diff --git a/blogs/2026-02-16.html b/blogs/2026-02-16.html index a31dafd0..57a3271c 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.

Published under AGPLv3

Copyright © 2024 Memoh

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DingTalk Channel Configuration ​

Memoh supports DingTalk bots for private chats and group chats. The adapter uses DingTalk's stream connection for inbound events and the official APIs for outbound replies and media.

Step 1: Create A DingTalk App ​

  1. Open the DingTalk developer platform for your organization.
  2. Create or choose the app that will act as your bot.
  3. Enable the bot / messaging capability for that app.
  4. Copy the app credentials:
    • App Key
    • App Secret

Depending on your DingTalk environment, you may also need to grant message permissions and publish the app before it is available to end users.

Step 2: Configure Memoh ​

  1. Open your bot in the Memoh Web UI.
  2. Go to Platforms.
  3. Click Add Channel and choose DingTalk.
  4. Fill in App Key and App Secret.
  5. Click Save and Enable.

Memoh maintains the DingTalk stream connection automatically. For the normal setup, you do not need to manually paste a webhook callback URL.

Step 3: Verify Messaging ​

After the channel is enabled:

  1. Send a private message to the DingTalk bot, or mention it in a supported group chat.
  2. Confirm the bot receives the message and can reply.

Features Supported ​

  • Private chats
  • Group chats
  • Text and Markdown-style output
  • Replies
  • Attachments and media

Current behavior note:

  • Outbound responses are non-streaming on DingTalk in Memoh.

Published under AGPLv3

Copyright © 2024 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, Server Members Intent and Presence Intent.
  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 Platforms 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).

Published under AGPLv3

Copyright © 2024 Memoh

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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 Platforms 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

Copyright © 2024 Memoh

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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:

  • Slack: Workspace messaging with Socket Mode, threads, files, and reactions.
  • Telegram: 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.
  • Matrix: Decentralized messaging protocol support for any Matrix homeserver.
  • Misskey: Federated social/chat style integration with replies and reactions.
  • DingTalk: Enterprise chat integration for private and group conversations.
  • WeCom (WeWork): Enterprise messaging integration for WeCom workspaces.
  • WeChat: Personal messaging via QR login.
  • WeChat Official Account: Official account webhook integration for private message scenarios.
  • Email: Connect via SMTP providers, Mailgun, or Gmail OAuth (configured through Email Providers).
  • Web: Built-in chat interface for immediate access.

WeChat vs WeChat Official Account ​

Memoh supports two different WeChat-related adapters:

  • WeChat (weixin) is the personal-account style integration that relies on QR login.
  • WeChat Official Account (wechatoa) is the official-account / webhook style integration that uses App ID, App Secret, Token, and optional AES settings.

Choose the one that matches your actual WeChat deployment model.

General Setup Flow ​

  1. Create an external app/bot: Register your bot on the target platform.
  2. Obtain credentials: Fetch API tokens, App IDs, app secrets, or access tokens.
  3. Configure in Memoh: Add the channel from your bot's Platforms tab.
  4. Save and enable: Activate the channel to start receiving and sending messages.

Depending on the platform, the final step may involve:

  • copying a webhook callback URL into the platform console
  • approving a QR login on mobile
  • leaving a long-lived stream/WebSocket connection running through Memoh

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

Published under AGPLv3

Copyright © 2024 Memoh

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  • Option B: Extract from a client

Important: Keep the access token secret. Anyone with this token can act as your bot account.

Step 2: Configure Memoh ​

  1. Go to your Bot's Platforms tab in the Memoh Web UI.
  2. Click Add Channel and select Matrix.
  3. Fill in the required fields:
FieldRequiredDescription
Homeserver URLYesThe base URL of your Matrix homeserver (e.g., https://matrix.org).
Access TokenYesThe bot account's access token.
User IDYesThe bot's Matrix user ID (e.g., @mybot:matrix.org).
Sync TimeoutNoLong-polling timeout in seconds (default: 30).
Auto Join InvitesNoAutomatically join rooms when invited (default: enabled).
  1. Click Save and Enable.

Step 3: Invite the Bot ​

  1. Open your Matrix client (Element, etc.).
  2. Invite the bot's user ID to a room, or start a direct message.
  3. If Auto Join Invites is enabled, the bot will automatically accept and join.

Features Supported ​

  • Message Content: Full access to text messages.
  • Rooms: Join and participate in group rooms.
  • Direct Messages: Private conversations with individual users.
  • Streaming: Responses are streamed as they are generated.
  • Markdown: Support for formatted text.
  • Media/Attachment: Bots can receive and send files or photo.
  • Check the roadmap for more features of Matrix.

Official Resources ​

Published under AGPLv3

Copyright © 2024 Memoh

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Misskey Channel Configuration ​

Memoh can connect a bot to a Misskey server so it can read inbound mentions and reply as a Misskey account. This adapter is best for text-first social interactions on self-hosted or public Misskey instances.

Step 1: Prepare A Misskey Account And Token ​

  1. Sign in to the Misskey instance you want to use.
  2. Create or choose the account that should represent your bot.
  3. Generate an Access Token for that account.

Memoh needs:

  • Instance URL: for example https://misskey.io
  • Access Token: a token for the bot account

The exact token-creation UI varies by Misskey instance. Make sure the token is allowed to read inbound events for the bot account and publish replies.

Step 2: Configure Memoh ​

  1. Open your bot in the Memoh Web UI.
  2. Go to Platforms.
  3. Click Add Channel and choose Misskey.
  4. Enter the Instance URL.
  5. Paste the Access Token.
  6. Click Save and Enable.

Memoh uses these credentials to discover the bot identity and start the Misskey connection.

Step 3: Start Chatting ​

After the channel is enabled, users can interact with the bot on that Misskey instance.

Misskey in Memoh is generally best suited for:

  • replies to users
  • text and Markdown-style output
  • reaction-aware social conversations

Features Supported ​

  • Text
  • Markdown
  • Replies
  • Reactions

Current limitations:

  • No attachments / media upload
  • No streaming output

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 Platforms 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) ​

Binding your QQ identity to your Memoh account enables cross-platform identity recognition — the bot will know who you are regardless of which channel you use.

  1. In Memoh Web UI, go to Profile > 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 ​

Published under AGPLv3

Copyright © 2024 Memoh

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Slack Channel Configuration ​

Connecting your Memoh Bot to Slack allows it to receive direct messages, participate in channels and threads, read attachments, send files, and use streaming replies.

Step 1: Create a Slack App ​

  1. Go to the Slack API dashboard and create a new app.
  2. Choose the workspace where you want to install the bot.
  3. Open Basic Information and keep this app page open for the next steps.

Step 2: Enable Socket Mode ​

Memoh's Slack adapter uses Socket Mode, so you need an app-level token in addition to the bot token.

  1. In Basic Information, enable Socket Mode.
  2. Create an App-Level Token with the connections:write scope.
  3. Copy the generated token. It starts with xapp-.

Step 3: Configure Bot Token Scopes ​

In OAuth & Permissions, add the bot token scopes required by the current Slack adapter:

  • app_mentions:read - receive bot mentions in channels
  • channels:history - read messages in public channels
  • groups:history - read messages in private channels
  • im:history - read direct messages
  • mpim:history - read group direct messages
  • chat:write - send replies and thread messages
  • files:read - read uploaded files and images
  • files:write - upload outbound files
  • reactions:write - add and remove reactions

You should also add these recommended scopes if you want Slack conversation names and metadata to show up more completely in Memoh:

  • channels:read
  • groups:read
  • im:read
  • mpim:read

Step 4: Subscribe to Bot Events ​

In Event Subscriptions, enable bot events and add:

  • app_mention
  • message.channels
  • message.groups
  • message.im
  • message.mpim

These are the inbound event types currently handled by the Slack adapter.

Step 5: Install the App to Your Workspace ​

  1. In OAuth & Permissions, click Install to Workspace.
  2. Review the permission screen.
  3. Authorize the app.
  4. Copy the Bot User OAuth Token. It starts with xoxb-.

Make sure the xoxb-... bot token and the xapp-... app-level token come from the same Slack app and workspace.

Step 6: Configure Memoh ​

  1. Open your Bot detail page in the Memoh Web UI.
  2. Go to the Platforms tab.
  3. Click Add Channel and select Slack.
  4. Fill in:
    • Bot Token: your xoxb-... token
    • App-Level Token: your xapp-... token
  5. Click Save and Enable.

Step 7: Add the Bot to Conversations ​

After the channel is enabled, the Slack app still needs to be present in the conversations where you want it to work.

  • For direct messages: open a DM with the app and send a message.
  • For public channels: invite the bot to the channel.
  • For private channels: invite the bot explicitly after installation.

If the bot can send messages but cannot read uploaded images or files, check that files:read is enabled. If it connects but receives no incoming messages, check the bot events and the matching history scopes again.

Features Supported ​

  • Direct Messages and Channels: Support for DMs, public channels, private channels, and threads.
  • Attachments: Read uploaded images and files from Slack, and send files back.

Published under AGPLv3

Copyright © 2024 Memoh

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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 Platforms 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) ​

Binding your Telegram identity to your Memoh account enables cross-platform identity recognition — the bot will know who you are regardless of which channel you use.

  1. In Memoh Web UI, go to Profile > 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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WeChat Official Account Channel Configuration ​

This guide covers the WeChat Official Account adapter in Memoh. It is different from the personal WeChat QR-login adapter: this one is for official-account webhook integration and is intended for inbound private-message scenarios.

Step 1: Prepare Official Account Credentials ​

From the WeChat Official Account platform, prepare:

  • App ID
  • App Secret
  • Token

You may also need:

  • Encoding AES Key if you use encrypted webhook delivery
  • an outbound HTTP Proxy URL if your deployment must reach WeChat APIs through a proxy

Step 2: Add The Channel In Memoh ​

  1. Open your bot in the Memoh Web UI.
  2. Go to Platforms.
  3. Click Add Channel and choose WeChat Official Account.
  4. Fill in the required fields:
    • App ID
    • App Secret
    • Token
  5. Choose the Encryption Mode.
  6. If you use safe or compat mode, also provide the Encoding AES Key.
  7. Save the channel.

Memoh generates a Webhook Callback URL after the channel has been saved.

Step 3: Configure The WeChat Platform ​

  1. Copy the Webhook Callback URL from Memoh.
  2. Paste it into the WeChat Official Account platform callback configuration.
  3. Make sure the WeChat platform and Memoh use the same:
    • Token
    • Encryption Mode
    • Encoding AES Key when encryption is enabled

WeChat will verify the callback before delivering real messages.

Step 4: Enable And Test ​

  1. Enable the channel in Memoh.
  2. Send a test private message from the official account side.
  3. Confirm that Memoh receives the message and the bot can reply.

Features Supported ​

  • Private chats
  • Replies
  • Attachments and media

Current behavior notes:

  • This adapter is intended for private-message conversations, not group-style chats.
  • Outbound responses are non-streaming on this channel.

Published under AGPLv3

Copyright © 2024 Memoh

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WeCom (WeWork) Channel Configuration ​

Connecting your Memoh Bot to WeCom (企业微信) allows it to interact with users within your organization's WeCom workspace.

Step 1: Create a WeCom Bot ​

  1. Log in to the WeCom Admin Console.
  2. Navigate to App Management > Custom Apps or Bot Management.
  3. Create a new bot application.
  4. Note the Bot ID and Secret credentials.

Step 2: Configure Memoh ​

  1. Go to your Bot's Platforms tab in the Memoh Web UI.
  2. Click Add Channel and select WeCom.
  3. Fill in the required fields:
FieldRequiredDescription
Bot IDYesThe WeCom bot identifier.
SecretYesThe bot's authentication credential.
WebSocket URLNoCustom WebSocket endpoint (uses default if empty).
  1. Click Save and Enable.

Step 3: Start Chatting ​

Once the channel is enabled, the bot will connect to WeCom via WebSocket and begin receiving messages. Users in your WeCom workspace can send messages to the bot directly or in group chats.

Features Supported ​

  • Message Content: Full support for text messages.
  • Direct Messages: Private conversations with individual users.
  • Group Chats: Participate in group conversations.
  • Streaming: Responses are streamed in real-time.

Official Resources ​

Published under AGPLv3

Copyright © 2024 Memoh

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WeChat Channel Configuration ​

Connecting your Memoh Bot to WeChat allows it to interact with users directly via WeChat personal messaging. Setup is simple — just scan a QR code.

Step 1: Add WeChat Channel in Memoh ​

  1. Go to your Bot's Platforms tab in the Memoh Web UI.
  2. Click Add Channel and select WeChat.

Step 2: Scan the QR Code ​

  1. Click Start QR Login. Memoh will fetch a QR code from the WeChat platform.
  2. Open WeChat on your phone and scan the displayed QR code.
  3. Confirm the login on your phone when prompted.
  4. Once confirmed, Memoh automatically saves the credentials — no manual token or API key entry is needed.

The QR code has a limited validity window. If it expires before you scan, click Start QR Login again to get a fresh one.

Step 3: Start Chatting ​

After the QR login succeeds, the channel is automatically enabled. Users can now send messages to the bot directly in WeChat.

Optional Settings ​

FieldDescription
Enable TypingShow typing indicators while generating responses.

Features Supported ​

  • Message Content: Full support for text messages.
  • Direct Messages: Private conversations with individual users.
  • Attachments: Support for images and media.
  • Typing Indicators: Visual feedback while generating responses (when enabled).

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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 define prioritized rules to allow or deny specific users, channel identities, or entire channel types — all from the bot's Access tab.

Quick Start: ACL Presets ​

When you create a bot, Memoh lets you start from an ACL preset. Presets are just a shortcut for common access patterns.

PresetResult
allow_allDefault effect is allow; anyone can chat unless you add deny rules later
private_onlyDefault effect is deny; private conversations are allowed
group_onlyDefault effect is deny; group conversations are allowed
group_and_thread_onlyDefault effect is deny; groups and threads are allowed
deny_allDefault effect is deny; nobody except the owner/admin path can chat until you add allow rules

These presets only define the starting point. After creation, you can refine everything from the Access tab.

Concepts ​

Default Effect ​

Each bot has a default effect (allow or deny) that applies when no ACL rule matches an incoming message. Configure this from the bot's Access tab.

  • Allow: Anyone can chat with the bot unless explicitly denied by a rule.
  • Deny: Only the bot owner, admins, and explicitly allowed subjects can chat.

Subject Types ​

ACL rules can target three kinds of subjects:

SubjectDescription
AllMatches every incoming message regardless of sender. Use this for global allow/deny rules.
Channel IdentityA specific identity on an external channel (e.g., a Telegram user, a Discord member). Useful for controlling access at the individual level.
Channel TypeAn entire channel platform (e.g., all Telegram users, all Discord users). Useful for platform-level access control.

Rule Effects ​

Each rule has an effect:

  • Allow — Grants the subject permission to chat with the bot.
  • Deny — Blocks the subject from chatting with the bot.

Priority-Based Evaluation ​

Rules are evaluated in priority order (top to bottom). The first matching rule determines the outcome:

  1. Bot owner or system admin → Always allowed (bypasses ACL).
  2. Rules are checked from highest priority (top) to lowest (bottom).
  3. The first rule whose subject matches the sender is applied.
  4. If no rule matches → the default effect is applied.

This means rule ordering matters. A deny rule placed above an allow rule will take precedence for matching subjects.

Managing Access ​

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

Start With A Preset, Then Refine ​

Recommended workflow:

  1. Pick an ACL preset when creating the bot.
  2. Open the Access tab.
  3. Confirm the resulting Default Effect.
  4. Add or reorder rules only where the preset is too broad or too narrow.

Adding Rules ​

  1. Click Add Rule.
  2. Select a subject type:
    • All: Applies to everyone.
    • Channel Identity: Search and select a specific channel identity the bot has seen before.
    • Channel Type: Select an entire channel platform.
  3. Choose the effect: allow or deny.
  4. Optionally set source scope to restrict the rule to a specific context:
    • Channel: Only applies when the message comes from a specific channel config.
    • Conversation Type: private, group, or thread.
    • Conversation ID: A specific chat/group ID.
    • Thread ID: A specific thread within a conversation (requires Conversation ID).
  5. Click Save.

Reordering Rules ​

Rules can be drag-and-dropped to change their priority. Higher rules (closer to the top) are evaluated first. After reordering, click Save to persist the new order.

Source Scope ​

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

  • Allow a user to chat only via Telegram, but not Discord.
  • Block an entire channel type 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.

What The Presets Actually Mean ​

This is the most useful mental model:

  • allow_all is best for open bots and public demos.
  • private_only is best when the bot should only answer in direct chats.
  • group_only is best for bots intended to live only in shared rooms.
  • group_and_thread_only is best for bots that should work in group spaces and threaded sub-conversations, but not in private DMs.
  • deny_all is best for highly restricted bots where you want to add every allow rule manually.

If you are unsure, start with allow_all for a personal test bot or deny_all for anything sensitive.

Examples ​

Open Bot (Anyone Can Chat) ​

  1. Choose preset allow_all, or set ACL Default Effect to allow.
  2. No rules needed — everyone is allowed by default.

Private Bot with Selected Users ​

  1. Choose preset deny_all, or set ACL Default Effect to deny.
  2. Add allow rules for each authorized channel identity.
  3. Only listed subjects (plus the bot owner and admins) can trigger the bot.

Open Bot with Blocked Users ​

  1. Choose preset allow_all, or set ACL Default Effect to allow.
  2. Add deny rules for problematic channel identities at the top of the list.
  3. Everyone except denied subjects can chat with the bot.

Platform-Specific Access ​

  1. Start from preset deny_all or private_only, depending on your goal.
  2. Add an allow rule with subject type Channel Type set to telegram.
  3. Only Telegram users can chat with the bot — messages from other channels are denied.

Channel-Scoped Access ​

  1. Add an allow rule for a specific channel identity.
  2. Set the Source Scope channel to your Telegram channel config.
  3. The user can only chat with the bot via that specific Telegram channel.

Debugging Access Decisions ​

When ACL behavior is confusing, use:

  • the Access tab to inspect rule order and default effect
  • the /access slash command to inspect the current identity, role, and ACL evaluation context

This is especially helpful when a user is linked across multiple channels or when group/thread scoping is involved.

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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 chats.
    • Avatar: A URL for the bot's profile picture.
    • Timezone: Optional per-bot timezone. If left empty, the bot inherits the user or system timezone.
    • ACL Preset: Quick-start access policy such as allow_all or private_only.
  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 specialized tabs.

Tab Overview ​

TabDescription
OverviewHealth checks for container, database, channels, and memory.
GeneralCore runtime settings: chat/title/image models, memory/search/browser/TTS bindings, timezone, language, reasoning, and danger zone.
ContainerContainer lifecycle (create/start/stop), snapshots, data export/import.
MemoryBrowse, search, create, edit, and compact memories.
PlatformsChannel configurations such as Telegram, Discord, Feishu, QQ, Matrix, WeCom, WeChat, Misskey, DingTalk, and Web.
AccessACL rules and default access behavior.
EmailEmail bindings and outbox.
TerminalInteractive terminal access to the bot's container.
FilesFile manager for the bot's container filesystem.
MCPMCP connection management (Stdio, Remote, OAuth).
HeartbeatHeartbeat configuration, model selection, and execution logs.
CompactionSession context compaction settings and logs.
ScheduleCron-based scheduled tasks and execution logs.
SkillsMarkdown-based skill files that define bot personality and capabilities.

Configuring the Bot's Core Settings ​

After creating a bot, the most important step is configuring its runtime settings. These settings are split across a few tabs instead of living in one giant form.

  1. Navigate to your bot's Detail Page.
  2. Start with the General tab for chat/runtime bindings.
  3. Use the Heartbeat tab for scheduled autonomous activity.
  4. Use the Compaction tab for session context compaction behavior.
  5. Use the Access tab to refine ACL rules after the initial ACL preset.

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

General Tab Reference ​

The General tab contains the settings that shape everyday conversation behavior.

FieldDescription
Chat ModelThe main LLM used for generating chat responses.
Title ModelOptional model used to generate session titles.
Image Generation ModelOptional model used by image-generation features. Pick a chat model that supports image-output.
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.
TTS ModelOptional speech model used for text-to-speech output. Speech models come from the TTS Providers flow, not the normal chat provider flow.
Browser ContextThe browser environment used for web automation, such as viewport, locale, and mobile behavior.
TimezonePer-bot timezone. If empty, Memoh inherits the user timezone and then falls back to the system timezone.
LanguageThe bot's primary communication language.
Reasoning EnabledAvailable when the selected chat model exposes reasoning compatibility.
Reasoning EffortSet the level of reasoning effort (low, medium, high).

Notes:

  • The Image Generation Model is intentionally separate from the normal chat model so you can dedicate an image-capable model only to visual generation tasks.
  • The TTS Model comes from the TTS Providers system and uses speech models such as Edge TTS voices.
  • The selected chat model's context_window influences session status reporting and Context Compaction.

Heartbeat Tab Reference ​

Heartbeat is configured from its own tab.

FieldDescription
Heartbeat EnabledEnable or disable periodic autonomous activity.
Heartbeat IntervalHow often the heartbeat runs, in minutes.
Heartbeat ModelOptional dedicated model for heartbeat tasks. This can differ from the main chat model.

The Heartbeat tab also includes heartbeat execution logs, so you can review what the bot did during autonomous runs.

Compaction Tab Reference ​

Compaction is now about session context compaction, not memory maintenance.

FieldDescription
Compaction EnabledEnable or disable automatic context compaction.
Compaction ThresholdEstimated token threshold that triggers compaction.
Compaction RatioHow aggressively the session context should be reduced.
Compaction ModelOptional dedicated model used to summarize old session context.

The Compaction tab also exposes compaction logs so you can see recent successful, pending, or failed runs.

For the runtime behavior, see Context Compaction.

Access And ACL ​

At creation time, the bot starts from an ACL preset. After that, use the Access tab for fine-grained control.

Two layers matter:

  • ACL Preset gives you a sensible starting policy for a new bot.
  • ACL Default Effect controls the default result when no rule matches.

Use the Access tab to refine conversation, group, and thread rules after the initial setup.

Most users only need the chat and discuss behavior described in Sessions.

If you manage bot settings through the API or custom automation, the settings schema also includes discuss_probe_model_id for discuss-mode specific setups. Treat it as an advanced setting rather than a required field for normal bot creation.

Terminal Tab ​

The Terminal tab provides interactive shell access to the bot's container:

  • Open multiple terminal tabs simultaneously.
  • Execute commands directly inside the container.
  • Requires the container to be running.

Deleting a Bot ​

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

  1. Navigate to the General 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.

Published under AGPLv3

Copyright © 2024 Memoh

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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 General 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, Matrix, and more simultaneously.

Supported Channels ​

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

Platform Guides ​

PlatformGuideNotes
TelegramTelegram ConfigurationStrong attachment and streaming support
Feishu (Lark)Feishu ConfigurationSupports webhook-style inbound mode
DiscordDiscord ConfigurationGood fit for communities and servers
QQQQ ConfigurationPersonal DM oriented
MatrixMatrix ConfigurationDecentralized homeserver support
MisskeyMisskey ConfigurationReplies and reactions, no streaming
DingTalkDingTalk ConfigurationEnterprise private/group chat
WeCom (WeWork)WeCom ConfigurationEnterprise workspace integration
WeChatWeChat ConfigurationPersonal QR login flow
WeChat Official AccountWeChat Official Account ConfigurationOfficial account webhook flow
SlackSlack ConfigurationReplies, no streaming

Two WeChat adapters exist on purpose:

  • WeChat is the QR-login personal messaging integration.
  • WeChat Official Account is the official account / webhook integration for private-message scenarios.

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.

4. Special Case: WeChat QR Login ​

If using WeChat:

  1. After enabling the channel, a QR code flow is provided for connecting.
  2. Scan the QR code with WeChat to link the bot.

5. Special Case: WeChat Official Account Webhook ​

If using WeChat Official Account:

  1. Create and save the channel first.
  2. Memoh generates a Webhook Callback URL for that channel.
  3. Copy the callback URL into the WeChat Official Account platform configuration.
  4. Keep the configured Token, Encryption Mode, and optional AES settings aligned between Memoh and WeChat.

6. Special Case: DingTalk Stream Connection ​

If using DingTalk:

  1. Configure App Key and App Secret.
  2. Save and enable the channel.
  3. Memoh maintains the stream connection for inbound events; you do not need to manage a separate webhook callback URL for the standard setup.

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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Context Compaction ​

Context Compaction reduces the prompt footprint of a single conversation session by summarizing older turns and keeping the active context smaller.

This page is about session context, not long-term memory storage. If you want to merge or rewrite stored memories in a memory provider, see Bot Memory Management.

Why It Exists ​

As a conversation grows, the bot needs to send more prior messages back to the model. That increases:

  • token usage
  • latency
  • pressure on the model's context window
  • the chance that older but still important turns will crowd out newer ones

Context compaction helps by replacing older conversational detail with a shorter summary that still preserves enough continuity for the next turns.

What It Changes ​

Context compaction affects the active session context only.

It does not:

  • delete the bot itself
  • change the configured memory provider
  • merge long-term memory records
  • replace the need for memory search

In practice, it changes how much historical session text is carried into future model calls.

Automatic Compaction ​

Configure automatic context compaction from the bot's General tab.

Relevant fields:

FieldDescription
Compaction EnabledEnable or disable automatic context compaction for this bot.
Compaction ThresholdEstimated token threshold that triggers background compaction.
Compaction RatioHow aggressively the session should be reduced during compaction.
Compaction ModelThe model used to summarize old session context.

When enabled, Memoh can compact context in the background after a turn when the estimated input size passes the configured threshold.

Memoh also uses the selected model's context_window to understand how close the session is to the available budget.

Immediate Compaction ​

You can trigger compaction immediately for the current session in two ways:

From The Session Status Panel ​

  1. Open the active conversation.
  2. Open the session status panel.
  3. Click Compact Now.

The status panel also shows the current context usage, cache hit rate, and used skills, which helps you decide whether compaction is useful right now.

From Slash Commands ​

Run:

text
/compact

or:

text
/compact run

This runs synchronous context compaction for the current session and returns a status result to chat.

Status And Logs ​

The Compaction tab in the bot detail page provides an audit trail for context compaction runs.

Typical fields include:

  • Status — whether the compaction finished successfully or failed
  • Summary — the compacted summary text or a summary preview
  • Message Count — how many messages were involved
  • Started / Completed Time — when the run happened
  • Model / Usage — metadata about the model and token usage when available

The log list is useful when you want to verify that automatic compaction is actually running or diagnose a failure.

Relationship To context_window ​

Memoh tracks the current session against the selected chat model's context_window.

You can see this in:

  • the Web UI session status panel
  • the /status slash command

Compaction becomes more valuable as the active session gets closer to the model's context limit. A dedicated compaction model can also be used to summarize more cheaply than the main chat model.

Context Compaction vs Memory Compaction ​

These two features sound similar but solve different problems:

FeatureScopeTriggerResult
Context CompactionOne active sessionSession panel or /compactSummarizes older chat history for future turns
Memory CompactionLong-term memory providerMemory tabRewrites stored memory entries

Use Context Compaction when one conversation has become too large.

Use Memory Compaction when the bot's stored memories themselves have become noisy or redundant.

Next Steps ​

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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). Progress is shown via real-time SSE feedback during image pull and creation.
  • 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.

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.
  • CDI Devices: The effective GPU CDI devices currently attached to the container, if any.

Advanced: Provide CDI Devices ​

Memoh can provide host devices to a bot container through CDI (Container Device Interface). This is an advanced capability for users who want to expose host-managed devices, most commonly GPUs, to the container runtime.

In the Web UI, this capability is placed under Advanced options in the Container tab. It is optional and only needs to be configured when the bot must access CDI-backed devices from the host.

Configure CDI Devices ​

  1. Open the Bot's Container tab.
  2. Click Create if the container does not exist, or recreate the container if you need to change GPU settings.
  3. Expand Advanced options.
  4. Enable GPU.
  5. Enter one or more CDI device names in CDI devices.

You can enter CDI device names one per line or separated with commas. Common GPU-related examples:

  • nvidia.com/gpu=0
  • nvidia.com/gpu=all
  • amd.com/gpu=0
  • amd.com/gpu=all

Host Requirements ​

Before configuring CDI devices in Memoh, the host machine must already provide working device drivers, vendor toolkit support where required, and valid CDI specs. In practice, this usually means:

  • the host GPU works normally outside the container
  • CDI spec files exist under /etc/cdi or /var/run/cdi
  • the device name you enter in Memoh matches a real CDI device on the host

To discover the exact CDI device names exposed by the host, use the vendor tool on the host machine:

  • NVIDIA: nvidia-ctk cdi list
  • AMD: amd-ctk cdi list

If Memoh reports an error such as unresolvable CDI devices, the configured device name does not match any CDI device visible to the container runtime.

Important Behavior ​

  • CDI device settings are applied when the container is created. Updating the setting later requires recreating the container.
  • Stopping and starting an existing container does not change its attached CDI devices.
  • The container image still needs the appropriate user-space libraries and tools if you want to run CUDA or ROCm software inside the container.
  • After creation, the Container tab shows the effective attached CDI devices for verification.

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.

Data Export and Import ​

The Container tab supports exporting and importing container data for backup, migration, or sharing purposes.

Export ​

  1. Click Export Data.
  2. The container's filesystem data is packaged into a downloadable archive.
  3. Save the archive to your local machine.

Import ​

  1. Click Import Data.
  2. Select an archive file from your local machine.
  3. The archive contents are extracted into the container's filesystem.

Restore ​

The Restore operation resets the container's data directory to a clean state. This is useful when the filesystem has become corrupted or you want to start fresh without recreating the container.

Container Versioning ​

Memoh tracks container versions to manage the lifecycle of the bot's runtime environment. Version information includes:

  • Current Version: The active container version.
  • Version History: A log of container version changes over time.

This helps with auditing and understanding when container configurations were updated.

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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.

You can configure connections manually from the MCP tab, or start from a curated template in Supermarket and then review the prefilled draft in the bot editor.

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 inside the bot's container 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).

OAuth Authentication ​

Some MCP servers require OAuth-based authentication. Memoh supports the full MCP OAuth flow:

  1. Click the OAuth button on a connection that requires authentication.
  2. Memoh will discover the server's OAuth configuration automatically.
  3. Click Authorize to open the provider's authorization page.
  4. After granting access, you are redirected back to Memoh.
  5. The token is exchanged and stored securely for the connection.

You can also:

  • Check status — View the current OAuth token status.
  • Revoke — Remove the stored OAuth token.

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.
  • Export: Export connection configurations to a JSON file for backup or sharing.
  • Toggle Active: Enable or disable specific connections without deleting them.
  • Search: Quickly find a connection by name or ID.
  • Batch Delete: Select multiple connections and delete them at once.

Tool Discovery ​

Once an MCP connection is active, Memoh automatically discovers the tools exposed by the server. You can:

  1. Click on a connection to view its details.
  2. The Tools section lists all available tools with their names and descriptions.
  3. The bot will use these tools automatically during conversations when relevant.

You can also use the Probe button to manually refresh the tool list from the server.

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 General 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.

This page is about long-term memory. It is separate from session context compaction, which reduces the prompt footprint of a single conversation session. See Context Compaction.

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 Compaction ​

Over time, long-term memories can accumulate and become redundant. The Compact action in the Memory tab rewrites the stored memory set itself.

This operation is provider-level memory maintenance. It is useful when you want to:

  • merge overlapping memories
  • remove stale or low-value entries
  • improve retrieval quality by reducing noise

Parameters:

  • Ratio — Compression ratio such as 0.8, 0.5, or 0.3. Lower values make compaction more aggressive.
  • Decay Days — Optionally restrict compaction to older memories only.

This is different from Context Compaction, which compresses the active prompt for one session rather than rewriting stored memories.

Rebuild ​

The Rebuild feature re-indexes all memories from scratch. This is useful when:

  • You have changed the memory provider's mode (e.g., switching from off to sparse).
  • The vector index has become inconsistent.
  • You want to re-process all memories with updated settings.

Click Rebuild in the Memory tab to start the process. You can monitor the rebuild status in real-time.

Status ​

The Memory tab shows the current status of the memory provider for this bot:

  • Connected — The memory backend is reachable and operational.
  • Error — There is an issue with the memory provider configuration or connectivity.

Use the status indicator to quickly verify that the memory system is working before troubleshooting other issues.

Usage Statistics ​

The Memory tab displays storage usage information:

  • Total Memories — The number of memory entries stored for this bot.
  • Index Status — Whether the vector index is up-to-date.

Memory vs Session Context ​

Memoh has two different "compaction" concepts:

ConceptScopeWhere to trigger itWhat it changes
Memory CompactionLong-term memory providerMemory tabRewrites stored memory entries
Context CompactionOne conversation sessionSession status panel or /compactSummarizes older session context for future model calls

If you are trying to reduce retrieval noise across many conversations, use Memory Compaction.

If you are trying to shorten the currently active conversation history, use Context Compaction.

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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Providers And Models ​

To use Memoh effectively, you usually configure:

  • one or more providers that define how Memoh talks to upstream APIs
  • one or more models under those providers
  • optional speech providers if you want text-to-speech

The Web UI manages chat and embedding providers/models from the Models page. Speech models are managed separately from TTS Providers.

Provider Basics ​

A provider stores connection information for one upstream service, such as:

  • the API protocol (client_type)
  • the base URL if the protocol needs one
  • credentials such as an API key or OAuth token

Typical examples include OpenAI-compatible endpoints, Anthropic, Google Gemini, OpenAI Codex, and GitHub Copilot.

Creating A Provider ​

  1. Open the Models page from the settings sidebar.
  2. Click Add Provider.
  3. Fill in the provider form.
  4. Save the provider.

Common fields:

FieldDescription
NameFriendly display name, such as OpenAI or Copilot.
Client TypeAPI protocol used by this provider.
Base URLRoot API endpoint, when required by the selected client type.
API KeyToken-based authentication, when the client type uses direct credentials.

Client Types ​

Memoh currently supports these client types:

Client TypeTypical Use
openai-responsesOpenAI Responses API style providers
openai-completionsOpenAI Chat Completions compatible providers
anthropic-messagesAnthropic Messages API
google-generative-aiGoogle Gemini API
openai-codexOpenAI Codex / ChatGPT-backed coding workflow with OAuth
github-copilotGitHub Copilot with device OAuth
edge-speechSpeech-only provider type for Microsoft Edge Read Aloud

edge-speech is for speech synthesis, not for chat. Configure it through TTS Providers, not as your main chat provider.

OAuth-Based Providers ​

Most provider types use a normal API key. Two notable exceptions are openai-codex and github-copilot.

OpenAI Codex ​

  • Uses the openai-codex client type
  • Authenticates through the provider form's OAuth flow instead of a normal API key workflow
  • The bundled preset points at https://chatgpt.com/backend-api

This is a good fit when you want Codex-style model access for coding-oriented workflows.

GitHub Copilot ​

  • Uses the github-copilot client type
  • Uses device authorization
  • The provider form shows a verification URL and a user code while authorization is pending
  • After authorization completes, the provider stores the linked GitHub account token

GitHub Copilot is especially useful if you already have access to Copilot-backed chat and embedding models and want to reuse that access from Memoh.

Importing Models ​

After creating a provider, you can import or add models under it.

Typical flow:

  1. Select the provider.
  2. Click Import Models if the provider can expose a model catalog.
  3. Choose the models you want to save into Memoh.

You can also add models manually when you already know the upstream model ID.

Model Types ​

Memoh distinguishes three model types:

TypePurpose
chatMain LLMs for conversation, tool use, reasoning, and image generation
embeddingVector models for memory and retrieval
speechText-to-speech models used by TTS providers

Important distinction:

  • The Models page is primarily where you manage chat and embedding models.
  • speech models are exposed through TTS Providers.

Chat Model Configuration ​

When adding a chat model, the most important fields are:

FieldDescription
Model IDExact upstream identifier, such as gpt-4o or claude-sonnet-4.6.
NameFriendly display name shown in the UI.
CompatibilitiesFeature flags such as vision, tool-call, image-output, and reasoning.
Context WindowApproximate maximum context budget for the model.

Compatibilities ​

Memoh uses compatibility flags to decide which features a model can safely power:

CompatibilityMeaning
visionModel can accept images as input
tool-callModel can call tools
image-outputModel can generate images
reasoningModel exposes explicit reasoning modes / effort levels

If a model supports reasoning, it may also declare reasoning_efforts such as none, low, medium, high, or xhigh.

context_window ​

context_window is important because Memoh uses it to:

  • calculate session context usage in the Web UI
  • power /status output
  • decide when a session is approaching its prompt limit
  • guide Context Compaction

If you leave context_window empty, the model can still be used, but Memoh cannot show an exact usage percentage for that model.

Image Generation Models ​

Memoh now lets you assign an Image Generation Model to a bot. This model must be a chat model whose compatibilities include image-output.

That keeps image generation separate from your default chat model when needed.

Embedding Models ​

Embedding models are used for semantic indexing and retrieval.

The required field is:

FieldDescription
DimensionsVector size for the embedding output, such as 1536.

Use embedding models with memory providers or any feature that relies on vector search.

Speech Models ​

Speech models are managed from TTS Providers, not from the standard chat provider flow.

Current built-in example:

  • Edge TTS via edge-speech

This separation matters because speech models have voice, format, speed, and pitch settings that do not apply to chat or embedding models.

For most bots, think in terms of three parallel model roles:

  • Chat model for normal conversations
  • Embedding model for memory search
  • Speech / image models for side capabilities such as TTS and image generation

You do not need to force one model to do everything.

Next Steps ​

  • To assign chat, image, browser, memory, and TTS settings to a bot, see Bot Management.
  • To configure speech providers and speech models, see TTS Providers.

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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.

Published under AGPLv3

Copyright © 2024 Memoh

- + \ No newline at end of file diff --git a/getting-started/search-provider.html b/getting-started/search-provider.html index da0cea8d..fcf5a38e 100644 --- a/getting-started/search-provider.html +++ b/getting-started/search-provider.html @@ -9,9 +9,9 @@ - + - + @@ -20,7 +20,7 @@
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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 General 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.

Published under AGPLv3

Copyright © 2024 Memoh

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Sessions ​

A Session is an independent conversation thread between a user and a bot. Each session maintains its own context window and message history, allowing the bot to focus on a specific topic or task without interference from other conversations.

Concept: Conversation Isolation ​

When you chat with a bot, your messages are grouped into a session. The bot uses the session's history to maintain context. Starting a new session resets this context, giving you a fresh conversation without losing the previous one.

Sessions are scoped per bot — each bot manages its own set of sessions independently.

Session Types ​

Memoh uses five session types to separate different kinds of bot activity:

TypeDescription
ChatStandard user-initiated conversations. This is the default session type when chatting with a bot.
DiscussObservation-oriented conversation mode. The bot may stay silent by default and only speaks when it decides to send a real reply into the conversation.
HeartbeatAutomatically created when a bot's heartbeat triggers. Contains the bot's periodic autonomous activity.
ScheduleCreated when a scheduled task fires. Contains the bot's execution of a cron-triggered command.
SubagentCreated when the bot delegates a task to a subagent. Contains the subagent's independent work context.

Only Chat and Discuss sessions are directly created from user conversation routes. The other session types are system-managed and appear as read-only records in the session list.

Chat vs Discuss ​

chat and discuss are the two session types you are most likely to see in normal conversation threads.

Chat means:

  • the conversation behaves like a normal direct assistant exchange
  • users expect a visible reply when they send a prompt
  • this is the default in the Web UI and in direct-message style conversations

Discuss means:

  • the bot is observing an ongoing conversation, often in a group
  • the model's direct text output is treated as internal monologue
  • the bot only speaks to the conversation when it explicitly issues a send action
  • staying silent is valid and often desirable

In practice, discuss is what makes Memoh feel less like a synchronous chatbot and more like a participant that can decide whether to join in.

Starting a New Session with /new ​

The /new slash command creates a fresh session on the current conversation route, resetting the active session context without deleting old history.

Supported forms:

  • /new — create a new session using the default session type for the current context
  • /new chat — force a normal chat session
  • /new discuss — force a discuss session

Default routing behavior:

  • Web UI local chat defaults to chat
  • private conversations default to chat
  • group conversations on channel adapters default to discuss

/new discuss is not supported from the built-in Web UI local channel. Use a real channel adapter such as Telegram, Discord, or Misskey if you want to explicitly create discuss sessions.

This works across supported channels:

In External Channels (Telegram, Discord, Feishu, etc.) ​

Send /new, /new chat, or /new discuss as a message to the bot. The bot will:

  1. Create a new session of the requested or inferred type.
  2. Route all subsequent messages from you to this new session.
  3. The previous session's history is preserved but no longer active.

This is especially useful when:

  • You want to change topics without the bot referencing old context.
  • The conversation has become too long and you want a clean start.
  • You are switching between different tasks.

In the Web UI ​

The Web UI provides a session sidebar where you can:

  • Click the New Session button to create a fresh chat session.
  • Switch between existing sessions by clicking on them.
  • Search sessions by content.
  • Filter sessions by type (chat, discuss, heartbeat, schedule, subagent).
  • Rename or delete sessions.

Managing Sessions ​

Viewing Sessions ​

In the Web UI, the session sidebar lists all sessions for the currently selected bot. Each entry shows:

  • Title — The session name (auto-generated or user-defined).
  • Type — The session type icon.
  • Last Activity — When the session was last active.

For everyday chat use, chat and discuss sessions are intentionally shown together because they both represent user-facing conversation threads.

Renaming Sessions ​

Click on a session title to rename it. This helps organize conversations by topic.

Deleting Sessions ​

Remove sessions you no longer need. Deleting a session removes its message history permanently.

Session Status Panel ​

The session status panel provides a compact runtime summary for the active session. It is the same information surfaced by /status.

Key fields include:

  • Messages — total message count in the session
  • Context Usage — current used tokens relative to the selected model's context_window
  • Cache Hit Rate — how much of the input came from cache reads
  • Cache Read / Cache Write — token counts associated with caching
  • Skills — effective skills used by the session

The panel also exposes Compact Now, which triggers immediate Context Compaction for the current session.

How Sessions Relate to Other Features ​

  • Discuss sessions are optimized for channels where the bot should observe and selectively speak, especially in group conversations.
  • Heartbeat sessions are created on each heartbeat trigger. You can view what the bot did during its autonomous activity by opening the corresponding heartbeat session.
  • Schedule sessions are created when a scheduled task runs. Check these to see the results of cron-triggered commands.
  • Subagent sessions track delegated tasks. They show the independent work context of each subagent invocation.
  • Memory is shared across all sessions for a bot — memories extracted from one session are available in all others.

Published under AGPLv3

Copyright © 2024 Memoh

- + \ No newline at end of file diff --git a/getting-started/skills.html b/getting-started/skills.html index 54441486..e87ce7b5 100644 --- a/getting-started/skills.html +++ b/getting-started/skills.html @@ -9,9 +9,9 @@ - + - + @@ -27,7 +27,7 @@ # Coder Skill You write clear code, explain trade-offs, and use file or command tools when they help complete the task.

Practical rules:

  • Use a simple ASCII skill name such as coder-skill, research, or docs-helper.
  • Avoid spaces in the name; Memoh uses it as the skill directory name.
  • The Markdown body is the actual instruction content injected into the bot runtime.

Where Skills Come From ​

Memoh distinguishes between managed skills and discovered skills:

  • Managed skills are the ones you create, edit, or install through Memoh. They live under /data/skills/<name>/SKILL.md.
  • Discovered skills are found from compatibility locations inside the bot environment, such as legacy skill directories from imported images or older setups.

Memoh scans these container-internal roots in order:

TypeRoot
Managed/data/skills/
Legacy discovered/data/.skills/
Compatibility discovered/data/.agents/skills/
Compatibility discovered/root/.agents/skills/

Within each root, Memoh looks for SKILL.md either directly under the root or inside a named subdirectory such as /data/skills/coder-skill/SKILL.md.

This matters because the same skill name can appear from multiple sources. Memoh resolves those duplicates into states.

Skill States ​

Each listed skill source has one of these states:

StateMeaning
effectiveThis is the version currently active for that skill name
shadowedAnother source with the same skill name takes precedence
disabledThis specific source has been disabled and will not be used

The important mental model is: the skill name is the identity. If Memoh finds multiple coder-skill sources, only one can be effective.

Typical Examples ​

  • A skill you just created in Memoh is usually managed + effective.
  • A legacy skill can show up as effective until you create or adopt a managed skill with the same name.
  • After you adopt a discovered skill into the managed directory, the managed copy becomes effective and the old source usually becomes shadowed.
  • If you disable the effective source, another source with the same name may become effective automatically.

Managing Skills In The UI ​

Open a bot, then go to Skills.

Add Skill ​

  1. Click Add Skill.
  2. Fill in the raw Markdown in the editor.
  3. Save it.

Memoh writes the file into its managed skills directory.

Edit Skill ​

  • Use Edit on a card to update the raw SKILL.md content.
  • Editing is most useful for managed skills you own directly in Memoh.

Delete Skill ​

  • Delete removes the managed skill directory for that skill name.
  • Deleting a managed skill can expose a discovered fallback source with the same name, making that fallback become effective.

Disable / Enable ​

  • Disable turns off one specific skill source without deleting it.
  • Enable re-enables a previously disabled source.

Use this when you want to test a fallback or temporarily remove a skill from the prompt without losing its content.

Adopt ​

Adopt copies a discovered skill into Memoh's managed skills directory so you can own and edit it from the UI.

Use adopt when:

  • a skill came from a legacy or compatibility source
  • you want that skill to become part of your Memoh-managed configuration
  • you want to edit it safely in the UI later

Adopt is not available once a managed skill with the same name already exists.

Effective Skills At Runtime ​

Only effective skills are loaded into the bot runtime.

That means:

  • shadowed skills are visible for inspection, but not used
  • disabled skills are ignored
  • the active prompt only sees the current effective set

In active sessions, the Session Status Panel can also show which skills were used during that session.

Supermarket And Imported Skills ​

Two common ways skills appear without being typed manually:

  • Supermarket install: Memoh downloads the selected skill into the managed skills directory, so it behaves like a normal managed skill.
  • Imported / legacy environment: Memoh discovers existing skills from compatibility paths and shows them as discovered sources.

If a discovered skill is useful but you want to fully manage it in Memoh, adopt it.

  1. Start with a small number of focused skills.
  2. Prefer clear names and short descriptions.
  3. Use Disable before Delete if you are unsure.
  4. Use Adopt for legacy skills you plan to keep.
  5. Install reusable skills from Supermarket instead of copy-pasting them repeatedly.

Published under AGPLv3

Copyright © 2024 Memoh

- + \ No newline at end of file diff --git a/getting-started/slash-commands.html b/getting-started/slash-commands.html index 090d927e..303f945d 100644 --- a/getting-started/slash-commands.html +++ b/getting-started/slash-commands.html @@ -9,9 +9,9 @@ - + - + @@ -32,7 +32,7 @@ /schedule disable morning-news

/skill ​

Lists the currently available bot skills.

Actions:

ActionUsage
list/skill or /skill list

/fs ​

Browses the bot container filesystem.

Actions:

ActionUsage
list/fs list [path]
read/fs read <path>

Examples:

text
/fs list /
 /fs list /home
 /fs read /home/bot/IDENTITY.md

Read output is truncated when the file is very large.

Context Compaction Command ​

/compact ​

Triggers immediate session context compaction for the current session. This is different from memory compaction:

  • context compaction reduces the active prompt/history footprint of one session
  • memory compaction rewrites long-term memory entries in the memory provider

Actions:

ActionUsage
run/compact or /compact run

Use this when the current conversation has grown long and you want Memoh to summarize older turns before continuing. See Context Compaction.

Published under AGPLv3

Copyright © 2024 Memoh

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Supermarket ​

Supermarket is Memoh's built-in catalog for skills and MCP templates.

Install A Skill ​

  1. Open Supermarket in the Web UI.
  2. Switch to the Skills tab.
  3. Choose a skill and click Install.
  4. Select the target bot.
  5. Confirm the install.
  6. The skill appears in that bot's Skills tab.

Install An MCP Template ​

  1. Open Supermarket in the Web UI.
  2. Switch to the MCP tab.
  3. Choose an entry and click Install.
  4. Select the target bot.
  5. Memoh opens that bot's MCP tab with a prefilled draft connection.
  6. Fill in any required secrets or OAuth details.
  7. Save the connection.
  8. Probe it if you want to refresh tool discovery.

Contribute ​

Contribute new skills or MCP templates here:

Published under AGPLv3

Copyright © 2024 Memoh

- + \ No newline at end of file diff --git a/hashmap.json b/hashmap.json index 46924173..d0439e6a 100644 --- a/hashmap.json +++ b/hashmap.json @@ -1 +1 @@ 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diff --git a/index.html b/index.html index ecebadb1..d1948214 100644 --- a/index.html +++ b/index.html @@ -9,9 +9,9 @@ - + - + @@ -20,7 +20,7 @@
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Memoh Documentation ​

Memoh(/ˈmemoʊ/) is a multi-member, structured long-memory, containerized AI agent platform. Create bots with isolated workspaces, persistent memory, tool access, and channel integrations across Telegram, Discord, Lark (Feishu), QQ, Matrix, Misskey, DingTalk, WeCom, WeChat, WeChat Official Account, Email, and Web.

Start Here ​

Feature Guides ​

  • Channels — Platform overview and links to per-channel guides.
  • Access Control — ACL presets, rule ordering, and scoped access.
  • Skills — Managed skills, effective/shadowed states, and adoption workflow.
  • Supermarket — Install skills and MCP templates from the catalog.
  • MCP — Configure tool servers, OAuth, probing, import/export.
  • Memory — Long-term memory providers and memory compaction.
  • Context Compaction — Reduce active session context size without changing stored memory.

Published under AGPLv3

Copyright © 2024 Memoh

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Docker Installation ​

Docker is the recommended way to run Memoh. The stack includes PostgreSQL, the main server (with embedded Containerd and in-process AI agent), and the 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 and in-process AI agent
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 v0.6.0

Or using the environment variable:

bash
curl -fsSL https://memoh.sh | sudo MEMOH_VERSION=v0.6.0 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=v0.6.0 USE_CN_MIRROR=true sh

Manual Install ​

bash
git clone https://github.com/memohai/Memoh.git
+    
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Docker Installation ​
Docker is the recommended way to run Memoh. The stack includes PostgreSQL, the main server (with embedded Containerd and in-process AI agent), and the 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 and in-process AI agent
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. Detect whether this is a first-time install, an upgrade, or a reinstall
  3. Prompt for configuration (workspace, data directory, admin credentials, JWT secret, Postgres password, sparse service toggle, browser core selection)
  4. Reuse the existing config.toml automatically during upgrades so database credentials stay aligned with the persisted PostgreSQL volume
  5. Offer a clean reinstall mode that removes Memoh Docker containers, volumes, and network before starting again
  6. Fetch the latest release tag from GitHub and clone the repository
  7. Generate config.toml from the Docker template with your settings when needed
  8. Pin Docker image versions to the release
  9. Select and pull the prebuilt browser image for the chosen cores and start all services
  10. Print recent postgres and migrate logs automatically if startup fails
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
If the script detects an existing Memoh installation in silent mode, it defaults to upgrade and reuses the previous config.toml. If Docker state exists but no reusable config.toml can be found, the script exits and asks you to choose an explicit reinstall.
Force a clean reinstall (removes Memoh Docker data before starting again):
bash
curl -fsSL https://memoh.sh | sudo MEMOH_INSTALL_MODE=reinstall sh
You can also pass the install mode as an argument:
bash
curl -fsSL https://memoh.sh | sudo sh -s -- --install-mode reinstall
Install a specific version:
bash
curl -fsSL https://memoh.sh | sudo sh -s -- --version v0.6.0
Or using the environment variable:
bash
curl -fsSL https://memoh.sh | sudo MEMOH_VERSION=v0.6.0 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=v0.6.0 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
[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
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.
Configuration Reference ​
The config.toml file controls all server behavior. Here is a summary of the available sections:
SectionDescription
[log]Logging level and format (info, debug; text, json)
[server]HTTP listen address (default :8080)
[admin]Admin account credentials (username, password, email)
[auth]JWT secret and token expiration
timezoneServer timezone (default UTC)
[containerd]Containerd socket path and namespace
[workspace]Container image, snapshotter, data paths, CNI config, optional registry mirror
[postgres]PostgreSQL connection (host, port, user, password, database, sslmode)
[qdrant]Qdrant vector database connection (base_url, api_key, timeout)
[sparse]Sparse encoding service URL
[registry]Provider definitions directory
[browser_gateway]Browser Gateway host, port, and server address
[web]Web frontend host and port
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
+docker compose down -v         # Stop and remove Memoh Docker data
 docker compose logs -f         # View logs
 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. v0.6.0). Also pins Docker image versions.
USE_CN_MIRRORfalseSet to true to use China mainland image mirrors
BROWSER_CORESchromium,firefoxBrowser engines to include in the browser image
BROWSER_TAGlatestDocker tag for the browser image
Published under AGPLv3
Copyright © 2024 Memoh

-    
+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. v0.6.0). Also pins Docker image versions.
MEMOH_INSTALL_MODEautoInstall mode: auto, fresh, upgrade, or reinstall
USE_CN_MIRRORfalseSet to true to use China mainland image mirrors
BROWSER_CORESchromium,firefoxBrowser engines to include in the browser image
BROWSER_TAGlatestDocker tag for the browser image

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 4ff31650..db940e71 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 General 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 General 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 General 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 General 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.

Published under AGPLv3

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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 General 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.

Published under AGPLv3

Copyright © 2024 Memoh

- + \ No newline at end of file diff --git a/tts-providers/edge.html b/tts-providers/edge.html index e4e98f94..0732515d 100644 --- a/tts-providers/edge.html +++ b/tts-providers/edge.html @@ -9,9 +9,9 @@ - + - + @@ -20,7 +20,7 @@
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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 General 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.

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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 General 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.

Published under AGPLv3

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了解 Memoh ​

Memoh 是什么 ​

Memoh 是面向多成员、可结构化长期记忆、容器化运行的 AI 智能体平台。你可以创建多个机器人，为每个机器人提供独立工作区和长期记忆，通过 Telegram、Discord、飞书、QQ、Matrix、Misskey、钉钉、企微、微信、微信公众号、邮件或自带网页界面使用。

每个机器人有各自的运行环境、工具、记忆与渠道。用起来更像「每人一台小电脑上的智能体」，而不是所有人共用一条聊天人格。

和其它方案不一样在哪 ​

多机器人、多用户 ​

  • 一个账号里可建多个机器人，分角色或分场景用。
  • 私聊、群聊、委派的流程里，人和机器人都可参与。
  • 共享对话里能区分不同用户；跨渠道绑定身份后，同一个人可被稳定识别。

容器化工作区 ​

每个机器人在自己的容器里跑，有独立文件系统和网络边界。机器人在工作区内读写文件、跑命令、用工具，不会踩到别的机器人。

长期记忆与会话负担 ​

这是两件不同的事：

  • 长期记忆通过各记忆提供方存事实、跨会话检索。
  • 会话上下文压缩是在「当前这一串对话」太长时，用摘要把活跃窗口缩小。

注意：压缩会话上下文，改的是当前对话窗口；记忆压缩（在记忆 tab）改的是存下来的记忆条目本身。

会话与 Discuss 模式 ​

每个机器人有多路 会话，自带上下文。Memoh 里常见五类：

  • Chat：普通面向人的对话。
  • Discuss：偏观察；模型先在内心里组织语言，只有在你用发送类动作时才算真正对频道说话。
  • Heartbeat：按间隔自动跑的任务会话。
  • Schedule：由 cron 触发的任务会话。
  • Subagent：委派子智能体时产生的会话。

在渠道里可以用 /new 等切会话，网页端也有会话侧栏，可看上下文占用、缓存命中、用到的技能等。

渠道覆盖面 ​

用统一的渠道适配，让一个机器人能同时在多个地方被叫到。当前可对接例如 Telegram、Discord、飞书、QQ、Matrix、Misskey、钉钉、企微、微信、公众号、邮件、Web。

个人微信扫码与公众号 Webhook 是两套不同适配，别混用。

工具、技能、MCP、超市 ​

内置能力包括：网页搜索与拉取、浏览器自动化、工作区内改文件/跑命令、记忆检索与管理、发消息/邮件、TTS、子智能体、可复用 技能 模块、外部 MCP 服务、以及从 超市 装技能和 MCP 模板。

供应商与模型 ​

支持多种对接方式，例如：OpenAI 系 Chat/Responses、Anthropic Messages、Google、Codex、GitHub Copilot、Edge 朗读等。模型按 chat / embedding / speech 分角色。文生图走兼容的 chat/图像能力，不单独做一层「图像供应商系统」。

运维与界面 ​

网页端尽量把日常事做完：机器人各 tab、供应商与模型（含部分 OAuth）、会话里即时压缩与状态、技能显隐、渠道里用斜杠命令。不必天天手改配置文件。

从哪开始 ​

Published under AGPLv3

Copyright © 2024 Memoh

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钉钉 ​

企业内私聊、群聊；入站多走钉钉 Stream，出站走官方 API，媒体按平台能力来。

1. 建钉钉应用 ​

  1. 进组织对应的钉钉开放平台/开发者后台。
  2. 建或选用一个应用，打开 机器人/消息 等能力（以钉钉当前流程为准）。
  3. 拿到 App Key、App Secret，并按需在组织内 发布/授权 给使用的人。

2. 在 Memoh 里填 ​

  1. 机器人 Platforms → Add Channel → DingTalk。
  2. 填 App Key、App Secret。
  3. Save and Enable。

Memoh 会维护与钉钉的 stream 长连接。常规部署不用你再去配一层 webhook 回调地址（与飞书公众号那种不同）。

3. 验证 ​

私聊给机器人发一句，或在支持的群里 @ 它，看能否收、能否回。

支持的能力 ​

  • 私聊、群聊
  • 文本、类 Markdown
  • 回复
  • 附件/媒体

说明：在 Memoh 里，钉钉侧出站回复多为非流式（整段出）。

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Discord ​

接上 Discord 后，机器人可进服务器、在频道和私聊里说话。

1. 建 Discord 应用 ​

  1. 打开 Discord Developer Portal。
  2. New Application，起名。
  3. 左侧 Bot → Reset Token 得到 Bot Token，保管好。

2. 开特权意图 ​

在 Privileged Gateway Intents 里打开：

  • Message Content Intent
  • Server Members Intent
  • Presence Intent

保存。

3. 把机器人拉进服务器 ​

  1. OAuth2 → URL Generator。
  2. 勾选 scope：bot、applications.commands。
  3. 权限里至少：发消息、读历史、嵌链接、发附件等按需要勾。
  4. 用生成 URL 在浏览器里打开，选服务器并授权。

文档入口：Discord Developer Portal - Bots

4. 在 Memoh 里填 ​

  1. 机器人 Platforms → Add Channel → Discord。
  2. 贴 Bot Token。
  3. Save and Enable。

支持的能力 ​

  • 读完整消息正文。
  • 附件图片、文件。
  • 若用 MCP 等可扩展 slash；具体以你 Memoh 版本与配置为准。

Published under AGPLv3

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飞书（Lark） ​

通过飞书开放平台接企业内机器人在群/私聊里用。

1. 建飞书应用 ​

  1. 打开 飞书开放平台 并登录。
  2. 创建企业自建应用，填名与简介。
  3. 左侧 应用能力 或 凭证与基础信息 里取 App ID、App Secret。

2. 开机器人能力 ​

控制台 应用能力 → 机器人 → 启用。

3. 开权限 ​

应用能力 → 权限管理，至少可开（按组织要求可能需审批）：

  • im:message（收发信息）
  • im:chat（群信息等）

需时点 申请权限。

4. 事件：Webhook 入站 ​

  1. Memoh 里该机器人 Platforms → 加 飞书，入站方式 选 webhook。
  2. 填 App ID、App Secret。
  3. Save，页面上会出现 Webhook 回调 URL，复制。
  4. 飞书后台 应用配置 → 事件订阅。
  5. 把上面 URL 填到 请求地址 等对应栏位并保存。
  6. 订阅如 接收消息（im.message.receive_v1 等，以当前控制台为准）。

官方： 飞书自定义机器人

5. 发布 ​

应用发布 → 版本管理与发布，建版本、走审批/发布。通过后机器人才能在租户里正常用。

Published under AGPLv3

Copyright © 2024 Memoh

- + \ No newline at end of file diff --git a/zh/channels/index.html b/zh/channels/index.html index d064b51e..2be8d482 100644 --- a/zh/channels/index.html +++ b/zh/channels/index.html @@ -9,9 +9,9 @@ - + - + @@ -20,7 +20,7 @@
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渠道总览 ​

渠道把 Memoh 机器人和外面连起来；配好以后，你就能在常用 IM 里用机器人。

当前支持：

  • Slack：Socket Mode、频道与 thread、文件、反应。
  • Telegram：流式、附件、格式等较全。
  • 飞书：开放平台，偏企业流程。
  • Discord：服务器与私聊。
  • QQ：官方机器人平台，个人 DM 向。
  • Matrix：任意 homeserver，去中心化协议。
  • Misskey：回复、反应；偏文字社交。
  • 钉钉：企业私聊/群，流式入站。
  • 企微 WeCom：企业微信工作区。
  • 微信：个人号，扫码登录。
  • 微信公众号：服务号/公众号 webhook，偏私聊入站。
  • 邮件：SMTP、Mailgun、Gmail OAuth 等，在「邮件提供方」里配，见 邮件。
  • Web：自带网页聊天。

个人微信 和 公众号 ​

两套东西不要混：

  • 微信（weixin）：个人号扫码连上那种。
  • 公众号（wechatoa）：要 App ID、App Secret、Token，可选加解密；走平台回调 URL。

按你实际部署选一种。

一般怎么配 ​

  1. 在目标平台注册应用/机器人，拿 token、密钥等。
  2. 在 Memoh 里该机器人的 Platforms 里加渠道，按表单填。
  3. 保存并启用。

最后一步因平台而异：有的要把 回调 URL 粘到控制台，有的要 手机扫码，有的要 长连接/WebSocket 一直由 Memoh 维护。下面分平台见各篇。

Published under AGPLv3

Copyright © 2024 Memoh

- + \ No newline at end of file diff --git a/zh/channels/matrix.html b/zh/channels/matrix.html index 21f903e1..60ab39a3 100644 --- a/zh/channels/matrix.html +++ b/zh/channels/matrix.html @@ -9,9 +9,9 @@ - + - + @@ -29,7 +29,7 @@ }, "password": "<password>" }'
  • 或从客户端里导出/查看 token（以客户端安全提示为准）。

注意：谁拿到 token 谁就能以该账号操作，当密码保管。

2. 在 Memoh 里填 ​

  1. Platforms → Add Channel → Matrix。
  2. 填表：
字段必填说明
Homeserver URL是如 https://matrix.org
Access Token是机器人账号 token
User ID是如 @mybot:matrix.org
Sync Timeout否长轮询秒数，默认如 30
Auto Join Invites否被邀请是否自动进房，默认多开
  1. Save and Enable。

3. 拉机器人进房 ​

在 Element 等里邀请该 User ID，或私聊。若开了 Auto Join Invites，邀请会自动接受。

支持的能力 ​

  • 房间、私聊、流式、Markdown、媒体/附件等（以当前 Memoh 版本为准）。
  • 更多路线可见 相关 issue/路线图。

参考 ​

Published under AGPLv3

Copyright © 2024 Memoh

- + \ No newline at end of file diff --git a/zh/channels/misskey.html b/zh/channels/misskey.html index b1bd459d..233bc698 100644 --- a/zh/channels/misskey.html +++ b/zh/channels/misskey.html @@ -9,9 +9,9 @@ - + - + @@ -20,7 +20,7 @@
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Misskey ​

把 Memoh 接到某台 Misskey 实例，以 Misskey 账号收 mention、发回复。适合偏文字、社交向的玩法。

1. 账号和 Token ​

  1. 登录目标 Misskey 实例。
  2. 用（或专门建）一个代表机器人的账号。
  3. 给该账号生成 Access Token（各实例「设置 → API」等位置名称可能略有不同）。

需要交给 Memoh 的主要是：

  • Instance URL，如 https://misskey.io
  • Access Token

Token 要允许：读入站、发回复等（以你实例上的权限勾选项为准）。

2. 在 Memoh 里填 ​

  1. 机器人 Platforms → Add Channel → Misskey。
  2. 填 Instance URL、Access Token。
  3. Save and Enable。

3. 用起来 ​

启用后，用户可在该实例上 at/互动。Misskey 在 Memoh 里更偏：回复、文字与类 Markdown、带反应的会话。

支持的能力 ​

  • 文本、Markdown、回复、反应。

当前限制（以版本为准）：

  • 无附件/媒体上传类能力
  • 无流式逐字输出

Published under AGPLv3

Copyright © 2024 Memoh

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QQ ​

走 QQ 开放平台的官方机器人，可和用户在 QQ 里互动。

1. 建 QQ 机器人 ​

  1. 打开 QQ 机器人开放平台，用 QQ 登录。
  2. 创建机器人（创建机器人），一般无需审批；每号最多约 5 个。
  3. 记下 AppID、AppSecret。

注意：AppSecret 往往只显示一次，存好。再看一次常会被迫重置。

2. 在 Memoh 里填 ​

  1. 机器人 Platforms → Add Channel → QQ。
  2. 填 AppID、AppSecret。
  3. 可选：
    • Markdown Support：开 Markdown（默认多开）。
    • Enable Input Hint：是否显示「正在输入」类提示（默认多开）。
  4. Save and Enable。

3. 绑定身份（可选） ​

跨渠道认出同一人时，可绑 Memoh 账号：

  1. Profile → Bind Code → 选 QQ → Generate。
  2. 在 QQ 里给机器人私聊发绑定码。

支持的能力 ​

  • 文本、Markdown、附件。
  • 正在输入类提示（可关）。
  • 场景：C2C 私聊、群、频道等（以平台与版本为准）。

参考 ​

Published under AGPLv3

Copyright © 2024 Memoh

- + \ No newline at end of file diff --git a/zh/channels/slack.html b/zh/channels/slack.html index 3769a159..ec5d7a19 100644 --- a/zh/channels/slack.html +++ b/zh/channels/slack.html @@ -9,9 +9,9 @@ - + - + @@ -20,7 +20,7 @@
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Slack ​

在 Slack 里收 DM、进频道、跟 thread、读附件、发文件、流式回复。Memoh 的 Slack 适配用 Socket Mode。

1. 建 Slack App ​

  1. 打开 Slack API 控制台，Create New App。
  2. 选要装的工作区。
  3. 在 Basic Information 等页保留窗口，下面要填。

2. 开 Socket Mode ​

  1. 在 Settings → 找到 Socket Mode 并开启。
  2. 建 App-Level Token，Scope 勾 connections:write。
  3. 复制以 xapp- 开头的 App-Level Token。

3. Bot 权限（OAuth Scopes） ​

在 OAuth & Permissions → Bot Token Scopes 至少加当前适配会用的，例如：

  • app_mentions:read：频道里 @
  • channels:history / groups:history / im:history / mpim:history：读各场景消息
  • chat:write：在频道/线程/DM 里发
  • files:read / files:write：收、发文件
  • reactions:write：动反应

建议再加（名子、元数据更齐）：

  • channels:read / groups:read / im:read / mpim:read

4. 订事件 ​

Event Subscriptions 打开，Subscribe to bot events 里加，例如：

  • app_mention
  • message.channels / message.groups / message.im / message.mpim

以你装的 Memoh 版本在文档/发行说明里写的为准；上面是常见集合。

5. 装到工作区 ​

  1. OAuth & Permissions → Install to Workspace。
  2. 授权后复制 Bot User OAuth Token（xoxb- 开头）。

同一个 Slack App 里 的 xoxb- 与 xapp- 要配对使用。

6. 在 Memoh 里填 ​

  1. 机器人 Platforms → Add Channel → Slack。
  2. 填 Bot Token（xoxb-）、App-Level Token（xapp-）。
  3. Save and Enable。

7. 把 app 加进对话 ​

  • DM：在 Slack 里和该 app 开对话并发一条。
  • 公开频道：把 app 邀请进频道。
  • 私聊频道：装好后显式邀请进频道。

能发不能收图/文件，多半缺 files:read；能连上没人任何入站，查 Event 和 history 类 scope 是否配对。

支持的能力 ​

  • DM、公开/私有频道、thread
  • 读/发附件与图片

Published under AGPLv3

Copyright © 2024 Memoh

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Telegram ​

把 Memoh 机器人接到 Telegram。Memoh 对 TG 支持较全：流式、Markdown、附件等。

1. 在 Telegram 建 Bot ​

先要有一个 API Token。

  1. 在 Telegram 里搜官方 @BotFather。
  2. 发 /newbot。
  3. 按提示填：
    • Name：展示名，如 My Memoh Bot。
    • Username：全局唯一、以 bot 结尾，如 my_memoh_bot。
  4. BotFather 会给 API Token（形如 123456789:ABC...）。不要泄露。

官方说明：Telegram Bot Tutorial

2. 在 Memoh 里填 ​

  1. 网页里打开机器人 详情 → Platforms。
  2. Add Channel → 选 Telegram。
  3. 把 API Token 填进凭据。
  4. Save and Enable。

3. 绑定身份（可选） ​

把 Telegram 身份绑到 Memoh 账号后，跨渠道也能认出同一个人。

  1. 网页 Profile → Bind Code。
  2. 选 Telegram，点 Generate，复制码。
  3. 在 Telegram 里跟你的机器人私聊，把绑定码发过去。
  4. 机器人应回复绑定成功。

支持的能力 ​

  • 流式：边生成边出字。
  • Markdown：粗体、斜体、代码块、链接等。
  • 附件：收图/文件，机器人也可发文件。
  • 回复链：跟贴上下文能用于推理。

Published under AGPLv3

Copyright © 2024 Memoh

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微信公众号 ​

与 个人微信扫码 那套不同：这是 服务号/公众号的 webhook 接入，适合平台把消息推到你部署的 URL，多用在私聊入站场景。

1. 准备平台侧信息 ​

在微信公众号平台准备至少：

  • App ID
  • App Secret
  • Token

若开加密传输，还要 Encoding AES Key；出网要绕代理时可能有 代理 URL 等，按你部署来。

2. 在 Memoh 里加渠道 ​

  1. 机器人 Platforms → Add Channel → WeChat Official Account（名称以界面为准）。
  2. 填 App ID、App Secret、Token。
  3. 选 加解密方式（与微信后台一致）。
  4. 若用 safe / compatible 等要密钥的模式，再填 Encoding AES Key。
  5. 先保存渠道。保存后 Memoh 会生成 URL(服务器配置) 用的回调地址，复制到微信平台。

3. 在微信平台填回调 ​

  1. 把 Memoh 的 Webhook 回调 URL 粘到公众号后台「服务器配置」的 URL。
  2. Token、Encoding、EncodingAESKey 两边必须完全一致。
  3. 微信会先做一次验证，通过后才真正推消息。

4. 启用与试发 ​

  1. Memoh 里打开该渠道。
  2. 用测试号或正式号走一条私聊入站，看 Memoh 是否收到、机器人能否回。

支持的能力 ​

  • 私聊场景
  • 回复
  • 附件/媒体

注意（以当前适配为准）：

  • 不是群聊那种产品形态
  • 出站多 非流式

Published under AGPLv3

Copyright © 2024 Memoh

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企业微信（WeCom / WeWork） ​

在企微工作区内收发消息，常走管理后台里自建/机器人应用。

1. 建企微侧凭据 ​

  1. 登录 企微管理后台 或开发文档里指引的入口。
  2. 在 应用管理 等位置创建 自建应用 或 机器人（以你组织实际菜单为准）。
  3. 记下 AgentId / Bot ID 与 Secret 等（字段名以 Memoh 表单与企微当前文档为准）。

2. 在 Memoh 里填 ​

  1. 机器人 Platforms → Add Channel → WeCom。
  2. 按表单填，常见包括：
字段必填说明
Bot ID是企微里该机器人的标识
Secret是鉴权用
WebSocket URL否不填多用默认端点
  1. Save and Enable。

3. 使用 ​

连上后，工作区内用户可私聊或拉群与机器人说话，行为以你企微与 Memoh 当前版本为准。

支持的能力 ​

  • 文本
  • 私聊、群聊
  • 流式回复（在 Memoh 里多为实时出字，以实际为准）

参考 ​

Published under AGPLv3

Copyright © 2024 Memoh

- + \ No newline at end of file diff --git a/zh/channels/weixin.html b/zh/channels/weixin.html index c18badb0..d9a9af45 100644 --- a/zh/channels/weixin.html +++ b/zh/channels/weixin.html @@ -9,9 +9,9 @@ - + - + @@ -20,7 +20,7 @@
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微信（个人号） ​

个人微信发消息、收附件。Memoh 侧是 扫二维码 登录，不手填长串 token。

1. 在 Memoh 里加渠道 ​

  1. 机器人 Platforms → Add Channel → WeChat。

2. 扫码 ​

  1. 点 Start QR Login，Memoh 会拉取二维码。
  2. 手机微信扫页面上的码。
  3. 手机上点确认。
  4. 成功后会话凭据由 Memoh 存好，一般不用你复制 API key。

码有过期时间；过期了再点 Start QR Login 重刷。

3. 使用 ​

登录成功后渠道一般会自动启用。用户即可在微信里跟机器人私聊。

可选 ​

字段说明
Enable Typing生成中是否显示「正在输入」类状态

支持的能力 ​

  • 私聊文本
  • 图片、媒体等附件
  • 可选「正在输入」提示

Published under AGPLv3

Copyright © 2024 Memoh

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访问控制 ​

Memoh 用 ACL 控制谁能跟你的机器人说话。可在机器人 Access tab 里按人、按平台账号、按整类渠道设规则，并设优先级。

快速入门：ACL 预设 ​

建机器人时可选 ACL 预设，只是常见起步方式：

预设效果
allow_all默认 允许；来者不拒，除非你再加拒绝规则
private_only默认 拒绝；私聊放行（视具体实现与后续规则）
group_only默认 拒绝；群聊放行
group_and_thread_only默认 拒绝；群与 thread 放行
deny_all默认 拒绝；除 owner/管理员路径外都要你显式允许

预设只是起点，之后都在 Access 里改。

概念 ​

默认效果 ​

每个机器人有一个 default effect（allow 或 deny）：没有任一条规则命中时怎么办。

  • Allow：默认谁都能聊，除非你按规则挡。
  • Deny：默认都挡，只放行 owner、管理员和你写允许规则的对象。

主体类型 ​

主体说明
All所有入站消息，用来做全局放行/拒绝
Channel Identity某个外部平台上的具体身份（如某个 TG 用户）
Channel Type某整类渠道（如所有 Telegram 用户）

效果 ​

每条规则不是 Allow 就是 Deny。

优先级 ​

按列表自上而下匹配，第一条命中的生效：

  1. 机器人 owner、系统管理员 → 始终允许（绕 ACL）。
  2. 再按规则从上往下，找第一个主体匹配的发送者。
  3. 若都不匹配 → 用 默认效果。

所以顺序很重要：同一个人，上面一条 deny 会盖住下面一条 allow（若先匹配到 deny）。

管理 ​

建议流程 ​

  1. 建机时选一个预设。
  2. 打开 Access，确认 Default Effect。
  3. 只有在太宽或太窄时再加/调规则。

加规则 ​

  1. Add Rule。
  2. 选主体：All / 某平台账号 / 某渠道类型。
  3. 选效果 allow 或 deny。
  4. 可选 来源范围（把规则限在更细的上下文）：
    • 某条渠道配置
    • 会话类型：私聊 / 群 / thread
    • 会话 ID
    • 线索程 ID（通常要配合会话 ID）
  5. 保存。

排序 ​

可拖拽改优先级；越靠上越先判。改完记得 Save。

来源范围 ​

用来写细规则，例如：

  • 只允许从 Telegram 进，不允许 Discord
  • 某渠道类型只在群里挡
  • 某个群、某条 thread 单独管

范围大致是：渠道 → 会话类型 → 会话 ID → 线程 ID；后几级可选，但 Thread 一般要有 Conversation ID。

预设怎么选 ​

  • allow_all：开放试用、公开展示。
  • private_only：只打算私聊用。
  • group_only：只打算在共享房间用。
  • group_and_thread_only：要群/线程，不要私聊。
  • deny_all：敏感用例，逐个加白名单。

吃不准时，个人测试机可 allow_all，敏感环境可 deny_all 再白名单。

例子 ​

谁都能聊 ​

  1. 预设 allow_all，或默认效果 allow。
  2. 不加规则也行。

只给少数私用 ​

  1. 预设 deny_all 或默认 deny。
  2. 对要放行的人加 allow 的 Channel Identity。

公开但拉黑几个人 ​

  1. 预设 allow_all 或默认 allow。
  2. 在列表上面加 deny 给那几个人（先判 deny）。

只开某一整平台 ​

  1. 从 deny_all 或 private_only 等起步。
  2. 加 Channel Type 的 allow，例如只开 telegram。

只让某人在某一渠道聊 ​

  1. 给该人 allow。
  2. 来源范围 里把渠道指到具体那条 Telegram/Discord 配置。

查为什么判成这样 ​

  • 看 Access 里规则顺序和默认效果。
  • 用 /access 看当前身份、角色、ACL 判定上下文。

多平台身份绑在一起、或群/thread 判晕时，这条特别有用。

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机器人 ​

机器人是 Memoh 里独立的智能体：自带容器、长期记忆、可配性格，并能通过各 渠道 对话、用工具做事。

创建 ​

  1. 侧栏进入 Bots。
  2. 点 Create Bot。
  3. 基本信息：
    • Display Name：对外的名字
    • Avatar：头像 URL
    • Timezone：可空；不填则继承用户或系统时区
    • ACL Preset：如 allow_all 只给自己用、private_only 等快捷策略
  4. 创建。

详情页 ​

点卡片进 详情，各 tab 管不同事：

Tab内容
Overview容器、库、渠道、记忆等健康检查
General主模型/标题/生图、记忆/搜索/浏览器/TTS、时区、语言、推理、危险区
Container起停、快照、导入导出
Memory浏览、搜、建、改、压记忆
Platforms各消息渠道（Telegram、Discord、飞书等）
AccessACL 与默认通过/拒绝
Email邮服绑定、发件箱
Terminal进容器 shell
Files容器内文件管理
MCP连接（Stdio/Remote/OAuth）
Heartbeat心跳间隔、模型、执行日志
Compaction会话压缩设置与记录
Schedulecron 与日志
Skills技能 Markdown

核心先配什么 ​

  1. 打开机器人 General，先管模型与各类绑定。
  2. Heartbeat 管周期自主跑。
  3. Compaction 管会话写不长时的压缩。
  4. Access 在 ACL 预设之后细调。

若这些资源还没有，先建好：

General 字段 ​

字段说明
Chat Model主对话模型
Title Model可选，生成会话标题
Image Generation Model可选，需带 image-output 的聊天模型
Memory Provider长期记忆后端；内置类型还可自带记忆/向量模型
Search Provider联网搜索用哪家
TTS Model来自 TTS 流，不是普通 chat 供应商里选
Browser Context自动化上网用的浏览器配置
Timezone不填则用户时区再落到系统
Language机器人主用语
Reasoning Enabled当前 chat 模型有 reasoning 时可用
Reasoning Effortlow / medium / high

注意：

  • 生图模型 故意与主聊天模型分开，好单独换「更擅长出图」的。
  • TTS 在 TTS 提供方 里用 speech 模型，例如 Edge。
  • context_window 会影响状态栏展示和 会话压缩 的体感。

Heartbeat 字段 ​

字段说明
Heartbeat Enabled开不开周期自主
Interval多少分钟一次
Heartbeat Model可与主 chat 不同

同 tab 可看各次执行日志。

Compaction 相关（此处指「会话」） ​

这里说的是 当前会话 的上下文压短，不是改记忆条目的那种。

字段说明
Compaction Enabled是否自动在会话里压摘要
Compaction Threshold触发的估算 token 阈值
Compaction Ratio压多狠
Compaction Model可选，专门做摘要的模型

细节见 会话上下文压缩。

访问与 ACL ​

创建时先给一个 ACL 预设，之后在 Access 里微调。预设 给一版默认策略，Default Effect 管「没命中规则时」放行还是挡。

会话 与 Discuss 的默认行为在那一页。若你用 API/自动化，配置里还可能有 discuss_probe_model_id 等进阶项，日常创建不必先动。

终端 ​

Terminal tab 开交互 shell，可多 tab；容器在跑时才能用。

删除 ​

General 最下 Danger Zone → Delete Bot，会删掉该机器人相关数据（含容器文件与记忆等），不可恢复。

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浏览器上下文 ​

Memoh 通过 Browser Gateway 给机器人无头浏览器能力。Browser Context 里存视口、语言、时区、是否模拟手机等。绑到机器人后，可用工具打开页面、点按、填表、截图、读 DOM 等。

Browser Gateway ​

基于 Playwright。一个 context 像一份可复用的「浏览器侧配置」。

适合：跳站、点链、填表、读渲染后内容、出图或 PDF 等。

建上下文 ​

侧栏 Browser Contexts：

  1. Add Browser Context
  2. 填 Name
  3. Create

配字段 ​

字段说明
Name展示名
Corechromium（默认）或 firefox
Viewport Width / Height像素
User Agent可选
Device Scale Factor可选 DPR
Locale如 en-US、zh-CN
Timezone ID如 UTC、Asia/Shanghai
Is Mobile手机行为
Ignore HTTPS Errors坏证书站是否还进

可 Edit、Delete。

绑到机器人 ​

  1. Bots → 打开机器人
  2. General
  3. Browser Context 下拉选
  4. 保存

之后调浏览器工具时会用这个 profile。

机器人侧 ​

常见内置如：

  • browser_action：导航、点击、填表、选、滚、多 tab、截图、PDF 等
  • browser_observe：看当前页，给模型用

让机器人能操作真页面，而不只有静态 HTML 或搜索摘要。

浏览器核 ​

镜像里可带 Chromium、Firefox 或两者。构建时由 BROWSER_CORES 决定。一键安装脚本里会问；手搓例如：

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

合法值：chromium、firefox、chromium,firefox（默认组合）。

接下来 ​

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机器人的渠道 ​

渠道把机器人接到各消息平台，让你用熟悉的 App 跟它说话。

统一接入 ​

Memoh 在中间做一层适配；一个机器人可以同时挂在 Telegram、Discord、Matrix 等多个平台。

支持哪些平台 ​

在机器人 Platforms tab 里配。分平台步骤见 渠道 下各篇：

平台指南备注
TelegramTelegram附件、流式较好
飞书飞书可走 webhook 入站
DiscordDiscord社群、服务器
QQQQ偏个人 DM
MatrixMatrix自建 homeserver
MisskeyMisskey回复、反应；无流式
钉钉钉钉企业私聊/群
企微企微企业微信工作区
微信微信个人扫码登录
公众号公众号公众号 webhook
SlackSlack有 thread；无流式

个人 微信 和 公众号 是两套适配，别混用。

配置流程 ​

1. 添加 ​

Add Channel → 选平台 → 按表单填（随平台变）。

2. 常见字段 ​

字段说明
Credentials各平台给的 token、密钥、机器人 key 等
Disabled不删配置，只关掉
Routing平台侧线程/会话与 Memoh 的对应关系

3. 飞书 Webhook 入站 ​

webhook 入站时，Memoh 会生成 Webhook 回调 URL，贴到飞书应用事件里，由飞书把消息推过来。

4. 个人微信 ​

启用后走扫码，用微信扫完连上。

5. 公众号 ​

先保存渠道，用 Memoh 给的 回调 URL 配到公众号后台；Token、加解密方式、可选 AES 要与 Memoh 里一致。

6. 钉钉 Stream ​

填 App Key / App Secret，保存并启用；标准用法下由 Memoh 维护长连接收事件，不必自己再管一层 webhook。

操作 ​

  • Save：改配置
  • Save and Enable：保存并立刻启用
  • 启停开关：不删配置地开/关
  • Delete：删掉该渠道配置

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会话上下文压缩 ​

上下文压缩只针对当前这一会话：把早先轮次压成摘要，让后面模型调用时带的活跃窗口小一点。

这和改记忆库里存的长记忆不是一码事。要动存储条目，看 长期记忆。

为什么需要 ​

对话一拉長，回给模型的历史就膨胀：token、延迟、吃满 context 都难受，老内容还可能挤掉新内容。压缩用摘要换掉一部分细节，还保留点连贯性即可。

它动什么、不动什么 ​

动的是本会话的活跃上下文。

不动：

  • 不删机器人本身
  • 不换记忆提供方配置
  • 不合并长期记忆条目的主流程（那是记忆 tab 里另一类 compaction）
  • 不替代「去记忆库里搜」

自动 ​

在 General（或你版本里放压缩设置的地方）里配：

字段说明
Compaction Enabled开不开自动压
Compaction Threshold估算超多少 token 就触发后台压缩
Compaction Ratio压多狠
Compaction Model用谁写摘要，可与主 chat 不同

打开后，Memoh 在某一轮之后，若估摸输入已超阈值，会后台做压缩。context_window 会参与「快满了」的感觉。

立刻压 ​

两路：

状态区 ​

  1. 打开当前对话。
  2. 打开会话状态区。
  3. 点 Compact Now。

上面也有上下文占用、缓存、技能等，方便你判断要不要现在压。

斜杠命令 ​

text
/compact

或

text
/compact run

同步压当前会话，结果会回到聊天里。

记录 ​

机器人详情 Compaction tab 可看各次：成功/失败、摘要或预览、涉多少条消息、起止时间、若有的模型与用量。用来确认自动有没有在跑、失败原因等。

和 context_window ​

Memoh 会拿当前选中的 chat 模型的 context_window 对照本会话。网页状态区、/status 都能看到离上限多近。越满，专门建一个便宜点的压缩模型写摘要，往往越划算。

和「记忆压缩」的区别 ​

作用范围怎么触发结果
上下文压缩当前活跃会话状态区 / /compact / 自动本会话里较早内容变摘要，方便后面几轮继续聊
记忆压缩长期记忆提供方Memory tab改库里记忆条目，不是单会话 prompt

一路聊太长了 → 上下文压缩。
存下来的记忆又脏又重 → 去 Memory 里做那类维护。

接下来 ​

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容器 ​

每个机器人在自己的容器里跑，文件系统、网络边界都隔开，互不影响，也跟宿主机隔离。

是什么 ​

可以把它想成机器人私用的一台小电脑：能存文件、装包、跑脚本、跨会话留状态。

操作 ​

在 Container tab。

生命周期 ​

  • Create：没有就按镜像建；拉镜像、建实例时会有 SSE 进度。
  • Start：要跑文件工具、终端等，多半要先起来。
  • Stop：省资源，优雅停。
  • Delete：删实例（数据行为视版本与设置而定，以界面为准）。

信息 ​

会显示如：容器 id、状态、用的镜像、宿主机/容器路径、后台任务数、若配了 CDI 设备（常见 GPU）也会列出来。

进阶：CDI 设备 ​

要把宿主机通过 CDI（常见是 GPU）透进容器，在 Container → Advanced 里配。一般只有确实要在里面跑 CUDA/ROCm 等才要动。

配法 ​

  1. 打开 Container。
  2. 没有容器先 Create；要改 GPU 类设置往往要重建容器。
  3. 展开 Advanced。
  4. 开 GPU，在 CDI devices 里写设备名。

可每行一个或逗号分隔，例如：

  • nvidia.com/gpu=0
  • nvidia.com/gpu=all
  • amd.com/gpu=0
  • amd.com/gpu=all

宿主要求 ​

宿主机上驱动、厂商工具、CDI spec 要已就绪。通常意味着：

  • 宿主机上 GPU 本来就能用
  • /etc/cdi 或 /var/run/cdi 里有 spec
  • 你填的名字和运行时看见的一致

查本机名：

  • NVIDIA：nvidia-ctk cdi list
  • AMD：amd-ctk cdi list

若报 unresolvable CDI devices，多半是名字对不上。

注意 ​

  • CDI 在创建时生效，改配置后常要重建容器；只停再起不会换已挂设备。
  • 镜像里仍要装对的用户态库，才能真跑算子。
  • 建好后 Container tab 会显示当前挂上的设备，便于核对。

快照 ​

Create Snapshot 把当前环境状态勾下来，方便回滚、试大改。
Restore 按某个快照回退。可删不要的快照。

导入导出 ​

  • Export Data：把容器内数据打成包下载。
  • Import Data：从本地上传打进文件系统。

Restore（数据侧） ​

Restore 在数据目录侧做「清到干净再灌」，适合盘坏了或想从零来而又不删容器实例时，以界面说明为准。

版本 ​

会跟 Current Version、Version History 等，方便审计环境何时、因何变过。

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邮件 ​

让机器人收/发邮件，分两步：邮服提供方 + 机器人上的 Email 绑定。

在做什么 ​

  1. 先配能连上的 Email Provider（如 Mailgun、泛用 SMTP 等）。
  2. 再在某个机器人上，把某邮箱绑定过去，并设读/写/删权限。

邮服 ​

侧栏 Email Provider。

新建 ​

  1. Add Email Provider
  2. 类型如 Mailgun（量大）、泛用 SMTP（传统邮局）
  3. 按表单填 domain/api_key 或 host/port 等
  4. 创建

机器人上的绑定 ​

机器人 Email tab。

添加 ​

  1. Add Binding
  2. 选已建的 Email Provider
  3. 填要绑的 Email Address
  4. 勾权限：
    • Can Read 收信、处理入站
    • Can Write 发信
    • Can Delete 管邮箱里删除等（视实现）
  5. 创建

发件箱 ​

Outbox 会记发出的邮件：收信人、主题、状态、时间，便于对账、排错。

和机器人 ​

  • 有权限时可用邮件发报告、回邮、或按新邮件做事。
  • 和聊天一样，是另一条通道，但仍是结构化、可审的。

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文件 ​

每个机器人在容器里有一份独立盘。在 Files tab 里直接管。

能做什么 ​

带工具栏、目录树、编辑器的 FileManager 常见能力：

浏览 ​

  • 面包屑上下级
  • Refresh 看机器人刚写的文件
  • New Folder 建目录

文件 ​

  • Upload 本机文件打进容器
  • Rename / Delete（目录可递归删）
  • Download 拉回本机

看与改 ​

FileViewer 里：

  • 文本类（如 .md、.js、.py、.toml）用 Monaco 打开，高亮、保存回写盘。
  • 图（.png 等）可预览。

和机器人 ​

机器人自己也能用技能、MCP 等改这些文件；Files 是你肉眼查看、手改的入口。

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心跳 ​

Heartbeat 让机器人按固定间隔自己跑一轮，不依赖你一直发消息。适合巡检查状态、收数据、清盘、发提醒等。

是什么 ​

到点触发一次，给机器人一句「例行走一圈」的语境，它可以用技能、MCP、工具。间隔用分钟计。

配置 ​

Heartbeat tab：

字段说明
Enabled开/关
Interval隔多少分钟，默认常是 30
Model用哪一聊天模型跑；可与主 chat 不同

日志 ​

每次执行有记录：状态（ok / alert / error）、时间、耗时、结果摘要 等。可按状态筛、刷新、清、翻页更多。

和机器人 ​

  • 心跳轮会带专门系统向的提示，让模型做「例行」事。
  • 技能、MCP 照常可用。
  • 日志就是自主行为留下的线索。

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MCP 连接 ​

Memoh 支持 Model Context Protocol (MCP)，让机器人接外部工具与上下文服务。每个机器人可以有自己的一组 MCP 连接。

在做什么 ​

MCP 把「外部数据、外部工具」用相对统一的方式接进来。你在机器人 MCP tab 里手配，或从 超市 装模板再进编辑器改。

连接类型 ​

1. Stdio（本机进程） ​

在机器人容器里起本地命令，用标准输入输出通信。

字段说明
Command可执行文件，如 npx、python3
Arguments参数列表
Env环境变量
CWD工作目录

2. Remote（HTTP/SSE） ​

走网络的远程 MCP。

字段说明
URL服务端点
Headers如鉴权头
Transporthttp 或 sse

OAuth ​

有的 MCP 服要走 OAuth。Memoh 会：

  1. 在需要认证的连接上点 OAuth。
  2. 自动 discover 服务方 OAuth 配置。
  3. Authorize 打开授权页。
  4. 用户同意后回跳，Memoh 换 token 并安全存好。

也可看状态、Revoke 清掉 token。

操作 ​

  • Add Connection：加一条，选类型，填表。
  • Import JSON：从标准 mcpServers JSON 一次导入多条。
  • Export：导出备份或分享。
  • Toggle Active：不删，只开/关某条。
  • Search：按名或 id 找。
  • Batch Delete：多选删除。

工具发现 ​

连接 active 后，Memoh 会拉该服暴露的工具。点进连接可看 Tools 列表。需要时点 Probe 手动刷新。

和机器人怎么配合 ​

连上后，对话里模型会按说明选用这些工具；发现到的工具会进推理流程。具体工具名、参数以服端为准。

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长期记忆 ​

Memoh 的结构化长期记忆让机器人在多路会话里也能用上以前留下的事实。用 Memory tab 前，先给机器人配好 Memory Provider。

先决条件 ​

  1. 在 记忆提供方 里建一个（内置、Mem0、OpenViking 等）。
  2. 打开机器人 General。
  3. Memory Provider 里选中。
  4. 保存。

没选提供方，就没有正在使用的记忆后端。

在做什么 ​

记忆条目的存、取、搜都由当前提供方实现；按类型和模式，可能是文件索引、稀疏向量、稠密向量、或外接 API。用户发消息时，Memoh 会尝试取出相关记忆塞进当次上下文。

本页说 长期记忆；会话写太长 要压短，是另一件事，见 会话上下文压缩。

在界面里 ​

建记忆 ​

  • New Memory：手打一条。
  • From Conversation：从已有对话里抽成记忆。

搜与管理 ​

  • Search：按 id 或文字筛。
  • Edit：改内容。
  • Delete：不要就删。

记忆压缩（注意：不是会话压缩） ​

积累多了可以在 Memory tab 对存储侧做 Compact：合并重复、去陈旧、压噪声。有 Ratio、Decay Days 等参数。

这和 会话上下文压缩 不同：后者是单路会话里把 prompt 压短，不改库里长期记忆条目的存法与合并结果。

Rebuild ​

Rebuild 会按当前设置整库重索引。换模式、索引乱了、想全量用新设置跑一遍时有用。页面上可跟进度。

状态 ​

Memory tab 会显示当前该机器人的记忆后端的 Connected / Error，排障时先看一眼。

用量 ​

可看 总条数、索引是否跟上 等，心里有数再调策略。

和「会话里那段对话」的对比 ​

概念作用范围在哪动改变什么
记忆压缩长期记忆提供方Memory tab改存储里条目怎么合并/精简
上下文压缩当前这一路会话状态区或 /compact用摘要把本会话历史变短，给后面轮次用

要减「跨会话噪声」、整理记忆库 → 用 记忆压缩。
要减「这一路聊太长了」→ 用 会话上下文压缩。

和机器人的关系 ​

  • 聊的时候会按配置去搜记忆。
  • 具体用哪种后端、什么模式、embedding 等，在提供方上配，见 记忆提供方。
  • 长期记忆是机器人「个性与事实」里很大一块来源。

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供应商与模型 ​

日常用 Memoh，多半要配好：

  • 一个或多个 供应商（怎么连上游 API）
  • 其下的 模型
  • 若要朗读，再配 语音相关（见 TTS）

聊天与 embedding 在 Models 页管理；语音模型在 TTS 流程里单走。

供应商基础 ​

供应商里存的是某一类上游的连法，例如：

  • 协议（client_type）
  • 需要时的 base URL
  • API Key 或 OAuth 等凭据

常见有 OpenAI 兼容站、Anthropic、Google、Codex、GitHub Copilot 等。

新建供应商 ​

  1. 侧栏打开 Models。
  2. 点 Add Provider。
  3. 按表单填完保存。

常用字段：

字段说明
Name展示名，如 OpenAI。
Client Type本供应商用的协议。
Base URL部分协议必填的根地址。
API Key走密钥时填。

客户端类型 ​

Client Type常见用途
openai-responsesOpenAI Responses 风格
openai-completionsChat Completions 兼容
anthropic-messagesAnthropic Messages
google-generative-aiGoogle Gemini
openai-codexCodex / ChatGPT 那套，OAuth
github-copilotCopilot，设备码 OAuth
edge-speech仅朗读，走 Edge

edge-speech 不能当主聊天用，请走 TTS 提供方。

走 OAuth 的供应商 ​

多数类型用普通 API Key。openai-codex 和 github-copilot 例外。

OpenAI Codex ​

  • 类型选 openai-codex
  • 在供应商表单里走 OAuth，不填普通 key
  • 预置会指向 https://chatgpt.com/backend-api

偏写代码、走 Codex 那套时合适。

GitHub Copilot ​

  • 类型 github-copilot
  • 设备码 授权
  • 等待时界面会给验证 URL 和用户码
  • 结束后存 GitHub 侧 token

你本来就有 Copilot 时，可复用进 Memoh。

导入模型 ​

建完供应商后可以导入或手加模型。常见：选中供应商 → Import Models（若上游有目录）→ 勾要保存的。已知上游 id 时也可手填。

模型类型 ​

类型用途
chat对话、工具、推理、文生图等
embedding向量化、记忆检索
speech朗读，挂在 TTS

Models 页主要管 chat / embedding；speech 在 TTS。

聊天模型上要注意的项 ​

字段说明
Model ID上游真实 id，如 gpt-4o。
Name界面展示名。
Compatibilities如 vision、tool-call、image-output、reasoning。
Context Window粗算上下文上限。

兼容性 ​

标记含义
vision能吃图
tool-call能调工具
image-output能出图
reasoning有显式推理/档位

有推理时可能还带 reasoning_efforts：none、low…xhigh 等。

context_window ​

Memoh 用来：

  • 在网页上算当前会话占了多少上下文
  • 驱动 /status 等
  • 判断是否逼近上限
  • 决定何时需要 会话上下文压缩

不填也能用，但百分比会没法精确给。

文生图模型 ​

机器人上可单挂 Image Generation Model，须是带 image-output 的 chat 模型。需要时与默认聊天模型分开。

Embedding 模型 ​

给语义索引用。必填如 Dimensions（向量维数，如 1536）。和记忆或其它向量检索能力绑在一起用。

语音模型 ​

在 TTS 提供方 配，不跟普通 chat 供应商混流。例如 Edge TTS 走 edge-speech。语音还有音色、格式、语速、音高，和 chat/embedding 不是一路设置。

怎么记省事 ​

对多数机器人，可以分三条线想：

  • Chat：日常说人话
  • Embedding：记忆
  • Speech / 生图模型：边能力

不必强行一模型全包。

接下来 ​

  • 给机器人绑聊天、生图、浏览器、记忆、朗读等：机器人
  • 配语音提供方与语音模型：TTS 提供方

Published under AGPLv3

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- + \ No newline at end of file diff --git a/zh/getting-started/schedule.html b/zh/getting-started/schedule.html index ad0e9c9a..130e93f6 100644 --- a/zh/getting-started/schedule.html +++ b/zh/getting-started/schedule.html @@ -9,9 +9,9 @@ - + - + @@ -32,7 +32,7 @@ "enabled": true, "max_calls": null }

（字段名以当前 API 为准。）

执行时发生什么 ​

  1. cron 到点
  2. current_calls +1
  3. 若设了 max_calls 且到顶，任务自动关
  4. 智能体收 command 与计划上下文
  5. 用工具发消息/搜网页等
  6. 结果可发到已连接渠道

和 Heartbeat 对比 ​

ScheduleHeartbeat
触发cron，时间点灵活固定间隔（分钟）
内容你写的一条自然语言指令偏「例行走一圈」的通用轮询
次数上限可设无上限那层概念
适合定点的具体事笼统的周期自检

Heartbeat 当「常转转」Schedule 当「几点要干啥」。两者可一起用。

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搜索提供方 ​

给机器人接 Search Provider 后，它能在对话里实时查网，用搜索引擎 API 当工具。

在做什么 ​

每个 Search Provider 是某一家的搜索 API（如 Brave、Google）。配好并绑到机器人后，需要新鲜事实时由模型去调。

支持哪些 ​

侧栏 Search Provider 里建。常见能力包括（以你装的版本为准）：

引擎说明
Brave隐私和速度常被提及
Bing覆盖面大
Google传统网页搜
Tavily偏 AI 研究向
SearxNG自建聚合
DuckDuckGo偏隐私
其它如搜狗、Serper、Jina、Exa、Bocha、Yandex 等

建一个 ​

  1. 侧栏 Search Provider。
  2. Add Search Provider。
  3. 填 Name、API Key，有的引擎还要 base_url（如 SearxNG）。

Edit 改、Delete 删。

绑到机器人 ​

  1. Bots → 机器人
  2. General
  3. Search Provider 下拉选中
  4. 保存

和机器人 ​

需要较新、较贴问题的公开信息时，会走搜索，再把结果融进回答；可多结果综合。具体策略由模型和工具设计决定。

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会话 ​

会话是用户与机器人之间的一路独立对话。每路有自己的上下文与历史，换话题或任务时常开新会话。

为什么要隔离 ​

你和机器人聊的内容都落在当前会话里；新开会话会换一块上下文，老记录还在，只是不再参与当前这一路的推理。

会话按机器人分；每个机器人各自一摞会话，互不掺。

会话类型 ​

类型说明
Chat最常见的用户发起对话，默认就它。
Discuss偏「旁观」：机器人默认可以不吱声，把模型输出当内心戏，只有用发送类动作时才算对频道真说话。
Heartbeat心跳触发生成，记自主行为。
Schedulecron 触发。
Subagent子智能体被委派时。

你日常在聊拉里直接看到的，多半是 Chat 和 Discuss；后三种多由系统建，在列表里像只读记录。

Chat 和 Discuss 差在哪 ​

Chat：

  • 像普通助理来回问
  • 用户发一句，通常就期待有一条可见回复
  • 网页、私聊默认多落在这类

Discuss：

  • 常出现在群里：机器人看着大家聊
  • 直接文本多算内部独白
  • 真发到频道要显式 send 之类动作
  • 保持沉默是正常选项

有 Discuss，机器人才更像「要不要接话我自己决定」的群成员，而不是每句都回的客服。

/new 开新会话 ​

同一路由下用 /new 可开新会话，不删老历史，只是当前换一块上下文。

  • /new：按当前场景默认类型
  • /new chat：强制普通 chat
  • /new discuss：强制 discuss

网页本地频道 默认 chat；私聊 多 chat；外接群聊 多 discuss。

内置网页本地 不支持 /new discuss，要 discuss 请用 Telegram、Discord、Misskey 等真实渠道。

在渠道里发这些命令会：建新会话、后续消息都进这路、旧会话保留但不再当「当前」。

网页 里也可用侧栏 New Session、切换、搜索、按类型筛、重命名、删除等。

管理 ​

列表 ​

当前机器人的会话在侧栏；每项可见标题、类型图标、最近活跃时间。日常你关心的 Chat / Discuss 会混在同一列表，都是「人能看到的那类线程」。

重命名 ​

点标题可改，方便按题目标注。

删 ​

删会话会永久去掉这一路的历史。

状态区 ​

状态区和 /status 是同一份信息：本会话消息数、上下文占用（相对 context_window）、缓存命中、读写量、本路用到的技能 等。这里有 立即压缩，触发的是 会话上下文压缩，不是改记忆条目的那种。

和其它功能的关系 ​

  • Discuss 给「群里多看少说」用。
  • Heartbeat 每触发一次，会有一条对应当次自主行为的会话，可点开看做了啥。
  • Schedule 到点也会生成会话，可看 cron 命令跑的结果。
  • Subagent 各自会话，跟委派任务对齐。
  • 长期记忆在机器人整级共享，从哪一路抽出来，别路也能检索到（在你配置允的前提下）。

Published under AGPLv3

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- + \ No newline at end of file diff --git a/zh/getting-started/skills.html b/zh/getting-started/skills.html index b5113c71..fc6c6e16 100644 --- a/zh/getting-started/skills.html +++ b/zh/getting-started/skills.html @@ -9,9 +9,9 @@ - + - + @@ -27,7 +27,7 @@ # Coder Skill 你写清楚代码、说明取舍，该用文件/命令工具时就用。

建议：

  • 名字用简单 ASCII，如 coder-skill、research，别带空格；Memoh 会拿它当目录名。
  • 正文就是真正要注入到运行时的说明。

从哪来 ​

分 托管 和 发现：

  • 托管：你在 Memoh 里建、改、从超市装的，放在 /data/skills/<name>/SKILL.md。
  • 发现：容器里旧目录、导入镜像里带来的兼容路径。

Memoh 按顺序扫这些根：

类型根路径
托管/data/skills/
旧版发现/data/.skills/
兼容/data/.agents/skills/
兼容/root/.agents/skills/

每个根下可以直接放 SKILL.md，或子目录如 /data/skills/coder-skill/SKILL.md。

同名技能可能多份来源，会进入不同状态（见下）。

状态 ​

每个来源会标：

状态含义
effective这个名字当前真在用的这一份
shadowed同名有别的来源优先了
disabled这一份被关掉，不用

记死一点：技能名是身份。多个 coder-skill 只能有一个 effective。

常见情况 ​

  • 刚在 Memoh 里建的多半是托管 + effective。
  • 老目录里有一份时，可能先由它 effective，直到你装了托管同名。
  • 把发现的那份 Adopt 成托管后，通常托管变 effective，旧来源变 shadowed。
  • 关掉 effective 那一份，同名里下一位可能顶上来。

在界面里 ​

添加 ​

Add Skill → 在编辑器里写 raw Markdown → 保存，写到托管目录。

编辑 ​

Edit 改 SKILL.md；自己托管的改起来最直接。

删除 ​

Delete 会去掉这份托管目录；若还有发现来源，可能那份又变成 effective。

禁用 / 启用 ​

Disable 关某一条来源不删；Enable 再开。适合试回退、暂时不用某套提示。

Adopt ​

把发现来源复制进托管目录，方便你长期用界面改。已有托管同名时不能 Adopt。

运行时 ​

只有 effective 会进当前机器人提示。

  • shadowed 能看、不进提示
  • disabled 当不存在
  • 活跃会话的 状态区 也会显示这条路上用过哪些技能

超市和导入 ​

  • 超市安装：下好的技能进托管目录，和手写一样。
  • 老环境/导入：可能只出现在发现路径，需要就 Adopt。

建议工作流 ​

  1. 先少而精，名字和描述写清楚。
  2. 不确定删不删时先 Disable。
  3. 要长期留用的发现项用 Adopt。
  4. 能复用就从 超市 装，少复制粘贴多份。

Published under AGPLv3

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/skill ​

动作用法
list/skill 或 /skill list

/fs ​

动作用法
list/fs list [path]
read/fs read <path>
text
/fs list /
 /fs list /home
 /fs read /home/bot/IDENTITY.md

文件太大时输出会截断。

/compact ​

立刻对当前会话做 会话上下文压缩，不是改记忆库里条目的那种记忆压缩。

动作用法
run/compact 或 /compact run

聊得很长、想先摘要再续时有用。

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超市 ​

Memoh 内置的 技能 与 MCP 模板 目录。

装技能 ​

  1. 网页里打开 Supermarket。
  2. 切到 Skills。
  3. 选一个，点 Install。
  4. 选目标机器人。
  5. 确认。
  6. 到该机器人 Skills tab 里能看到。

装 MCP 模板 ​

  1. 打开 Supermarket。
  2. 切到 MCP。
  3. 选一项，Install。
  4. 选机器人。
  5. 会跳转到该机器人 MCP tab，并带上草稿连接。
  6. 补全密钥、OAuth 等。
  7. 保存。
  8. 需要时点 Probe 刷新工具列表。

贡献 ​

新技能或 MCP 模板可提到：

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Memoh 中文文档 ​

Memoh（读作 /ˈmemoʊ/）是面向多角色、长记忆、容器化场景的 AI 智能体平台。你可以创建多个机器人，为每个机器人准备独立工作区与长期记忆，并通过 Telegram、Discord、飞书、QQ、Matrix、Misskey、钉钉、企微、微信、公众号、邮件或内置网页端接入。

起步 ​

  • 产品概览：平台在做什么、和其它方案差别在哪。
  • Docker 安装：推荐的一键/Compose 部署方式。
  • 供应商与模型：配置上游 API、模型类型与能力标记。
  • 机器人：创建机器人并配置各标签页。
  • 会话：聊天与 Discuss 模式、状态区、路由。
  • 斜杠命令：命令结构、权限与速查表。

功能指南 ​

  • 渠道总览：支持的平台与各平台分篇（Telegram、飞书、企微、微信等）。
  • 访问控制：ACL 预设、规则顺序与按来源限定。
  • 技能：托管/发现、生效/被遮蔽、从超市安装。
  • 超市：技能与 MCP 模板安装。
  • MCP：Stdio/远程、OAuth、探测与导入导出。
  • 长期记忆：记忆提供方与在界面里的操作。
  • 会话上下文压缩：缩小当前会话占用，不动存储里的长期记忆。

其它「入门」子页已翻译：容器、文件、浏览器、心跳、计划任务、搜索、邮件、Supermarket 等，见下栏 入门（中文）。

记忆与语音提供方 ​

各页链接以本中文站路径为主；个别尚未翻译的英文页仍可从英文站侧栏进入。

Published under AGPLv3

Copyright © 2024 Memoh

- + \ No newline at end of file diff --git a/zh/installation/docker.html b/zh/installation/docker.html index d9ea4f2b..d54b2346 100644 --- a/zh/installation/docker.html +++ b/zh/installation/docker.html @@ -9,9 +9,9 @@ - + - + @@ -28,7 +28,7 @@ docker compose logs -f # 看日志 docker compose ps # 状态 docker compose pull && docker compose up -d # 更新镜像再起

环境变量 ​

变量默认说明
POSTGRES_PASSWORDmemoh123须与 config.toml 里 postgres.password 一致
MEMOH_CONFIG./config.toml配置文件路径
MEMOH_VERSION最新发版要装的 git 标签，也用于钉死镜像
USE_CN_MIRRORfalse是否用大陆镜像
BROWSER_CORESchromium,firefox浏览器镜像里包含的引擎
BROWSER_TAGlatest浏览器镜像 tag

Published under AGPLv3

Copyright © 2024 Memoh

- + \ No newline at end of file diff --git a/zh/memory-providers/builtin.html b/zh/memory-providers/builtin.html index 909ebb48..f2b8b664 100644 --- a/zh/memory-providers/builtin.html +++ b/zh/memory-providers/builtin.html @@ -9,9 +9,9 @@ - + - + @@ -25,7 +25,7 @@ [sparse] base_url = "http://sparse:8085"

Dense ​

要 embedding 模型（在提供方里配）+ Qdrant：

bash
docker compose --profile qdrant up -d
toml
[qdrant]
 base_url = "http://qdrant:6334"

（稠密模式细节、embedding 在 UI 里选哪条，以你当前版本为准。）

绑到机器人 ​

  1. Bots → 机器人
  2. General → Memory Provider
  3. 保存

若未选，运行层面不会用这条提供方。

配好之后 ​

在 Memory tab 可手建、从对话抽、搜、改、压、重建等。日常操作见 长期记忆。

Published under AGPLv3

Copyright © 2024 Memoh

- + \ No newline at end of file diff --git a/zh/memory-providers/index.html b/zh/memory-providers/index.html index ca76810a..72029654 100644 --- a/zh/memory-providers/index.html +++ b/zh/memory-providers/index.html @@ -9,9 +9,9 @@ - + - + @@ -20,7 +20,7 @@
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记忆提供方 ​

Memory Provider 决定机器人怎么存、怎么取、怎么管长期记忆。在机器人 General 里绑一个，即成为抽取与检索记忆的后端。

有哪些 ​

  • 内置：默认自带，可关、稀疏、稠密三档，全可自建。
  • Mem0：走 Mem0 云 API，要密钥。
  • OpenViking：自建或 SaaS，自有 API。

一般步骤 ​

  1. 侧栏 Memory Providers。
  2. 选类型，建一个实例。
  3. 配好参数。
  4. 机器人 General → Memory Provider 选中。
  5. 在 Memory tab 里管具体条目。

接下来 ​

Published under AGPLv3

Copyright © 2024 Memoh

- + \ No newline at end of file diff --git a/zh/memory-providers/mem0.html b/zh/memory-providers/mem0.html index 9359400e..90f7382f 100644 --- a/zh/memory-providers/mem0.html +++ b/zh/memory-providers/mem0.html @@ -9,9 +9,9 @@ - + - + @@ -20,7 +20,7 @@
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Mem0 记忆 ​

把机器人接到 Mem0 云端，由对方管存储、索引、检索，本机不搭那套库。

建提供方 ​

  1. Memory Providers → Add Memory Provider。
  2. Name、Provider Type 选 mem0。
  3. Create。

配置 ​

字段必填说明
Base URL否空则默认 https://api.mem0.ai
API Key是鉴权，按密钥存
Organization ID否工作区用
Project ID否工作区用

绑到机器人 ​

  1. Bots → 机器人 → General
  2. Memory Provider 选 Mem0
  3. 保存

使用 ​

绑上后，抽取、搜索、管理都走 Mem0 API。日常见 长期记忆。

Published under AGPLv3

Copyright © 2024 Memoh

- + \ No newline at end of file diff --git a/zh/memory-providers/openviking.html b/zh/memory-providers/openviking.html index 198240c1..c0d5981e 100644 --- a/zh/memory-providers/openviking.html +++ b/zh/memory-providers/openviking.html @@ -9,9 +9,9 @@ - + - + @@ -20,7 +20,7 @@
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OpenViking 记忆 ​

可自建或接 SaaS 的独立记忆 API，给需要专用记忆后端的场景多一个选项。

建提供方 ​

  1. Memory Providers → Add Memory Provider。
  2. Name、Provider Type 选 openviking。
  3. Create。

配置 ​

字段必填说明
Base URL是如 http://openviking:8088
API Key否需要时填，按密钥存

绑到机器人 ​

  1. Bots → 机器人 → General
  2. Memory Provider 选 OpenViking
  3. 保存

使用 ​

绑上后，记忆相关 API 都走 OpenViking。日常见 长期记忆。

Published under AGPLv3

Copyright © 2024 Memoh

- + \ No newline at end of file diff --git a/zh/tts-providers/edge.html b/zh/tts-providers/edge.html index 1c453419..409378bc 100644 --- a/zh/tts-providers/edge.html +++ b/zh/tts-providers/edge.html @@ -9,9 +9,9 @@ - + - + @@ -20,7 +20,7 @@
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Edge TTS ​

用 Edge 对外公开的朗读接口做合成：免费、无 API key，语言/声音很多。

建提供方 ​

  1. TTS Providers → Add。
  2. 类型选 edge。
  3. Create。

通常会自带默认模型 edge-read-aloud。

调模型 ​

点 edge-read-aloud：

字段说明
Voice语言 + 声线 id，默认如 en-US-EmmaMultilingualNeural
Format如 audio-24khz-48kbitrate-mono-mp3、webm 等
Speed如 0.5、1.0、2.0
Pitch-100 ～ +100，默认 0

绑到机器人 ​

  1. 机器人 General → TTS Model
  2. 选配好的 Edge 模型
  3. 保存

试听 ​

模型页上一般有试合成按钮，绑给机器人前可先听效果。

Published under AGPLv3

Copyright © 2024 Memoh

- + \ No newline at end of file diff --git a/zh/tts-providers/index.html b/zh/tts-providers/index.html index de72ec15..d26bbe59 100644 --- a/zh/tts-providers/index.html +++ b/zh/tts-providers/index.html @@ -9,9 +9,9 @@ - + - + @@ -20,7 +20,7 @@
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语音（TTS） ​

Memoh 支持把字变成声音。可以分三层想：

  • TTS Provider：一种服务类型（如 Edge TTS），在 TTS 页建具名实例。
  • TTS Model：某实例下的具体声音/模型，可配音色、格式、变速、音高。
  • 机器人绑定：在机器人 General 里选 TTS Model，之后对话里可朗读。

一般步骤 ​

  1. 侧栏 TTS Providers。
  2. Add，选类型（如 edge）。
  3. Create（常会自动导入默认模型）。
  4. 点进模型，调音色、格式等。
  5. 用页面试听。
  6. 机器人 General 里选这个 TTS Model 并保存。

当前文档里有的 ​

提供方说明
Edge TTS走 Edge 公开朗读接口，无 key，多语音

接下来 ​

Published under AGPLv3

Copyright © 2024 Memoh

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