TeamClaw

A high-performance Agent subsystem for complex multi-agent orchestration. It provides a visual workflow canvas (OASIS) to coordinate OpenClaw agents, automated computer use tasks, and real-time monitoring via a dedicated Web UI. Supports Telegram/QQ bot integrations and Cloudflare Tunnel for secure remote access.

hou xinyuan@avalon-467

Install

openclaw skills install @avalon-467/teamclaw

TeamClaw Agent Subsystem Skill

https://github.com/Avalon-467/Teamclaw

Introduction

TeamClaw is an OpenClaw-like multi-agent sub-platform with a built-in lightweight agent (similar to OpenClaw's), featuring computer use capabilities and social platform integrations (e.g., Telegram). It can run independently without blocking the main agent, or be directly controlled by an OpenClaw agent to orchestrate the built-in OASIS collaboration platform. It also supports exposing the frontend to the public internet via Cloudflare, enabling remote visual multi-agent workflow programming from mobile devices or any browser.

TeamClaw is a versatile AI Agent service providing:

Conversational Agent: A LangGraph-based multi-tool AI assistant supporting streaming/non-streaming conversations
OASIS Forum: A multi-expert parallel discussion/execution engine for orchestrating multiple agents
Scheduled Tasks: An APScheduler-based task scheduling center
Bark Push: Mobile push notifications
Frontend Web UI: A complete chat interface

Skill Scripts

All scripts are located in selfskill/scripts/, invoked uniformly via the run.sh entry point, all non-interactive.

text

selfskill/scripts/
 run.sh          # Main entry (start/stop/status/setup/add-user/configure)
 adduser.py      # Non-interactive user creation
 configure.py    # Non-interactive .env configuration management

Quick Start

All commands are executed in the project root directory.

Three-step launch flow: setup → configure → start

1. First Deployment

bash

# Install dependencies
bash selfskill/scripts/run.sh setup

# Initialize configuration file
bash selfskill/scripts/run.sh configure --init

# Configure LLM (required)
bash selfskill/scripts/run.sh configure --batch \
  LLM_API_KEY=sk-your-key \
  LLM_BASE_URL=https://api.deepseek.com \
  LLM_MODEL=deepseek-chat

# ⚠️ Create user account (REQUIRED — without this you CANNOT log in to the Web UI or call API)
bash selfskill/scripts/run.sh add-user system MySecurePass123

⚠️ You MUST create at least one user account before starting the service!

The Web UI login page requires username + password.

All API calls require Authorization: Bearer <user_id>:<password> (or INTERNAL_TOKEN:<user_id>).

If you skip this step, you will be locked out of the entire system.

You can create multiple users. The first argument is the username, the second is the password.

2. Start / Stop / Status

bash

bash selfskill/scripts/run.sh start     # Start in background
bash selfskill/scripts/run.sh status    # Check status
bash selfskill/scripts/run.sh stop      # Stop service

3. Bark Push vs Chatbot (Telegram/QQ) — Startup Differences

Component	How it starts	Configuration needed	Notes
Bark Push (port 58010)	Automatically started by `launcher.py`	None — works out of the box	A standalone binary (`bin/bark-server`). Auto-downloaded on first `setup`. No env vars needed.
Telegram Bot	Requires manual setup	`TELEGRAM_BOT_TOKEN`, `TELEGRAM_ALLOWED_USERS` in `.env`	`launcher.py` calls `chatbot/setup.py` which has an interactive menu (`input()`). In headless/background mode this will block. To avoid blocking, configure the bot tokens in `.env` beforehand and start the bot separately: `nohup python chatbot/telegrambot.py > logs/telegrambot.log 2>&1 &`
QQ Bot	Requires manual setup	`QQ_APP_ID`, `QQ_BOT_SECRET`, `QQ_BOT_USERNAME` in `.env`	Same as Telegram — interactive setup will block in headless mode. Start separately: `nohup python chatbot/QQbot.py > logs/qqbot.log 2>&1 &`

⚠️ Important for Agent/headless usage: The chatbot/setup.py script contains interactive input() prompts. When launcher.py runs in the background (via run.sh start), if chatbot/setup.py exists it will be called and block indefinitely waiting for user input. To prevent this:

Either remove/rename chatbot/setup.py before starting, OR

Pre-configure all bot tokens in .env and start bots independently (bypassing setup.py).

4. Configuration Management

bash

# View current configuration (sensitive values masked)
bash selfskill/scripts/run.sh configure --show

# Set a single item
bash selfskill/scripts/run.sh configure PORT_AGENT 51200

# Batch set
bash selfskill/scripts/run.sh configure --batch TTS_MODEL=gemini-2.5-flash-preview-tts TTS_VOICE=charon

Configuration Options

Option	Description	Default
`LLM_API_KEY`	LLM API key (required)
`LLM_BASE_URL`	LLM API URL	`https://api.deepseek.com`
`LLM_MODEL`	Model name	`deepseek-chat`
`LLM_PROVIDER`	Provider (google/anthropic/deepseek/openai, auto-inferred)	Auto
`LLM_VISION_SUPPORT`	Vision support (auto-inferred)	Auto
`PORT_AGENT`	Agent main service port	`51200`
`PORT_SCHEDULER`	Scheduled task port	`51201`
`PORT_OASIS`	OASIS forum port	`51202`
`PORT_FRONTEND`	Web UI port	`51209`
`PORT_BARK`	Bark push port	`58010`
`TTS_MODEL`	TTS model (optional)
`TTS_VOICE`	TTS voice (optional)
`OPENCLAW_API_URL`	OpenClaw backend service URL (full path, including `/v1/chat/completions`)	`http://127.0.0.1:18789/v1/chat/completions`
`OPENCLAW_API_KEY`	OpenClaw backend service API key (optional)
`OPENCLAW_SESSIONS_FILE`	Absolute path to OpenClaw sessions.json file (required when using OpenClaw)	`None`
`INTERNAL_TOKEN`	Internal communication secret (auto-generated)	Auto

Ports & Services

Port	Service
51200	AI Agent main service
51201	Scheduled tasks
51202	OASIS forum
51209	Web UI

API Authentication

Method 1: User Authentication

text

Authorization: Bearer <user_id>:<password>

Method 2: Internal Token (for inter-service calls, recommended)

text

Authorization: Bearer <INTERNAL_TOKEN>:<user_id>

INTERNAL_TOKEN is auto-generated on first startup; view it via configure --show-raw.

Core API

Base URL: http://127.0.0.1:51200

Chat (OpenAI-compatible)

text

POST /v1/chat/completions
Authorization: Bearer <token>

{"model":"mini-timebot","messages":[{"role":"user","content":"Hello"}],"stream":true,"session_id":"my-session"}

System Trigger (internal call)

text

POST /system_trigger
X-Internal-Token: <INTERNAL_TOKEN>

{"user_id":"system","text":"Please execute a task","session_id":"task-001"}

Cancel Session

text

POST /cancel

{"user_id":"<user_id>","session_id":"<session_id>"}

OASIS Four Operating Modes (Default: Discussion Mode)

📖 Dedicated OASIS usage guide (especially for OpenClaw agent integration): OASIS_GUIDE.md

The "four modes" are two orthogonal switches:

Discussion vs Execution: Determines whether expert output is "forum-style discussion/voting" or "workflow-style execution/deliverables".

Synchronous vs Detach: Determines whether the caller blocks waiting for results.

1) Discussion Mode vs Execution Mode

Discussion Mode (discussion=true, default)

Purpose: Multiple experts provide different perspectives, pros/cons analysis, clarify disputes, and can form consensus.
Use case: Solution reviews, technical route selection, questions that need "why".

Execution Mode (discussion=false)

Purpose: Use OASIS as an orchestrator to complete tasks in planned sequential/parallel order, emphasizing direct output (code/scripts/checklists/finalized plans).
Use case: Delivery tasks with clear objectives that don't need debate.

2) Synchronous Mode vs Detach Mode

Detach (detach=true, default)

Behavior: Returns topic_id immediately, continues running/discussing in the background; later use check_oasis_discussion(topic_id) to track progress and results.
Use case: Most tasks, especially multi-round/multi-expert/long-running/tool-calling tasks.

Synchronous (detach=false)

Behavior: After calling post_to_oasis, waits for completion and returns the final result directly.
Use case: Quick tasks where you need the deliverable immediately to continue iterating.

3) Auto-selection Rules (Recommended Default Strategy)

When not explicitly specified, the following default strategy is recommended:

Default = Discussion + Detach
- discussion=true
- detach=true
Switch to Execution Mode when these signals appear:
- "Give me the final version / copy-pasteable / executable script / just conclusions no discussion"
- "Generate SOP / checklist / table step by step and finalize"
Switch to Synchronous Mode when these signals appear:
- "Wait for the result / I need it now / give me the answer directly"
- Quick single-round tasks where the deliverable is needed immediately

4) Four Combinations Quick Reference

Combination	Parameters	Returns	Use Case
Discussion + Detach (default)	discussion=true, detach=true	topic_id, check later	Decision/review/collect opinions
Discussion + Sync	discussion=true, detach=false	See discussion & conclusion on the spot	Quick discussion needing immediate result
Execution + Detach	discussion=false, detach=true	topic_id, check later	Long execution/complex pipelines
Execution + Sync	discussion=false, detach=false	Direct deliverables	Generate code/plans/checklists

OASIS Four Agent Types

OASIS supports four types of agents, distinguished by the name format in schedule_yaml:

#	Type	Name Format	Engine Class	Description
1	Direct LLM	`tag#temp#N`	`ExpertAgent`	Stateless single LLM call. Each round reads all posts one LLM call publish + vote. No cross-round memory. `tag` maps to preset expert name/persona, `N` is instance number (same expert can have multiple copies).
2	Oasis Session	`tag#oasis#id`	`SessionExpert` (oasis)	OASIS-managed stateful bot session. `tag` maps to preset expert, persona injected as system prompt on first round. Bot retains conversation memory across rounds (incremental context). `id` can be any string; new ID auto-creates session on first use.
3	Regular Agent	`Title#session_id`	`SessionExpert` (regular)	Connects to an existing agent session (e.g., `Assistant#default`, `Coder#my-project`). No identity injectionthe session's own system prompt defines the agent. Suitable for bringing personal bot sessions into discussions.
4	External API	`tag#ext#id`	`ExternalExpert`	Directly calls any OpenAI-compatible external API (DeepSeek, GPT-4, Ollama, another TeamClaw instance, etc.). Does not go through local agent. External service assumed stateful. Supports custom request headers via YAML `headers` field.

Session ID Format

text

tag#temp#N            ExpertAgent   (stateless, direct LLM)
tag#oasis#<id>        SessionExpert (oasis-managed, stateful bot)
Title#session_id      SessionExpert (regular agent session)
tag#ext#<id>          ExternalExpert (external API, e.g. OpenClaw agent)

Special Suffix:

Appending #new to the end of any session name forces creation of a brand new session (ID replaced with random UUID, ensuring no reuse):
- creative#oasis#abc#new #new stripped, ID replaced with UUID
- Assistant#my-session#new Same processing

Oasis Session Conventions:

Oasis sessions are identified by #oasis# in session_id (e.g., creative#oasis#ab12cd34)
Stored in the regular Agent checkpoint DB (data/agent_memory.db), no separate storage
Auto-created on first use, no pre-creation needed
tag part maps to preset expert configuration to find persona

YAML Example

yaml

version: 1
plan:
  # Type 1: Direct LLM (stateless, fast)
  - expert: "creative#temp#1"
  - expert: "critical#temp#2"

  # Type 2: Oasis session (stateful, with memory)
  - expert: "data#oasis#analysis01"
  - expert: "synthesis#oasis#new#new"   # Force new session

  # Type 3: Regular agent session (your existing bot)
  - expert: "Assistant#default"
  - expert: "Coder#my-project"

  # Type 4: External API (DeepSeek, GPT-4, etc.)
  # Note: api_key is auto-read from OPENCLAW_API_KEY env var; use "****" mask in YAML (never write plaintext keys)
  - expert: "deepseek#ext#ds1"

  # Type 4: OpenClaw External API (local Agent service)
  # api_key auto-resolved from OPENCLAW_API_KEY env var when set to "****"
  - expert: "coder#ext#oc1"
    api_url: "http://127.0.0.1:23001/v1/chat/completions"
    api_key: "****"              # Masked — real key read from OPENCLAW_API_KEY env var at runtime
    model: "agent:main:test1"    # agent:<agent_name>:<session>, session auto-created if not exists

  # Parallel execution
  - parallel:
      - expert: "creative#temp#1"
        instruction: "Analyze from innovation perspective"
      - expert: "critical#temp#2"
        instruction: "Analyze from risk perspective"

  # All experts speak + manual injection
  - all_experts: true
  - manual:
      author: "Moderator"
      content: "Please focus on feasibility"

DAG Mode — Dependency-Driven Parallel Execution

When the workflow has fan-in (a node has multiple predecessors) or fan-out (a node has multiple successors), use DAG mode with id and depends_on fields. The engine maximizes parallelism — each node starts as soon as all its dependencies are satisfied.

DAG YAML Example:

yaml

version: 1
repeat: false
plan:
  - id: research
    expert: "creative#temp#1"                # Root — starts immediately
  - id: analysis
    expert: "critical#temp#1"                # Root — runs in PARALLEL with research
  - id: synthesis
    expert: "synthesis#temp#1"
    depends_on: [research, analysis]         # Fan-in: waits for BOTH to complete
  - id: review
    expert: "data#temp#1"
    depends_on: [synthesis]                  # Runs after synthesis

DAG Rules:

Every step must have a unique id field.
depends_on is a list of step ids that must complete before this step starts. Omit for root nodes.
The graph must be acyclic (no circular dependencies).
Steps with no dependency relationship run in parallel automatically.
The visual Canvas auto-detects fan-in/fan-out and generates DAG format.
manual steps can also have id/depends_on.

External API (Type 4) Detailed Configuration

Type 4 external agents support additional configuration fields in YAML steps:

yaml

version: 1
plan:
  - expert: "#ext#analyst"
    api_url: "https://api.deepseek.com"          # Required: External API base URL (auto-completes to /v1/chat/completions)
    api_key: "****"                               # Masked — real key auto-read from OPENCLAW_API_KEY env var at runtime
    model: "deepseek-chat"                        # Optional: Model name, default gpt-3.5-turbo
    headers:                                      # Optional: Custom HTTP headers (key-value dict)
      X-Custom-Header: "value"

🔒 API Key Security: You no longer need to write plaintext API keys in YAML. Set api_key: "****" (or omit it entirely) and the system will automatically read the real key from the OPENCLAW_API_KEY environment variable at runtime. The frontend canvas also displays **** instead of the real key. If you do write a plaintext key, it will still work (backward compatible). Configuration Field Description:

Field	Required	Description
`api_url`		External API address, auto-completes path to `/v1/chat/completions`
`api_key`		Use `****` mask — auto-read from `OPENCLAW_API_KEY` env var. Plaintext keys also supported (backward compatible)
`model`		Default `gpt-3.5-turbo`
`headers`		Any key-value dict, merged into HTTP request headers

OpenClaw-specific Configuration:

OpenClaw is a locally running OpenAI-compatible Agent service. After setting up OpenClaw-specific endpoints in .env, the frontend orchestration panel will auto-fill api_url and api_key when dragging in an OpenClaw expert, no manual input needed:

bash

# Configure OpenClaw endpoint and sessions file path
bash selfskill/scripts/run.sh configure --batch \
OPENCLAW_SESSIONS_FILE=./data/sessions.json \
  OPENCLAW_API_URL=http://127.0.0.1:18789/v1/chat/completions \
  OPENCLAW_API_KEY=your-openclaw-key-if-needed

** Note:**

OPENCLAW_SESSIONS_FILE is a prerequisite for using the OpenClaw feature and must point to the absolute path of OpenClaw's sessions.json file. The frontend orchestration panel will not load OpenClaw sessions if unconfigured.

Path Convention: ./agents/main/sessions/sessions.json is a common path structure for OpenClaw agent sessions. This path convention allows the system to properly access and orchestrate OpenClaw agents.

Session Management: Accessing session information is a necessary process for OpenClaw agent orchestration, enabling multi-agent workflow coordination and visual canvas operations.

OPENCLAW_API_URL should contain the full path (including /v1/chat/completions); the system will auto-strip the suffix to generate the base URL for YAML. The api_url field in YAML only needs the base URL (e.g., http://127.0.0.1:18789); the engine auto-completes the path.

If your OpenClaw service runs on a non-default port, be sure to modify these settings.

OpenClaw model Field Format:

text

agent:<agent_name>:<session_name>

agent_name: Agent name in OpenClaw, usually main
session_name: Session name, e.g., test1, default, etc. You can enter a non-existent session name to auto-create

Examples:

agent:main:default Use main agent's default session
agent:main:test1 Use main agent's test1 session (auto-created if not exists)
agent:main:code-review Use main agent's code-review session

Request Header Assembly Logic: Final request headers = Content-Type: application/json + Authorization: Bearer <api_key> (if present) + all key-value pairs from YAML headers.

x-openclaw-session-key — Deterministic OpenClaw Session Routing:

When calling an OpenClaw agent via External API (Type 4), the x-openclaw-session-key HTTP header is the key mechanism for routing requests to a specific, deterministic OpenClaw session. Without this header, OpenClaw may not correctly associate the request with the intended session.

The frontend orchestration panel automatically sets this header when you drag an OpenClaw session onto the canvas.
When writing YAML manually or calling the API programmatically, you must include this header in the headers field to ensure session determinism.

yaml

# Example: Connecting to a specific OpenClaw session
- expert: "coder#ext#oc1"
  api_url: "http://127.0.0.1:18789"
  api_key: "****"                                      # ← Masked; real key from OPENCLAW_API_KEY env var
  model: "agent:main:my-session"
  headers:
    x-openclaw-session-key: "agent:main:my-session"   # ← This header determines the exact OpenClaw session

The value of x-openclaw-session-key should match the model field's session identifier (format: agent:<agent_name>:<session_name>). This ensures the external request is routed to the correct OpenClaw agent session, maintaining conversation continuity and state.

Using OASIS Server Independently

The OASIS Server (port 51202) can be used independently of the Agent main service. External scripts, other services, or manual curl can directly operate all OASIS features without going through MCP tools or Agent conversations.

Independent Use Scenarios:

Initiate multi-expert discussions/executions from external scripts
Debug workflow orchestration
Integrate OASIS as a microservice into other systems
Manage experts, sessions, workflows, and other resources

Prerequisites:

OASIS service is running (bash selfskill/scripts/run.sh start starts all services simultaneously)
All endpoints use user_id parameter for user isolation (no Authorization header needed)

API Overview:

Function	Method	Path
List experts	GET	`/experts?user_id=xxx`
Create custom expert	POST	`/experts/user`
Update/delete custom expert	PUT/DELETE	`/experts/user/{tag}`
List oasis sessions	GET	`/sessions/oasis?user_id=xxx`
Save workflow	POST	`/workflows`
List workflows	GET	`/workflows?user_id=xxx`
YAML Layout	POST	`/layouts/from-yaml`
Create discussion/execution	POST	`/topics`
View discussion details	GET	`/topics/{topic_id}?user_id=xxx`
Get conclusion (blocking)	GET	`/topics/{topic_id}/conclusion?user_id=xxx&timeout=300`
SSE real-time stream	GET	`/topics/{topic_id}/stream?user_id=xxx`
Cancel discussion	DELETE	`/topics/{topic_id}?user_id=xxx`
List all topics	GET	`/topics?user_id=xxx`

These endpoints share the same backend implementation as MCP tools, ensuring consistent behavior.

OASIS Discussion/Execution

text

POST http://127.0.0.1:51202/topics

{"question":"Discussion topic","user_id":"system","max_rounds":3,"discussion":true,"schedule_file":"...","schedule_yaml":"...","callback_url":"http://127.0.0.1:51200/system_trigger","callback_session_id":"my-session"}

Prefer using schedule_yaml to avoid repeated YAML input; this is the absolute path to the YAML workflow file, usually under /XXXXX/TeamClaw/data/user_files/username.

Externally Participating in OASIS Server via curl (Complete Methods)

The OASIS Server (port 51202), in addition to being called by MCP tools, also supports direct curl operations for external scripts or debugging. All endpoints use user_id parameter for user isolation.

1. Expert Management

bash

# List all experts (public + user custom)
curl 'http://127.0.0.1:51202/experts?user_id=xinyuan'

# Create custom expert
curl -X POST 'http://127.0.0.1:51202/experts/user' \
  -H 'Content-Type: application/json' \
  -d '{"user_id":"xinyuan","name":"Product Manager","tag":"pm","persona":"You are an experienced product manager skilled in requirements analysis and product planning","temperature":0.7}'

# Update custom expert
curl -X PUT 'http://127.0.0.1:51202/experts/user/pm' \
  -H 'Content-Type: application/json' \
  -d '{"user_id":"xinyuan","persona":"Updated expert description"}'

# Delete custom expert
curl -X DELETE 'http://127.0.0.1:51202/experts/user/pm?user_id=xinyuan'

2. Session Management

bash

# List OASIS-managed expert sessions (sessions containing #oasis#)
curl 'http://127.0.0.1:51202/sessions/oasis?user_id=xinyuan'

3. Workflow Management

bash

# List user's saved workflows
curl 'http://127.0.0.1:51202/workflows?user_id=xinyuan'

# Save workflow (auto-generate layout)
curl -X POST 'http://127.0.0.1:51202/workflows' \
  -H 'Content-Type: application/json' \
  -d '{"user_id":"xinyuan","name":"trio_discussion","schedule_yaml":"version:1\nplan:\n - expert: \"creative#temp#1\"","description":"Trio discussion","save_layout":true}'

4. Layout Generation

bash

# Generate layout from YAML
curl -X POST 'http://127.0.0.1:51202/layouts/from-yaml' \
  -H 'Content-Type: application/json' \
  -d '{"user_id":"xinyuan","yaml_source":"version:1\nplan:\n - expert: \"creative#temp#1\"","layout_name":"trio_layout"}'

5. Discussion/Execution

bash

# Create discussion topic (synchronous, wait for conclusion)
curl -X POST 'http://127.0.0.1:51202/topics' \
  -H 'Content-Type: application/json' \
  -d '{"user_id":"xinyuan","question":"Discussion topic","max_rounds":3,"schedule_yaml":"version:1\nplan:\n - expert: \"creative#temp#1\"","discussion":true}'

# Create discussion topic (async, returns topic_id)
curl -X POST 'http://127.0.0.1:51202/topics' \
  -H 'Content-Type: application/json' \
  -d '{"user_id":"xinyuan","question":"Discussion topic","max_rounds":3,"schedule_yaml":"version:1\nplan:\n - expert: \"creative#temp#1\"","discussion":true,"callback_url":"http://127.0.0.1:51200/system_trigger","callback_session_id":"my-session"}'

# View discussion details
curl 'http://127.0.0.1:51202/topics/{topic_id}?user_id=xinyuan'

# Get discussion conclusion (blocking wait)
curl 'http://127.0.0.1:51202/topics/{topic_id}/conclusion?user_id=xinyuan&timeout=300'

# Cancel discussion
curl -X DELETE 'http://127.0.0.1:51202/topics/{topic_id}?user_id=xinyuan'

# List all discussion topics
curl 'http://127.0.0.1:51202/topics?user_id=xinyuan'

6. Real-time Stream

bash

# SSE real-time update stream (discussion mode)
curl 'http://127.0.0.1:51202/topics/{topic_id}/stream?user_id=xinyuan'

Storage Locations:

Workflows (YAML): data/user_files/{user}/oasis/yaml/{file}.yaml (canvas layouts are converted from YAML in real-time, no longer stored as separate layout JSON)
User custom experts: data/oasis_user_experts/{user}.json
Discussion records: data/oasis_topics/{user}/{topic_id}.json

Note: These endpoints share the same backend implementation as MCP tools list_oasis_experts, add_oasis_expert, update_oasis_expert, delete_oasis_expert, list_oasis_sessions, set_oasis_workflow, list_oasis_workflows, yaml_to_layout, post_to_oasis, check_oasis_discussion, cancel_oasis_discussion, list_oasis_topics, ensuring consistent behavior.

Example Configuration Reference

Below is an actual running configuration example (sensitive info redacted):

bash

bash selfskill/scripts/run.sh configure --init
bash selfskill/scripts/run.sh configure --batch \
  LLM_API_KEY=sk-xxxxxxxxxxxxxxxxxxxxxxxxxxxx4c74 \
  LLM_BASE_URL=https://deepseek.com \
  LLM_MODEL=deepseek-chat \
  LLM_VISION_SUPPORT=true \
  TTS_MODEL=gemini-2.5-flash-preview-tts \
  TTS_VOICE=charon \
  PORT_AGENT=51200 \
  PORT_SCHEDULER=51201 \
  PORT_OASIS=51202 \
  PORT_FRONTEND=51209 \
  PORT_BARK=58010 \
  OPENCLAW_API_URL=http://127.0.0.1:18789/v1/chat/completions \
  OPENAI_STANDARD_MODE=false
bash selfskill/scripts/run.sh add-user system <your-password>

Output after configure --show:

text

  PORT_SCHEDULER=51201
  PORT_AGENT=51200
  PORT_FRONTEND=51209
  PORT_OASIS=51202
  OASIS_BASE_URL=http://127.0.0.1:51202
  PORT_BARK=58010
  INTERNAL_TOKEN=f1aa****57e7          # Auto-generated, do not leak
  LLM_API_KEY=sk-7****4c74
  LLM_BASE_URL=https://deepseek.com
  LLM_MODEL=deepseek-chat
  LLM_VISION_SUPPORT=true
  TTS_MODEL=gemini-2.5-flash-preview-tts
  TTS_VOICE=charon
  OPENAI_STANDARD_MODE=false

Note: INTERNAL_TOKEN is auto-generated on first startup; PUBLIC_DOMAIN / BARK_PUBLIC_URL are auto-written by the tunnel; no manual configuration needed.

Typical Usage Flow

bash

cd /home/avalon/TeamClaw

# First-time configuration
bash selfskill/scripts/run.sh setup
bash selfskill/scripts/run.sh configure --init
bash selfskill/scripts/run.sh configure --batch LLM_API_KEY=sk-xxx LLM_BASE_URL=https://api.deepseek.com LLM_MODEL=deepseek-chat
bash selfskill/scripts/run.sh add-user system MyPass123

# Start
bash selfskill/scripts/run.sh start

# Call API
curl -X POST http://127.0.0.1:51200/v1/chat/completions \
  -H "Content-Type: application/json" \
  -H "Authorization: Bearer system:MyPass123" \
  -d '{"model":"mini-timebot","messages":[{"role":"user","content":"Hello"}],"stream":false,"session_id":"default"}'

# Stop
bash selfskill/scripts/run.sh stop

Important Notes

All skill scripts are in selfskill/scripts/, not affecting original project functionality
Process management via PID files, start supports idempotent calls
Do not leak INTERNAL_TOKEN
Log path: logs/launcher.log
Be sure to tell users how to open the visual interface and how to log in to the account for discussions
The OpenClaw session file path can be inferred from the example path and current skill path. If you are an OpenClaw agent, be sure to complete the full OpenClaw-related configuration

TeamClaw Agent Skill

TeamClaw OpenClaw Agent OpenClaw Agent computer use Telegram agent OpenClaw agent OASIS Agent Cloudflare Agent

TeamClaw AI Agent

** Agent** LangGraph AI /
**OASIS **/ Agent
**** APScheduler
**Bark **
** Web UI**

Skill

selfskill/scripts/ run.sh ****

text

selfskill/scripts/
 run.sh          # start/stop/status/setup/add-user/configure
 adduser.py      # 
 configure.py    #  .env

****setup configure start

1.

bash

# 
bash selfskill/scripts/run.sh setup

# 
bash selfskill/scripts/run.sh configure --init

#  LLM
bash selfskill/scripts/run.sh configure --batch \
  LLM_API_KEY=sk-your-key \
  LLM_BASE_URL=https://api.deepseek.com \
  LLM_MODEL=deepseek-chat

# 
bash selfskill/scripts/run.sh add-user system MySecurePass123

2. //

bash

bash selfskill/scripts/run.sh start     # 
bash selfskill/scripts/run.sh status    # 
bash selfskill/scripts/run.sh stop      #

3.

bash

# 
bash selfskill/scripts/run.sh configure --show

# 
bash selfskill/scripts/run.sh configure PORT_AGENT 51200

# 
bash selfskill/scripts/run.sh configure --batch TTS_MODEL=gemini-2.5-flash-preview-tts TTS_VOICE=charon


`LLM_API_KEY`	LLM API ****
`LLM_BASE_URL`	LLM API	`https://api.deepseek.com`
`LLM_MODEL`		`deepseek-chat`
`LLM_PROVIDER`	google/anthropic/deepseek/openai
`LLM_VISION_SUPPORT`
`PORT_AGENT`	Agent	`51200`
`PORT_SCHEDULER`		`51201`
`PORT_OASIS`	OASIS	`51202`
`PORT_FRONTEND`	Web UI	`51209`
`PORT_BARK`	Bark	`58010`
`TTS_MODEL`	TTS
`TTS_VOICE`	TTS
`OPENCLAW_API_URL`	OpenClaw `/v1/chat/completions`	`http://127.0.0.1:18789/v1/chat/completions`
`OPENCLAW_API_KEY`	OpenClaw API Key
`OPENCLAW_SESSIONS_FILE`	OpenClaw sessions.json OpenClaw	`None`
`INTERNAL_TOKEN`


51200	AI Agent
51201
51202	OASIS
51209	Web UI

API

1

text

Authorization: Bearer <user_id>:<password>

2 Token

text

Authorization: Bearer <INTERNAL_TOKEN>:<user_id>

INTERNAL_TOKEN configure --show-raw

API

Base URL: http://127.0.0.1:51200

OpenAI

text

POST /v1/chat/completions
Authorization: Bearer <token>

{"model":"mini-timebot","messages":[{"role":"user","content":""}],"stream":true,"session_id":"my-session"}

text

POST /system_trigger
X-Internal-Token: <INTERNAL_TOKEN>

{"user_id":"system","text":"","session_id":"task-001"}

text

POST /cancel

{"user_id":"<user_id>","session_id":"<session_id>"}

OASIS

📖 专注 OASIS 使用的独立指引文档（尤其是 OpenClaw agent 集成）: OASIS_GUIDE.md

""

** vs "/""/("> - ** vs detach

1) vs

discussion=true

discussion=false

OASIS ////

2) vs detach

detach=true

topic_id/ check_oasis_discussion(topic_id)

detach=false

post_to_oasis
/

3)

** = + **
- discussion=true
- detach=true

" / / / "
" SOP / / "

" / / / "
/

4)


+ ()	discussion=true, detach=true	topic_id	//
+	discussion=true, detach=false		/
+	discussion=false, detach=true	topic_id	/
+	discussion=false, detach=false		//

OASIS

OASIS **** schedule_yaml name

#		Name
1	Direct LLM	`tag#temp#N`	`ExpertAgent`	LLM LLM + `tag` /`N`
2	Oasis Session	`tag#oasis#id`	`SessionExpert` (oasis)	OASIS bot session`tag` system promptBot `id` ID session
3	Regular Agent	`Title#session_id`	`SessionExpert` (regular)	agent session #default``Coder#my-projectsession system prompt agent bot session
4	External API	`tag#ext#id`	`ExternalExpert`	OpenAI APIDeepSeekGPT-4Ollama TeamClaw agent YAML `headers`

Session ID

text

tag#temp#N            ExpertAgent   (, LLM)
tag#oasis#<id>        SessionExpert (oasis, bot)
Title#session_id      SessionExpert (agent session)
tag#ext#<id>          ExternalExpert (APIopenclaw agent)

session #new ** session**ID UUID
creative#oasis#abc#new #new ID UUID
#my-session#new

**Oasis session **

Oasis session session_id #oasis# creative#oasis#ab12cd34
Agent checkpoint DBdata/agent_memory.db
tag

YAML

yaml

version: 1
plan:
  # Type 1: Direct LLM
  - expert: "creative#temp#1"
  - expert: "critical#temp#2"

  # Type 2: Oasis session
  - expert: "data#oasis#analysis01"
  - expert: "synthesis#oasis#new#new"   # session

  # Type 3: Regular agent sessionbot
  - expert: "#default"
  - expert: "Coder#my-project"

  # Type 4: External APIDeepSeek, GPT-4
  # 注意：api_key 自动从 OPENCLAW_API_KEY 环境变量读取；YAML 中使用 "****" 掩码（切勿写入明文密钥）
  - expert: "deepseek#ext#ds1"

  # Type 4: OpenClaw External API Agent 
  # api_key 从 OPENCLAW_API_KEY 环境变量自动读取，YAML 中使用 "****" 掩码
  - expert: "coder#ext#oc1"
    api_url: "http://127.0.0.1:23001/v1/chat/completions"
    api_key: "****"              # 掩码 — 运行时自动从 OPENCLAW_API_KEY 环境变量读取真实密钥
    model: "agent:main:test1"    # agent:<agent_name>:<session>session 

  # 
  - parallel:
      - expert: "creative#temp#1"
        instruction: ""
      - expert: "critical#temp#2"
        instruction: ""

  #  + 
  - all_experts: true
  - manual:
      author: ""
      content: ""

DAG 模式 — 依赖驱动的并行执行

当工作流存在 fan-in（一个节点有多个前驱）或 fan-out（一个节点有多个后继）时，使用带 id 和 depends_on 字段的 DAG 模式。引擎会最大化并行——每个节点在所有依赖完成后立即启动，无需等待无关节点。

DAG YAML 示例：

yaml

version: 1
repeat: false
plan:
  - id: research
    expert: "creative#temp#1"                # 根节点 — 立即启动
  - id: analysis
    expert: "critical#temp#1"                # 根节点 — 与 research 并行运行
  - id: synthesis
    expert: "synthesis#temp#1"
    depends_on: [research, analysis]         # Fan-in：等待两者都完成
  - id: review
    expert: "data#temp#1"
    depends_on: [synthesis]                  # synthesis 完成后执行

DAG 规则：

每个步骤必须有唯一的 id 字段。
depends_on 是该步骤启动前必须完成的步骤 id 列表。根节点不需要此字段。
图必须无环（禁止循环依赖）。
没有依赖关系的步骤自动并行执行。
可视化画布自动检测 fan-in/fan-out 并生成 DAG 格式。
manual 步骤同样支持 id/depends_on。

External API (Type 4)

Type 4 agent YAML

yaml

version: 1
plan:
  - expert: "#ext#analyst"
    api_url: "https://api.deepseek.com"          #  API  base URL /v1/chat/completions
    api_key: "****"                               # 掩码 — 运行时自动从 OPENCLAW_API_KEY 环境变量读取真实密钥
    model: "deepseek-chat"                        #  gpt-3.5-turbo
    headers:                                      #  HTTP key-value 
      X-Custom-Header: "value"

🔒 API Key 安全机制：YAML 中无需再写入明文 API Key。设置 api_key: "****"（或完全省略）即可，系统运行时会自动从 OPENCLAW_API_KEY 环境变量读取真实密钥。前端画布也仅显示 **** 而非真实密钥。如果你仍然写入明文密钥，也能正常工作（向后兼容）。


`api_url`		API `/v1/chat/completions`
`api_key`		使用 `****` 掩码 — 自动从 `OPENCLAW_API_KEY` 环境变量读取。也支持直接写入明文密钥（向后兼容）
`model`		`gpt-3.5-turbo`
`headers`		key-value HTTP

**OpenClaw **

OpenClaw OpenAI Agent .env OpenClaw endpoint OpenClaw **** api_url api_key

bash

#  OpenClaw endpoint  sessions 
bash selfskill/scripts/run.sh configure --batch \
OPENCLAW_SESSIONS_FILE=./data/sessions.json \
  OPENCLAW_API_URL=http://127.0.0.1:18789/v1/chat/completions \
  OPENCLAW_API_KEY=your-openclaw-key-if-needed

OPENCLAW_SESSIONS_FILE OpenClaw **** OpenClaw sessions.json OpenClaw sessions

Path Convention: ./agents/main/sessions/sessions.json OpenClaw agent sessions OpenClaw agents

Session Management: Accessing session information is a necessary process for OpenClaw agent orchestration, enabling multi-agent workflow coordination and visual canvas operations.

OPENCLAW_API_URL **** /v1/chat/completions base URL YAMLYAML api_url base URL http://127.0.0.1:18789

OpenClaw

**OpenClaw model **

text

agent:<agent_name>:<session_name>

agent_nameOpenClaw agent main
session_name test1``default ** session **
agent:main:default main agent default session
agent:main:test1 main agent test1 session
agent:main:code-review main agent code-review session

= Content-Type: application/json + Authorization: Bearer <api_key> + YAML headers

x-openclaw-session-key —— OpenClaw 确定性 Session 路由：

通过 External API（Type 4）调用 OpenClaw agent 时，x-openclaw-session-key HTTP header 是将请求路由到指定 OpenClaw session 的关键机制。缺少此 header，OpenClaw 可能无法正确关联到目标 session。

前端编排面板在拖拽 OpenClaw session 到画布时会自动设置此 header。
手动编写 YAML 或通过 API 调用时，必须在 headers 字段中包含此 header 以确保 session 的确定性。

yaml

# 示例：连接到指定的 OpenClaw session
- expert: "coder#ext#oc1"
  api_url: "http://127.0.0.1:18789"
  api_key: "****"                                      # ← 掩码；真实密钥从 OPENCLAW_API_KEY 环境变量读取
  model: "agent:main:my-session"
  headers:
    x-openclaw-session-key: "agent:main:my-session"   # ← 此 header 决定了目标 OpenClaw session

x-openclaw-session-key 的值应与 model 字段的 session 标识符一致（格式：agent:<agent_name>:<session_name>）。这确保外部请求被路由到正确的 OpenClaw agent session，保持对话连续性和状态。

OASIS Server

OASIS Server 51202** Agent ** curl OASIS MCP Agent

/
workflow
OASIS
workflow

OASIS bash selfskill/scripts/run.sh start
user_id Authorization header

**API **


	GET	`/experts?user_id=xxx`
	POST	`/experts/user`
/	PUT/DELETE	`/experts/user/{tag}`
oasis sessions	GET	`/sessions/oasis?user_id=xxx`
workflow	POST	`/workflows`
workflows	GET	`/workflows?user_id=xxx`
YAML Layout	POST	`/layouts/from-yaml`
/	POST	`/topics`
	GET	`/topics/{topic_id}?user_id=xxx`
	GET	`/topics/{topic_id}/conclusion?user_id=xxx&timeout=300`
SSE	GET	`/topics/{topic_id}/stream?user_id=xxx`
	DELETE	`/topics/{topic_id}?user_id=xxx`
	GET	`/topics?user_id=xxx`

MCP

OASIS /

text

POST http://127.0.0.1:51202/topics

{"question":"","user_id":"system","max_rounds":3,"discussion":true,"schedule_file":"...","schedule_yaml":"...","callback_url":"http://127.0.0.1:51200/system_trigger","callback_session_id":"my-session"}

schedule_yamlyamlyaml/XXXXX/TeamClaw/data/user_files/username

curl OASIS

OASIS 51202 MCP curl user_id

1.

bash

#  + 
curl 'http://127.0.0.1:51202/experts?user_id=xinyuan'

# 
curl -X POST 'http://127.0.0.1:51202/experts/user' \
  -H 'Content-Type: application/json' \
  -d '{"user_id":"xinyuan","name":"","tag":"pm","persona":"","temperature":0.7}'

# 
curl -X PUT 'http://127.0.0.1:51202/experts/user/pm' \
  -H 'Content-Type: application/json' \
  -d '{"user_id":"xinyuan","persona":""}'

# 
curl -X DELETE 'http://127.0.0.1:51202/experts/user/pm?user_id=xinyuan'

2.

bash

#  OASIS  #oasis#  session
curl 'http://127.0.0.1:51202/sessions/oasis?user_id=xinyuan'

3. Workflow

bash

#  workflows
curl 'http://127.0.0.1:51202/workflows?user_id=xinyuan'

#  workflow layout
curl -X POST 'http://127.0.0.1:51202/workflows' \
  -H 'Content-Type: application/json' \
  -d '{"user_id":"xinyuan","name":"trio_discussion","schedule_yaml":"version:1\nplan:\n - expert: \"creative#temp#1\"","description":"","save_layout":true}'

4. Layout

bash

#  YAML  layout
curl -X POST 'http://127.0.0.1:51202/layouts/from-yaml' \
  -H 'Content-Type: application/json' \
  -d '{"user_id":"xinyuan","yaml_source":"version:1\nplan:\n - expert: \"creative#temp#1\"","layout_name":"trio_layout"}'

5. /

bash

# 
curl -X POST 'http://127.0.0.1:51202/topics' \
  -H 'Content-Type: application/json' \
  -d '{"user_id":"xinyuan","question":"","max_rounds":3,"schedule_yaml":"version:1\nplan:\n - expert: \"creative#temp#1\"","discussion":true}'

#  topic_id
curl -X POST 'http://127.0.0.1:51202/topics' \
  -H 'Content-Type: application/json' \
  -d '{"user_id":"xinyuan","question":"","max_rounds":3,"schedule_yaml":"version:1\nplan:\n - expert: \"creative#temp#1\"","discussion":true,"callback_url":"http://127.0.0.1:51200/system_trigger","callback_session_id":"my-session"}'

# 
curl 'http://127.0.0.1:51202/topics/{topic_id}?user_id=xinyuan'

# 
curl 'http://127.0.0.1:51202/topics/{topic_id}/conclusion?user_id=xinyuan&timeout=300'

# 
curl -X DELETE 'http://127.0.0.1:51202/topics/{topic_id}?user_id=xinyuan'

# 
curl 'http://127.0.0.1:51202/topics?user_id=xinyuan'

6.

bash

# SSE 
curl 'http://127.0.0.1:51202/topics/{topic_id}/stream?user_id=xinyuan'

Workflows (YAML): data/user_files/{user}/oasis/yaml/{file}.yaml YAML layout JSON
: data/oasis_user_experts/{user}.json
: data/oasis_topics/{user}/{topic_id}.json

**** MCP list_oasis_experts``add_oasis_expert``update_oasis_expert``delete_oasis_expert``list_oasis_sessions``set_oasis_workflow``list_oasis_workflows``yaml_to_layout``post_to_oasis``check_oasis_discussion``cancel_oasis_discussion``list_oasis_topics

bash

bash selfskill/scripts/run.sh configure --init
bash selfskill/scripts/run.sh configure --batch \
  LLM_API_KEY=sk-xxxxxxxxxxxxxxxxxxxxxxxxxxxx4c74 \
  LLM_BASE_URL=https://deepseek.com \
  LLM_MODEL=deepseek-chat \
  LLM_VISION_SUPPORT=true \
  TTS_MODEL=gemini-2.5-flash-preview-tts \
  TTS_VOICE=charon \
  PORT_AGENT=51200 \
  PORT_SCHEDULER=51201 \
  PORT_OASIS=51202 \
  PORT_FRONTEND=51209 \
  PORT_BARK=58010 \
  OPENCLAW_API_URL=http://127.0.0.1:18789/v1/chat/completions \
  OPENAI_STANDARD_MODE=false
bash selfskill/scripts/run.sh add-user system <your-password>

configure --show

text

  PORT_SCHEDULER=51201
  PORT_AGENT=51200
  PORT_FRONTEND=51209
  PORT_OASIS=51202
  OASIS_BASE_URL=http://127.0.0.1:51202
  PORT_BARK=58010
  INTERNAL_TOKEN=f1aa****57e7          # 
  LLM_API_KEY=sk-7****4c74
  LLM_BASE_URL=https://deepseek.com
  LLM_MODEL=deepseek-chat
  LLM_VISION_SUPPORT=true
  TTS_MODEL=gemini-2.5-flash-preview-tts
  TTS_VOICE=charon
  OPENAI_STANDARD_MODE=false

INTERNAL_TOKEN PUBLIC_DOMAIN / BARK_PUBLIC_URL tunnel

bash

cd /home/avalon/TeamClaw

# 
bash selfskill/scripts/run.sh setup
bash selfskill/scripts/run.sh configure --init
bash selfskill/scripts/run.sh configure --batch LLM_API_KEY=sk-xxx LLM_BASE_URL=https://api.deepseek.com LLM_MODEL=deepseek-chat
bash selfskill/scripts/run.sh add-user system MyPass123

# 
bash selfskill/scripts/run.sh start

#  API
curl -X POST http://127.0.0.1:51200/v1/chat/completions \
  -H "Content-Type: application/json" \
  -H "Authorization: Bearer system:MyPass123" \
  -d '{"model":"mini-timebot","messages":[{"role":"user","content":""}],"stream":false,"session_id":"default"}'

# 
bash selfskill/scripts/run.sh stop

skill selfskill/scripts/
PID start
INTERNAL_TOKEN
: logs/launcher.log
openclaw session fileskillopenclaw agentopenclaw

⚠️ Before First Launch — Required Configuration

Before starting TeamClaw for the first time, the following environment variables must be configured. Without them the service will not function correctly.

1. LLM Configuration (Required)

⚠️ LLM API ≠ OpenClaw API — They are two completely separate sets of credentials!

LLM_API_KEY / LLM_BASE_URL / LLM_MODEL → Your LLM provider (DeepSeek, OpenAI, Google, etc.). Used for the built-in Agent's conversations and OASIS experts.

OPENCLAW_API_URL / OPENCLAW_API_KEY → Your local OpenClaw service endpoint. Used only for orchestrating OpenClaw agents on the visual Canvas.

Do NOT mix them up. They point to different services, use different keys, and serve different purposes.

Variable	Description	Example
`LLM_API_KEY`	Your LLM provider's API key. This is mandatory.	`sk-xxxxxxxxxxxxxxxx`
`LLM_BASE_URL`	Base URL of your LLM provider's API endpoint.	`https://api.deepseek.com`
`LLM_MODEL`	The model name to use for conversations.	`deepseek-chat` / `gpt-4o` / `gemini-2.5-flash`

bash

bash selfskill/scripts/run.sh configure --batch \
  LLM_API_KEY=sk-your-key \
  LLM_BASE_URL=https://api.deepseek.com \
  LLM_MODEL=deepseek-chat

2. OpenClaw Integration (Required for visual workflow orchestration)

⚠️ Reminder: OpenClaw API is NOT the same as LLM API above!

The OPENCLAW_* variables below point to your locally running OpenClaw service, not to an external LLM provider. They have completely different URLs, keys, and purposes.

These variables are required if you intend to use the OASIS visual Canvas to orchestrate OpenClaw agents:

Variable	Description	Example
`OPENCLAW_SESSIONS_FILE`	Absolute path to the OpenClaw `sessions.json` file. Used to discover existing OpenClaw agent sessions and make them available for drag-and-drop orchestration on the visual Canvas. The frontend orchestration panel will NOT load OpenClaw sessions if this is not set.	`/projects/.moltbot/agents/main/sessions/sessions.json`
`OPENCLAW_API_URL`	The OpenClaw backend API endpoint. This changes with the gateway port. You MUST first enable OpenClaw's OpenAI-compatible API interface before configuring this. Include the full path with `/v1/chat/completions`.	`http://127.0.0.1:18789/v1/chat/completions`
`OPENCLAW_API_KEY`	The API key for accessing OpenClaw via its OpenAI-compatible endpoint. Required if your OpenClaw instance has authentication enabled.	`your-openclaw-key`

Important: OPENCLAW_API_URL changes whenever the OpenClaw gateway port changes. Always verify the port is correct and that the OpenClaw OpenAI-compatible interface is enabled before starting TeamClaw.

bash

bash selfskill/scripts/run.sh configure --batch \
  OPENCLAW_SESSIONS_FILE=/projects/.moltbot/agents/main/sessions/sessions.json \
  OPENCLAW_API_URL=http://127.0.0.1:18789/v1/chat/completions \
  OPENCLAW_API_KEY=your-openclaw-key-if-needed

3. Cloudflare Tunnel (Optional — for remote access)

To expose the Web UI to the public internet for remote visual workflow programming (e.g., from a mobile phone):

The tunnel.py script will automatically write PUBLIC_DOMAIN and BARK_PUBLIC_URL into .env when a Cloudflare Tunnel is established.
No manual configuration is needed — just run the tunnel script and the frontend becomes accessible via HTTPS on the public domain.
Non-blocking start: tunnel.py blocks the terminal by default (main thread joins tunnel threads). To start it without blocking the agent or terminal, run it in the background:

bash

nohup python scripts/tunnel.py > logs/tunnel.log 2>&1 &
sleep 30  # Wait for tunnels to be established and PUBLIC_DOMAIN written to .env

⚠️ 首次启动前 — 必须配置项

首次启动 TeamClaw 之前，以下环境变量必须配置完毕，否则服务无法正常运行。

1. LLM 配置（必填）

⚠️ LLM API ≠ OpenClaw API —— 这是两组完全不同的配置！

LLM_API_KEY / LLM_BASE_URL / LLM_MODEL → 你的 LLM 服务商（DeepSeek、OpenAI、Google 等）。用于内置 Agent 对话和 OASIS 专家调用。

OPENCLAW_API_URL / OPENCLAW_API_KEY → 你的 本地 OpenClaw 服务 端点。仅用于在可视化画布上编排 OpenClaw Agent。

切勿混淆！ 它们指向不同的服务，使用不同的密钥，用途完全不同。

变量	说明	示例
`LLM_API_KEY`	LLM 服务商的 API 密钥，必填项。	`sk-xxxxxxxxxxxxxxxx`
`LLM_BASE_URL`	LLM 服务商的 API 基础地址。	`https://api.deepseek.com`
`LLM_MODEL`	使用的模型名称。	`deepseek-chat` / `gpt-4o` / `gemini-2.5-flash`

bash

bash selfskill/scripts/run.sh configure --batch \
  LLM_API_KEY=sk-your-key \
  LLM_BASE_URL=https://api.deepseek.com \
  LLM_MODEL=deepseek-chat

2. OpenClaw 集成配置（使用可视化编排时必填）

⚠️ 再次提醒：OpenClaw API 和上面的 LLM API 不是同一个东西！

下面的 OPENCLAW_* 变量指向你 本地运行的 OpenClaw 服务，而非外部 LLM 服务商。它们的 URL、密钥和用途完全不同。

如果你需要使用 OASIS 可视化画布来编排 OpenClaw Agent，以下变量必须配置：

变量	说明	示例
`OPENCLAW_SESSIONS_FILE`	OpenClaw `sessions.json` 文件的绝对路径。用于获取已有的 OpenClaw Agent session 号，使其可以在可视化画布中被拖拽使用。未配置此项时前端编排面板将无法加载 OpenClaw sessions。	`/projects/.moltbot/agents/main/sessions/sessions.json`
`OPENCLAW_API_URL`	OpenClaw 后端 API 地址。该地址随 gateway 端口变化而变化。必须先开启 OpenClaw 的 OpenAI 兼容接口，填写包含 `/v1/chat/completions` 的完整路径。	`http://127.0.0.1:18789/v1/chat/completions`
`OPENCLAW_API_KEY`	通过 OpenAI 兼容接口访问 OpenClaw 时使用的 API Key。如果你的 OpenClaw 实例启用了鉴权，则此项必填。	`your-openclaw-key`

重要提醒：OPENCLAW_API_URL 会随着 OpenClaw gateway 端口的改变而改变，启动前请务必确认端口正确，且 OpenClaw 的 OpenAI 兼容接口已开启。

bash

bash selfskill/scripts/run.sh configure --batch \
  OPENCLAW_SESSIONS_FILE=/projects/.moltbot/agents/main/sessions/sessions.json \
  OPENCLAW_API_URL=http://127.0.0.1:18789/v1/chat/completions \
  OPENCLAW_API_KEY=your-openclaw-key-if-needed

3. Cloudflare Tunnel（可选 — 用于远程访问）

如需将前端 Web UI 通过公网 HTTPS 安全暴露，以便在手机或其他远程设备上进行可视化多 Agent 工作流编排：

运行 tunnel.py 脚本后，Cloudflare Tunnel 会自动建立，并将 PUBLIC_DOMAIN 和 BARK_PUBLIC_URL 写入 .env。
无需手动配置，启动隧道后即可通过 HTTPS 公网域名访问前端。
非阻塞启动：tunnel.py 默认会阻塞终端（主线程 join 等待隧道线程）。如需避免阻塞 Agent 或终端，请后台启动：

bash

nohup python scripts/tunnel.py > logs/tunnel.log 2>&1 &
sleep 30  # 等待隧道建立完成，PUBLIC_DOMAIN 写入 .env

TeamClaw

Install

TeamClaw Agent Subsystem Skill

Introduction

Skill Scripts

Quick Start

1. First Deployment

2. Start / Stop / Status

3. Bark Push vs Chatbot (Telegram/QQ) — Startup Differences

4. Configuration Management

Configuration Options

Ports & Services

API Authentication

Method 1: User Authentication

Method 2: Internal Token (for inter-service calls, recommended)

Core API

Chat (OpenAI-compatible)

System Trigger (internal call)

Cancel Session

OASIS Four Operating Modes (Default: Discussion Mode)

1) Discussion Mode vs Execution Mode

2) Synchronous Mode vs Detach Mode

3) Auto-selection Rules (Recommended Default Strategy)

4) Four Combinations Quick Reference

OASIS Four Agent Types

Session ID Format

YAML Example

DAG Mode — Dependency-Driven Parallel Execution

External API (Type 4) Detailed Configuration

Using OASIS Server Independently

OASIS Discussion/Execution

Externally Participating in OASIS Server via curl (Complete Methods)

1. Expert Management

2. Session Management

3. Workflow Management

4. Layout Generation

5. Discussion/Execution

6. Real-time Stream

Example Configuration Reference

Typical Usage Flow

Important Notes

TeamClaw Agent Skill

Skill

1.

2. //

3.

API

1

2 Token

API

OpenAI

OASIS

1) vs

discussion=true

2) vs detach

3)

4)

OASIS

Session ID

YAML

DAG 模式 — 依赖驱动的并行执行

External API (Type 4)

OASIS Server

OASIS /

curl OASIS

1.

2.

3. Workflow

4. Layout

5. /

6.

⚠️ Before First Launch — Required Configuration

1. LLM Configuration (Required)

2. OpenClaw Integration (Required for visual workflow orchestration)

3. Cloudflare Tunnel (Optional — for remote access)

⚠️ 首次启动前 — 必须配置项

1. LLM 配置（必填）

2. OpenClaw 集成配置（使用可视化编排时必填）

3. Cloudflare Tunnel（可选 — 用于远程访问）

Related skills