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/

run.sh

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

# 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 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

launcher.py

bin/bark-server

setup

TELEGRAM_BOT_TOKEN

TELEGRAM_ALLOWED_USERS

.env

launcher.py

chatbot/setup.py

input()

.env

nohup python chatbot/telegrambot.py > logs/telegrambot.log 2>&1 &

QQ_APP_ID

QQ_BOT_SECRET

QQ_BOT_USERNAME

.env

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:

1. Either remove/rename

   chatbot/setup.py

   before starting, OR
2. Pre-configure all bot tokens in

   .env

   and start bots independently (bypassing

   setup.py

   ).

4. Configuration Management

# 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

LLM_API_KEY

LLM_BASE_URL

https://api.deepseek.com

LLM_MODEL

deepseek-chat

LLM_PROVIDER

LLM_VISION_SUPPORT

PORT_AGENT

51200

PORT_SCHEDULER

51201

PORT_OASIS

51202

PORT_FRONTEND

51209

PORT_BARK

58010

TTS_MODEL

TTS_VOICE

OPENCLAW_API_URL

/v1/chat/completions

http://127.0.0.1:18789/v1/chat/completions

OPENCLAW_API_KEY

OPENCLAW_SESSIONS_FILE

None

INTERNAL_TOKEN

Ports & Services

API Authentication

Method 1: User Authentication

Authorization: Bearer <user_id>:<password>

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

Authorization: Bearer <INTERNAL_TOKEN>:<user_id>

INTERNAL_TOKEN

configure --show-raw

Core API

Base URL:

http://127.0.0.1:51200

Chat (OpenAI-compatible)

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)

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

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

Cancel Session

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:

1. Default = Discussion + Detach

   • discussion=true

   • detach=true

2. 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"

3. 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

OASIS Four Agent Types

OASIS supports four types of agents, distinguished by the

name

schedule_yaml

tag#temp#N

ExpertAgent

tag

N

tag#oasis#id

SessionExpert

tag

id

Title#session_id

SessionExpert

Assistant#default

Coder#my-project

tag#ext#id

ExternalExpert

headers

Session ID Format

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

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

depends_on

DAG YAML Example:

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:

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:

api_url

/v1/chat/completions

api_key

****

OPENCLAW_API_KEY

model

gpt-3.5-turbo

headers

OpenClaw-specific Configuration:

OpenClaw is a locally running OpenAI-compatible Agent service. After setting up OpenClaw-specific endpoints in

.env

api_url

api_key

# 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

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>

headers

x-openclaw-session-key

When calling an OpenClaw agent via External API (Type 4), the

x-openclaw-session-key

• 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.

# 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:

/experts?user_id=xxx

/experts/user

/experts/user/{tag}

/sessions/oasis?user_id=xxx

/workflows

/workflows?user_id=xxx

/layouts/from-yaml

/topics

/topics/{topic_id}?user_id=xxx

/topics/{topic_id}/conclusion?user_id=xxx&timeout=300

/topics/{topic_id}/stream?user_id=xxx

/topics/{topic_id}?user_id=xxx

/topics?user_id=xxx

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

OASIS Discussion/Execution

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

1. Expert Management

# 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

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

3. Workflow Management

# 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

# 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

# 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

# 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

Example Configuration Reference

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

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

  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

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

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

****

setup

configure

start

1.

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

3.

# 
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_BASE_URL

https://api.deepseek.com

LLM_MODEL

deepseek-chat

LLM_PROVIDER

LLM_VISION_SUPPORT

PORT_AGENT

51200

PORT_SCHEDULER

51201

PORT_OASIS

51202

PORT_FRONTEND

51209

PORT_BARK

58010

TTS_MODEL

TTS_VOICE

OPENCLAW_API_URL

/v1/chat/completions

http://127.0.0.1:18789/v1/chat/completions

OPENCLAW_API_KEY

OPENCLAW_SESSIONS_FILE

None

INTERNAL_TOKEN

API

1

Authorization: Bearer <user_id>:<password>

2 Token

Authorization: Bearer <INTERNAL_TOKEN>:<user_id>

INTERNAL_TOKEN

configure --show-raw

API

Base URL:

http://127.0.0.1:51200

OpenAI

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

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

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

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

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)

1. ** = + **

   • discussion=true

   • detach=true

2. ---

• " / / / "
• " SOP / / "

• " / / / "
• /

4)

OASIS

OASIS ****

schedule_yaml

name

tag#temp#N

ExpertAgent

tag

N

tag#oasis#id

SessionExpert

tag

id

Title#session_id

SessionExpert

#default``Coder#my-project

tag#ext#id

ExternalExpert

headers

Session ID

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 DB

  data/agent_memory.db

• tag

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

depends_on

DAG YAML 示例：

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

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

/v1/chat/completions

api_key

****

OPENCLAW_API_KEY

model

gpt-3.5-turbo

headers

**OpenClaw **

OpenClaw OpenAI Agent

.env

api_url

api_key

#  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

agent:<agent_name>:<session_name>

• agent_name

  OpenClaw 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>

headers

x-openclaw-session-key

通过 External API（Type 4）调用 OpenClaw agent 时，

x-openclaw-session-key

• 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。

# 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 **

/experts?user_id=xxx

/experts/user

/experts/user/{tag}

/sessions/oasis?user_id=xxx

/workflows

/workflows?user_id=xxx

/layouts/from-yaml

/topics

/topics/{topic_id}?user_id=xxx

/topics/{topic_id}/conclusion?user_id=xxx&timeout=300

/topics/{topic_id}/stream?user_id=xxx

/topics/{topic_id}?user_id=xxx

/topics?user_id=xxx

MCP

OASIS /

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.

#  + 
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.

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

3. Workflow

#  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

#  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. /

# 
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.

# 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 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

  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

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.

LLM_API_KEY

sk-xxxxxxxxxxxxxxxx

LLM_BASE_URL

https://api.deepseek.com

LLM_MODEL

deepseek-chat

gpt-4o

gemini-2.5-flash

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:

OPENCLAW_SESSIONS_FILE

sessions.json

/projects/.moltbot/agents/main/sessions/sessions.json

OPENCLAW_API_URL

/v1/chat/completions

http://127.0.0.1:18789/v1/chat/completions

OPENCLAW_API_KEY

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 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:

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。

LLM_API_KEY

sk-xxxxxxxxxxxxxxxx

LLM_BASE_URL

https://api.deepseek.com

LLM_MODEL

deepseek-chat

gpt-4o

gemini-2.5-flash

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：

OPENCLAW_SESSIONS_FILE

sessions.json

/projects/.moltbot/agents/main/sessions/sessions.json

OPENCLAW_API_URL

/v1/chat/completions

http://127.0.0.1:18789/v1/chat/completions

OPENCLAW_API_KEY

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 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：

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