PraisonAI

Name: PraisonAI
Rating: 5 (6363 reviews)
Author: mervinpraison

AI 与智能体编辑精选

by mervinpraison

PraisonAI 是一个支持自反思和多 LLM 的低代码 AI 智能体框架。

如果你需要快速搭建一个能 24/7 运行的 AI 智能体团队来处理复杂任务（比如自动研究或代码生成），PraisonAI 的低代码设计和多平台集成（如 Telegram）让它上手极快。但作为非官方项目，它的生态成熟度可能不如 LangChain 等主流框架，适合愿意尝鲜的开发者。

6.4kGitHub

什么是 PraisonAI？

PraisonAI 是一个支持自反思和多 LLM 的低代码 AI 智能体框架。

README

PraisonAI 🦞

</div>

PraisonAI 🦞 — Automate and solve complex challenges with AI agent teams that plan, research, code, and deliver results to Telegram, Discord, and WhatsApp — running 24/7. A low-code, production-ready multi-agent framework with handoffs, guardrails, memory, RAG, and 100+ LLM providers, built around simplicity, customisation, and effective human-agent collaboration.

<div align="center"> <a href="https://x.com/elonmusk/status/1893870468249141688" target="_blank"> <img src="https://img.shields.io/badge/Highlighted_by_Elon_Musk-000000?style=for-the-badge&logo=x&logoColor=white" alt="Highlighted by Elon Musk" /> </a> "Grok 3 customer support" — <a href="https://x.com/elonmusk/status/1893870468249141688">Elon Musk quoting PraisonAI's tutorial</a> </div> <img src=".github/images/dashboard.png" alt="PraisonAI Dashboard" width="800" /> <img src=".github/images/agentflow.gif" alt="PraisonAI AgentFlow" width="800" />

code

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 pip install praisonai

* export TAVILY_API_KEY=xxxxx

Quick Paths:

🆕 New here? → Quick Start (1 minute to first agent)

📦 Installing? → Installation

🐍 Python SDK? → Python Examples

📄 YAML/No-Code? → YAML Examples

🎯 CLI user? → CLI Quick Reference

🤝 Contributing? → Contributing

⚡ Performance

PraisonAI is built for speed, with agent instantiation in under 4μs. This reduces overhead, improves responsiveness, and helps multi-agent systems scale efficiently in real-world production workloads.

Performance Metric	PraisonAI
Avg Instantiation Time	3.77 μs

🎯 Use Cases

AI agents solving real-world problems across industries:

Use Case	Description
🔍 Research & Analysis	Conduct deep research, gather information, and generate insights from multiple sources automatically
💻 Code Generation	Write, debug, and refactor code with AI agents that understand your codebase and requirements
✍️ Content Creation	Generate blog posts, documentation, marketing copy, and technical writing with multi-agent teams
📊 Data Pipelines	Extract, transform, and analyze data from APIs, databases, and web sources automatically
🤖 Customer Support	Deploy 24/7 support bots on Telegram, Discord, Slack with memory and knowledge-backed responses
⚙️ Workflow Automation	Automate multi-step business processes with agents that hand off tasks, verify results, and self-correct

Supported Providers

PraisonAI supports 100+ LLM providers through seamless integration:

<img src="https://img.shields.io/badge/OpenAI-412991?style=flat&logo=openai&logoColor=white" alt="OpenAI" /> <img src="https://img.shields.io/badge/Anthropic-191919?style=flat&logo=anthropic&logoColor=white" alt="Anthropic" /> <img src="https://img.shields.io/badge/Google_Gemini-4285F4?style=flat&logo=google&logoColor=white" alt="Google Gemini" /> <img src="https://img.shields.io/badge/DeepSeek-566AB2?style=flat" alt="DeepSeek" /> <img src="https://img.shields.io/badge/Azure-0078D4?style=flat&logo=microsoftazure&logoColor=white" alt="Azure" /> <img src="https://img.shields.io/badge/Ollama-000000?style=flat" alt="Ollama" /> <img src="https://img.shields.io/badge/Groq-F05237?style=flat" alt="Groq" /> <img src="https://img.shields.io/badge/Mistral-FF7000?style=flat" alt="Mistral" /> <img src="https://img.shields.io/badge/Cerebras-F05A28?style=flat" alt="Cerebras" /> <img src="https://img.shields.io/badge/Cohere-39594D?style=flat" alt="Cohere" /> <img src="https://img.shields.io/badge/OpenRouter-6467F2?style=flat" alt="OpenRouter" /> <img src="https://img.shields.io/badge/Perplexity-20808D?style=flat" alt="Perplexity" /> <img src="https://img.shields.io/badge/Fireworks-FF6B35?style=flat" alt="Fireworks" /> <img src="https://img.shields.io/badge/AWS_Bedrock-FF9900?style=flat&logo=amazonaws&logoColor=white" alt="AWS Bedrock" /> <img src="https://img.shields.io/badge/xAI_Grok-000000?style=flat" alt="xAI Grok" /> <img src="https://img.shields.io/badge/Vertex_AI-4285F4?style=flat&logo=googlecloud&logoColor=white" alt="Vertex AI" /> <img src="https://img.shields.io/badge/HuggingFace-FFD21E?style=flat&logo=huggingface&logoColor=black" alt="HuggingFace" /> <img src="https://img.shields.io/badge/Together_AI-000000?style=flat" alt="Together AI" /> <img src="https://img.shields.io/badge/Databricks-FF3621?style=flat&logo=databricks&logoColor=white" alt="Databricks" /> <img src="https://img.shields.io/badge/Replicate-262626?style=flat" alt="Replicate" /> <img src="https://img.shields.io/badge/Cloudflare-F38020?style=flat&logo=cloudflare&logoColor=white" alt="Cloudflare" /> <details> <summary>View all 24 providers with examples</summary>

Provider	Example
OpenAI	Example
Anthropic	Example
Google Gemini	Example
Ollama	Example
Groq	Example
DeepSeek	Example
xAI Grok	Example
Mistral	Example
Cohere	Example
Perplexity	Example
Fireworks	Example
Together AI	Example
OpenRouter	Example
HuggingFace	Example
Azure OpenAI	Example
AWS Bedrock	Example
Google Vertex	Example
Databricks	Example
Cloudflare	Example
AI21	Example
Replicate	Example
SageMaker	Example
Moonshot	Example
vLLM	Example

</details>

🌟 Why PraisonAI?

	Feature	How
🔌	MCP Protocol — stdio, HTTP, WebSocket, SSE	`tools=MCP("npx ...")`
🧠	Planning Mode — plan → execute → reason	`planning=True`
🔍	Deep Research — multi-step autonomous research	Docs
🤖	External Agents — orchestrate Claude Code, Gemini CLI, Codex	Docs
🔄	Agent Handoffs — seamless conversation passing	`handoff=True`
🛡️	Guardrails — input/output validation	Docs
	Web Search + Fetch — native browsing	`web_search=True`
🪞	Self Reflection — agent reviews its own output	Docs
🔀	Workflow Patterns — route, parallel, loop, repeat	Docs
🧠	Memory (zero deps) — works out of the box	`memory=True`

<details> <summary>View all 25 features</summary>

	Feature	How
💡	Prompt Caching — reduce latency + cost	`prompt_caching=True`
💾	Sessions + Auto-Save — persistent state across restarts	`auto_save="my-project"`
💭	Thinking Budgets — control reasoning depth	`thinking_budget=1024`
📚	RAG + Quality-Based RAG — auto quality scoring retrieval	Docs
📊	Model Router — auto-routes to cheapest capable model	Docs
🧊	Shadow Git Checkpoints — auto-rollback on failure	Docs
📡	A2A Protocol — agent-to-agent interop	Docs
📏	Context Compaction — never hit token limits	Docs
📡	Telemetry — OpenTelemetry traces, spans, metrics	Docs
📜	Policy Engine — declarative agent behavior control	Docs
🔄	Background Tasks — fire-and-forget agents	Docs
🔁	Doom Loop Detection — auto-recovery from stuck agents	Docs
🕸️	Graph Memory — Neo4j-style relationship tracking	Docs
🏖️	Sandbox Execution — isolated code execution	Docs
🖥️	Bot Gateway — multi-agent routing across channels	Docs

</details> <div align="center"> <a href="https://x.com/elonmusk/status/1893870468249141688" target="_blank"> <img src=".github/images/elon_musk_praisonai.png" alt="Highlighted by Elon Musk" width="600" /> </a> "Grok 3 customer support" — <a href="https://x.com/elonmusk/status/1893870468249141688">Elon Musk quoting PraisonAI's tutorial</a> </div>

🚀 Quick Start

Get started with PraisonAI in under 1 minute:

bash

# Install
pip install praisonaiagents

# Set API key
export OPENAI_API_KEY=your_key_here

# Create a simple agent
python -c "from praisonaiagents import Agent; Agent(instructions='You are a helpful AI assistant').start('Write a haiku about AI')"

Next Steps: Single Agent Example | Multi Agents | Full Docs

📦 Installation

Python SDK

Lightweight package dedicated for coding:

bash

pip install praisonaiagents

For the full framework with CLI support:

bash

pip install praisonai

🦞 PraisonAI Claw — full UI with bots, memory, knowledge, and gateway:

bash

pip install "praisonai[claw]"
praisonai claw

🔗 PraisonAI Flow — Langflow Visual Flow Builder:

bash

pip install "praisonai[flow]"
praisonai flow

🤖 PraisonAI UI — Clean chat interface:

bash

pip install "praisonai[ui]"
praisonai ui

JavaScript SDK

bash

npm install praisonai

📘 Using Python Code

1. Single Agent

python

from praisonaiagents import Agent
agent = Agent(instructions="You are a helpful AI assistant")
agent.start("Write a movie script about a robot in Mars")

2. Multi Agents

python

from praisonaiagents import Agent, Agents

research_agent = Agent(instructions="Research about AI")
summarise_agent = Agent(instructions="Summarise research agent's findings")
agents = Agents(agents=[research_agent, summarise_agent])
agents.start()

3. MCP (Model Context Protocol)

python

from praisonaiagents import Agent, MCP

# stdio - Local NPX/Python servers
agent = Agent(tools=MCP("npx @modelcontextprotocol/server-memory"))

# Streamable HTTP - Production servers
agent = Agent(tools=MCP("https://api.example.com/mcp"))

# WebSocket - Real-time bidirectional
agent = Agent(tools=MCP("wss://api.example.com/mcp", auth_token="token"))

# With environment variables
agent = Agent(
    tools=MCP(
        command="npx",
        args=["-y", "@modelcontextprotocol/server-brave-search"],
        env={"BRAVE_API_KEY": "your-key"}
    )
)

📖 Full MCP docs — stdio, HTTP, WebSocket, SSE transports

4. Custom Tools

python

from praisonaiagents import Agent, tool

@tool
def search(query: str) -> str:
    """Search the web for information."""
    return f"Results for: {query}"

@tool
def calculate(expression: str) -> float:
    """Evaluate a math expression."""
    return eval(expression)

agent = Agent(
    instructions="You are a helpful assistant",
    tools=[search, calculate]
)
agent.start("Search for AI news and calculate 15*4")

📖 Full tools docs — BaseTool, tool packages, 100+ built-in tools

5. Persistence (Databases)

python

from praisonaiagents import Agent, db

agent = Agent(
    name="Assistant",
    db=db(database_url="postgresql://localhost/mydb"),
    session_id="my-session"
)
agent.chat("Hello!")  # Auto-persists messages, runs, traces

📖 Full persistence docs — PostgreSQL, MySQL, SQLite, MongoDB, Redis, and 20+ more

6. PraisonAI Claw 🦞 (Dashboard UI)

Connect your AI agents to Telegram, Discord, Slack, WhatsApp and more — all from a single command.

bash

pip install "praisonai[claw]"
praisonai claw

Open http://localhost:8082 — the dashboard comes with 13 built-in pages: Chat, Agents, Memory, Knowledge, Channels, Guardrails, Cron, and more. Add messaging channels directly from the UI.

📖 Full Claw docs — platform tokens, CLI options, Docker, and YAML agent mode

7. Langflow Integration 🔗 (Visual Flow Builder)

Build multi-agent workflows visually with drag-and-drop components in Langflow.

bash

pip install "praisonai[flow]"
praisonai flow

Open http://localhost:7861 — use the Agent and Agent Team components to create sequential or parallel workflows. Connect Chat Input → Agent Team → Chat Output for instant multi-agent pipelines.

📖 Full Flow docs — visual agent building, component reference, and deployment

8. PraisonAI UI 🤖 (Clean Chat)

Lightweight chat interface for your AI agents.

bash

pip install "praisonai[ui]"
praisonai ui

📄 Using YAML (No Code)

Example 1: Two Agents Working Together

Create agents.yaml:

yaml

framework: praisonai
topic: "Write a blog post about AI"

agents:
  researcher:
    role: Research Analyst
    goal: Research AI trends and gather information
    instructions: "Find accurate information about AI trends"
    
  writer:
    role: Content Writer
    goal: Write engaging blog posts
    instructions: "Write clear, engaging content based on research"

Run with:

bash

praisonai agents.yaml

The agents automatically work together sequentially

Example 2: Agent with Custom Tool

Create two files in the same folder:

agents.yaml:

yaml

framework: praisonai
topic: "Calculate the sum of 25 and 15"

agents:
  calculator_agent:
    role: Calculator
    goal: Perform calculations
    instructions: "Use the add_numbers tool to help with calculations"
    tools:
      - add_numbers

tools.py:

python

def add_numbers(a: float, b: float) -> float:
    """
    Add two numbers together.
    
    Args:
        a: First number
        b: Second number
    
    Returns:
        The sum of a and b
    """
    return a + b

Run with:

bash

praisonai agents.yaml

💡 Tips:

Use the function name (e.g., add_numbers) in the tools list, not the file name

Tools in tools.py are automatically discovered

The function's docstring helps the AI understand how to use it

🎯 CLI Quick Reference

Category	Commands
Execution	`praisonai`, `--auto`, `--interactive`, `--chat`
Research	`research`, `--query-rewrite`, `--deep-research`
Planning	`--planning`, `--planning-tools`, `--planning-reasoning`
Workflows	`workflow run`, `workflow list`, `workflow auto`
Memory	`memory show`, `memory add`, `memory search`, `memory clear`
Knowledge	`knowledge add`, `knowledge query`, `knowledge list`
Sessions	`session list`, `session resume`, `session delete`
Tools	`tools list`, `tools info`, `tools search`
MCP	`mcp list`, `mcp create`, `mcp enable`
Development	`commit`, `docs`, `checkpoint`, `hooks`
Scheduling	`schedule start`, `schedule list`, `schedule stop`

📖 Full CLI reference

✨ Key Features

<details open> <summary>🤖 Core Agents</summary>

Feature	Code	Docs
Single Agent	Example	📖
Multi Agents	Example	📖
Auto Agents	Example	📖
Self Reflection AI Agents	Example	📖
Reasoning AI Agents	Example	📖
Multi Modal AI Agents	Example	📖

</details> <details> <summary>🔄 Workflows</summary>

Feature	Code	Docs
Simple Workflow	Example	📖
Workflow with Agents	Example	📖
Agentic Routing (`route()`)	Example	📖
Parallel Execution (`parallel()`)	Example	📖
Loop over List/CSV (`loop()`)	Example	📖
Evaluator-Optimizer (`repeat()`)	Example	📖
Conditional Steps	Example	📖
Workflow Branching	Example	📖
Workflow Early Stop	Example	📖
Workflow Checkpoints	Example	📖

</details> <details> <summary>💻 Code & Development</summary>

Feature	Code	Docs
Code Interpreter Agents	Example	📖
AI Code Editing Tools	Example	📖
External Agents (All)	Example	📖
Claude Code CLI	Example	📖
Gemini CLI	Example	📖
Codex CLI	Example	📖
Cursor CLI	Example	📖

</details> <details> <summary>🧠 Memory & Knowledge</summary>

Feature	Code	Docs
Memory (Short & Long Term)	Example	📖
File-Based Memory	Example	📖
Claude Memory Tool	Example	📖
Add Custom Knowledge	Example	📖
RAG Agents	Example	📖
Chat with PDF Agents	Example	📖
Data Readers (PDF, DOCX, etc.)	CLI	📖
Vector Store Selection	CLI	📖
Retrieval Strategies	CLI	📖
Rerankers	CLI	📖
Index Types (Vector/Keyword/Hybrid)	CLI	📖
Query Engines (Sub-Question, etc.)	CLI	📖

</details> <details> <summary>🔬 Research & Intelligence</summary>

Feature	Code	Docs
Deep Research Agents	Example	📖
Query Rewriter Agent	Example	📖
Native Web Search	Example	📖
Built-in Search Tools	Example	📖
Unified Web Search	Example	📖
Web Fetch (Anthropic)	Example	📖

</details> <details> <summary>📋 Planning & Execution</summary>

Feature	Code	Docs
Planning Mode	Example	📖
Planning Tools	Example	📖
Planning Reasoning	Example	📖
Prompt Chaining	Example	📖
Evaluator Optimiser	Example	📖
Orchestrator Workers	Example	📖

</details> <details> <summary>👥 Specialized Agents</summary>

Feature	Code	Docs
Data Analyst Agent	Example	📖
Finance Agent	Example	📖
Shopping Agent	Example	📖
Recommendation Agent	Example	📖
Wikipedia Agent	Example	📖
Programming Agent	Example	📖
Math Agents	Example	📖
Markdown Agent	Example	📖
Prompt Expander Agent	Example	📖

</details> <details> <summary>🎨 Media & Multimodal</summary>

Feature	Code	Docs
Image Generation Agent	Example	📖
Image to Text Agent	Example	📖
Video Agent	Example	📖
Camera Integration	Example	📖

</details> <details> <summary>🔌 Protocols & Integration</summary>

Feature	Code	Docs
MCP Transports	Example	📖
WebSocket MCP	Example	📖
MCP Security	Example	📖
MCP Resumability	Example	📖
MCP Config Management	Docs	📖
LangChain Integrated Agents	Example	📖

</details> <details> <summary>🛡️ Safety & Control</summary>

Feature	Code	Docs
Guardrails	Example	📖
Human Approval	Example	📖
Rules & Instructions	Docs	📖

</details> <details> <summary>⚙️ Advanced Features</summary>

Feature	Code	Docs
Async & Parallel Processing	Example	📖
Parallelisation	Example	📖
Repetitive Agents	Example	📖
Agent Handoffs	Example	📖
Stateful Agents	Example	📖
Autonomous Workflow	Example	📖
Structured Output Agents	Example	📖
Model Router	Example	📖
Prompt Caching	Example	📖
Fast Context	Example	📖

</details> <details> <summary>🛠️ Tools & Configuration</summary>

Feature	Code	Docs
100+ Custom Tools	Example	📖
YAML Configuration	Example	📖
100+ LLM Support	Example	📖
Callback Agents	Example	📖
Hooks	Example	📖
Middleware System	Example	📖
Configurable Model	Example	📖
Rate Limiter	Example	📖
Injected Tool State	Example	📖
Shadow Git Checkpoints	Example	📖
Background Tasks	Example	📖
Policy Engine	Example	📖
Thinking Budgets	Example	📖
Output Styles	Example	📖
Context Compaction	Example	📖

</details> <details> <summary>📊 Monitoring & Management</summary>

Feature	Code	Docs
Sessions Management	Example	📖
Auto-Save Sessions	Docs	📖
History in Context	Docs	📖
Telemetry	Example	📖
Project Docs (.praison/docs/)	Docs	📖
AI Commit Messages	Docs	📖
@Mentions in Prompts	Docs	📖

</details> <details> <summary>🖥️ CLI Features</summary>

Feature	Code	Docs
Slash Commands	Example	📖
Autonomy Modes	Example	📖
Cost Tracking	Example	📖
Repository Map	Example	📖
Interactive TUI	Example	📖
Git Integration	Example	📖
Sandbox Execution	Example	📖
CLI Compare	Example	📖
Profile/Benchmark	Docs	📖
Auto Mode	Docs	📖
Init	Docs	📖
File Input	Docs	📖
Final Agent	Docs	📖
Max Tokens	Docs	📖

</details> <details> <summary>🧪 Evaluation</summary>

Feature	Code	Docs
Accuracy Evaluation	Example	📖
Performance Evaluation	Example	📖
Reliability Evaluation	Example	📖
Criteria Evaluation	Example	📖

</details> <details> <summary>🎯 Agent Skills</summary>

Feature	Code	Docs
Skills Management	Example	📖
Custom Skills	Example	📖

</details> <details> <summary>⏰ 24/7 Scheduling</summary>

Feature	Code	Docs
Agent Scheduler	Example	📖

</details>

💻 Using JavaScript Code

bash

npm install praisonai
export OPENAI_API_KEY=xxxxxxxxxxxxxxxxxxxxxx

javascript

const { Agent } = require('praisonai');
const agent = new Agent({ instructions: 'You are a helpful AI assistant' });
agent.start('Write a movie script about a robot in Mars');

⭐ Star History

🎓 Video Tutorials

Learn PraisonAI through our comprehensive video series:

<details> <summary>View all 22 video tutorials</summary>

Topic	Video
AI Agents with Self Reflection
Reasoning Data Generating Agent
AI Agents with Reasoning
Multimodal AI Agents
AI Agents Workflow
Async AI Agents
Mini AI Agents
AI Agents with Memory
Repetitive Agents
Introduction
Tools Overview
Custom Tools
Firecrawl Integration
User Interface
Crawl4AI Integration
Chat Interface
Code Interface
Mem0 Integration
Training
Realtime Voice Interface
Call Interface
Reasoning Extract Agents

</details>

👥 Contributing

We welcome contributions! Fork the repo, create a branch, and submit a PR → Contributing Guide.

❓ FAQ & Troubleshooting

<details> <summary>ModuleNotFoundError: No module named 'praisonaiagents'</summary>

Install the package:

bash

pip install praisonaiagents

</details> <details> <summary>API key not found / Authentication error</summary>

Ensure your API key is set:

bash

export OPENAI_API_KEY=your_key_here

For other providers, see Models docs.

</details> <details> <summary>How do I use a local model (Ollama)?</summary>

bash

# Start Ollama server first
ollama serve

# Set environment variable
export OPENAI_BASE_URL=http://localhost:11434/v1

See Models docs for more details.

</details> <details> <summary>How do I persist conversations to a database?</summary>

Use the db parameter:

python

from praisonaiagents import Agent, db

agent = Agent(
    name="Assistant",
    db=db(database_url="postgresql://localhost/mydb"),
    session_id="my-session"
)

See Persistence docs for supported databases.

</details> <details> <summary>How do I enable agent memory?</summary>

python

from praisonaiagents import Agent

agent = Agent(
    name="Assistant",
    memory=True,  # Enables file-based memory (no extra deps!)
    user_id="user123"
)

See Memory docs for more options.

</details> <details> <summary>How do I run multiple agents together?</summary>

python

from praisonaiagents import Agent, Agents

agent1 = Agent(instructions="Research topics")
agent2 = Agent(instructions="Summarize findings")
agents = Agents(agents=[agent1, agent2])
agents.start()

See Agents docs for more examples.

</details> <details> <summary>How do I use MCP tools?</summary>

python

from praisonaiagents import Agent, MCP

agent = Agent(
    tools=MCP("npx @modelcontextprotocol/server-memory")
)

See MCP docs for all transport options.

</details>

Getting Help

<div align="center"> Made with ❤️ by the PraisonAI Team <a href="https://docs.praison.ai">📚 Documentation</a> • <a href="https://github.com/MervinPraison/PraisonAI">GitHub</a> • <a href="https://youtube.com/@MervinPraison">▶️ YouTube</a> • <a href="https://x.com/MervinPraison">𝕏 X</a> • <a href="https://linkedin.com/in/mervinpraison">💼 LinkedIn</a> </div>

常见问题

PraisonAI 是什么？

AI Agents Framework with Self Reflection and MCP support

PraisonAI

什么是 PraisonAI？

README

PraisonAI 🦞

⚡ Performance

🎯 Use Cases

Supported Providers

🌟 Why PraisonAI?

🚀 Quick Start

📦 Installation

Python SDK

JavaScript SDK

📘 Using Python Code

1. Single Agent

2. Multi Agents

3. MCP (Model Context Protocol)

4. Custom Tools

5. Persistence (Databases)

6. PraisonAI Claw 🦞 (Dashboard UI)

7. Langflow Integration 🔗 (Visual Flow Builder)

8. PraisonAI UI 🤖 (Clean Chat)

📄 Using YAML (No Code)

Example 1: Two Agents Working Together

Example 2: Agent with Custom Tool

🎯 CLI Quick Reference

✨ Key Features

💻 Using JavaScript Code

⭐ Star History

🎓 Video Tutorials

👥 Contributing

❓ FAQ & Troubleshooting

Getting Help

常见问题

PraisonAI 是什么？

相关 Skills

Claude接口

提示工程专家

智能体流程设计

相关 MCP Server

顺序思维

知识图谱记忆

ai.klavis/strata

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