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PraisonAI

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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。

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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

<p align="center"> <picture> <source media="(prefers-color-scheme: dark)" srcset=".github/images/logo_dark.png" /> <source media="(prefers-color-scheme: light)" srcset=".github/images/logo_light.png" /> <img alt="PraisonAI Logo" src=".github/images/logo_light.png" width="250" /> </picture> </p> <!-- mcp-name: io.github.MervinPraison/praisonai --> <p align="center"> <a href="https://github.com/MervinPraison/PraisonAI"><img src="https://static.pepy.tech/badge/PraisonAI" alt="Total Downloads" /></a> <a href="https://github.com/MervinPraison/PraisonAI"><img src="https://img.shields.io/github/v/release/MervinPraison/PraisonAI" alt="Latest Stable Version" /></a> <a href="https://github.com/MervinPraison/PraisonAI"><img src="https://img.shields.io/badge/License-MIT-yellow.svg" alt="License" /></a> <a href="https://registry.modelcontextprotocol.io/servers/io.github.MervinPraison/praisonai"><img src="https://img.shields.io/badge/MCP-Registry-blue" alt="MCP Registry" /></a> </p> <div align="center">

PraisonAI 🦞

<a href="https://trendshift.io/repositories/9130" target="_blank"><img src="https://trendshift.io/api/badge/repositories/9130" alt="MervinPraison%2FPraisonAI | Trendshift" style="width: 250px; height: 55px;" width="250" height="55"/></a>

</div>

PraisonAI 🦞 — Hire a 24/7 AI Workforce. Stop writing boilerplate and start shipping autonomous, self-improving agents that research, plan, and execute tasks across your apps. From one agent to an entire organization, deployed in 5 lines of code.

bash
curl -fsSL https://praison.ai/install.sh | bash
<div align="center"> <br> <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> <br> </div> <p align="center"> <img src=".github/images/dashboard.png" alt="PraisonAI Dashboard" width="800" /> </p> <p align="center"> <img src=".github/images/agentflow.gif" alt="PraisonAI AgentFlow" width="800" /> </p>
code
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 pip install praisonai
<p align="center"> <img src=".github/images/latest_ai_news_and_crawl_each_url_to_find_info.gif" alt="PraisonAI command execution" width="800" /> </p>

* export TAVILY_API_KEY=xxxxx

<div align="center"> <a href="https://docs.praison.ai"> <p align="center"> <img src="https://img.shields.io/badge/📚_Documentation-Visit_docs.praison.ai-blue?style=for-the-badge&logo=bookstack&logoColor=white" alt="Documentation" /> </p> </a> </div>

🎯 Use Cases

AI agents solving real-world problems across industries:

Use CaseDescription
🔍 Research & AnalysisConduct deep research, gather information, and generate insights from multiple sources automatically
💻 Code GenerationWrite, debug, and refactor code with AI agents that understand your codebase and requirements
✍️ Content CreationGenerate blog posts, documentation, marketing copy, and technical writing with multi-agent teams
📊 Data PipelinesExtract, transform, and analyze data from APIs, databases, and web sources automatically
🤖 Customer SupportDeploy 24/7 support bots on Telegram, Discord, Slack with memory and knowledge-backed responses
⚙️ Workflow AutomationAutomate multi-step business processes with agents that hand off tasks, verify results, and self-correct

🚀 Meet your first Agent (Under 1 Minute)

  1. Install the lightweight core SDK:
bash
pip install praisonaiagents
export OPENAI_API_KEY="your-api-key"
  1. Run your first autonomous agent:
python
from praisonaiagents import Agent

# Give your agent a goal, and watch it work.
agent = Agent(instructions="You are a senior data analyst.")
agent.start("Analyze the top 3 tech trends of 2026 and format as a markdown table.")

🌌 The PraisonAI Ecosystem

Start simple with the core SDK, or expand to full visual builders and dashboards when you're ready.

  • Core SDK (praisonaiagents): For pure Python development. pip install praisonaiagents
  • 💻 PraisonAI CLI (praisonai): For terminal-based developers. pip install praisonai
  • 🦞 Claw Dashboard: Connect agents directly to Telegram, Slack, or Discord. pip install "praisonai[claw]"
  • 🔗 Flow Visual Builder: Drag-and-drop workflow creation. pip install "praisonai[flow]"
  • 🤖 PraisonAI UI: Clean chat interface. pip install "praisonai[ui]"

JavaScript SDK

bash
npm install praisonai

🧠 Supported Providers & Features

Powered by 100+ LLMs (OpenAI, Anthropic, Gemini & local models).

<p align="center"> <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" /> </p> <details> <summary><strong>View all 24 providers with examples</strong></summary>
ProviderExample
OpenAIExample
AnthropicExample
Google GeminiExample
OllamaExample
GroqExample
DeepSeekExample
xAI GrokExample
MistralExample
CohereExample
PerplexityExample
FireworksExample
Together AIExample
OpenRouterExample
HuggingFaceExample
Azure OpenAIExample
AWS BedrockExample
Google VertexExample
DatabricksExample
CloudflareExample
AI21Example
ReplicateExample
SageMakerExample
MoonshotExample
vLLMExample
</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> <p><em>"Grok 3 customer support" — <a href="https://x.com/elonmusk/status/1893870468249141688">Elon Musk quoting PraisonAI's tutorial</a></em></p> </div> <br>

🌟 Why PraisonAI?

FeatureHow
🔌MCP Protocol — stdio, HTTP, WebSocket, SSEtools=MCP("npx ...")
🧠Planning Mode — plan → execute → reasonplanning=True
🔍Deep Research — multi-step autonomous researchDocs
🤖External Agents — orchestrate Claude Code, Gemini CLI, CodexDocs
🔄Agent Handoffs — seamless conversation passinghandoff=True
🛡️Guardrails — input/output validationDocs
Web Search + Fetch — native browsingweb_search=True
🪞Self Reflection — agent reviews its own outputDocs
🔀Workflow Patterns — route, parallel, loop, repeatDocs
🧠Memory (zero deps) — works out of the boxmemory=True
<details> <summary><strong>View all 25 features</strong></summary>
FeatureHow
💡Prompt Caching — reduce latency + costprompt_caching=True
💾Sessions + Auto-Save — persistent state across restartsauto_save="my-project"
💭Thinking Budgets — control reasoning depththinking_budget=1024
📚RAG + Quality-Based RAG — auto quality scoring retrievalDocs
📊Model Router — auto-routes to cheapest capable modelDocs
🧊Shadow Git Checkpoints — auto-rollback on failureDocs
📡A2A Protocol — agent-to-agent interopDocs
📏Context Compaction — never hit token limitsDocs
📡Telemetry — OpenTelemetry traces, spans, metricsDocs
📜Policy Engine — declarative agent behavior controlDocs
🔄Background Tasks — fire-and-forget agentsDocs
🔁Doom Loop Detection — auto-recovery from stuck agentsDocs
🕸️Graph Memory — Neo4j-style relationship trackingDocs
🏖️Sandbox Execution — isolated code executionDocs
🖥️Bot Gateway — multi-agent routing across channelsDocs
</details>

📘 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:
    """Safely evaluate a numeric arithmetic expression."""
    import ast
    import operator
    
    # Define allowed operations
    _OPS = {
        ast.Add: operator.add,
        ast.Sub: operator.sub,
        ast.Mult: operator.mul,
        ast.Div: operator.truediv,
        ast.Pow: operator.pow,
        ast.USub: operator.neg,
        ast.UAdd: operator.pos,
    }
    
    def _safe_eval(node):
        if isinstance(node, ast.Constant) and isinstance(node.value, (int, float)):
            return node.value
        elif isinstance(node, ast.BinOp) and type(node.op) in _OPS:
            return _OPS[type(node.op)](_safe_eval(node.left), _safe_eval(node.right))
        elif isinstance(node, ast.UnaryOp) and type(node.op) in _OPS:
            return _OPS[type(node.op)](_safe_eval(node.operand))
        else:
            raise ValueError("Unsupported expression")
    
    try:
        return _safe_eval(ast.parse(expression, mode="eval").body)
    except (ValueError, SyntaxError, TypeError, ZeroDivisionError, OverflowError):
        raise ValueError("Invalid arithmetic expression")

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

⚠️ Security Note: Never use eval(), exec(), or subprocess in tool functions that process LLM-generated or user-supplied input. Always validate and sanitize inputs to prevent code injection attacks. 📖 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

Required Environment Variables

Copy .env.example to .env and configure the following variables:

VariableRequiredDescription
OPENAI_API_KEYYesOpenAI API key for all LLM calls
TAVILY_API_KEYYes (Claw)Tavily key for the built-in web-search tool. Get one free at https://app.tavily.com

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

CategoryCommands
Executionpraisonai, --auto, --interactive, --chat
Researchresearch, --query-rewrite, --deep-research
Planning--planning, --planning-tools, --planning-reasoning
Workflowsworkflow run, workflow list, workflow auto
Memorymemory show, memory add, memory search, memory clear
Knowledgeknowledge add, knowledge query, knowledge list
Sessionssession list, session resume, session delete
Toolstools list, tools info, tools search
MCPmcp list, mcp create, mcp enable
Developmentcommit, docs, checkpoint, hooks
Schedulingschedule start, schedule list, schedule stop

📖 Full CLI reference

✨ Key Features

<details open> <summary><strong>🤖 Core Agents</strong></summary>
FeatureCodeDocs
Single AgentExample📖
Multi AgentsExample📖
Auto AgentsExample📖
Self Reflection AI AgentsExample📖
Reasoning AI AgentsExample📖
Multi Modal AI AgentsExample📖
</details> <details> <summary><strong>🔄 Workflows</strong></summary>
FeatureCodeDocs
Simple WorkflowExample📖
Workflow with AgentsExample📖
Agentic Routing (route())Example📖
Parallel Execution (parallel())Example📖
Loop over List/CSV (loop())Example📖
Evaluator-Optimizer (repeat())Example📖
Conditional StepsExample📖
Workflow BranchingExample📖
Workflow Early StopExample📖
Workflow CheckpointsExample📖
</details> <details> <summary><strong>💻 Code & Development</strong></summary>
FeatureCodeDocs
Code Interpreter AgentsExample📖
AI Code Editing ToolsExample📖
External Agents (All)Example📖
Claude Code CLIExample📖
Gemini CLIExample📖
Codex CLIExample📖
Cursor CLIExample📖
</details> <details> <summary><strong>🧠 Memory & Knowledge</strong></summary>
FeatureCodeDocs
Memory (Short & Long Term)Example📖
File-Based MemoryExample📖
Claude Memory ToolExample📖
Add Custom KnowledgeExample📖
RAG AgentsExample📖
Chat with PDF AgentsExample📖
Data Readers (PDF, DOCX, etc.)CLI📖
Vector Store SelectionCLI📖
Retrieval StrategiesCLI📖
RerankersCLI📖
Index Types (Vector/Keyword/Hybrid)CLI📖
Query Engines (Sub-Question, etc.)CLI📖
</details> <details> <summary><strong>🔬 Research & Intelligence</strong></summary>
FeatureCodeDocs
Deep Research AgentsExample📖
Query Rewriter AgentExample📖
Native Web SearchExample📖
Built-in Search ToolsExample📖
Unified Web SearchExample📖
Web Fetch (Anthropic)Example📖
</details> <details> <summary><strong>📋 Planning & Execution</strong></summary>
FeatureCodeDocs
Planning ModeExample📖
Planning ToolsExample📖
Planning ReasoningExample📖
Prompt ChainingExample📖
Evaluator OptimiserExample📖
Orchestrator WorkersExample📖
</details> <details> <summary><strong>👥 Specialized Agents</strong></summary>
FeatureCodeDocs
Data Analyst AgentExample📖
Finance AgentExample📖
Shopping AgentExample📖
Recommendation AgentExample📖
Wikipedia AgentExample📖
Programming AgentExample📖
Math AgentsExample📖
Markdown AgentExample📖
Prompt Expander AgentExample📖
</details> <details> <summary><strong>🎨 Media & Multimodal</strong></summary>
FeatureCodeDocs
Image Generation AgentExample📖
Image to Text AgentExample📖
Video AgentExample📖
Camera IntegrationExample📖
</details> <details> <summary><strong>🔌 Protocols & Integration</strong></summary>
FeatureCodeDocs
MCP TransportsExample📖
WebSocket MCPExample📖
MCP SecurityExample📖
MCP ResumabilityExample📖
MCP Config ManagementDocs📖
LangChain Integrated AgentsExample📖
</details> <details> <summary><strong>🛡️ Safety & Control</strong></summary>
FeatureCodeDocs
GuardrailsExample📖
Human ApprovalExample📖
Rules & InstructionsDocs📖
</details> <details> <summary><strong>⚙️ Advanced Features</strong></summary>
FeatureCodeDocs
Async & Parallel ProcessingExample📖
ParallelisationExample📖
Repetitive AgentsExample📖
Agent HandoffsExample📖
Stateful AgentsExample📖
Autonomous WorkflowExample📖
Structured Output AgentsExample📖
Model RouterExample📖
Prompt CachingExample📖
Fast ContextExample📖
</details> <details> <summary><strong>🛠️ Tools & Configuration</strong></summary>
FeatureCodeDocs
100+ Custom ToolsExample📖
YAML ConfigurationExample📖
100+ LLM SupportExample📖
Callback AgentsExample📖
HooksExample📖
Middleware SystemExample📖
Configurable ModelExample📖
Rate LimiterExample📖
Injected Tool StateExample📖
Shadow Git CheckpointsExample📖
Background TasksExample📖
Policy EngineExample📖
Thinking BudgetsExample📖
Output StylesExample📖
Context CompactionExample📖
</details> <details> <summary><strong>📊 Monitoring & Management</strong></summary>
FeatureCodeDocs
Sessions ManagementExample📖
Auto-Save SessionsDocs📖
History in ContextDocs📖
TelemetryExample📖
Langfuse TracingDocs📖
Project Docs (.praison/docs/)Docs📖
AI Commit MessagesDocs📖
@Mentions in PromptsDocs📖
</details> <details> <summary><strong>🖥️ CLI Features</strong></summary>
FeatureCodeDocs
Slash CommandsExample📖
Autonomy ModesExample📖
Cost TrackingExample📖
Repository MapExample📖
Interactive TUIExample📖
Git IntegrationExample📖
Sandbox ExecutionExample📖
CLI CompareExample📖
Profile/BenchmarkDocs📖
Auto ModeDocs📖
InitDocs📖
File InputDocs📖
Final AgentDocs📖
Max TokensDocs📖
</details> <details> <summary><strong>🧪 Evaluation</strong></summary>
FeatureCodeDocs
Accuracy EvaluationExample📖
Performance EvaluationExample📖
Reliability EvaluationExample📖
Criteria EvaluationExample📖
</details> <details> <summary><strong>🎯 Agent Skills</strong></summary>
FeatureCodeDocs
Skills ManagementExample📖
Custom SkillsExample📖
</details> <details> <summary><strong>⏰ 24/7 Scheduling</strong></summary>
FeatureCodeDocs
Agent SchedulerExample📖
</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');

⚡ Performance

PraisonAI is built for speed, with agent instantiation in around 14μs. This reduces overhead, improves responsiveness, and helps multi-agent systems scale efficiently in real-world production workloads.

Performance MetricPraisonAI
Avg Instantiation Time14 μs

⭐ Star History

Star History Chart

🔍 Langfuse Tracing

bash
pip install "praisonai[langfuse]"
praisonai langfuse
<p align="center"> <img src=".github/images/langfuse.png" alt="PraisonAI Langfuse Tracing" width="800" /> </p>

🎓 Video Tutorials

Learn PraisonAI through our comprehensive video series:

<details> <summary><strong>View all 22 video tutorials</strong></summary>
TopicVideo
AI Agents with Self ReflectionSelf Reflection
Reasoning Data Generating AgentReasoning Data
AI Agents with ReasoningReasoning
Multimodal AI AgentsMultimodal
AI Agents WorkflowWorkflow
Async AI AgentsAsync
Mini AI AgentsMini
AI Agents with MemoryMemory
Repetitive AgentsRepetitive
IntroductionIntroduction
Tools OverviewTools Overview
Custom ToolsCustom Tools
Firecrawl IntegrationFirecrawl
User InterfaceUI
Crawl4AI IntegrationCrawl4AI
Chat InterfaceChat
Code InterfaceCode
Mem0 IntegrationMem0
TrainingTraining
Realtime Voice InterfaceRealtime
Call InterfaceCall
Reasoning Extract AgentsReasoning Extract
</details>

👥 Contributing

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

❓ FAQ & Troubleshooting

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

Install the package:

bash
pip install praisonaiagents
</details> <details> <summary><strong>API key not found / Authentication error</strong></summary>

Ensure your API key is set:

bash
export OPENAI_API_KEY=your_key_here

For other providers, see Models docs.

</details> <details> <summary><strong>How do I use a local model (Ollama)?</strong></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><strong>How do I persist conversations to a database?</strong></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><strong>How do I enable agent memory?</strong></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><strong>How do I run multiple agents together?</strong></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><strong>How do I use MCP tools?</strong></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"> <p><strong>Made with ❤️ by the PraisonAI Team</strong></p> <p> <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> </p> </div>

常见问题

PraisonAI 是什么？

AI Agents Framework with Self Reflection and MCP support

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