PraisonAI：多代理任务自动化框架

PraisonAI 🦞 — Hire a 24/7 AI Workforce. Stop writing boilerplate and start shipping autonomous self-improving agents that research, plan,…

PraisonAI 🦞 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. curl -fsSL https://praison.ai/install.sh | bash ██████╗ ██████╗ █████╗ ██╗███████╗ ██████╗ ███╗ ██╗ █████╗ ██╗ ██╔══██╗██╔══██╗██╔══██╗██║██╔════╝██╔═══██╗████╗ ██║ ██╔══██╗██║ ██████╔╝██████╔╝███████║██║███████╗██║ ██║██╔██╗ ██║ ███████║██║ ██╔═══╝ ██╔══██╗██╔══██║██║╚════██║██║ ██║██║╚██╗██║ ██╔══██║██║ ██║ ██║ ██║██║ ██║██║███████║╚██████╔╝██║ ╚████║ ██║ ██║██║ ╚═╝ ╚═╝ ╚═╝╚═╝ ╚═╝╚═╝╚══════╝ ╚═════╝ ╚═╝ ╚═══╝ ╚═╝ ╚═╝╚═╝ pip install praisonai 🎯 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 🚀 Meet your first Agent (Under 1 Minute) Install the lightweight core SDK: pip install praisonaiagents export OPENAI_API_KEY="your-api-key" Run your first autonomous agent: 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 Five-Layer Agent Stack Most frameworks hand you one or two layers and leave the rest as homework. PraisonAI covers all five — plus the outer layer that decides where your agent actually runs. Each layer wraps the one inside it. When an agent misbehaves, the layer tells you where to look. ┌─────────────────────────────────────────────────────────────────┐ │ ⬡ MANAGED AGENTS — Where does it actually run? │ │ ┌─────────────────────────────────────────────────────────────┐ │ │ │ 5 · GRAPH — Who runs when, and who checks whom? │ │ │ │ ┌─────────────────────────────────────────────────────────┐ │ │ │ │ │ 4 · LOOP — When do we stop? │ │ │ │ │ │ ┌─────────────────────────────────────────────────────┐ │ │ │ │ │ │ │ 3 · HARNESS — Can it act, and be checked? │ │ │ │ │ │ │ │ ┌─────────────────────────────────────────────────┐ │ │ │ │ │ │ │ │ │ 2 · CONTEXT — Is the right thing in the window? │ │ │ │ │ │ │ │ │ │ ┌─────────────────────────────────────────────┐ │ │ │ │ │ │ │ │ │ │ │ 1 · PROMPT — Did I say it clearly? │ │ │ │ │ │ │ │ │ │ │ └─────────────────────────────────────────────┘ │ │ │ │ │ │ │ │ │ └─────────────────────────────────────────────────┘ │ │ │ │ │ │ │ └─────────────────────────────────────────────────────┘ │ │ │ │ │ └─────────────────────────────────────────────────────────┘ │ │ │ └─────────────────────────────────────────────────────────────┘ │ └─────────────────────────────────────────────────────────────────┘ Layer The question it answers PraisonAI 1 · Prompt Did I say it clearly? instructions=, role/goal/backstory, output=, templates= 2 · Context Is the right thing in the window? memory=, knowledge=, context=, handoff ContextPolicy 3 · Harness Can it act, and be checked? tools=, MCP(), guardrails=, approval=, hooks=, sandbox= 4 · Loop When do we stop? execution=ExecutionConfig(...), reflection=, autonomy=, doom-loop detection 5 · Graph Who runs when, and who checks whom? AgentFlow, route(), parallel(), loop(), repeat() ⬡ Managed Where does it actually run? tools_run_on="docker" — one shared sandbox for the tools, or run_on="anthropic" for the whole agent Layer 1 · Prompt — Did I say it clearly? Role, instructions, examples, output format. from praisonaiagents import Agent agent = Agent( role="Senior Data Analyst", goal="Turn raw numbers into decisions", output="verbose", # markdown-formatted output ) agent.start("Summarise Q3 revenue trends") Layer 2 · Context — Is the right thing in the window? Write, select, compress, isolate — the four context operations, one parameter each. from praisonaiagents import Agent agent = Agent( instructions="You are a support engineer.", memory={"user_id": "u-42"}, # write — persists across runs (needs a user_id) knowledge=["docs/"], # select — retrieves only what's relevant context="summarize", # compress — auto-compacts before the limit ) Isolate is handoffs=[specialist] — a sub-agent inherits the last few messages and the intersection of your tools, not your whole transcript. 📖 Handoffs Layer 3 · Harness — Can it act, and be checked? Agent = Model + Harness. Tool dispatch, plus the guides that steer before acting and the sensors that observe after. from praisonaiagents import Agent, MCP, tool @tool def deploy(env: str) -> str: """Deploy the current build to an environment.""" return f"Deployed to {env}" agent = Agent( name="ReleaseEngineer", instructions="You are a release engineer.", tools=[deploy, MCP("npx -y @modelcontextprotocol/server-filesystem /tmp")], approval=True, # guide — human gate before risky tools run ) agent.start("Deploy to staging, then list the files you can read") Layer 4 · Loop — When do we stop? Hard iteration caps, budget ceilings, no-progress detection and completion checks — every brake is explicit. from praisonaiagents import Agent, ExecutionConfig agent = Agent( instructions="Fix the failing tests.", execution=ExecutionConfig(max_iter=30, max_budget=0.50, on_budget_exceeded="stop"), autonomy=True, # required to drive the loop with run_autonomous() ) result = agent.run_autonomous("Refactor the auth module", max_iterations=5) print(result.completion_reason) #