PraisonAI

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.")

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")

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)

from praisonaiagents import Agent, MCP, tool
@tooldef 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")

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)# goal | no_tool_calls | max_iterations | timeout | doom_loop | needs_help | error# (with on_budget_exceeded="stop", hitting the cap raises BudgetExceededError,#  surfaced here as completion_reason="error")

from praisonaiagents import AgentFlowfrom praisonaiagents.workflows import route, parallel, repeat
flow = AgentFlow(steps=[    classifier,    route({"bug": [bug_agent], "feature": [feature_agent], "default": [triage]}),    parallel([reviewer, tester]),                      # fan out, join automatically    repeat(editor, until=lambda ctx: "approved" in ctx.previous_result.lower(),           max_iterations=3),                          # evaluator–optimizer])flow.run("Ticket #123: login fails on Safari")

from praisonaiagents import Agent, AgentFlow
writer = Agent(name="Writer", instructions="You write files.")reader = Agent(name="Reader", instructions="You read files.")
flow = AgentFlow(tools_run_on="docker", steps=[writer, reader])  # or e2b | modal | daytona | flyioflow.run("Write 'hello' to /workspace/note.txt, then read it back")

from praisonaiagents import Agent
# A. Only the TOOLS move. Thinking stays on your machine.agent = Agent(name="builder", instructions="You build things.",              tools_run_on="docker")   # docker | e2b | modal | daytona | flyio                                       # tenki | sandlock | ssh | novita | subprocess
# B. The WHOLE agent moves — model calls, loop and toolsagent = Agent(name="teacher", instructions="You teach.", run_on="anthropic")  # hostedagent = Agent(name="builder", instructions="You build.", run_on="docker")     # self-hostedagent.start("Write a Python script that prints the first 10 primes, then run it")

>>> Agent(name="builder", instructions="x", tools_run_on="docker")Agent(name='builder', thinks_on='this machine', tools_run_on='a Docker container')
>>> agent.where_does_it_run()Thinking (the AI model calls) happens on this machine.Tools run on a Docker container.Your own tools (check_db) still run on this machine -- only shell, file andcode tools move. They read and write this machine's files.

>>> Agent(name="x", instructions="i", run_on="e2b")TypeError: Agent(run_on='e2b') is not valid: run_on= places the whole agent-- model calls, loop and tools -- on a managed runtime, and 'e2b' runscommands but cannot host an agent loop.  To run only the tools there:  Agent(tools_run_on='e2b')

agent.execute_code_sync("print(6 * 7)", run_in="sandlock")   # kernel-enforced

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

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()

from praisonaiagents import Agent, MCP
# stdio - Local NPX/Python serversagent = Agent(tools=MCP("npx @modelcontextprotocol/server-memory"))
# Streamable HTTP - Production serversagent = Agent(tools=MCP("https://api.example.com/mcp"))
# WebSocket - Real-time bidirectionalagent = Agent(tools=MCP("wss://api.example.com/mcp", auth_token="token"))
# With environment variablesagent = Agent(    tools=MCP(        command="npx",        args=["-y", "@modelcontextprotocol/server-brave-search"],        env={"BRAVE_API_KEY": "your-key"}    ))

from praisonaiagents import Agent, tool
@tooldef search(query: str) -> str:    """Search the web for information."""    return f"Results for: {query}"
@tooldef 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")

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

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