Five-Layer Agent Stack

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

@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")

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"),
    reflection=True,               # completion check — the agent grades its own answer
    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 AgentFlow, 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 praisonai import Agent, ManagedAgent, LocalManagedConfig

# A. Tools run in a remote sandbox; the agent loop stays local
sandboxed = ManagedAgent(
    provider="local", compute="e2b",   # or modal | daytona | flyio | docker | tenki
    config=LocalManagedConfig(model="gpt-4o-mini", name="RemoteTools"),
)
agent = Agent(name="builder", backend=sandboxed)

# B. The entire agent loop runs in the cloud (needs ANTHROPIC_API_KEY;
#    with no key set, ManagedAgent() falls back to a local loop)
agent = Agent(name="teacher", backend=ManagedAgent())
agent.start("Write a Python script that prints the first 10 primes, then run it")