AgentTeam Supported Params

AgentTeam applies orchestration params at the team level, but six feature params only work when set on individual Agent(...) instances.

from praisonaiagents import Agent, Task, PraisonAIAgents

researcher = Agent(name="Researcher", instructions="Research the topic.")
writer = Agent(name="Writer", instructions="Write a summary.")

team = PraisonAIAgents(agents=[researcher, writer])
team.start()

Quick Start

Team-level params work

from praisonaiagents import Agent, PraisonAIAgents

researcher = Agent(name="Researcher", instructions="Research the topic.")
writer = Agent(name="Writer", instructions="Write a summary.")

team = PraisonAIAgents(
    agents=[researcher, writer],
    process="sequential",
    memory=True,
    planning=True,
)
team.start()

agents, process, memory, planning, and the other orchestration params take effect for the whole team.

Feature params belong on each Agent

from praisonaiagents import Agent, PraisonAIAgents

researcher = Agent(
    name="Researcher",
    instructions="Research the topic.",
    knowledge=["notes.pdf"],
    guardrails=lambda o: (True, o),
)
writer = Agent(name="Writer", instructions="Write a summary.")

team = PraisonAIAgents(agents=[researcher, writer])
team.start()

Set knowledge, guardrails, web, reflection, caching, and learn on the Agent(...) that needs them.

What Applies Where

AgentTeam accepts the six feature params for API symmetry with Agent, but only stores them — it never enforces them at the team level. Only orchestration params and autonomy fan out.

Param	Scope	Notes
`agents`	Team	Team roster
`tasks`	Team	Auto-generated from agents if omitted
`process`	Team	`"sequential"`, `"workflow"`, `"hierarchical"`
`manager_llm`	Team	Manager LLM for hierarchical process
`llm`	Team	Default LLM for all agents
`name`	Team	Team name
`variables`	Team	Global variables for substitution
`memory`	Team	Multi-agent memory
`planning`	Team	Multi-agent planning
`context`	Team	Context management
`output`	Team	Output/verbosity config
`execution`	Team	Iteration/retry limits
`hooks`	Team	Multi-agent hooks
`autonomy`	Team	Propagated per agent via `_init_autonomy`
`run_on`	Team	Shared sandbox for all agents
`guardrails`	Per-agent only	Warns if passed to the team
`web`	Per-agent only	Warns if passed to the team
`reflection`	Per-agent only	Warns if passed to the team
`caching`	Per-agent only	Warns if passed to the team
`learn`	Per-agent only	Warns if passed to the team
`knowledge`	Per-agent only	Warns if passed to the team

Wrong vs Right

Passing feature params to the team compiles fine — no TypeError — but silently does nothing. Move them onto the agents that need them.

from praisonaiagents import Agent, PraisonAIAgents

researcher = Agent(name="Researcher", instructions="Research.")
writer = Agent(name="Writer", instructions="Write.")

# knowledge/guardrails/etc. are NOT applied at the team level
team = PraisonAIAgents(
    agents=[researcher, writer],
    knowledge=["notes.pdf"],
    guardrails=lambda o: (True, o),
)

from praisonaiagents import Agent, PraisonAIAgents

researcher = Agent(
    name="Researcher",
    instructions="Research.",
    knowledge=["notes.pdf"],
    guardrails=lambda o: (True, o),
)
writer = Agent(name="Writer", instructions="Write.")

team = PraisonAIAgents(agents=[researcher, writer])

Passing any of guardrails, web, reflection, caching, learn, or knowledge to AgentTeam logs:

AgentTeam received ['knowledge', 'guardrails'] but does not yet apply them at the team level; pass these to individual Agent(...) instances instead.

The team still runs — those settings are simply ignored at the team level.

Why It’s This Way

These six params exist on AgentTeam for parity with the Agent API surface, so the same call shape works in both places. There is no team-level orchestrator for guardrails, web, reflection, caching, learn, or knowledge yet, so the team stores the values but cannot enforce them. Each Agent already enforces them individually, which is where they belong.

Best Practices

Put feature params on the Agent that needs them

A researcher needs knowledge; a validator needs guardrails. Configure each Agent(...) for its own job instead of hoping the team fans settings out.

Watch the logs when a feature seems inactive

If knowledge or guardrails appear to do nothing on a team, check for the “does not yet apply them at the team level” warning — it means the param landed on the team, not an agent.

Use team params for orchestration only

Reserve AgentTeam(...) for process, memory, planning, context, output, execution, hooks, and autonomy — the params that actually fan out.

Guardrails

Validate agent output per agent.

Knowledge Sources

What Agent(knowledge=...) accepts.

Web

Enable web search and fetch per agent.

Reflection

Self-reflection per agent.

Caching

Response and prompt caching per agent.

Learn

Continuous learning per agent.

Getting Started

Install

Core Concepts

Guides

Features

Models

Databases

Observability

Memory

Telemetry & Analytics

Knowledge

RAG

Persistence

Tools

Other Features

Developers

Configuration

Best Practices

Getting Started (No Code)

AgentTeam Supported Params

Quick Start

What Applies Where

Wrong vs Right

Why It’s This Way

Best Practices

Guardrails

Knowledge Sources

Web

Reflection

Caching

Learn

​Quick Start

​What Applies Where

​Wrong vs Right

​Why It’s This Way

​Best Practices

​Related

Guardrails

Knowledge Sources

Web

Reflection

Caching

Learn

Quick Start

What Applies Where

Wrong vs Right

Why It’s This Way

Best Practices

Related