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framework: agno connects PraisonAI’s YAML and CLI to the Agno multi-agent framework, routing tasks through Agno’s Team(mode=route) with a single install.

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Quick Start

from praisonaiagents import Agent

agent = Agent(
    name="Helper",
    instructions="Answer briefly.",
    llm="openai/gpt-4o-mini",
)

agent.start("Reply with exactly OK.")
The same task expressed as a framework: agno YAML runs through Agno’s Team(mode=route) router — keep reading for YAML.
1

Install

pip install "praisonai[agno]"
2

Set API Key

export OPENAI_API_KEY=your-key
3

Create agents.yaml

framework: agno
topic: Quick task
roles:
  helper:
    role: Helper
    goal: Answer briefly
    backstory: Helpful assistant
    tasks:
      answer:
        description: Reply with exactly OK.
        expected_output: OK
4

Run

praisonai agents.yaml --framework agno
Requires praisonai-frameworks 0.1.8+. Install with pip install "praisonai[agno]" or pip install "praisonai-frameworks[agno]".

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How Agno Works

Agno wraps each role into an agno.Agent, then assembles a Team(mode=route) that routes tasks to the appropriate specialist. For handoff topologies, the router agent delegates to sub-agents at runtime.

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Capabilities

CapabilitySupported
agent_creation✅
tool_execution✅
sequential_execution✅
handoff✅
tool_loop✅
Workflow YAML (steps-style) is not supported. Using framework: agno in a workflow YAML raises:
ValueError: framework='agno' in workflow YAML is not supported for workflow execution
Use the roles: agents.yaml shape shown in the Quick Start above.

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Sequential Context (Task Chaining)

Tasks reference outputs of earlier tasks with context: [task_name]: 
framework: agno
topic: numbers
roles:
  writer:
    role: Writer
    goal: Write numbers only
    backstory: Concise writer
    tasks:
      draft:
        description: Reply with only the number 3.
        expected_output: "3"
      polish:
        description: Add 3 to the previous result. Reply with only the number.
        expected_output: "6"
        context:
          - draft
Multi-task configs without handoff.to run sequentially; context wires task outputs as inputs to subsequent tasks.

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Handoffs

Use handoff.to with a single router task; specialists are routed via Agno Team(mode=route): 
framework: agno
topic: language help
roles:
  triage:
    role: Triage Agent
    handoff:
      to:
        - English Agent
    tasks:
      route:
        description: Help with {topic}
  english:
    role: English Agent
    goal: Reply in English only
    backstory: English specialist.
Multi-task configs with handoff.to fall back to sequential execution.

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Direct Adapter Use (Advanced)

Call the adapter without the CLI or YAML loader: 
from praisonai_frameworks.agno.adapter import AgnoAdapter

config = {
    "framework": "agno",
    "topic": "Quick test",
    "roles": {
        "helper": {
            "role": "Assistant",
            "goal": "Answer briefly",
            "backstory": "Helpful assistant",
            "tasks": {
                "answer": {
                    "description": "Reply with exactly the word OK.",
                    "expected_output": "OK",
                }
            },
        }
    },
}
llm_config = [{"model": "openai/gpt-4o-mini", "api_key": ""}]   # use $OPENAI_API_KEY
result = AgnoAdapter().run(config, llm_config, "Quick test", tools_dict={})
Most users should use the CLI / YAML flow instead. Direct adapter calls are for advanced integration scenarios.

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Verify Installation

Check availability via praisonai doctor: 
$ praisonai doctor
✓ Runtime 'agno' available
  name: Agno
  capabilities: agent_creation, tool_execution, sequential_execution, handoff, tool_loop
Or check from Python: 
from praisonai._framework_availability import is_available

if is_available("agno"):
    print("Agno is installed and importable")
_agno_probe() checks three things in order:
  1. importlib.metadata.distribution("agno") — the PyPI dist must be installed.
  2. importlib.util.find_spec("agno") — the agno import namespace must be discoverable.
  3. from agno.agent import Agent — the Agno Agent symbol must import without error.
True from is_available("agno") guarantees the adapter can run, not just that the package is on disk.

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Pip Extras Reference

ExtraInstallsRequired for
praisonai[agno]praisonai-frameworks[agno]>=0.1.8, praisonai-tools>=0.1.0Probe + doctor recognition + adapter dispatch for Agno
praisonai-frameworks[agno] (transitive)Agno adapter implementation registered via entry-point group, plus the agno PyPI distActually executing framework: agno
The install-hint key maps agno → agno (same name for both the YAML key and PyPI dist). The YAML key, CLI flag value, and probe name are all agno.

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Troubleshooting

framework='agno' is not a valid choice — you are running a pre-PR-#2501 PraisonAI version. Upgrade: pip install -U praisonai. Framework 'agno' was requested but is not installed — run pip install 'praisonai[agno]' (from your project rather than praisonai-frameworks[agno] alone, so praisonai-tools is also pulled in). ValueError: framework='agno' in workflow YAML is not supported for workflow execution — switch the file to the roles: agents.yaml shape used in the Quick Start above. agno imports successfully but is_available("agno") returns False — the probe also requires agno to be visible to importlib.metadata.distribution(...). If the package was installed in editable or namespace-only mode without a real dist-info, the probe refuses. Install the published agno PyPI package.

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Best Practices

Choose agno when you want Agno’s Team(mode=route) routing semantics with OpenAI-compatible models. Agno supports handoffs, sequential tasks, and tool loops — making it a strong alternative to autogen_v4 or openai_agents for multi-agent routing topologies.
Use handoff.to in your YAML when you need a router agent to delegate to specialists at runtime. Multi-task configs fall back to sequential execution. See Handoffs for the full conceptual model and which frameworks support it.
Always use the roles: agents.yaml shape. The workflow steps: format raises a ValueError at validation time and is explicitly not supported for framework: agno.
Declare task dependencies with context: [task_name] in your YAML. This keeps the config declarative and lets the adapter wire task outputs as inputs automatically.
The Agno adapter runs synchronously (sync run() only). Async task graphs are not supported in the current adapter version.

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Framework Availability

Probe API for checking installed frameworks

Framework Adapter Plugins

Register custom adapters via entry points

Handoffs

Handoff conceptual model and which frameworks support it

PraisonAI Agents

PraisonAI native agents framework