Code Migration

AI-powered migration of your agent code to PraisonAI

Migrate your existing agent projects to PraisonAI using an AI-driven workflow that analyzes, converts, and validates your codebase automatically.

from praisonaiagents import Agent

agent = Agent(name="migration-helper", instructions="Help migrate from older PraisonAI versions.")
agent.start("Migrate my agents.yaml from v1 format to v2 format.")

How It Works

The user points the CLI at a project; AgentFlow runs four agents in sequence — Analyzer reads the codebase, Converter transforms it, Evaluator scores the result (1–10), and Error Agent diagnoses failures and retries up to 3 times.

Quick Start

1. Point to Your Project

   praisonai migrate ./my-agent-project

2. AI Analyzes All Files

   The Analyzer Agent reads your entire codebase to understand relationships between files.

3. Intelligent Conversion

   The Converter Agent transforms your code using learned patterns.

4. Automatic Validation

   The Evaluator Agent tests the converted code. If issues are found, the Error Agent analyzes and retries (up to 3 times).

The Migration Agents

CLI Reference

praisonai migrate <path> [options]

Feature Mapping

The migration uses a feature specification to map patterns:

Evaluation Loop

Programmatic API

from praisonai.cli.features.migrate import MigrationFlow

# Create migration flow
flow = MigrationFlow(
    source_path="./my-project",
    output_path="./converted",
    max_retries=3
)

# Run the agent-driven migration
result = flow.run()

if result.success:
    print(f"Migration complete: {result.files_converted} files")
else:
    print(f"Issues found: {result.errors}")

Migration Tips

Troubleshooting

API Migration: verbose= to output=

The verbose= parameter has been consolidated into the output= parameter across all PraisonAI components.

Quick Migration

# ❌ Old (raises TypeError)
agent = Agent(instructions="...", verbose=True)

# ✅ New
agent = Agent(instructions="...", output="verbose")

# ❌ Old (raises TypeError)
team = AgentTeam(agents=[...], tasks=[...], verbose=True)

# ✅ New
team = AgentTeam(agents=[...], tasks=[...], output="verbose")

# ⚠️ Old (still works, backward compat)
process = Process(tasks={...}, agents=[...], verbose=True)

# ✅ New (preferred)
process = Process(tasks={...}, agents=[...], output="verbose")

What the error looks like

Since PraisonAI 1.x (PR #4347), the error message names the supported replacement. If you already pass one of the removed kwargs, the traceback tells you exactly what to change:

TypeError: Agent.__init__() got unexpected keyword argument(s): stream, verbose
  stream: streaming moved into output=; use output=OutputConfig(stream=True).
  verbose: verbosity moved into output=; use output='verbose' (or output=OutputConfig(verbose=True)).

Unknown kwargs are still rejected outright rather than silently swallowed — this is intentional so a typo can never disable the behaviour it was meant to configure. The hint is added only for the two names people reach for most (they are accepted by six sibling classes in PraisonAI, and by CrewAI and LangChain).

Output Presets

Component Status

API Migration: max_iter= / session=

AgentTeam also removed the max_iter= and session= kwargs. Both now raise TypeError — use execution= for iteration limits and variables= for session state.

max_iter= → execution=

Replace max_iter= with an execution= preset ("fast" sets max_iter=10, "balanced" sets max_iter=20) or pass an explicit value.

from praisonaiagents import Agent, Task, AgentTeam

# ❌ Old (raises TypeError)
# team = AgentTeam(agents=[...], tasks=[...], max_iter=20)

# ✅ New — preset ("fast"=10 | "balanced"=20)
team = AgentTeam(
    agents=[Agent(name="Analyst", instructions="Analyse the input")],
    tasks=[Task(description="Analyse Q3 metrics", expected_output="Insights")],
    execution="balanced",
)
team.start()

from praisonaiagents import Agent, Task, AgentTeam

# ❌ Old (raises TypeError)
# team = AgentTeam(agents=[...], tasks=[...], max_iter=30)

# ✅ New — explicit value via dict
team = AgentTeam(
    agents=[Agent(name="Analyst", instructions="Analyse the input")],
    tasks=[Task(description="Analyse Q3 metrics", expected_output="Insights")],
    execution={"max_iter": 30},
)
team.start()

session= → variables=

The session= kwarg is gone. Pass session state through variables= and call session.save() explicitly.

from praisonaiagents import Agent, Task, AgentTeam

# ❌ Old (raises TypeError)
# team = AgentTeam(agents=[...], tasks=[...], session=my_session)

# ✅ New — session state via variables=
team = AgentTeam(
    agents=[Agent(name="Writer", instructions="Personalise the response")],
    tasks=[Task(description="Greet {{user}} about {{topic}}", expected_output="Message")],
    variables={"user": "Alice", "topic": "PraisonAI"},
)
team.start()

# Persist session state explicitly when you need it
# my_session.save()

Removed kwargs at a glance

Back-compat class aliases

AgentTeam is the canonical multi-agent class in v1.0+. The SDK also exposes three silent aliases that resolve to the same class, so old snippets, older docs, and downstream integrations keep working unchanged.

All four are importable from the package root and point to the same object:

from praisonaiagents import AgentTeam, AgentManager, Agents, PraisonAIAgents

assert AgentTeam is AgentManager is Agents is PraisonAIAgents   # True

Best Practices

Related