praisonai-train Package

Train agents or fine-tune LLMs without installing the full PraisonAI wrapper

Train agents or fine-tune LLMs without installing the full PraisonAI wrapper.

The praisonai-train PyPI package (import: praisonai_train) is Tier 2c — it sits on top of praisonaiagents and gives you the train CLI group and a standalone praisonai-train console script.

Quick Start

1. Agent Training

   Improve an agent iteratively — no ML dependencies required.

   from praisonaiagents import Agent
   
   agent = Agent(instructions="You are a helpful assistant.")
   

   pip install praisonai-train
   
   praisonai-train agents --input "What is Python?"
   
2. LLM Fine-tuning

   Add the [llm] extra to pull the Unsloth/torch stack.

   pip install "praisonai-train[llm]"
   
   praisonai-train llm dataset.json \
       --model unsloth/Meta-Llama-3.1-8B-Instruct-bnb-4bit
   

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When to Use praisonai-train vs praisonai train

Install the standalone package when you only need training; use the wrapper’s praisonai train when you already run the full stack.

Both entry points expose the same commands: every praisonai train <sub> also runs as praisonai-train <sub>.

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CLI Subcommands

Five subcommands cover fine-tuning and agent training.

Subcommand	Purpose
praisonai-train llm DATASET	Fine-tune an LLM via Unsloth
praisonai-train agents [AGENT_FILE]	Iteratively train an agent
praisonai-train list	List training sessions
praisonai-train show SESSION_ID	Show a session’s iterations and best score
praisonai-train apply SESSION_ID	Apply learned suggestions to an agent

See Train CLI for full flags.

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Common Patterns

Train, review, apply

Run a training session, inspect the iterations, then bake the best one into your agent.

# --iterations N is a max — training stops early on a score >= 9.5 in LLM mode
praisonai-train agents --input "Explain AI" --human
praisonai-train list
praisonai-train show train-abc123 --iterations
praisonai-train apply train-abc123 --run "Explain AI"

Apply in Python

Apply a session’s suggestions to an agent directly.

from praisonaiagents import Agent
from praisonai_train.train.agents import apply_training

agent = Agent(instructions="You are a helpful assistant.")
apply_training(agent, session_id="train-abc123")

Train on any console

The same commands run identically on macOS, Linux, and Windows — no encoding configuration needed.

# Works identically on macOS/Linux/Windows — no encoding config needed
from praisonaiagents import Agent

agent = Agent(instructions="You are a helpful assistant.")

# On a Windows cp1252 console, the summary renders as plain ASCII
# and the session is still persisted for later review.
praisonai-train agents --input "What is Python?"
praisonai-train show train-abc123

Force all iterations

Benchmarks, regression tests, and demos that need to observe the feedback loop across every iteration should pass --no-early-stop (CLI) or no_early_stop=True (Python) so the 9.5 threshold is bypassed.

# CLI — always runs all 3 iterations
praisonai-train agents --input "What is Python?" --iterations 3 --no-early-stop

from praisonaiagents import Agent
from praisonai_train.train.agents import AgentTrainer, TrainingScenario

agent = Agent(instructions="You are a helpful assistant.")
trainer = AgentTrainer(agent=agent, iterations=3, no_early_stop=True)
trainer.scenarios.append(TrainingScenario(input_text="What is Python?"))
report = trainer.run()

Without this flag, --iterations behaves as a maximum in LLM-as-Judge mode — training stops as soon as any iteration scores ≥ 9.5.

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Windows & non-UTF-8 Consoles

praisonai-train agents renders its summary table with emoji (✅ PASSED, ❌ NEEDS WORK, ★ best-iteration marker) when stdout supports UTF-8, and automatically falls back to ASCII (PASSED, NEEDS WORK, *) when it doesn’t. It detects the console’s encoding at runtime.

The ASCII summary is the correct output on a cp1252 Windows console — not a truncation. The session is saved either way; praisonai-train show <session-id> re-renders it in whichever encoding your current console supports.

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Exit Codes

praisonai-train agents reports three distinct outcomes.

Exit	Meaning
0	Training completed and the report was persisted (praisonai-train list will show it). A warning is printed if only the console display failed to encode.
1	Training itself failed — the run raised an exception (including encoding errors that hit before save_report) or arguments were invalid. No report is persisted.
130	Interrupted by Ctrl-C.

On a cp1252 Windows console, a completed training session now exits 0 even if Rich cannot render the summary emoji — you’ll see Training complete but summary could not be displayed: 'charmap' codec can't encode .... Run praisonai-train show <session-id> to inspect the persisted result. If you want the full emoji summary, run chcp 65001 first or set PYTHONIOENCODING=utf-8.

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Best Practices

Install the base package for agent training

pip install praisonai-train pulls praisonaiagents plus litellm (needed for LLM-as-Judge grading) — enough for agents, list, show, and apply. Add [llm] only when you need Unsloth fine-tuning.

Use the standalone script when you don't want the wrapper

The praisonai-train console script exposes the full train group without installing praisonai. Ideal for lightweight training-only environments.

Old imports keep working

Existing praisonai.train.*, praisonai.train_vision, and praisonai.upload_vision imports still resolve to the same module objects in praisonai_train. Nothing to migrate.

Backward-compatible: if you already have the wrapper installed, praisonai.train.* imports and the setup-conda-env entry point continue to work unchanged.

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Related

Train

Training overview and fine-tuning setup.

Train CLI

Full flag reference for the five subcommands.

Installation Extras

The train install matrix.

Package Tiers

How the six packages stack.

Windows Terminal Encoding

Fix Rich crashes and ASCII rendering on legacy Windows consoles.