> ## Documentation Index > Fetch the complete documentation index at: https://praison.ai/docs/llms.txt > Use this file to discover all available pages before exploring further. # 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. ```mermaid theme={"theme":{"light":"vitesse-light","dark":"vitesse-dark"}} graph LR subgraph "praisonai-train" Agent[🤖 Agent] --> Train[⚙️ praisonai-train agents] Train --> Grade[⭐ Grader / Human] Grade --> Better[✅ Improved Agent] end classDef input fill:#8B0000,stroke:#7C90A0,color:#fff classDef process fill:#189AB4,stroke:#7C90A0,color:#fff classDef grade fill:#F59E0B,stroke:#7C90A0,color:#fff classDef output fill:#10B981,stroke:#7C90A0,color:#fff class Agent input class Train process class Grade grade class Better output ``` 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 Improve an agent iteratively — no ML dependencies required. ```python theme={"theme":{"light":"vitesse-light","dark":"vitesse-dark"}} from praisonaiagents import Agent agent = Agent(instructions="You are a helpful assistant.") ``` ```bash theme={"theme":{"light":"vitesse-light","dark":"vitesse-dark"}} pip install praisonai-train praisonai-train agents --input "What is Python?" ``` Add the `[llm]` extra to pull the Unsloth/torch stack. ```bash theme={"theme":{"light":"vitesse-light","dark":"vitesse-dark"}} pip install "praisonai-train[llm]" praisonai-train llm dataset.json \ --model unsloth/Meta-Llama-3.1-8B-Instruct-bnb-4bit ``` *** ## 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. ```mermaid theme={"theme":{"light":"vitesse-light","dark":"vitesse-dark"}} graph TB Q1{What do I need?} Q1 -->|Already have the
full wrapper| P1[praisonai train ...] Q1 -->|Just agent training| P2[pip install praisonai-train] Q1 -->|Agent training +
LLM fine-tuning| P3["pip install praisonai-train[llm]"] Q1 -->|Full stack +
fine-tuning deps| P4["pip install praisonai[train]"] classDef question fill:#F59E0B,stroke:#7C90A0,color:#fff classDef wrapper fill:#10B981,stroke:#7C90A0,color:#fff classDef runtime fill:#189AB4,stroke:#7C90A0,color:#fff class Q1 question class P1,P4 wrapper class P2,P3 runtime ``` Both entry points expose the same commands: every `praisonai train ` also runs as `praisonai-train `. *** ## 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](/docs/cli/train) for full flags. *** ## Common Patterns ### Train, review, apply Run a training session, inspect the iterations, then bake the best one into your agent. ```bash theme={"theme":{"light":"vitesse-light","dark":"vitesse-dark"}} # --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. ```python theme={"theme":{"light":"vitesse-light","dark":"vitesse-dark"}} 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. ```python theme={"theme":{"light":"vitesse-light","dark":"vitesse-dark"}} # Works identically on macOS/Linux/Windows — no encoding config needed from praisonaiagents import Agent agent = Agent(instructions="You are a helpful assistant.") ``` ```bash theme={"theme":{"light":"vitesse-light","dark":"vitesse-dark"}} # 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. ```bash theme={"theme":{"light":"vitesse-light","dark":"vitesse-dark"}} # CLI — always runs all 3 iterations praisonai-train agents --input "What is Python?" --iterations 3 --no-early-stop ``` ```python theme={"theme":{"light":"vitesse-light","dark":"vitesse-dark"}} 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. *** ## 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. ```mermaid theme={"theme":{"light":"vitesse-light","dark":"vitesse-dark"}} graph TB Start[praisonai-train agents] --> Detect{stdout
encoding?} Detect -->|UTF-8| Rich[Rich table + emoji
PASSED tick / star marker] Detect -->|cp1252 / ASCII| Safe[Rich table + ASCII labels
PASSED / * marker] Safe --> Fallback{Rich still
UnicodeEncodeError?} Fallback -->|No| Safe Fallback -->|Yes| Plain[Plain-text summary
Key: value lines] classDef input fill:#6366F1,stroke:#7C90A0,color:#fff classDef decision fill:#F59E0B,stroke:#7C90A0,color:#fff classDef success fill:#10B981,stroke:#7C90A0,color:#fff classDef fallback fill:#189AB4,stroke:#7C90A0,color:#fff class Start input class Detect,Fallback decision class Rich success class Safe,Plain fallback ``` The ASCII summary is the correct output on a cp1252 Windows console — not a truncation. The session is saved either way; `praisonai-train show ` re-renders it in whichever encoding your current console supports. *** ## 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 ` to inspect the persisted result. If you want the full emoji summary, run `chcp 65001` first or set `PYTHONIOENCODING=utf-8`. *** ## Best Practices `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. The `praisonai-train` console script exposes the full `train` group without installing `praisonai`. Ideal for lightweight training-only environments. 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. *** ## Related Training overview and fine-tuning setup. Full flag reference for the five subcommands. The train install matrix. How the six packages stack. Fix Rich crashes and ASCII rendering on legacy Windows consoles.