> ## 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"}}
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")
```
***
## Best Practices
`pip install praisonai-train` pulls only `praisonaiagents` — 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 five packages stack.