⚒️ The observability platform for agentic systems.

AlphaTrion is an open-source framework for building and optimizing GenAI applications. Track experiments, monitor performance, analyze model usage, and manage artifacts—all through an intuitive dashboard. Named after the oldest and wisest Transformer.

Trusted By

Features

• 🔬 Experiment Tracking - Organize ML experiments with hierarchical teams, experiments, and runs
• 📊 Performance Monitoring - Track metrics, visualize trends, and monitor experiment status
• 🔍 Distributed Tracing - Automatic OpenTelemetry integration for LLM calls with token usage and span analysis
• 🪝 Post-Run Hooks - Automatically sync metadata and status after run completion
• 🎯 Interactive Dashboard - Modern web UI for exploring experiments and traces
• 🔌 Easy Integration - Simple Python API with async/await support

Core Concepts

• Organization - Top-level entity for grouping teams and users
• Team - Collaborative workspace for organizing experiments and runs
• User - Individual account with secure authentication and team memberships
• Experiment - Logical grouping of runs with shared purpose, organized by labels
• Run - Individual execution instance with configuration and metrics

Quick Start

1. Installation

# From PyPI
pip install alphatrion

# Or from source
git clone https://github.com/inftyai/alphatrion.git && cd alphatrion
source start.sh

2. Setup

# Start PostgreSQL, ClickHouse, and Registry
cp .env.example .env
make up

# Wait for services to be ready, then run migrations
make migrate-all

# Initialize your organization, team, and user account
alphatrion init

Optional Tools:

• pgAdmin: http://localhost:8081 (alphatrion@inftyai.com / alphatr1on)
• Registry UI: http://localhost:80
• Grafana: http://localhost:3000 (admin / admin) - LLM metrics dashboard
• Prometheus: http://localhost:9090 - Metrics explorer

3. Run Your First Experiment

import alphatrion as alpha
from alphatrion.experiment import CraftExperiment

# Initialize with your user ID
alpha.init(user_id="<your_user_id>")

async def my_task():
    # Your code here
    await alpha.log_metrics({"accuracy": 0.95, "loss": 0.12})

async with CraftExperiment.start(name="my_experiment") as exp:
    run = exp.run(my_task)
    await exp.wait()

4. Launch Dashboard

# Start backend server (terminal 1)
alphatrion server

# Launch dashboard (terminal 2)
alphatrion dashboard

Access the dashboard at http://127.0.0.1:5173 and log in with your email and password to explore experiments, visualize metrics, and analyze traces.

5. View Traces

AlphaTrion automatically captures distributed tracing data for all LLM calls, including latency, token usage, and span relationships.

6. Using Post-Run Hooks (Optional)

Automatically sync metadata and status after run completion.

from alphatrion.experiment import CraftExperiment
from alphatrion.run import PostRunHookFn

async def train_model():
    # Your training code
    return {
        "metadata": {"accuracy": 0.95, "loss": 0.05},
        "status": "COMPLETED",
    }

async with CraftExperiment.start("training") as exp:
    run = exp.run(
        train_model,
        post_run_hooks=[PostRunHookFn.sync_metadata, PostRunHookFn.sync_status]
    )
    await exp.wait()

7. Cleanup

make down

References

• Architecture: Diagrams
• Dashboard: Setup Guide | CLI Reference | Architecture
• Development: Contributing Guide
• Claude Code Integration: Hooks Setup

Contributing

We welcome contributions! Check out our development guide to get started.