osiris-pipeline 0.5.1

LLM-first conversational ETL pipeline generator

Osiris Pipeline v0.5.1

The deterministic compiler for AI-native data pipelines. You describe outcomes in plain English; Osiris compiles them into reproducible, production-ready manifests that run with the same behavior everywhere (local or cloud).

🚀 Quick Start

# Setup
python3 -m venv .venv
source .venv/bin/activate
pip install -r requirements.txt

# Initialize configuration
osiris init

# Start MCP server for AI integration (Claude Desktop, etc.)
osiris mcp

🎯 What Makes Osiris Different

• Compiler, not orchestrator - Others schedule what you hand-craft. Osiris generates, validates, and compiles pipelines from plain English.
• Determinism as a contract - Fingerprinted manifests guarantee reproducibility across environments.
• Conversational → executable - Describe intent; Osiris interrogates real systems and proposes a feasible plan.
• Run anywhere, same results - Transparent adapters deliver execution parity (local and E2B today).
• Boring by design - Predictable, explainable, portable — industrial-grade AI, not magical fragility.

📊 Visual Overview

Pipeline Execution Dashboard

Interactive HTML dashboard showing pipeline execution metrics and performance

Run Overview with E2B Integration

Comprehensive run overview showing E2B cloud execution with <1% overhead

Step-by-Step Pipeline Execution

Detailed view of pipeline steps with row counts and execution times

Example Usage via MCP

# Start the MCP server
$ osiris mcp

# Use with Claude Desktop or any MCP-compatible client to:
# - Discover database schemas and sample data
# - Generate SQL queries and transformations
# - Validate and compile pipelines
# - Execute with deterministic, reproducible results

# Or run pipelines directly:
$ osiris run examples/inactive_customers.yaml

✨ Key Features

• AI-native pipeline generation from plain English descriptions
• Deterministic compilation with fingerprinted, reproducible manifests
• Run anywhere with identical behavior (local or E2B cloud)
• Interactive HTML reports with comprehensive observability
• AI Operation Package (AIOP) for LLM-friendly debugging and analysis
• LLM-friendly with machine-readable documentation for AI assistants

🤖 LLM-Friendly Documentation

Osiris provides machine-readable documentation for AI assistants:

• For Users: Share docs/user-guide/llms.txt with ChatGPT/Claude to generate pipelines
• For Developers: Use docs/developer-guide/llms.txt for AI-assisted development
• Pro Mode: Customize AI behavior with osiris dump-prompts --export

🚀 E2B Cloud Execution

Run pipelines in isolated E2B sandboxes with <1% overhead:

# Run in cloud sandbox
osiris run pipeline.yaml --e2b

# With custom resources
osiris run pipeline.yaml --e2b --e2b-cpu 4 --e2b-mem 8

See the User Guide for complete E2B documentation.

🤖 AI Operation Package (AIOP)

Every pipeline run automatically generates a comprehensive AI Operation Package for LLM analysis:

# View AIOP export after any run
osiris logs aiop --last

# Generate human-readable summary
osiris logs aiop --last --format md

# Configure in osiris.yaml
aiop:
  enabled: true  # Auto-export after each run
  policy: core   # ≤300KB for LLM consumption

AIOP provides four semantic layers for AI understanding:

• Evidence Layer: Timestamped events, metrics, and artifacts
• Semantic Layer: DAG structure and component relationships
• Narrative Layer: Natural language descriptions with citations
• Metadata Layer: LLM primer and configuration

See AIOP Architecture for details.

📚 Documentation

For comprehensive documentation, visit the Documentation Hub:

• Quickstart - 10-minute setup guide
• User Guide - Complete usage documentation
• Architecture - Technical deep-dive with diagrams
• Developer Guide - Module patterns and LLM contracts
• Examples - Ready-to-use pipelines

🚦 Roadmap

• v0.2.0 ✅ - Conversational agent, deterministic compiler, E2B parity
• v0.3.0 ✅ - AI Operation Package (AIOP) for LLM-friendly debugging
• v0.3.1 ✅ - Fixed validation warnings for ADR-0020 compliant configs
• v0.3.5 ✅ - GraphQL extractor, DuckDB processor, test infrastructure improvements
• v0.5.1 (Current) ✅ - Critical bug fixes batch 2: OML sample, OSIRIS_HOME, Windows shell, PYTHONPATH, Guide references
• M2 - Production workflows, approvals, orchestrator integration
• M3 - Streaming, parallelism, enterprise scale
• M4 - Iceberg tables, intelligent DWH agent

See docs/roadmap/ for details.

🛠️ Contributing

See CONTRIBUTING.md for development workflow, code quality standards, and commit guidelines.

License

Apache-2.0