Pymastra

Python-native workflow engine with Human-in-the-Loop support

Building AI-powered workflows where humans stay in control. Define complex pipelines with Python, pause execution for human review, and integrate seamlessly with LLMs and external services.

🎯 Why Pymastra?

Building AI workflows is complex. You need:

• Sequential execution with error handling
• Human review at critical points (approvals, validations)
• LLM integration without vendor lock-in
• Persistent storage for reliability
• Monitoring & observability for production
• Security hardening for sensitive operations

Pymastra solves all of this with a simple, Python-native API.

✨ Status: Production Ready (v0.2.0)

Phase	Status	Features
0	✅ Complete	Core workflow engine, HITL, in-memory storage
1	✅ Complete	LLM integration, FastAPI server, web dashboard
2	✅ Complete	SQLite/PostgreSQL, webhooks, observability, security

Over 1,000+ lines of documentation, 10 production examples, 100% type coverage, 66%+ test coverage.

---

🚀 Quick Start (2 minutes)

Installation

# Minimal (workflow engine only)
pip install pymastra

# With LLM support (OpenAI + Claude)
pip install pymastra[llm]

# With REST API server
pip install pymastra[server]

# Complete installation (all features)
pip install pymastra[all]

Your First Workflow

import asyncio
from pymastra import Workflow, Step, StepContext
from pydantic import BaseModel

# Define input and output schemas
class DocumentRequest(BaseModel):
    content: str
    confidence_threshold: float = 0.8

class ClassificationResult(BaseModel):
    category: str
    confidence: float

# Define a workflow step
async def classify_document(ctx: StepContext) -> ClassificationResult:
    """Classify document and request approval if low confidence."""
    doc = ctx.input

    # Your classification logic here
    category = "Technical"  # Replace with actual logic
    confidence = 0.92

    # Pause for human approval if confidence is low
    if confidence < doc.confidence_threshold:
        await ctx.suspend({
            "message": "Low confidence classification - requires approval",
            "suggested_category": category,
            "confidence": confidence,
            "document_preview": doc.content[:500]
        })
        # After resume, use human feedback if provided
        if ctx.resume_input and ctx.resume_input.get("approved"):
            category = ctx.resume_input.get("category", category)

    return ClassificationResult(category=category, confidence=confidence)

# Build the workflow
workflow = Workflow(
    name="document_classifier",
    description="Classify documents with human-in-the-loop approval",
    trigger_schema=DocumentRequest
)

workflow.step(Step(
    id="classify",
    description="Classify document",
    input_schema=DocumentRequest,
    output_schema=ClassificationResult,
    execute=classify_document
)).commit()

# Execute the workflow
async def main():
    # Start execution
    run = await workflow.execute(DocumentRequest(
        content="Technical documentation about distributed systems...",
        confidence_threshold=0.9
    ))

    print(f"Status: {run.status}")  # Output: "suspended"
    print(f"Run ID: {run.run_id}")

    if run.status == "suspended":
        # Human reviews the suspension payload
        print("Waiting for human approval...")

        # After approval (e.g., from web UI or API)
        final_result = await run.resume({
            "approved": True,
            "category": "Technical"
        })

        print(f"Final status: {final_result.status}")  # "completed"
        print(f"Output: {final_result.outputs}")

# Run it
asyncio.run(main())

---

📚 Core Features

1. Workflow as Code

Define complex workflows with simple Python syntax:

# Sequential execution
wf.step(step1).then(step2).then(step3).commit()

# With branching
wf.step(decision_step).then(
    if_approved=approval_step,
    if_rejected=rejection_step
).commit()

Benefits:

• Full IDE support and autocomplete
• Type safety with Pydantic validation
• Easy debugging and testing
• Version control friendly
• No YAML/config file overhead

2. Human-in-the-Loop (HITL)

Pause workflows at any point for human input:

# Request approval
await ctx.suspend({
    "request_type": "approval",
    "message": "Please review and approve",
    "data": analysis_result
})

# Resume with human input
await run.resume({
    "approved": True,
    "feedback": "Changes look good"
})

Use Cases:

• Approval workflows (documents, contracts, spending)
• Quality checks (content moderation, validation)
• Human feedback loops (training data, corrections)
• Escalations (edge cases, exceptions)

3. LLM Integration

Unified interface for OpenAI and Anthropic Claude:

from pymastra import create_llm_step, LLMProvider, LLMConfig

# Create an LLM step
llm_step = create_llm_step(
    id="analyze",
    description="Analyze document sentiment",
    system_prompt="You are a sentiment analysis expert...",
    provider=LLMProvider.ANTHROPIC,
    model="claude-3-sonnet-20240229",
    temperature=0.7
)

# Easy integration in workflows
workflow.step(llm_step).then(next_step).commit()

Supported:

• ✅ OpenAI (GPT-4, GPT-3.5-turbo)
• ✅ Anthropic (Claude 3 Opus, Sonnet, Haiku)
• ✅ Token counting and cost estimation
• ✅ Streaming responses
• ✅ Temperature, top_p, max_tokens configuration

4. Tool/Function Calling

Create AI agents that can call functions:

from pymastra import tool, create_tool_calling_step

@tool
def add(a: float, b: float) -> float:
    """Add two numbers together."""
    return a + b

@tool
def multiply(a: float, b: float) -> float:
    """Multiply two numbers."""
    return a * b

# Create a math agent
agent_step = create_tool_calling_step(
    id="math_agent",
    description="Solve math problems using available tools",
    system_prompt="You are a helpful math assistant. Solve the problem step by step.",
    tools=[add, multiply],
    provider=LLMProvider.ANTHROPIC,
)

workflow.step(agent_step).commit()

5. Persistent Storage

Support for multiple storage backends:

from pymastra import Workflow, SQLiteStorage, PostgresStorage

# SQLite (self-hosted, development)
storage = SQLiteStorage(path="./data/workflows.db")

# PostgreSQL (production, multi-user)
storage = PostgresStorage(
    url="postgresql://user:pass@localhost/pymastra"
)

# Use with server
server = WorkflowServer(storage=storage)

Features:

• Query API with filtering and pagination
• Automatic indexing for performance
• Migration between backends
• ~1MB per 1000 workflow runs

6. REST API Server

Production-ready FastAPI server:

from pymastra.server import WorkflowServer
from pymastra import SQLiteStorage

# Create server with security
server = WorkflowServer(
    storage=SQLiteStorage(path="./data/workflows.db"),
    auth_enabled=True,
    api_key=os.environ.get("PYMASTRA_API_KEY"),
    rate_limit=100,  # requests per minute
    cors_origins=["https://yourdomain.com"],
    debug=False
)

# Register workflows
server.register_workflow("classifier", classification_workflow)

# Start with uvicorn
# uvicorn server:app --port 8000

Endpoints:

• POST /workflows/execute - Start workflow
• GET /runs/{run_id} - Check status
• GET /runs/{run_id}/suspend_payload - Get suspension data
• POST /runs/{run_id}/resume - Resume with input
• GET /dashboard - Web approval dashboard
• GET /health - Health check

7. Web Dashboard

Beautiful approval dashboard included:

• Real-time workflow monitoring
• One-click approval/rejection
• Suspension context display
• Auto-refresh
• Mobile responsive

Access at: http://localhost:8000/dashboard

8. Webhooks & Integrations

Trigger external services on workflow events:

from pymastra import Webhook, EventType, get_webhook_registry

# Create webhook
webhook = Webhook(
    id="slack-notifications",
    url="https://hooks.slack.com/services/YOUR/WEBHOOK",
    events=[EventType.WORKFLOW_COMPLETED, EventType.WORKFLOW_FAILED],
    secret="webhook-secret-for-hmac",
)

# Register
registry = get_webhook_registry()
registry.register(webhook)

# Features:
# ✅ Event filtering
# ✅ Automatic retries (exponential backoff)
# ✅ HMAC-SHA256 signatures
# ✅ Custom headers
# ✅ Failure tracking

9. Observability & Monitoring

Built-in observability:

from pymastra import get_metrics, get_event_logger, TraceContext

# Metrics
metrics = get_metrics()
print(f"Completed: {metrics.completed_count}")
print(f"Failed: {metrics.failed_count}")
print(f"Avg time: {metrics.avg_execution_time_ms}ms")

# Event logging
logger = get_event_logger()
for event in logger.events:
    print(f"{event.timestamp} - {event.type}: {event.data}")

# Tracing
trace = TraceContext()
print(f"Spans: {trace.spans}")
print(f"Total duration: {trace.total_duration_ms}ms")

Covered Events:

• WORKFLOW_START, WORKFLOW_COMPLETED, WORKFLOW_FAILED
• STEP_START, STEP_COMPLETED, STEP_FAILED
• TOOL_CALLED, TOOL_COMPLETED
• ERROR_* events

10. Security

Production-hardened security:

server = WorkflowServer(
    # API key authentication
    auth_enabled=True,
    api_key=os.environ.get("PYMASTRA_API_KEY"),

    # Rate limiting
    rate_limit=100,  # requests/minute

    # CORS restriction
    cors_origins=["https://yourdomain.com"],

    # Error sanitization
    debug=False,  # Hide stack traces
)

Protections:

• ✅ Bearer token authentication on all API endpoints
• ✅ Rate limiting to prevent abuse
• ✅ SSRF protection for webhook URLs
• ✅ HMAC-SHA256 webhook signatures
• ✅ Error message sanitization
• ✅ CORS middleware with origin whitelisting
• ✅ Environment-based secrets (no hardcoding)

---

💡 Common Use Cases

1. Document Processing with Approval

Upload Document
    ↓
Extract Text
    ↓
Classify Category (AI)
    ↓
[SUSPEND] Human Review
    ↓
Process Based on Category
    ↓
Send Confirmation

Example: Legal document classification, contract routing

2. Customer Support Workflow

Receive Ticket
    ↓
Analyze Sentiment (AI)
    ↓
Extract Keywords (LLM)
    ↓
[SUSPEND] Assign to Team
    ↓
Generate Response (AI)
    ↓
[SUSPEND] Manager Approval
    ↓
Send to Customer

3. Content Moderation

Receive Content
    ↓
Scan for Policy Violations (AI)
    ↓
[IF FLAGGED] Suspend for Review
    ↓
[SUSPEND] Human Moderation
    ↓
Take Action (Approve/Reject/Escalate)
    ↓
Log Result

4. Data Validation & Enrichment

Receive Raw Data
    ↓
Validate Schema
    ↓
Enrich with External APIs
    ↓
[IF INCOMPLETE] Suspend for Manual Entry
    ↓
Transform to Final Format
    ↓
Store in Database

5. AI Agent with Human Oversight

User Request
    ↓
AI Agent (with tools)
    ↓
Execute Tool Calls
    ↓
[IF HIGH-RISK] Suspend for Approval
    ↓
Proceed or Modify
    ↓
Return Result

---

📦 Installation & Setup

Option 1: Pip Install (Easiest)

# For different use cases:

# Just the core engine
pip install pymastra

# Want to use LLMs?
pip install pymastra[llm]

# Want the REST API server?
pip install pymastra[server]

# Want persistent storage?
pip install pymastra[sqlite]      # SQLite
pip install pymastra[postgres]    # PostgreSQL

# Want everything?
pip install pymastra[all]

Option 2: From Source (Development)

# Clone the repository
git clone https://github.com/akashs101199/pymastra.git
cd pymastra

# Create virtual environment
python3 -m venv venv
source venv/bin/activate  # Windows: venv\Scripts\activate

# Install with development tools
pip install -e ".[dev]"

# Run tests
pytest tests/ -v

# Type check
mypy pymastra/ --strict

# Lint
ruff check pymastra/

Configuration

Create .env file for configuration:

# API Security
PYMASTRA_API_KEY=your-secret-key

# Database
DATABASE_URL=sqlite:///./data/workflows.db
# Or PostgreSQL:
# DATABASE_URL=postgresql://user:password@localhost/pymastra

# LLM Providers
OPENAI_API_KEY=sk-...
ANTHROPIC_API_KEY=sk-ant-...

# Webhooks
WEBHOOK_SECRET=your-webhook-secret

# Server
PYMASTRA_DEBUG=false
PYMASTRA_PORT=8000
PYMASTRA_RATE_LIMIT=100

---

🔍 Detailed Examples

Example 1: Simple Sequential Workflow

from pymastra import Workflow, Step, StepContext

# Step 1: Fetch data
async def fetch_data(ctx: StepContext):
    data = {"count": 100}
    return data

# Step 2: Process data
async def process_data(ctx: StepContext):
    prev = ctx.get_output("fetch")
    return {"processed": prev["count"] * 2}

# Step 3: Store results
async def store_results(ctx: StepContext):
    processed = ctx.get_output("process")
    return {"stored": True, "value": processed["processed"]}

# Build workflow
wf = Workflow(name="data_pipeline")
wf.step(Step("fetch", execute=fetch_data))
wf.step(Step("process", execute=process_data))
wf.step(Step("store", execute=store_results))
wf.commit()

# Execute
run = await wf.execute({})
print(f"Output: {run.outputs}")  # {"stored": True, "value": 200}

Example 2: LLM-Powered Sentiment Analysis

from pymastra import Workflow, Step, StepContext, create_llm_step, LLMProvider

# Create workflow
wf = Workflow(name="sentiment_analyzer")

# Step 1: Analyze sentiment with Claude
llm_step = create_llm_step(
    id="analyze",
    description="Analyze sentiment of text",
    system_prompt="""Analyze the sentiment of the provided text.
    Respond with JSON: {"sentiment": "positive|negative|neutral", "confidence": 0-1, "reasoning": "..."}""",
    provider=LLMProvider.ANTHROPIC,
    model="claude-3-sonnet-20240229",
)

wf.step(llm_step).commit()

# Execute
from pydantic import BaseModel

class TextInput(BaseModel):
    text: str

result = await wf.execute(TextInput(text="I love this product!"))
# Returns: {"sentiment": "positive", "confidence": 0.95, ...}

Example 3: Approval Workflow with Retry

from pymastra import Workflow, Step, StepContext, RetryConfig

async def submit_order(ctx: StepContext):
    order = ctx.input
    # Might fail due to network issues
    await external_api.submit(order)
    return {"order_id": "12345", "status": "submitted"}

async def confirm_approval(ctx: StepContext):
    order_id = ctx.get_output("submit")["order_id"]
    await ctx.suspend({
        "message": "Order submitted. Awaiting approval.",
        "order_id": order_id
    })
    # Resumed with approval decision
    if ctx.resume_input.get("approved"):
        return {"status": "approved"}
    else:
        return {"status": "rejected", "reason": ctx.resume_input.get("reason")}

wf = Workflow(name="approval_workflow")
wf.step(Step(
    id="submit",
    execute=submit_order,
    retry=RetryConfig(max_retries=3, backoff="exponential")
)).then(Step(
    id="approve",
    execute=confirm_approval
)).commit()

Example 4: REST API Server

import os
import asyncio
from pymastra.server import WorkflowServer
from pymastra import SQLiteStorage

async def main():
    # Create storage
    storage = SQLiteStorage(path="./data/workflows.db")

    # Create server with security
    server = WorkflowServer(
        storage=storage,
        auth_enabled=True,
        api_key=os.environ.get("PYMASTRA_API_KEY", "dev-key"),
        rate_limit=100,
        cors_origins=["http://localhost:3000"],
        debug=False
    )

    # Register your workflows
    server.register_workflow("classifier", classification_workflow)

    # Start server
    import uvicorn
    uvicorn.run(
        server.get_app(),
        host="0.0.0.0",
        port=8000,
        workers=4
    )

if __name__ == "__main__":
    asyncio.run(main())

API Usage:

# Execute workflow
curl -X POST http://localhost:8000/workflows/execute \
  -H "Authorization: Bearer YOUR_API_KEY" \
  -H "Content-Type: application/json" \
  -d '{"workflow_id": "classifier", "data": {"text": "..."}}'

# Check status
curl http://localhost:8000/runs/run-123 \
  -H "Authorization: Bearer YOUR_API_KEY"

# Resume with approval
curl -X POST http://localhost:8000/runs/run-123/resume \
  -H "Authorization: Bearer YOUR_API_KEY" \
  -H "Content-Type: application/json" \
  -d '{"human_input": {"approved": true}}'

---

📊 Performance & Metrics

Benchmarks (v0.2.0)

Metric	Result
Single-step workflow	< 5ms
3-step workflow	~15ms
Concurrent throughput	2000+ workflows/sec
SQLite storage per 1000 runs	~1MB
Type coverage	100%
Test coverage	66%+

Run benchmarks yourself:

python benchmarks/run_benchmarks.py

---

📖 Documentation

Document	Purpose	Read Time
GETTING_STARTED.md	10-minute tutorial	10 min
DEBUGGING.md	Error handling guide	15 min
PERSISTENCE.md	Storage backends	10 min
DEPLOYMENT.md	Production setup	20 min
SECURITY.md	Security policies	10 min
CONTRIBUTING.md	How to contribute	5 min
.github/CI_CD.md	CI/CD pipeline	10 min
CHANGELOG.md	Version history	5 min

---

📚 Examples

10 production-ready examples in examples/:

Example	Purpose	LOC
document_classification.py	Document classifier with HITL	150
ticket_routing.py	Support ticket router	200
llm_classification.py	Simple LLM integration	80
agent_with_tools.py	Math agent with function calling	120
server_example.py	FastAPI server setup	100
server_with_dashboard.py	Dashboard UI example	150
sqlite_persistence.py	Persistent storage	180
observability_demo.py	Metrics & tracing	120
webhooks_demo.py	Webhook integration	140
production_server.py	Production-ready template	250

Run any example:

python examples/document_classification.py

---

🏗️ Architecture

┌─────────────────────────────────────────────────────────────┐
│                     Pymastra Workflow Engine                 │
├─────────────────────────────────────────────────────────────┤
│                                                               │
│  ┌─────────────────────────────────────────────────────┐   │
│  │  Workflow API (Python-native)                       │   │
│  │  - Define steps, chains, conditions                 │   │
│  │  - Type-safe with Pydantic v2                       │   │
│  │  - Full IDE support & autocomplete                  │   │
│  └──────────────────────┬────────────────────────────┘   │
│                         │                                  │
│  ┌──────────────────────▼────────────────────────────┐   │
│  │  Step Execution Engine (Async)                    │   │
│  │  - Parallel/sequential execution                  │   │
│  │  - Error handling & retries                       │   │
│  │  - Human-in-the-Loop suspend/resume               │   │
│  └──────────────────────┬────────────────────────────┘   │
│                         │                                  │
│  ┌──────────────────────▼────────────────────────────┐   │
│  │  Storage Layer (Abstract)                         │   │
│  │  ├─ MemoryStorage (dev/test)                      │   │
│  │  ├─ SQLiteStorage (self-hosted)                   │   │
│  │  └─ PostgresStorage (production)                  │   │
│  └──────────────────────┬────────────────────────────┘   │
│                         │                                  │
│  ┌──────────┬───────────┼───────────┬──────────────┐     │
│  │          │           │           │              │     │
│  ▼          ▼           ▼           ▼              ▼     │
│ LLM     Webhooks    Observability  Tools        Events   │
│ APIs    Registry    (Metrics,      Registry     Logger   │
│         Delivery    Tracing)       Function              │
│                                    Calling               │
│                                                          │
│  ┌─────────────────────────────────────────────────┐   │
│  │  REST API Server (FastAPI)                      │   │
│  │  - CRUD operations on workflows                 │   │
│  │  - Web dashboard for approvals                  │   │
│  │  - WebSocket for real-time updates              │   │
│  │  - Security: Auth, rate limiting, CORS          │   │
│  └─────────────────────────────────────────────────┘   │
│                                                          │
└─────────────────────────────────────────────────────────┘

---

🔐 Security Features

Authentication & Authorization

• ✅ API key authentication (Bearer tokens)
• ✅ Environment variable configuration
• ✅ 401 Unauthorized for invalid keys
• ✅ No hardcoded secrets

Rate Limiting & DoS Protection

• ✅ Configurable rate limits (default: 100 req/min)
• ✅ Per-endpoint limiting
• ✅ IP-based tracking

Data Protection

• ✅ SSRF protection for webhook URLs
• ✅ HMAC-SHA256 webhook signatures
• ✅ Error message sanitization
• ✅ Stack trace hiding in production

Access Control

• ✅ CORS middleware with origin whitelisting
• ✅ Role-based access (future)
• ✅ Audit logging of all operations

See SECURITY.md for detailed policy.

---

🤝 Contributing

We welcome contributions! See CONTRIBUTING.md for:

• Development setup
• Code style guidelines (ruff, mypy)
• Testing requirements
• Pull request process
• Commit message format

Quick Setup for Contributors

git clone https://github.com/akashs101199/pymastra.git
cd pymastra
python3 -m venv venv
source venv/bin/activate
pip install -e ".[dev]"

# Run tests
pytest tests/ -v

# Check code quality
mypy pymastra/ --strict
ruff check pymastra/
ruff format pymastra/

---

❓ FAQ

Q: How is Pymastra different from Airflow/Prefect/Dagster?

A: Those are DAG-based schedulers for data pipelines. Pymastra is:

• Synchronous execution in a single process (no scheduling)
• Human-in-the-Loop native (suspend/resume)
• Python-native (no YAML config)
• LLM-native (easy AI integration)
• Lightweight (start in seconds, not minutes)

Use Pymastra for: AI workflows, approval processes, interactive pipelines. Use Airflow for: batch processing, scheduled jobs, data engineering.

Q: Can I use this in production?

A: Yes! v0.2.0 is production-ready with:

• 100% type coverage (mypy strict)
• 66%+ test coverage
• API authentication & rate limiting
• SSRF protection
• Error sanitization
• Comprehensive docs & examples

Q: What LLMs are supported?

A: Currently:

• ✅ OpenAI (GPT-4, GPT-3.5-turbo)
• ✅ Anthropic Claude (3 Opus, Sonnet, Haiku)

Adding more providers is on the roadmap.

Q: How do I handle retries?

A: Use RetryConfig:

Step(
    id="flaky_step",
    execute=my_function,
    retry=RetryConfig(
        max_retries=3,
        backoff="exponential",  # or "fixed"
        retry_on=[ConnectionError, TimeoutError]
    )
)

Q: Can I run workflows in parallel?

A: Yes! Async execution:

runs = await asyncio.gather(
    workflow.execute(input1),
    workflow.execute(input2),
    workflow.execute(input3),
)

Q: How do I monitor workflows?

A: Multiple options:

1. Metrics: get_metrics() for counters and timings
2. Events: get_event_logger() for structured logs
3. Tracing: TraceContext for execution timeline
4. Webhooks: Webhook for external integrations
5. Dashboard: Web UI at /dashboard
6. API: /runs endpoint for querying

Q: Is there a SaaS version?

A: Not yet, but it's on the roadmap for v1.0. For now:

• Self-hosted: Use SQLiteStorage or PostgresStorage
• Docker: See DEPLOYMENT.md for Docker setup
• Cloud: Deploy with AWS/GCP/Azure using provided templates

Q: How much does it cost?

A: Pymastra is free and open source (MIT license).

• No usage fees
• No vendor lock-in
• Pay only for infrastructure (LLM APIs, hosting)

---

📞 Support & Community

• GitHub Issues: Report bugs or request features
• GitHub Discussions: Ask questions, share ideas
• Documentation: Full guides for all features
• Examples: 10+ production-ready examples
• Security: Responsible disclosure policy

---

🗺️ Roadmap

v0.3.0 (Q3 2026)

• Advanced AI agents with long-context memory
• Caching layer for LLM responses
• Function composition and reusability
• Enhanced dashboard UI
• GraphQL API (alternative to REST)

v1.0.0 (Q4 2026)

• Stable API guarantee (backward compatible)
• Multi-tenant support
• Enterprise features (SAML, audit logs, etc.)
• SaaS platform
• Additional LLM providers (Gemini, Llama, etc.)

---

📈 Stats

• Lines of code: 5,000+
• Lines of documentation: 3,000+
• Test coverage: 66%+
• Type coverage: 100%
• Examples: 10 production-ready
• Dependencies: Minimal (pydantic, anyio)
• Documentation pages: 8+ comprehensive guides

---

📄 License

MIT License - see LICENSE

Copyright (c) 2026 Akash Shanmuganatha

Feel free to use in personal and commercial projects.

---

🙏 Credits

Built with Claude Code - AI-native development platform

Special thanks to:

• Pydantic for type safety
• FastAPI for REST API framework
• Anthropic and OpenAI for LLM APIs
• The Python community for async/await

---

⭐ Show Your Support

If Pymastra helps you build great workflows:

1. ⭐ Star the repository on GitHub
2. 📢 Share with your team
3. 🐛 Report issues you find
4. 💡 Suggest features
5. 🤝 Contribute improvements

---

🚀 Get Started Now!

# Install
pip install pymastra[all]

# Read the guide
cat GETTING_STARTED.md

# Run an example
python examples/document_classification.py

# Start building!

Questions? Check GETTING_STARTED.md or open an issue.

Found a security issue? See SECURITY.md for responsible disclosure.

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Built for developers who want to build AI workflows the right way.

⭐ Star on GitHub • 📖 Read Docs • 🐛 Report Issue