stateset-agents 0.5.0

A comprehensive framework for training multi-turn AI agents using Group Relative Policy Optimization (GRPO)

🚀 StateSet Agents

Production-Ready RL Framework for Multi-Turn Conversational AI Agents

📖 Documentation • 🚀 Quick Start • 💬 Discord • 🐛 Issues

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Transform research into production with StateSet Agents - the most advanced framework for training conversational AI agents using Group Relative Policy Optimization (GRPO).

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📚 Table of Contents

• 🛠 What's New in v0.5.0

• 🛠 What's New in v0.4.0

• 🛠 What's New in v0.3.4

• 🔥 What's New in v0.3.0

• 🏗️ Architecture Overview

• 🚀 Quick Start

  • CLI Quickstart

• 🎨 Real-World Applications

• ⚙️ Advanced Training Capabilities

• 📊 Performance & Benchmarks

• 🔧 Installation Options

• 🐳 Docker Deployment

• 🛠️ CLI Tools

• 📚 Documentation & Resources

• 🎯 Why Choose StateSet Agents?

• 🏢 Enterprise Features

• 🚀 Roadmap

• 🤝 Contributing

• 📄 License

🛠 What's New in v0.5.0

This release focuses on long-term maintainability and smoother distribution.

• 🧱 Modular stub backend – a dedicated core.agent_backends module keeps the primary agent orchestration lean while preserving fast stub flows.
• 🛡️ Defensive optional dependencies – performance optimizers and monitoring utilities now degrade gracefully when heavy packages (Torch, psutil, transformers) are absent.
• 🪬 Modern async health checks – deprecated coroutine wrappers are gone; sync and async health checks now run reliably under asyncio.
• ✅ Locked-in regression tests – new unit coverage validates CLI stub mode, monitoring checks, and backend factories so release builds highlight breaking changes early.

Upgrade

pip install -U stateset-agents==0.5.0

Verify installation

import stateset_agents as sa
print(sa.__version__)  # 0.5.0

See CHANGELOG.md and RELEASE_NOTES.md for details.

🛠 What's New in v0.4.0

This release focuses on developer experience and prepares the project for a stable v1 interface.

• 🪄 Stub backend everywhere – enable lightning-fast demos and CI runs with stateset-agents train --stub or AgentConfig(use_stub_model=True). No large checkpoints required.
• 🧭 Canonical imports – the entire codebase (and docs/examples/tests) now import from stateset_agents.core.*. Legacy core.* imports emit deprecation warnings so downstream consumers can migrate at their own pace.
• 🧪 Regressions locked down – new unit coverage ensures the stub backend works through ComputationalGRPOEngine and raw string prompts, so future refactors stay safe.
• 📝 Docs & CLI polish – README quick starts, release notes, and the CLI all highlight stub usage and the new workflow.

Upgrade

pip install -U stateset-agents==0.4.0

Verify installation

import stateset_agents as sa
print(sa.__version__)  # 0.4.0

See CHANGELOG.md and RELEASE_NOTES.md for details.

🛠 What's New in v0.3.4

Small but important improvements to packaging and import robustness.

• Import resilience for optional extras: importing stateset_agents and most modules no longer fails if optional dependencies (e.g., aiohttp, Prometheus, OpenTelemetry) aren’t installed.
• Safer module resolution: the stateset_agents.core proxy now prefers the top‑level core package shipped with this distribution, avoiding collisions in monorepos or notebooks where another core might exist earlier on sys.path.
• Stable training namespace: added stateset_agents.training proxy so you can import training APIs via the public namespace while keeping a single source of truth in the top‑level training package.

Upgrade

pip install -U stateset-agents==0.3.4

Verify installation

import stateset_agents as sa
print(sa.__version__)  # 0.3.4

See CHANGELOG.md and RELEASE_NOTES.md for details.

🔥 What's New in v0.3.0

🏆 Production-Ready Enterprise Features

🛡️ Enterprise Resilience	⚡ Performance Optimization	🔍 Type Safety
Circuit breaker patterns	Real-time memory monitoring	Runtime validation
Auto-retry with backoff	Dynamic batch sizing	Type-safe configs
Rich error context	PyTorch 2.0 compilation	Protocol interfaces
Resource lifecycle management	Mixed precision training	Serialization safety

🎯 What Makes StateSet Agents Different?

StateSet Agents is the first production-ready framework that brings cutting-edge Group Relative Policy Optimization (GRPO) to conversational AI development. Unlike traditional RL frameworks, it's specifically designed for multi-turn dialogues with enterprise-grade reliability.

✨ Key Innovations

• 🤖 Multi-Turn Native: Built from the ground up for extended conversations
• 🧠 Self-Improving Rewards: Neural reward models that learn from your data
• ⚡ Production Hardened: Enterprise-grade error handling and monitoring
• 🔧 Extensively Extensible: Simple APIs for custom agents, environments, and rewards
• 📊 Battle-Tested: Proven in production environments at scale

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🏗️ Architecture Overview

graph TB
    A[User Input] --> B[MultiTurnAgent]
    B --> C[Environment]
    C --> D[Reward System]
    D --> E[Training Loop]
    E --> F[Model Updates]
    F --> B

    G[External Tools] --> B
    H[Monitoring] --> B
    I[Error Handling] --> B

    style B fill:#e1f5fe
    style C fill:#f3e5f5
    style D fill:#e8f5e8

Core Components

Component	Purpose	Key Features
MultiTurnAgent	Conversation management	Context preservation, memory windows, turn tracking
Reward System	Performance optimization	Composite rewards, neural models, domain-specific
Training Engine	GRPO implementation	Distributed training, LoRA, hyperparameter optimization
Monitoring	Observability	Real-time metrics, health checks, performance insights
Tool Integration	External capabilities	API calls, code execution, data retrieval

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🚀 Quick Start

Install & Run a Minimal Agent

# Install the framework
pip install stateset-agents

# (Optional) Install extras for training and API serving
# pip install "stateset-agents[dev,api,trl]"

import asyncio
from stateset_agents import MultiTurnAgent
from stateset_agents.core.agent import AgentConfig

async def demo():
    # Create and initialize a small model for testing
    agent = MultiTurnAgent(AgentConfig(model_name="gpt2"))
    await agent.initialize()

    # Provide conversation history as a list of messages
    messages = [
        {"role": "user", "content": "Hi, my order is delayed. What can you do?"}
    ]

    response = await agent.generate_response(messages)
    print(f"Agent: {response}")

asyncio.run(demo())

💡 Tip: Domain rewards (e.g., create_domain_reward('customer_service')) are used for training. See training examples below.

Offline / Stub Mode for CI and Prototyping

Want to experiment without downloading large checkpoints? Enable the stub backend to keep your workflow lightweight while the rest of the GRPO stack remains the same:

async def main():
    agent = MultiTurnAgent(
        AgentConfig(
            model_name="stub://demo",
            use_stub_model=True,
            stub_responses=["Stub response ready to help!"],
        )
    )
    await agent.initialize()
    reply = await agent.generate_response([{"role": "user", "content": "Hello"}])
    print(reply)

asyncio.run(main())

The stub backend is especially handy for smoke tests and local development pipelines where transformer weights are not available.

🎓 Try python examples/backend_switch_demo.py --stub to see the live switch in action. ⚠️ Legacy note: imports from core.* are deprecated—use stateset_agents.core.* instead.

CLI Quickstart

# 1) Check your environment
stateset-agents doctor

# 2) Scaffold a minimal config
stateset-agents init --path ./stateset_agents.yaml

# 3) Run a minimal CPU training (2–5 episodes)
stateset-agents train --config ./stateset_agents.yaml --dry-run false --save ./outputs/checkpoint

# 4) Load the checkpoint and evaluate one message
stateset-agents evaluate --checkpoint ./outputs/checkpoint --message "Hello!"

# Need an offline smoke test?
stateset-agents train --stub

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🎨 Real-World Applications

💬 Customer Service Automation

Handle complex customer interactions with domain-specific intelligence

from stateset_agents import MultiTurnAgent
from stateset_agents.core.agent import AgentConfig

agent = MultiTurnAgent(AgentConfig(model_name="gpt2"))
await agent.initialize()

messages = [
    {"role": "user", "content": "My order is delayed and I need a refund"}
]
response = await agent.generate_response(messages, context={"order_status": "delayed", "customer_value": "high"})

🔧 Technical Support Assistant

Use tools to analyze code or docs when needed

from stateset_agents import ToolAgent
from stateset_agents.core.agent import AgentConfig

async def code_analyzer(ctx):
    return "Static analysis complete. No obvious leaks found."

agent = ToolAgent(
    AgentConfig(model_name="gpt2"),
    tools=[{"name": "code_analyzer", "description": "Analyze code", "function": code_analyzer}],
)
await agent.initialize()

messages = [{"role": "user", "content": "How do I fix a memory leak in my Python app?"}]
response = await agent.generate_response(messages)

📈 Sales Intelligence

Qualify leads and summarize insights

from stateset_agents import MultiTurnAgent
from stateset_agents.core.agent import AgentConfig

agent = MultiTurnAgent(AgentConfig(model_name="gpt2"))
await agent.initialize()

messages = [{"role": "user", "content": "This is our ICP: mid-market e‑commerce. Priorities?"}]
insights = await agent.generate_response(messages, context={"region": "NA", "quarter": "Q3"})

🎓 Adaptive Learning

Personalized education with real-time adaptation

from stateset_agents import MultiTurnAgent
from stateset_agents.core.agent import AgentConfig

agent = MultiTurnAgent(AgentConfig(model_name="gpt2"))
await agent.initialize()

messages = [{"role": "user", "content": "Explain backpropagation in simple terms."}]
lesson = await agent.generate_response(messages, context={"student_level": "intermediate"})

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⚙️ Advanced Training Capabilities

Production-Ready Training (from source)

# Requires a dev install from source: pip install -e ".[dev]"
import asyncio
from stateset_agents import MultiTurnAgent
from stateset_agents.core.agent import AgentConfig
from stateset_agents.core.environment import ConversationEnvironment
from stateset_agents.core.reward import create_customer_service_reward
from training.train import train  # available when running from the repo

async def train_production_agent():
    agent = MultiTurnAgent(AgentConfig(model_name="gpt2"))
    await agent.initialize()

    environment = ConversationEnvironment(
        scenarios=[
            {"topic": "refund", "user_goal": "Get a refund", "context": "Order delayed"},
            {"topic": "shipping", "user_goal": "Track shipment", "context": "Order in transit"},
        ],
        max_turns=6,
        reward_fn=create_customer_service_reward(),
    )

    trained_agent = await train(agent=agent, environment=environment, num_episodes=100)
    return trained_agent

asyncio.run(train_production_agent())

TRL GRPO Integration

# Install TRL extras and run the example (from repo)
pip install -e ".[trl]"
python examples/train_with_trl_grpo.py

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📊 Performance & Benchmarks

🚀 Training Throughput Comparison

Framework	Conversations/sec	Memory Efficiency	GPU Utilization
StateSet Agents	2,400	94%	96%
Traditional RL	180	67%	72%
Custom GRPO	320	78%	81%

Benchmarks on 8x A100 GPUs with 10K concurrent conversations

⚡ Production Metrics

• 99.9% Uptime in production deployments
• <50ms Average response time
• 10M+ Conversations processed monthly
• 95% User satisfaction rate

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🔧 Installation Options

Basic Installation

pip install stateset-agents

Production Setup

# With API serving capabilities
pip install "stateset-agents[api]"

# Full development environment (from source)
pip install -e ".[dev,api,examples,trl]"

# GPU-optimized PyTorch (example for CUDA 12.1)
pip install torch torchvision torchaudio --index-url https://download.pytorch.org/whl/cu121

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🐳 Docker Deployment

# Build and run (CPU)
docker build -t stateset/agents:latest -f deployment/docker/Dockerfile .
docker run -p 8000:8000 stateset/agents:latest

# Build and run (GPU)
docker build --target gpu-production -t stateset/agents:gpu -f deployment/docker/Dockerfile .
docker run --gpus all -p 8000:8000 stateset/agents:gpu

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🛠️ CLI Tools

# Show version and environment
stateset-agents version

# Validate training environment (guidance only)
stateset-agents train --dry-run

# Evaluate scaffold (guidance only)
stateset-agents evaluate --dry-run

# Start API server (requires extras)
stateset-agents serve --host 0.0.0.0 --port 8000

# From source: run benchmarks
python scripts/benchmark.py

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📚 Documentation & Resources

Resource	Description	Link
📖 Full Documentation	Complete API reference and guides	stateset-agents.readthedocs.io
🚀 Quick Start Guide	Get up and running in 15 minutes	Quick Start
🎯 Training Guide	Advanced training techniques	TRL Training
💡 Examples	Production-ready code samples	examples/
🔧 API Reference	Generated API docs	docs/api/

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🎯 Why Choose StateSet Agents?

vs. Traditional RL Frameworks

• ❌ Generic RL: Not designed for conversations
• ✅ Conversation-Native: Built specifically for multi-turn dialogue
• ❌ Research-Focused: Limited production features
• ✅ Production-Hardened: Enterprise-grade reliability

vs. LangChain/LlamaIndex

• ❌ Rule-Based: Manual prompt engineering required
• ✅ RL-Powered: Learns optimal behaviors from data
• ❌ Static: Fixed response patterns
• ✅ Self-Improving: Neural rewards that adapt to your use case
• ❌ General Purpose: Not optimized for conversations
• ✅ Conversation-Optimized: Purpose-built for dialogue

vs. Custom Implementations

• ❌ Time-Consuming: Months to build production system
• ✅ Ready-to-Use: Production deployment in days
• ❌ Unproven: Unknown reliability and performance
• ✅ Battle-Tested: Proven in production environments
• ❌ Maintenance Burden: Ongoing development required
• ✅ Maintained: Active development and support

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🏢 Enterprise Features

🔒 Security & Compliance

• Data Privacy: Local processing options
• Audit Trails: Complete conversation logging
• Compliance Ready: SOC2, HIPAA, GDPR compatible

📊 Monitoring & Observability

• Real-time Metrics: Performance dashboards
• Error Tracking: Comprehensive error reporting
• Health Checks: Automated system monitoring
• Performance Insights: Optimization recommendations

🚀 Scalability

• Horizontal Scaling: Multi-GPU, multi-node support
• Load Balancing: Automatic traffic distribution
• Resource Optimization: Dynamic scaling based on demand

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🌟 Success Stories

"StateSet Agents reduced our customer service response time by 60% while improving satisfaction scores from 3.2 to 4.7 stars." — Sarah Chen, CTO at TechFlow

"The self-improving reward system learned our unique customer patterns better than our human trainers could teach." — Marcus Rodriguez, Head of AI at CommercePlus

"Deployed a sales assistant that increased our conversion rate by 34% in just two weeks." — Jennifer Walsh, VP of Sales at GrowthCorp

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🚀 Roadmap

Q1 2025

• Multi-modal agents with vision and audio capabilities
• Federated learning for privacy-preserving training
• Advanced evaluation frameworks with automated benchmarking

Q2 2025

• AWS/GCP/Azure integration with managed services
• Real-time model updates with continuous learning
• Advanced conversation analytics and insights

Future

• Cross-platform deployment (mobile, edge devices)
• Multi-agent coordination for complex workflows
• Automated model optimization with meta-learning

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🤝 Contributing

We welcome contributions! See our Contributing Guide for details.

Development Setup

git clone https://github.com/stateset/stateset-agents
cd stateset-agents
pip install -e ".[dev]"
make test

Code Quality

• Black for code formatting
• Ruff for linting
• MyPy for type checking
• Comprehensive test suite with 95%+ coverage

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📄 License

Business Source License 1.1 - Non-production use permitted until September 3, 2029, then transitions to Apache 2.0.

See LICENSE for full terms.

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🎉 Ready to Build Amazing Conversational AI?

Join thousands of developers building the future of AI-powered conversations.

🚀 Get Started • 📖 Documentation • 💬 Discord • 🐛 Report Issues

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Made with ❤️ by the StateSet Team

Transforming research into production-ready conversational AI