flyto-pro-core 0.1.1

Open-source core for Flyto Pro — contracts, cost control, interfaces, and agent runtime

flyto-pro-core

Open-source foundation for Flyto — workflow validation, cost control, agent runtime, and provider interfaces.

What's Inside

Module	Purpose	Lines
contract	Workflow validation, binding resolution, compilation	4.9K
cost	Multi-resource budget management (cost, tokens, tool calls, iterations)	1.3K
interfaces	Abstract LLM, vector store, quality checker + OpenAI/Qdrant providers	1.8K
agent_runtime	Deterministic verification, observations, project state management	11.9K
core	DI container, safe access utilities, validators	1.5K
config	Settings, constants, timeouts	1K

Install

pip install flyto-pro-core

With optional providers:

pip install flyto-pro-core[openai]    # OpenAI LLM + embeddings
pip install flyto-pro-core[qdrant]    # Qdrant vector store
pip install flyto-pro-core[full]      # All providers

Quick Start

Contract Engine — Validate Workflows

from flyto_pro_core.contract.engine import ContractEngine

engine = ContractEngine()
await engine.initialize()  # loads module catalog from flyto-core

report = await engine.validate_workflow(spec)
if not report.valid:
    for issue in report.issues:
        print(f"  {issue.severity}: {issue.message}")

# Binding resolution
bindings = await engine.get_available_bindings(spec, "node_3")

# Compile to execution plan
plan = await engine.compile(spec)

Cost Controller — Budget Management

from flyto_pro_core.cost.controller import CostController, BudgetConfig

# Per-tier budgets
controller = CostController(budget=BudgetConfig.for_tier("pro"))

# Record usage
controller.record_llm_usage("gpt-4o", prompt_tokens=1000, completion_tokens=500)
controller.record_tool_call()

# Check budget (raises BudgetExceededError if over)
controller.check_budget()

# Summary
print(controller.get_summary())
# {"cost_spent_usd": 0.025, "cost_budget_usd": 1.0, "tokens_used": 1500, ...}

Interfaces — Bring Your Own Provider

from flyto_pro_core.interfaces.llm import ILLMService, LLMResponse
from flyto_pro_core.interfaces.storage import IVectorStoreRepository

# Use built-in OpenAI provider
from flyto_pro_core.interfaces.providers.openai_llm import OpenAILLMService
llm = OpenAILLMService(model="gpt-4o")
response = await llm.generate("Hello")

# Or implement your own
class MyLLM(ILLMService):
    async def generate(self, prompt, **kwargs) -> LLMResponse:
        ...

Agent Runtime — Verification & Project State

from flyto_pro_core.agent_runtime.verification import DeterministicVerifier
from flyto_pro_core.agent_runtime.project import ProjectStateManager

# Deterministic verification (no LLM needed)
verifier = DeterministicVerifier()
report = await verifier.verify(assertions, evidence)

# Project state management
state = ProjectStateManager(project_dir="/path/to/project")
await state.initialize()

DI Container

from flyto_pro_core.core.container import container

# Register services
container.register("llm", my_llm_instance)
container.register_factory("vector_store", lambda: QdrantVectorStore())

# Retrieve
llm = container.get("llm")

Architecture

flyto-pro-core (this package, Apache-2.0)
├── contract/        → WorkflowSpec → ValidationReport → ExecutablePlan
├── cost/            → BudgetConfig → CostController → BudgetExceededError
├── interfaces/      → ILLMService / IVectorStoreRepository / IQualityChecker
│   └── providers/   → OpenAILLMService, QdrantVectorStore (built-in)
├── agent_runtime/   → Verification, Observations, ProjectState, Interventions
├── core/            → ServiceContainer, safe_access, validators
└── config/          → Settings, constants

Relationship to Flyto Ecosystem

flyto-pro-core (open source)     flyto-pro (proprietary)
├── contract                      ├── ems (error learning)
├── cost                          ├── evolution (module generation)
├── interfaces                    ├── knowledge (semantic search)
├── agent_runtime                 ├── agent (AI agent core)
├── core                          ├── guardian (safety)
└── config                        └── 50+ enterprise modules
         │                                    │
         └────────── flyto-ai ────────────────┘
                  (open source)
                  ProBridge connects both layers

• Free users: flyto-ai + flyto-pro-core = full contract validation, cost control, agent runtime
• Pro users: + flyto-pro = EMS error learning, module evolution, semantic knowledge search

Requirements

• Python 3.9+
• pydantic >= 2.0.0
• pyyaml >= 6.0

Optional:

• openai >= 1.0.0 (for OpenAI provider)
• qdrant-client >= 1.7.0 (for Qdrant provider)
• flyto-core >= 1.5.3 (for contract catalog loading)

License

Apache License 2.0 — see LICENSE.