"""
purpose_agent — The World's First SLM-Native Self-Improving Agentic Framework

Works with both Small Language Models (SLMs, 0.6B-3B params, local) and
Large Language Models (LLMs, cloud APIs) with equal efficiency.

Architecture based on 8 published papers:
  - MUSE (arxiv:2510.08002): 3-tier hierarchical memory
  - LATS (arxiv:2310.04406): LLM-as-value-function
  - REMEMBERER (arxiv:2306.07929): Q-value experience replay
  - Reflexion (arxiv:2303.11366): Verbal reinforcement
  - SPC (arxiv:2504.19162): Anti-reward-hacking
  - CER (arxiv:2506.06698): Contextual experience distillation
  - MemRL (arxiv:2601.03192): Two-phase retrieval
  - TinyAgent (arxiv:2409.00608): SLM-native agent patterns

Modules:
  Core:       types, llm_backend, actor, purpose_function, experience_replay, optimizer, orchestrator
  SLM:        slm_backends (Ollama, llama-cpp, prompt compression)
  Streaming:  streaming (async generators, event streaming)
  Tools:      tools (Tool base class, built-in tools, Tool RAG)
  Observe:    observability (cost tracking, callbacks, metrics)
  Multi:      multi_agent (shared memory, agent delegation, teams)
  HITL:       hitl (checkpoint, interrupt, resume, Φ overrides)
  Eval:       evaluation (benchmark runner, improvement curves)
"""

__version__ = "0.2.0"

# Core
from purpose_agent.types import (
    State, Action, Trajectory, TrajectoryStep,
    Heuristic, PurposeScore, MemoryRecord, MemoryTier,
)
from purpose_agent.llm_backend import (
    LLMBackend, MockLLMBackend, HFInferenceBackend,
    OpenAICompatibleBackend, ChatMessage,
)
from purpose_agent.actor import Actor
from purpose_agent.purpose_function import PurposeFunction
from purpose_agent.experience_replay import ExperienceReplay
from purpose_agent.optimizer import HeuristicOptimizer
from purpose_agent.orchestrator import Orchestrator, Environment, SimpleEnvironment, TaskResult

# SLM-Native Backends
from purpose_agent.slm_backends import (
    OllamaBackend, LlamaCppBackend, SLMPromptCompressor,
    create_slm_backend, SLM_REGISTRY,
)

# Streaming & Async
from purpose_agent.streaming import StreamingMixin, StreamEvent, AsyncOrchestrator

# Tools
from purpose_agent.tools import (
    Tool, FunctionTool, ToolResult, ToolRegistry,
    CalculatorTool, PythonExecTool, ReadFileTool, WriteFileTool,
)

# Observability
from purpose_agent.observability import (
    CostTracker, TokenUsage, CallbackManager,
    AgentEvent, EventType, LoggingCallback, MetricsCollector,
)

# Multi-Agent
from purpose_agent.multi_agent import AgentSpec, AgentTeam

# Human-in-the-Loop
from purpose_agent.hitl import (
    HITLOrchestrator, Checkpoint, HumanInputHandler,
    CLIInputHandler, AutoApproveHandler, InterruptType,
)

# Evaluation
from purpose_agent.evaluation import BenchmarkTask, BenchmarkRunner, BenchmarkResult

# Plugin Registry
from purpose_agent.registry import (
    PluginRegistry, backend_registry, callback_registry, model_registry,
    EmbeddingBackend, default_embedding,
)

# Unified Capabilities (LangGraph + CrewAI + AutoGen + OpenAI SDK + LlamaIndex)
from purpose_agent.unified import (
    Agent, Graph, parallel, Conversation, KnowledgeStore,
    START, END, Message,
)

# Easy API (the only thing beginners need)
from purpose_agent.easy import purpose, Team, quickstart, TEAM_TEMPLATES

__all__ = [
    # Core
    "State", "Action", "Trajectory", "TrajectoryStep", "Heuristic",
    "PurposeScore", "MemoryRecord", "MemoryTier",
    "LLMBackend", "MockLLMBackend", "HFInferenceBackend",
    "OpenAICompatibleBackend", "ChatMessage",
    "Actor", "PurposeFunction", "ExperienceReplay", "HeuristicOptimizer",
    "Orchestrator", "Environment", "SimpleEnvironment", "TaskResult",
    # SLM
    "OllamaBackend", "LlamaCppBackend", "SLMPromptCompressor",
    "create_slm_backend", "SLM_REGISTRY",
    # Streaming
    "StreamingMixin", "StreamEvent", "AsyncOrchestrator",
    # Tools
    "Tool", "FunctionTool", "ToolResult", "ToolRegistry",
    "CalculatorTool", "PythonExecTool", "ReadFileTool", "WriteFileTool",
    # Observability
    "CostTracker", "TokenUsage", "CallbackManager",
    "AgentEvent", "EventType", "LoggingCallback", "MetricsCollector",
    # Multi-Agent
    "AgentSpec", "AgentTeam",
    # HITL
    "HITLOrchestrator", "Checkpoint", "HumanInputHandler",
    "CLIInputHandler", "AutoApproveHandler", "InterruptType",
    # Evaluation
    "BenchmarkTask", "BenchmarkRunner", "BenchmarkResult",
    # Plugin Registry
    "PluginRegistry", "backend_registry", "callback_registry", "model_registry",
    "EmbeddingBackend", "default_embedding",
    # Unified Capabilities
    "Agent", "Graph", "parallel", "Conversation", "KnowledgeStore",
    "START", "END", "Message",
    # Easy API
    "purpose", "Team", "quickstart", "TEAM_TEMPLATES",
]