Add purpose_agent package init

purpose_agent/__init__.py

@@ -0,0 +1,50 @@
+"""
+purpose_agent — A Self-Improving Agentic Framework via State-Value Evaluation
+
+Architecture based on:
+  - MUSE (arxiv:2510.08002): 3-tier hierarchical memory (strategic/procedural/tool)
+  - LATS (arxiv:2310.04406): LLM-as-value-function V(s) = λ·LM_score + (1-λ)·SC_score
+  - REMEMBERER (arxiv:2306.07929): Q-value experience replay with Bellman updates
+  - Reflexion (arxiv:2303.11366): Verbal reinforcement via episodic self-reflection
+  - SPC (arxiv:2504.19162): Anti-reward-hacking via adversarial critic patterns
+
+Core philosophy: The agent improves via a "Purpose Function" Φ(s) that evaluates
+intermediate state improvements (distance to goal) rather than binary outcome success.
+No real-time backprop — improvement comes from expanding external memory with
+learned heuristics extracted from high-reward trajectories.
+"""
+
+__version__ = "0.1.0"
+
+from purpose_agent.types import (
+    State,
+    Action,
+    Trajectory,
+    TrajectoryStep,
+    Heuristic,
+    PurposeScore,
+    MemoryRecord,
+)
+from purpose_agent.llm_backend import LLMBackend, MockLLMBackend
+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
+
+__all__ = [
+    "State",
+    "Action",
+    "Trajectory",
+    "TrajectoryStep",
+    "Heuristic",
+    "PurposeScore",
+    "MemoryRecord",
+    "LLMBackend",
+    "MockLLMBackend",
+    "Actor",
+    "PurposeFunction",
+    "ExperienceReplay",
+    "HeuristicOptimizer",
+    "Orchestrator",
+]