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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
__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",
]
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