from SimpleLLMFunc.builtin import SelfReference

selfref = SelfReference()
selfref.bind_history("agent_main", initial_history)

@llm_chat(self_reference_key="agent_main")
                    │
                    ▼
        ┌─ SelfRefSession（每次调用） ──────────────────┐
        │                                                │
        │  每次编译前：                                    │
        │    → collect_context_mutations()               │
        │    → 可能产出 Experience/Summary 补丁            │
        │                                                │
        │  轮次结束后：                                    │
        │    → finalize()                                │
        │    → 将更新后的历史持久化到后端                    │
        │                                                │
        └────────────────────────────────────────────────┘
                    │
                    ▼
        ┌─ SelfReference（持久后端）────────────────────┐
        │                                                │
        │  Memory["agent_main"]:                         │
        │    - history: [...]                            │
        │    - experiences: [{id, text}, ...]            │
        │    - summary: {...}                            │
        │                                                │
        └────────────────────────────────────────────────┘

@dataclass(frozen=True)
class DataFromSelfRef:
    base_system_prompt: str          # System prompt (may include experience markers)
    experiences: List[Dict[str, str]]  # [{id: "...", text: "..."}, ...]
    summary: Optional[Dict[str, Any]]  # Compaction metadata
    summary_message: Optional[Dict[str, Any]]  # Summary as a message
    working_messages: NormalizedMessageList  # Post-compaction working transcript

System Prompt:
  You are a coding agent...

  <experiences>
    <exp id="exp_001">User prefers dark mode terminal output</exp>
    <exp id="exp_002">Always run tests before committing</exp>
  </experiences>

# Inside execute_code:
payload = runtime.selfref.context.compact(
    goal="Build the authentication module",
    instruction="Continue implementing OAuth flow",
    discoveries=["API requires PKCE", "Token refresh interval is 1h"],
    completed=["Set up project structure", "Added OAuth dependency"],
    current_status="Implementing token exchange endpoint",
    likely_next_work="Add refresh token logic, then write tests",
    relevant_files_directories=["src/auth/", "tests/test_auth.py"],
    remember=["OAuth endpoint requires PKCE challenge"]
)

# Spawn (inside execute_code)
handle = runtime.selfref.fork.spawn(
    task="Review the auth module for security issues",
    instruction="Check for: SQL injection, XSS, auth bypass. Return findings as a list.",
)
print(handle["fork_id"])  # "fork_abc123"

# ... do other work ...

# Gather (when you need results)
results = runtime.selfref.fork.gather_all()
for fork_id, result in results.items():
    print(f"{fork_id}: {result['status']} - {result['response']}")

@llm_chat(
    llm_interface=llm,
    toolkit=[*repl.toolset, *file_tools],
    self_reference_key="agent_main",
)
async def agent(message: str, history: list):
    """Your agent prompt here."""
    pass