payload = runtime.selfref.context.compact(
    goal="Implement user authentication with OAuth",
    instruction="Continue with refresh token logic. Token exchange is working.",
    discoveries=[
        "OAuth provider requires PKCE challenge",
        "Access tokens expire in 1 hour",
        "Refresh endpoint: POST /oauth/token with grant_type=refresh_token",
    ],
    completed=[
        "Set up project structure",
        "Implemented PKCE challenge generation",
        "Built token exchange endpoint (tested, working)",
    ],
    current_status="Token exchange works. Next: refresh token flow.",
    likely_next_work="Implement refresh token rotation, add token storage, write integration tests",
    relevant_files_directories=[
        "src/auth/oauth.py",
        "src/auth/token_store.py",
        "tests/test_auth.py",
    ],
    remember=["OAuth provider requires PKCE — never skip it"],
)

COMPACTION_THRESHOLD = 0.3  # 上下文窗口的 30%

def should_compact(llm) -> bool:
    used = llm.input_token_count + llm.output_token_count
    return used > llm.context_window * COMPACTION_THRESHOLD

def prepare_message(message: str) -> str:
    if should_compact(llm):
        return message + "\n\nAfter completing this task, compact your context."
    return message

# 在 execute_code 内部：

# 创建子任务
handle_a = runtime.selfref.fork.spawn(
    task="Review src/auth/ for security issues",
    instruction="Check for: injection, auth bypass, secret leakage. Return a list of findings.",
)

handle_b = runtime.selfref.fork.spawn(
    task="Write unit tests for src/auth/oauth.py",
    instruction="Cover: token exchange, PKCE validation, error cases. Write to tests/test_oauth.py.",
)

print(f"Spawned: {handle_a['fork_id']}, {handle_b['fork_id']}")

# ... 父任务继续处理其他工作 ...

# 准备好后收集结果
results = runtime.selfref.fork.gather_all()
for fork_id, result in results.items():
    print(f"{fork_id}: {result['status']}")
    if result['status'] == 'completed':
        print(f"  Response: {result['response'][:200]}")

from SimpleLLMFunc.builtin import SelfReference

selfref = SelfReference()

# 将不同的历史绑定到不同的键
selfref.bind_history("coding", coding_history)
selfref.bind_history("research", research_history)

# 查看当前的 experiences
snapshot = runtime.selfref.context.inspect()
for exp in snapshot["experiences"]:
    print(f"  {exp['id']}: {exp['text']}")

# 移除过时的
runtime.selfref.context.forget("exp_old_001")

repl = PyRepl(working_directory=workspace)
file_tools = FileToolset(workspace).toolset

@llm_chat(
    llm_interface=llm,
    toolkit=[*repl.toolset, *file_tools],
    stream=True,
    self_reference_key="project_agent",
)
async def project_agent(message: str, history: list | None = None):
    """
    Long-running project agent with durable memory.

    Use runtime.selfref.context.remember(...) for durable project facts.
    Use runtime.selfref.context.compact(...) at milestones.
    Use runtime.selfref.fork.spawn(...) for parallelizable subtasks.
    """
    pass