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0b9dc2e | 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 71 72 73 74 75 76 77 78 79 80 81 82 83 84 85 86 87 88 89 90 91 92 93 94 95 96 97 98 99 100 101 102 103 104 105 106 107 108 109 110 111 112 113 114 115 116 117 118 119 120 121 122 123 124 125 126 127 128 129 130 131 132 133 134 135 136 137 138 139 140 141 142 143 144 145 146 147 148 149 150 151 152 153 154 155 156 157 158 159 160 161 162 163 164 165 166 167 168 169 170 171 172 173 174 175 176 177 178 179 180 181 182 183 184 185 186 187 188 189 190 191 192 193 194 195 196 197 198 199 200 201 202 203 204 205 206 207 208 209 210 211 212 213 214 215 216 217 218 219 220 221 222 223 224 225 226 227 228 229 230 231 232 233 234 235 236 237 238 239 | # -*- coding: utf-8 -*-
"""AgenticMemoryMiddleware end-to-end demo.
The demo uses a single Agent with the filesystem-backed long-term memory
middleware and the built-in ``Read`` / ``Write`` tools across two turns:
1. The Agent receives mock user input that explicitly asks it to remember
durable user information. The middleware injects memory instructions and the
Agent writes Markdown files under ``demo_workspace/Memory``.
2. The same Agent is then asked to recall the earlier user information. The
answer is grounded by the Markdown files persisted on disk by the
middleware.
Requires:
pip install agentscope
export DASHSCOPE_API_KEY=sk-...
"""
import asyncio
import os
import shutil
from pathlib import Path
from pydantic import SecretStr
from agentscope.agent import Agent
from agentscope.credential import DashScopeCredential
from agentscope.event import (
TextBlockDeltaEvent,
ToolCallDeltaEvent,
ToolCallStartEvent,
ToolResultEndEvent,
ToolResultTextDeltaEvent,
)
from agentscope.message import UserMsg
from agentscope.middleware import AgenticMemoryMiddleware
from agentscope.model import DashScopeChatModel
from agentscope.permission import AdditionalWorkingDirectory, PermissionMode
from agentscope.tool import Read, Toolkit, Write
RESET_DEMO_WORKSPACE = True
DEMO_ROOT = Path(__file__).with_name("demo_workspace")
FIRST_USER_MESSAGE = """
Please remember these durable facts for future conversations in this
workspace:
- My name is Alice Chen.
- I live in Hangzhou.
- I prefer concise Chinese answers.
- When evaluating examples, I like seeing a fresh Agent instance prove that
long-term memory was persisted outside the current conversation state.
Use the filesystem memory instructions in your system prompt: create or update
a topic Markdown memory file with frontmatter, and update MEMORY.md with a
short pointer to that file. Read MEMORY.md first if you need to update it.
""".strip()
SECOND_USER_MESSAGE = """
What do you remember about my name, location, answer style, and how I like
examples to demonstrate long-term memory? Read the relevant memory files if
you need details before answering.
""".strip()
def _configure_demo_permissions(agent: Agent, workspace_root: Path) -> None:
"""Allow the demo Agent to read and write inside the demo workspace.
Args:
agent (`Agent`):
The Agent whose permission context should be configured.
workspace_root (`Path`):
The directory containing the demo memory files.
"""
agent.state.permission_context.mode = PermissionMode.ACCEPT_EDITS
agent.state.permission_context.working_directories[
str(workspace_root)
] = AdditionalWorkingDirectory(
path=str(workspace_root),
source="file-system-memory-demo",
)
def _build_agent(model: DashScopeChatModel, workspace_root: Path) -> Agent:
"""Build a fresh Agent attached to one filesystem memory workspace.
Args:
model (`DashScopeChatModel`):
The chat model used by both the Agent and memory relevance
selection.
workspace_root (`Path`):
The directory that stores ``Memory/MEMORY.md`` and topic files.
Returns:
`Agent`:
A newly initialized Agent instance.
"""
memory = AgenticMemoryMiddleware(workdir=str(workspace_root))
agent = Agent(
name="memory_assistant",
system_prompt=(
"You are a concise assistant. When the user asks you to remember "
"durable preferences or profile facts, persist them using the "
"filesystem memory instructions. Use the Read and Write tools for "
"memory files."
),
model=model,
toolkit=Toolkit(tools=[Read(), Write()]),
middlewares=[memory],
)
_configure_demo_permissions(agent, workspace_root)
return agent
async def _run_turn(agent: Agent, text: str) -> str:
"""Run one streamed turn and print tool activity.
Args:
agent (`Agent`):
The Agent to run.
text (`str`):
The user message.
Returns:
`str`:
The concatenated assistant text response.
"""
tool_names: dict[str, str] = {}
tool_args: dict[str, str] = {}
tool_results: dict[str, str] = {}
reply_parts: list[str] = []
async for event in agent.reply_stream(UserMsg("alice", text)):
if isinstance(event, ToolCallStartEvent):
tool_names[event.tool_call_id] = event.tool_call_name
tool_args[event.tool_call_id] = ""
tool_results[event.tool_call_id] = ""
elif isinstance(event, ToolCallDeltaEvent):
tool_args[event.tool_call_id] += event.delta
elif isinstance(event, ToolResultTextDeltaEvent):
tool_results[event.tool_call_id] += event.delta
elif isinstance(event, ToolResultEndEvent):
tool_id = event.tool_call_id
name = tool_names.pop(tool_id, "<unknown>")
arguments = tool_args.pop(tool_id, "")
result = tool_results.pop(tool_id, "")
print(f"[tool] {name}({arguments}) -> {event.state}")
for line in result.splitlines():
print(f" {line}")
elif isinstance(event, TextBlockDeltaEvent):
reply_parts.append(event.delta)
return "".join(reply_parts)
def _print_memory_files(workspace_root: Path) -> None:
"""Print the Markdown files persisted by the memory middleware.
Args:
workspace_root (`Path`):
The demo workspace root.
"""
memory_root = workspace_root / "Memory"
print(f"\n[Markdown memory files] {memory_root}")
if not memory_root.exists():
print(" The Memory directory has not been created yet.")
return
for path in sorted(memory_root.rglob("*.md")):
relative = path.relative_to(workspace_root)
print(f"\n--- {relative} ---")
print(path.read_text(encoding="utf-8").strip())
def _print_soft_verification(workspace_root: Path) -> None:
"""Print a lightweight check that expected memory keywords were saved.
Args:
workspace_root (`Path`):
The demo workspace root.
"""
memory_root = workspace_root / "Memory"
combined = (
"\n".join(
path.read_text(encoding="utf-8", errors="replace")
for path in sorted(memory_root.rglob("*.md"))
)
if memory_root.exists()
else ""
)
checks = {
"MEMORY.md exists": (memory_root / "MEMORY.md").exists(),
"mentions Alice Chen": "Alice Chen" in combined,
"mentions Hangzhou": "Hangzhou" in combined,
"mentions concise Chinese answers": (
"concise Chinese" in combined or "Chinese answers" in combined
),
}
print("\n[Soft verification]")
for label, ok in checks.items():
print(f" {'PASS' if ok else 'WARN'} - {label}")
async def main() -> None:
"""Run the agentic memory demo."""
api_key = os.environ["DASHSCOPE_API_KEY"]
if RESET_DEMO_WORKSPACE:
print(f"=== resetting demo workspace: {DEMO_ROOT} ===")
shutil.rmtree(DEMO_ROOT, ignore_errors=True)
else:
print(f"=== reusing demo workspace: {DEMO_ROOT} ===")
DEMO_ROOT.mkdir(parents=True, exist_ok=True)
model = DashScopeChatModel(
credential=DashScopeCredential(api_key=SecretStr(api_key)),
model="qwen3.7-max",
stream=False,
)
print("\n=== Turn 1: ask the Agent to persist user memory ===")
agent = _build_agent(model, DEMO_ROOT)
print(f"[user]\n{FIRST_USER_MESSAGE}\n")
first_reply = await _run_turn(agent, FIRST_USER_MESSAGE)
print(f"\n[assistant]\n{first_reply}")
_print_memory_files(DEMO_ROOT)
_print_soft_verification(DEMO_ROOT)
print("\n=== Turn 2: ask the same Agent to recall memory ===")
print(f"[user]\n{SECOND_USER_MESSAGE}\n")
second_reply = await _run_turn(agent, SECOND_USER_MESSAGE)
print(f"\n[assistant]\n{second_reply}")
if __name__ == "__main__":
asyncio.run(main())
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