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feat(agent): two-stage memory system with Dream consolidation

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Replace single-stage MemoryConsolidator with a two-stage architecture:

- Consolidator: lightweight token-budget triggered summarization,
  appends to HISTORY.md with cursor-based tracking
- Dream: cron-scheduled two-phase processor that analyzes HISTORY.md
  and updates SOUL.md, USER.md, MEMORY.md via AgentRunner with
  edit_file tools for surgical, fault-tolerant updates

New files: MemoryStore (pure file I/O), Dream class, DreamConfig,
/dream and /dream-log commands. 89 tests covering all components.
This commit is contained in:
chengyongru 2026-03-31 10:58:57 +08:00
parent cda627f956
commit 5da86258cc
19 changed files with 863 additions and 735 deletions
Show Stats Download Patch File Download Diff File Expand all files Collapse all files

3
nanobot/agent/__init__.py
View File

@ -3,7 +3,7 @@
from nanobot.agent.context import ContextBuilder
from nanobot.agent.hook import AgentHook, AgentHookContext, CompositeHook
from nanobot.agent.loop import AgentLoop
from nanobot.agent.memory import MemoryStore
from nanobot.agent.memory import Consolidator, Dream, MemoryStore
from nanobot.agent.skills import SkillsLoader
from nanobot.agent.subagent import SubagentManager
@ -13,6 +13,7 @@ __all__ = [
"AgentLoop",
"CompositeHook",
"ContextBuilder",
"Dream",
"MemoryStore",
"SkillsLoader",
"SubagentManager",

4
nanobot/agent/context.py
View File

@ -82,8 +82,8 @@ You are nanobot, a helpful AI assistant.
## Workspace
Your workspace is at: {workspace_path}
- Long-term memory: {workspace_path}/memory/MEMORY.md (write important facts here)
- History log: {workspace_path}/memory/HISTORY.md (grep-searchable). Each entry starts with [YYYY-MM-DD HH:MM].
- Long-term memory: {workspace_path}/memory/MEMORY.md (automatically managed by Dream — do not edit directly)
- History log: {workspace_path}/memory/history.jsonl (append-only JSONL, not grep-searchable).
- Custom skills: {workspace_path}/skills/{{skill-name}}/SKILL.md
{platform_policy}

19
nanobot/agent/loop.py
View File

@ -15,7 +15,7 @@ from loguru import logger
from nanobot.agent.context import ContextBuilder
from nanobot.agent.hook import AgentHook, AgentHookContext, CompositeHook
from nanobot.agent.memory import MemoryConsolidator
from nanobot.agent.memory import Consolidator, Dream
from nanobot.agent.runner import AgentRunSpec, AgentRunner
from nanobot.agent.subagent import SubagentManager
from nanobot.agent.tools.cron import CronTool
@ -230,8 +230,8 @@ class AgentLoop:
self._concurrency_gate: asyncio.Semaphore | None = (
asyncio.Semaphore(_max) if _max > 0 else None
)
self.memory_consolidator = MemoryConsolidator(
workspace=workspace,
self.consolidator = Consolidator(
store=self.context.memory,
provider=provider,
model=self.model,
sessions=self.sessions,
@ -240,6 +240,11 @@ class AgentLoop:
get_tool_definitions=self.tools.get_definitions,
max_completion_tokens=provider.generation.max_tokens,
)
self.dream = Dream(
store=self.context.memory,
provider=provider,
model=self.model,
)
self._register_default_tools()
self.commands = CommandRouter()
register_builtin_commands(self.commands)
@ -491,7 +496,7 @@ class AgentLoop:
logger.info("Processing system message from {}", msg.sender_id)
key = f"{channel}:{chat_id}"
session = self.sessions.get_or_create(key)
await self.memory_consolidator.maybe_consolidate_by_tokens(session)
await self.consolidator.maybe_consolidate_by_tokens(session)
self._set_tool_context(channel, chat_id, msg.metadata.get("message_id"))
history = session.get_history(max_messages=0)
current_role = "assistant" if msg.sender_id == "subagent" else "user"
@ -506,7 +511,7 @@ class AgentLoop:
)
self._save_turn(session, all_msgs, 1 + len(history))
self.sessions.save(session)
self._schedule_background(self.memory_consolidator.maybe_consolidate_by_tokens(session))
self._schedule_background(self.consolidator.maybe_consolidate_by_tokens(session))
return OutboundMessage(channel=channel, chat_id=chat_id,
content=final_content or "Background task completed.")
@ -522,7 +527,7 @@ class AgentLoop:
if result := await self.commands.dispatch(ctx):
return result
await self.memory_consolidator.maybe_consolidate_by_tokens(session)
await self.consolidator.maybe_consolidate_by_tokens(session)
self._set_tool_context(msg.channel, msg.chat_id, msg.metadata.get("message_id"))
if message_tool := self.tools.get("message"):
@ -559,7 +564,7 @@ class AgentLoop:
self._save_turn(session, all_msgs, 1 + len(history))
self.sessions.save(session)
self._schedule_background(self.memory_consolidator.maybe_consolidate_by_tokens(session))
self._schedule_background(self.consolidator.maybe_consolidate_by_tokens(session))
if (mt := self.tools.get("message")) and isinstance(mt, MessageTool) and mt._sent_in_turn:
return None

579
nanobot/agent/memory.py
View File

@ -1,4 +1,4 @@
"""Memory system for persistent agent memory."""
"""Memory system: pure file I/O store, lightweight Consolidator, and Dream processor."""
from __future__ import annotations
@ -11,94 +11,181 @@ from typing import TYPE_CHECKING, Any, Callable
from loguru import logger
from nanobot.utils.helpers import ensure_dir, estimate_message_tokens, estimate_prompt_tokens_chain
from nanobot.utils.helpers import ensure_dir, estimate_message_tokens, estimate_prompt_tokens_chain, strip_think
from nanobot.agent.runner import AgentRunSpec, AgentRunner
from nanobot.agent.tools.registry import ToolRegistry
if TYPE_CHECKING:
from nanobot.providers.base import LLMProvider
from nanobot.session.manager import Session, SessionManager
_SAVE_MEMORY_TOOL = [
{
"type": "function",
"function": {
"name": "save_memory",
"description": "Save the memory consolidation result to persistent storage.",
"parameters": {
"type": "object",
"properties": {
"history_entry": {
"type": "string",
"description": "A paragraph summarizing key events/decisions/topics. "
"Start with [YYYY-MM-DD HH:MM]. Include detail useful for grep search.",
},
"memory_update": {
"type": "string",
"description": "Full updated long-term memory as markdown. Include all existing "
"facts plus new ones. Return unchanged if nothing new.",
},
},
"required": ["history_entry", "memory_update"],
},
},
}
]
def _ensure_text(value: Any) -> str:
"""Normalize tool-call payload values to text for file storage."""
return value if isinstance(value, str) else json.dumps(value, ensure_ascii=False)
def _normalize_save_memory_args(args: Any) -> dict[str, Any] | None:
"""Normalize provider tool-call arguments to the expected dict shape."""
if isinstance(args, str):
args = json.loads(args)
if isinstance(args, list):
return args[0] if args and isinstance(args[0], dict) else None
return args if isinstance(args, dict) else None
_TOOL_CHOICE_ERROR_MARKERS = (
"tool_choice",
"toolchoice",
"does not support",
'should be ["none", "auto"]',
)
def _is_tool_choice_unsupported(content: str | None) -> bool:
"""Detect provider errors caused by forced tool_choice being unsupported."""
text = (content or "").lower()
return any(m in text for m in _TOOL_CHOICE_ERROR_MARKERS)
# ---------------------------------------------------------------------------
# MemoryStore — pure file I/O layer
# ---------------------------------------------------------------------------
class MemoryStore:
"""Two-layer memory: MEMORY.md (long-term facts) + HISTORY.md (grep-searchable log)."""
"""Pure file I/O for memory files: MEMORY.md, history.jsonl, SOUL.md, USER.md."""
_MAX_FAILURES_BEFORE_RAW_ARCHIVE = 3
_DEFAULT_MAX_HISTORY = 1000
def __init__(self, workspace: Path):
def __init__(self, workspace: Path, max_history_entries: int = _DEFAULT_MAX_HISTORY):
self.workspace = workspace
self.max_history_entries = max_history_entries
self.memory_dir = ensure_dir(workspace / "memory")
self.memory_file = self.memory_dir / "MEMORY.md"
self.history_file = self.memory_dir / "HISTORY.md"
self._consecutive_failures = 0
self.history_file = self.memory_dir / "history.jsonl"
self.soul_file = workspace / "SOUL.md"
self.user_file = workspace / "USER.md"
self._dream_log_file = self.memory_dir / ".dream-log.md"
self._cursor_file = self.memory_dir / ".cursor"
self._dream_cursor_file = self.memory_dir / ".dream_cursor"
def read_long_term(self) -> str:
if self.memory_file.exists():
return self.memory_file.read_text(encoding="utf-8")
return ""
# -- generic helpers -----------------------------------------------------
def write_long_term(self, content: str) -> None:
@staticmethod
def read_file(path: Path) -> str:
try:
return path.read_text(encoding="utf-8")
except FileNotFoundError:
return ""
# -- MEMORY.md (long-term facts) -----------------------------------------
def read_memory(self) -> str:
return self.read_file(self.memory_file)
def write_memory(self, content: str) -> None:
self.memory_file.write_text(content, encoding="utf-8")
def append_history(self, entry: str) -> None:
with open(self.history_file, "a", encoding="utf-8") as f:
f.write(entry.rstrip() + "\n\n")
# -- SOUL.md -------------------------------------------------------------
def read_soul(self) -> str:
return self.read_file(self.soul_file)
def write_soul(self, content: str) -> None:
self.soul_file.write_text(content, encoding="utf-8")
# -- USER.md -------------------------------------------------------------
def read_user(self) -> str:
return self.read_file(self.user_file)
def write_user(self, content: str) -> None:
self.user_file.write_text(content, encoding="utf-8")
# -- context injection (used by context.py) ------------------------------
def get_memory_context(self) -> str:
long_term = self.read_long_term()
long_term = self.read_memory()
return f"## Long-term Memory\n{long_term}" if long_term else ""
# -- history.jsonl — append-only, JSONL format ---------------------------
def append_history(self, entry: str) -> int:
"""Append *entry* to history.jsonl and return its auto-incrementing cursor."""
cursor = self._next_cursor()
ts = datetime.now().strftime("%Y-%m-%d %H:%M")
record = {"cursor": cursor, "timestamp": ts, "content": strip_think(entry.rstrip()) or entry.rstrip()}
with open(self.history_file, "a", encoding="utf-8") as f:
f.write(json.dumps(record, ensure_ascii=False) + "\n")
self._cursor_file.write_text(str(cursor), encoding="utf-8")
return cursor
def _next_cursor(self) -> int:
"""Read the current cursor counter and return next value."""
if self._cursor_file.exists():
try:
return int(self._cursor_file.read_text(encoding="utf-8").strip()) + 1
except (ValueError, OSError):
pass
# Fallback: read last line's cursor from the JSONL file.
last = self._read_last_entry()
if last:
return last["cursor"] + 1
return 1
def read_unprocessed_history(self, since_cursor: int) -> list[dict[str, Any]]:
"""Return history entries with cursor > *since_cursor*."""
return [e for e in self._read_entries() if e["cursor"] > since_cursor]
def compact_history(self) -> None:
"""Drop oldest entries if the file exceeds *max_history_entries*."""
if self.max_history_entries <= 0:
return
entries = self._read_entries()
if len(entries) <= self.max_history_entries:
return
kept = entries[-self.max_history_entries:]
self._write_entries(kept)
# -- JSONL helpers -------------------------------------------------------
def _read_entries(self) -> list[dict[str, Any]]:
"""Read all entries from history.jsonl."""
entries: list[dict[str, Any]] = []
try:
with open(self.history_file, "r", encoding="utf-8") as f:
for line in f:
line = line.strip()
if line:
try:
entries.append(json.loads(line))
except json.JSONDecodeError:
continue
except FileNotFoundError:
pass
return entries
def _read_last_entry(self) -> dict[str, Any] | None:
"""Read the last entry from the JSONL file efficiently."""
try:
with open(self.history_file, "rb") as f:
f.seek(0, 2)
size = f.tell()
if size == 0:
return None
read_size = min(size, 4096)
f.seek(size - read_size)
data = f.read().decode("utf-8")
lines = [l for l in data.split("\n") if l.strip()]
if not lines:
return None
return json.loads(lines[-1])
except (FileNotFoundError, json.JSONDecodeError):
return None
def _write_entries(self, entries: list[dict[str, Any]]) -> None:
"""Overwrite history.jsonl with the given entries."""
with open(self.history_file, "w", encoding="utf-8") as f:
for entry in entries:
f.write(json.dumps(entry, ensure_ascii=False) + "\n")
# -- dream cursor --------------------------------------------------------
def get_last_dream_cursor(self) -> int:
if self._dream_cursor_file.exists():
try:
return int(self._dream_cursor_file.read_text(encoding="utf-8").strip())
except (ValueError, OSError):
pass
return 0
def set_last_dream_cursor(self, cursor: int) -> None:
self._dream_cursor_file.write_text(str(cursor), encoding="utf-8")
# -- dream log -----------------------------------------------------------
def read_dream_log(self) -> str:
return self.read_file(self._dream_log_file)
def append_dream_log(self, entry: str) -> None:
with open(self._dream_log_file, "a", encoding="utf-8") as f:
f.write(f"{entry.rstrip()}\n\n")
# -- message formatting utility ------------------------------------------
@staticmethod
def _format_messages(messages: list[dict]) -> str:
lines = []
@ -111,107 +198,10 @@ class MemoryStore:
)
return "\n".join(lines)
async def consolidate(
self,
messages: list[dict],
provider: LLMProvider,
model: str,
) -> bool:
"""Consolidate the provided message chunk into MEMORY.md + HISTORY.md."""
if not messages:
return True
current_memory = self.read_long_term()
prompt = f"""Process this conversation and call the save_memory tool with your consolidation.
## Current Long-term Memory
{current_memory or "(empty)"}
## Conversation to Process
{self._format_messages(messages)}"""
chat_messages = [
{"role": "system", "content": "You are a memory consolidation agent. Call the save_memory tool with your consolidation of the conversation."},
{"role": "user", "content": prompt},
]
try:
forced = {"type": "function", "function": {"name": "save_memory"}}
response = await provider.chat_with_retry(
messages=chat_messages,
tools=_SAVE_MEMORY_TOOL,
model=model,
tool_choice=forced,
)
if response.finish_reason == "error" and _is_tool_choice_unsupported(
response.content
):
logger.warning("Forced tool_choice unsupported, retrying with auto")
response = await provider.chat_with_retry(
messages=chat_messages,
tools=_SAVE_MEMORY_TOOL,
model=model,
tool_choice="auto",
)
if not response.has_tool_calls:
logger.warning(
"Memory consolidation: LLM did not call save_memory "
"(finish_reason={}, content_len={}, content_preview={})",
response.finish_reason,
len(response.content or ""),
(response.content or "")[:200],
)
return self._fail_or_raw_archive(messages)
args = _normalize_save_memory_args(response.tool_calls[0].arguments)
if args is None:
logger.warning("Memory consolidation: unexpected save_memory arguments")
return self._fail_or_raw_archive(messages)
if "history_entry" not in args or "memory_update" not in args:
logger.warning("Memory consolidation: save_memory payload missing required fields")
return self._fail_or_raw_archive(messages)
entry = args["history_entry"]
update = args["memory_update"]
if entry is None or update is None:
logger.warning("Memory consolidation: save_memory payload contains null required fields")
return self._fail_or_raw_archive(messages)
entry = _ensure_text(entry).strip()
if not entry:
logger.warning("Memory consolidation: history_entry is empty after normalization")
return self._fail_or_raw_archive(messages)
self.append_history(entry)
update = _ensure_text(update)
if update != current_memory:
self.write_long_term(update)
self._consecutive_failures = 0
logger.info("Memory consolidation done for {} messages", len(messages))
return True
except Exception:
logger.exception("Memory consolidation failed")
return self._fail_or_raw_archive(messages)
def _fail_or_raw_archive(self, messages: list[dict]) -> bool:
"""Increment failure count; after threshold, raw-archive messages and return True."""
self._consecutive_failures += 1
if self._consecutive_failures < self._MAX_FAILURES_BEFORE_RAW_ARCHIVE:
return False
self._raw_archive(messages)
self._consecutive_failures = 0
return True
def _raw_archive(self, messages: list[dict]) -> None:
def raw_archive(self, messages: list[dict]) -> None:
"""Fallback: dump raw messages to HISTORY.md without LLM summarization."""
ts = datetime.now().strftime("%Y-%m-%d %H:%M")
self.append_history(
f"[{ts}] [RAW] {len(messages)} messages\n"
f"[RAW] {len(messages)} messages\n"
f"{self._format_messages(messages)}"
)
logger.warning(
@ -219,8 +209,14 @@ class MemoryStore:
)
class MemoryConsolidator:
"""Owns consolidation policy, locking, and session offset updates."""
# ---------------------------------------------------------------------------
# Consolidator — lightweight token-budget triggered consolidation
# ---------------------------------------------------------------------------
class Consolidator:
"""Lightweight consolidation: summarizes evicted messages, appends to HISTORY.md."""
_MAX_CONSOLIDATION_ROUNDS = 5
@ -228,7 +224,7 @@ class MemoryConsolidator:
def __init__(
self,
workspace: Path,
store: MemoryStore,
provider: LLMProvider,
model: str,
sessions: SessionManager,
@ -237,7 +233,7 @@ class MemoryConsolidator:
get_tool_definitions: Callable[[], list[dict[str, Any]]],
max_completion_tokens: int = 4096,
):
self.store = MemoryStore(workspace)
self.store = store
self.provider = provider
self.model = model
self.sessions = sessions
@ -245,16 +241,14 @@ class MemoryConsolidator:
self.max_completion_tokens = max_completion_tokens
self._build_messages = build_messages
self._get_tool_definitions = get_tool_definitions
self._locks: weakref.WeakValueDictionary[str, asyncio.Lock] = weakref.WeakValueDictionary()
self._locks: weakref.WeakValueDictionary[str, asyncio.Lock] = (
weakref.WeakValueDictionary()
)
def get_lock(self, session_key: str) -> asyncio.Lock:
"""Return the shared consolidation lock for one session."""
return self._locks.setdefault(session_key, asyncio.Lock())
async def consolidate_messages(self, messages: list[dict[str, object]]) -> bool:
"""Archive a selected message chunk into persistent memory."""
return await self.store.consolidate(messages, self.provider, self.model)
def pick_consolidation_boundary(
self,
session: Session,
@ -294,14 +288,48 @@ class MemoryConsolidator:
self._get_tool_definitions(),
)
async def archive_messages(self, messages: list[dict[str, object]]) -> bool:
"""Archive messages with guaranteed persistence (retries until raw-dump fallback)."""
async def archive(self, messages: list[dict]) -> bool:
"""Summarize messages via LLM and append to HISTORY.md.
Returns True on success (or degraded success), False if nothing to do.
"""
if not messages:
return False
try:
formatted = MemoryStore._format_messages(messages)
response = await self.provider.chat_with_retry(
model=self.model,
messages=[
{
"role": "system",
"content": (
"Extract key facts from this conversation. "
"Only output items matching these categories, skip everything else:\n"
"- User facts: personal info, preferences, stated opinions, habits\n"
"- Decisions: choices made, conclusions reached\n"
"- Events: plans, deadlines, notable occurrences\n"
"- Preferences: communication style, tool preferences\n\n"
"Priority: user corrections and preferences > decisions > events > environment facts. "
"The most valuable memory prevents the user from having to repeat themselves.\n\n"
"Skip: code patterns derivable from source, git history, debug steps already in code, "
"or anything already captured in existing memory.\n\n"
"Output as concise bullet points, one fact per line. "
"No preamble, no commentary.\n"
"If nothing noteworthy happened, output: (nothing)"
),
},
{"role": "user", "content": formatted},
],
tools=None,
tool_choice=None,
)
summary = response.content or "[no summary]"
self.store.append_history(summary)
return True
except Exception:
logger.warning("Consolidation LLM call failed, raw-dumping to history")
self.store.raw_archive(messages)
return True
for _ in range(self.store._MAX_FAILURES_BEFORE_RAW_ARCHIVE):
if await self.consolidate_messages(messages):
return True
return True
async def maybe_consolidate_by_tokens(self, session: Session) -> None:
"""Loop: archive old messages until prompt fits within safe budget.
@ -356,7 +384,7 @@ class MemoryConsolidator:
source,
len(chunk),
)
if not await self.consolidate_messages(chunk):
if not await self.archive(chunk):
return
session.last_consolidated = end_idx
self.sessions.save(session)
@ -364,3 +392,186 @@ class MemoryConsolidator:
estimated, source = self.estimate_session_prompt_tokens(session)
if estimated <= 0:
return
# ---------------------------------------------------------------------------
# Dream — heavyweight cron-scheduled memory consolidation
# ---------------------------------------------------------------------------
class Dream:
"""Two-phase memory processor: analyze HISTORY.md, then edit files via AgentRunner.
Phase 1 produces an analysis summary (plain LLM call).
Phase 2 delegates to AgentRunner with read_file / edit_file tools so the
LLM can make targeted, incremental edits instead of replacing entire files.
"""
_PHASE1_SYSTEM = (
"Compare conversation history against current memory files. "
"Output one line per finding:\n"
"[FILE] atomic fact or change description\n\n"
"Files: USER (identity, preferences, habits), "
"SOUL (bot behavior, tone), "
"MEMORY (knowledge, project context, tool patterns)\n\n"
"Rules:\n"
"- Only new or conflicting information — skip duplicates and ephemera\n"
"- Prefer atomic facts: \"has a cat named Luna\" not \"discussed pet care\"\n"
"- Corrections: [USER] location is Tokyo, not Osaka\n"
"- Also capture confirmed approaches: if the user validated a non-obvious choice, note it\n\n"
"If nothing needs updating: [SKIP] no new information"
)
_PHASE2_SYSTEM = (
"Update memory files based on the analysis below.\n\n"
"## Quality standards\n"
"- Every line must carry standalone value — no filler\n"
"- Concise bullet points under clear headers\n"
"- Remove outdated or contradicted information\n\n"
"## Editing\n"
"- File contents provided below — edit directly, no read_file needed\n"
"- Batch changes to the same file into one edit_file call\n"
"- Surgical edits only — never rewrite entire files\n"
"- Do NOT overwrite correct entries — only add, update, or remove\n"
"- If nothing to update, stop without calling tools"
)
def __init__(
self,
store: MemoryStore,
provider: LLMProvider,
model: str,
max_batch_size: int = 20,
max_iterations: int = 10,
):
self.store = store
self.provider = provider
self.model = model
self.max_batch_size = max_batch_size
self.max_iterations = max_iterations
self._runner = AgentRunner(provider)
self._tools = self._build_tools()
# -- tool registry -------------------------------------------------------
def _build_tools(self) -> ToolRegistry:
"""Build a minimal tool registry for the Dream agent."""
from nanobot.agent.tools.filesystem import EditFileTool, ReadFileTool
tools = ToolRegistry()
workspace = self.store.workspace
tools.register(ReadFileTool(workspace=workspace, allowed_dir=workspace))
tools.register(EditFileTool(workspace=workspace, allowed_dir=workspace))
return tools
# -- main entry ----------------------------------------------------------
async def run(self) -> bool:
"""Process unprocessed history entries. Returns True if work was done."""
last_cursor = self.store.get_last_dream_cursor()
entries = self.store.read_unprocessed_history(since_cursor=last_cursor)
if not entries:
return False
batch = entries[: self.max_batch_size]
logger.info(
"Dream: processing {} entries (cursor {}→{}), batch={}",
len(entries), last_cursor, batch[-1]["cursor"], len(batch),
)
# Build history text for LLM
history_text = "\n".join(
f"[{e['timestamp']}] {e['content']}" for e in batch
)
# Current file contents
current_memory = self.store.read_memory() or "(empty)"
current_soul = self.store.read_soul() or "(empty)"
current_user = self.store.read_user() or "(empty)"
file_context = (
f"## Current MEMORY.md\n{current_memory}\n\n"
f"## Current SOUL.md\n{current_soul}\n\n"
f"## Current USER.md\n{current_user}"
)
# Phase 1: Analyze
phase1_prompt = (
f"## Conversation History\n{history_text}\n\n{file_context}"
)
try:
phase1_response = await self.provider.chat_with_retry(
model=self.model,
messages=[
{"role": "system", "content": self._PHASE1_SYSTEM},
{"role": "user", "content": phase1_prompt},
],
tools=None,
tool_choice=None,
)
analysis = phase1_response.content or ""
logger.debug("Dream Phase 1 complete ({} chars)", len(analysis))
except Exception:
logger.exception("Dream Phase 1 failed")
return False
# Phase 2: Delegate to AgentRunner with read_file / edit_file
phase2_prompt = f"## Analysis Result\n{analysis}\n\n{file_context}"
tools = self._tools
messages: list[dict[str, Any]] = [
{"role": "system", "content": self._PHASE2_SYSTEM},
{"role": "user", "content": phase2_prompt},
]
try:
result = await self._runner.run(AgentRunSpec(
initial_messages=messages,
tools=tools,
model=self.model,
max_iterations=self.max_iterations,
fail_on_tool_error=True,
))
logger.debug(
"Dream Phase 2 complete: stop_reason={}, tool_events={}",
result.stop_reason, len(result.tool_events),
)
except Exception:
logger.exception("Dream Phase 2 failed")
result = None
# Build changelog from tool events
changelog: list[str] = []
if result and result.tool_events:
for event in result.tool_events:
if event["status"] == "ok":
changelog.append(f"{event['name']}: {event['detail']}")
# Advance cursor — always, to avoid re-processing Phase 1
new_cursor = batch[-1]["cursor"]
self.store.set_last_dream_cursor(new_cursor)
self.store.compact_history()
if result and result.stop_reason == "completed":
logger.info(
"Dream done: {} change(s), cursor advanced to {}",
len(changelog), new_cursor,
)
else:
reason = result.stop_reason if result else "exception"
logger.warning(
"Dream incomplete ({}): cursor advanced to {}",
reason, new_cursor,
)
# Write dream log
ts = datetime.now().strftime("%Y-%m-%d %H:%M")
if changelog:
log_entry = f"## {ts}\n"
for change in changelog:
log_entry += f"- {change}\n"
self.store.append_dream_log(log_entry)
else:
self.store.append_dream_log(f"## {ts}\nNo changes.\n")
return True

25
nanobot/cli/commands.py
View File

@ -22,6 +22,7 @@ if sys.platform == "win32":
pass
import typer
from loguru import logger
from prompt_toolkit import PromptSession, print_formatted_text
from prompt_toolkit.application import run_in_terminal
from prompt_toolkit.formatted_text import ANSI, HTML
@ -640,6 +641,15 @@ def gateway(
# Set cron callback (needs agent)
async def on_cron_job(job: CronJob) -> str | None:
"""Execute a cron job through the agent."""
# Dream is an internal job — run directly, not through the agent loop.
if job.name == "dream":
try:
await agent.dream.run()
logger.info("Dream cron job completed")
except Exception:
logger.exception("Dream cron job failed")
return None
from nanobot.agent.tools.cron import CronTool
from nanobot.agent.tools.message import MessageTool
from nanobot.utils.evaluator import evaluate_response
@ -759,6 +769,21 @@ def gateway(
console.print(f"[green]✓[/green] Heartbeat: every {hb_cfg.interval_s}s")
# Register Dream cron job (always-on, idempotent on restart)
dream_cfg = config.agents.defaults.dream
if dream_cfg.model:
agent.dream.model = dream_cfg.model
agent.dream.max_batch_size = dream_cfg.max_batch_size
agent.dream.max_iterations = dream_cfg.max_iterations
from nanobot.cron.types import CronJob, CronPayload, CronSchedule
cron.register_system_job(CronJob(
id="dream",
name="dream",
schedule=CronSchedule(kind="cron", expr=dream_cfg.cron, tz=config.agents.defaults.timezone),
payload=CronPayload(kind="system_event"),
))
console.print(f"[green]✓[/green] Dream: cron {dream_cfg.cron}")
async def run():
try:
await cron.start()

42
nanobot/command/builtin.py
View File

@ -47,7 +47,7 @@ async def cmd_status(ctx: CommandContext) -> OutboundMessage:
session = ctx.session or loop.sessions.get_or_create(ctx.key)
ctx_est = 0
try:
ctx_est, _ = loop.memory_consolidator.estimate_session_prompt_tokens(session)
ctx_est, _ = loop.consolidator.estimate_session_prompt_tokens(session)
except Exception:
pass
if ctx_est <= 0:
@ -75,13 +75,47 @@ async def cmd_new(ctx: CommandContext) -> OutboundMessage:
loop.sessions.save(session)
loop.sessions.invalidate(session.key)
if snapshot:
loop._schedule_background(loop.memory_consolidator.archive_messages(snapshot))
loop._schedule_background(loop.consolidator.archive(snapshot))
return OutboundMessage(
channel=ctx.msg.channel, chat_id=ctx.msg.chat_id,
content="New session started.",
)
async def cmd_dream(ctx: CommandContext) -> OutboundMessage:
"""Manually trigger a Dream consolidation run."""
loop = ctx.loop
try:
did_work = await loop.dream.run()
content = "Dream completed." if did_work else "Dream: nothing to process."
except Exception as e:
content = f"Dream failed: {e}"
return OutboundMessage(
channel=ctx.msg.channel, chat_id=ctx.msg.chat_id, content=content,
)
async def cmd_dream_log(ctx: CommandContext) -> OutboundMessage:
"""Show the Dream consolidation log."""
loop = ctx.loop
store = loop.consolidator.store
log = store.read_dream_log()
if not log:
# Check if Dream has ever processed anything
if store.get_last_dream_cursor() == 0:
content = "Dream has not run yet."
else:
content = "No dream log yet."
else:
content = f"## Dream Log\n\n{log}"
return OutboundMessage(
channel=ctx.msg.channel,
chat_id=ctx.msg.chat_id,
content=content,
metadata={"render_as": "text"},
)
async def cmd_help(ctx: CommandContext) -> OutboundMessage:
"""Return available slash commands."""
return OutboundMessage(
@ -100,6 +134,8 @@ def build_help_text() -> str:
"/stop — Stop the current task",
"/restart — Restart the bot",
"/status — Show bot status",
"/dream — Manually trigger Dream consolidation",
"/dream-log — Show Dream consolidation log",
"/help — Show available commands",
]
return "\n".join(lines)
@ -112,4 +148,6 @@ def register_builtin_commands(router: CommandRouter) -> None:
router.priority("/status", cmd_status)
router.exact("/new", cmd_new)
router.exact("/status", cmd_status)
router.exact("/dream", cmd_dream)
router.exact("/dream-log", cmd_dream_log)
router.exact("/help", cmd_help)

10
nanobot/config/schema.py
View File

@ -28,6 +28,15 @@ class ChannelsConfig(Base):
send_max_retries: int = Field(default=3, ge=0, le=10) # Max delivery attempts (initial send included)
class DreamConfig(Base):
"""Dream memory consolidation configuration."""
cron: str = "0 */2 * * *" # Every 2 hours
model: str | None = None # Override model for Dream
max_batch_size: int = Field(default=20, ge=1) # Max history entries per run
max_iterations: int = Field(default=10, ge=1) # Max tool calls per Phase 2
class AgentDefaults(Base):
"""Default agent configuration."""
@ -42,6 +51,7 @@ class AgentDefaults(Base):
max_tool_iterations: int = 40
reasoning_effort: str | None = None # low / medium / high - enables LLM thinking mode
timezone: str = "UTC" # IANA timezone, e.g. "Asia/Shanghai", "America/New_York"
dream: DreamConfig = Field(default_factory=DreamConfig)
class AgentsConfig(Base):

14
nanobot/cron/service.py
View File

@ -351,6 +351,20 @@ class CronService:
logger.info("Cron: added job '{}' ({})", name, job.id)
return job
def register_system_job(self, job: CronJob) -> CronJob:
"""Register an internal system job (idempotent on restart)."""
store = self._load_store()
now = _now_ms()
job.state = CronJobState(next_run_at_ms=_compute_next_run(job.schedule, now))
job.created_at_ms = now
job.updated_at_ms = now
store.jobs = [j for j in store.jobs if j.id != job.id]
store.jobs.append(job)
self._save_store()
self._arm_timer()
logger.info("Cron: registered system job '{}' ({})", job.name, job.id)
return job
def remove_job(self, job_id: str) -> bool:
"""Remove a job by ID."""
store = self._load_store()

37
nanobot/skills/memory/SKILL.md
View File

@ -1,6 +1,6 @@
---
name: memory
description: Two-layer memory system with grep-based recall.
description: Two-layer memory system with Dream-managed knowledge files.
always: true
---
@ -8,30 +8,23 @@ always: true
## Structure
- `memory/MEMORY.md` — Long-term facts (preferences, project context, relationships). Always loaded into your context.
- `memory/HISTORY.md` — Append-only event log. NOT loaded into context. Search it with grep-style tools or in-memory filters. Each entry starts with [YYYY-MM-DD HH:MM].
- `SOUL.md` — Bot personality and communication style. **Managed by Dream.** Do NOT edit.
- `USER.md` — User profile and preferences. **Managed by Dream.** Do NOT edit.
- `memory/MEMORY.md` — Long-term facts (project context, important events). **Managed by Dream.** Do NOT edit.
- `memory/history.jsonl` — append-only JSONL, not loaded into context. search with `jq`-style tools.
- `memory/.dream-log.md` — Changelog of what Dream changed. View with `/dream-log`.
## Search Past Events
Choose the search method based on file size:
`memory/history.jsonl` is JSONL format — each line is a JSON object with `cursor`, `timestamp`, `content`.
- Small `memory/HISTORY.md`: use `read_file`, then search in-memory
- Large or long-lived `memory/HISTORY.md`: use the `exec` tool for targeted search
Examples (replace `keyword`):
- **Python (cross-platform):** `python -c "import json; [print(json.loads(l).get('content','')) for l in open('memory/history.jsonl','r',encoding='utf-8') if l.strip() and 'keyword' in l.lower()][-20:]"`
- **jq:** `cat memory/history.jsonl | jq -r 'select(.content | test("keyword"; "i")) | .content' | tail -20`
- **grep:** `grep -i "keyword" memory/history.jsonl`
Examples:
- **Linux/macOS:** `grep -i "keyword" memory/HISTORY.md`
- **Windows:** `findstr /i "keyword" memory\HISTORY.md`
- **Cross-platform Python:** `python -c "from pathlib import Path; text = Path('memory/HISTORY.md').read_text(encoding='utf-8'); print('\n'.join([l for l in text.splitlines() if 'keyword' in l.lower()][-20:]))"`
## Important
Prefer targeted command-line search for large history files.
## When to Update MEMORY.md
Write important facts immediately using `edit_file` or `write_file`:
- User preferences ("I prefer dark mode")
- Project context ("The API uses OAuth2")
- Relationships ("Alice is the project lead")
## Auto-consolidation
Old conversations are automatically summarized and appended to HISTORY.md when the session grows large. Long-term facts are extracted to MEMORY.md. You don't need to manage this.
- **Do NOT edit SOUL.md, USER.md, or MEMORY.md.** They are automatically managed by Dream.
- If you notice outdated information, it will be corrected when Dream runs next.
- Users can view Dream's activity with the `/dream-log` command.

2
nanobot/utils/helpers.py
View File

@ -296,7 +296,7 @@ def sync_workspace_templates(workspace: Path, silent: bool = False) -> list[str]
if item.name.endswith(".md") and not item.name.startswith("."):
_write(item, workspace / item.name)
_write(tpl / "memory" / "MEMORY.md", workspace / "memory" / "MEMORY.md")
_write(None, workspace / "memory" / "HISTORY.md")
_write(None, workspace / "memory" / "history.jsonl")
(workspace / "skills").mkdir(exist_ok=True)
if added and not silent:

20
tests/agent/test_consolidate_offset.py
View File

@ -506,7 +506,7 @@ class TestNewCommandArchival:
@pytest.mark.asyncio
async def test_new_clears_session_immediately_even_if_archive_fails(self, tmp_path: Path) -> None:
"""/new clears session immediately; archive_messages retries until raw dump."""
"""/new clears session immediately; archive is fire-and-forget."""
from nanobot.bus.events import InboundMessage
loop = self._make_loop(tmp_path)
@ -518,12 +518,12 @@ class TestNewCommandArchival:
call_count = 0
async def _failing_consolidate(_messages) -> bool:
async def _failing_summarize(_messages) -> bool:
nonlocal call_count
call_count += 1
return False
loop.memory_consolidator.consolidate_messages = _failing_consolidate # type: ignore[method-assign]
loop.consolidator.archive = _failing_summarize # type: ignore[method-assign]
new_msg = InboundMessage(channel="cli", sender_id="user", chat_id="test", content="/new")
response = await loop._process_message(new_msg)
@ -535,7 +535,7 @@ class TestNewCommandArchival:
assert len(session_after.messages) == 0
await loop.close_mcp()
assert call_count == 3 # retried up to raw-archive threshold
assert call_count == 1
@pytest.mark.asyncio
async def test_new_archives_only_unconsolidated_messages(self, tmp_path: Path) -> None:
@ -551,12 +551,12 @@ class TestNewCommandArchival:
archived_count = -1
async def _fake_consolidate(messages) -> bool:
async def _fake_summarize(messages) -> bool:
nonlocal archived_count
archived_count = len(messages)
return True
loop.memory_consolidator.consolidate_messages = _fake_consolidate # type: ignore[method-assign]
loop.consolidator.archive = _fake_summarize # type: ignore[method-assign]
new_msg = InboundMessage(channel="cli", sender_id="user", chat_id="test", content="/new")
response = await loop._process_message(new_msg)
@ -578,10 +578,10 @@ class TestNewCommandArchival:
session.add_message("assistant", f"resp{i}")
loop.sessions.save(session)
async def _ok_consolidate(_messages) -> bool:
async def _ok_summarize(_messages) -> bool:
return True
loop.memory_consolidator.consolidate_messages = _ok_consolidate # type: ignore[method-assign]
loop.consolidator.archive = _ok_summarize # type: ignore[method-assign]
new_msg = InboundMessage(channel="cli", sender_id="user", chat_id="test", content="/new")
response = await loop._process_message(new_msg)
@ -604,12 +604,12 @@ class TestNewCommandArchival:
archived = asyncio.Event()
async def _slow_consolidate(_messages) -> bool:
async def _slow_summarize(_messages) -> bool:
await asyncio.sleep(0.1)
archived.set()
return True
loop.memory_consolidator.consolidate_messages = _slow_consolidate # type: ignore[method-assign]
loop.consolidator.archive = _slow_summarize # type: ignore[method-assign]
new_msg = InboundMessage(channel="cli", sender_id="user", chat_id="test", content="/new")
await loop._process_message(new_msg)

78
tests/agent/test_consolidator.py Normal file
View File

@ -0,0 +1,78 @@
"""Tests for the lightweight Consolidator — append-only to HISTORY.md."""
import pytest
import asyncio
from unittest.mock import AsyncMock, MagicMock, patch
from nanobot.agent.memory import Consolidator, MemoryStore
@pytest.fixture
def store(tmp_path):
return MemoryStore(tmp_path)
@pytest.fixture
def mock_provider():
p = MagicMock()
p.chat_with_retry = AsyncMock()
return p
@pytest.fixture
def consolidator(store, mock_provider):
sessions = MagicMock()
sessions.save = MagicMock()
return Consolidator(
store=store,
provider=mock_provider,
model="test-model",
sessions=sessions,
context_window_tokens=1000,
build_messages=MagicMock(return_value=[]),
get_tool_definitions=MagicMock(return_value=[]),
max_completion_tokens=100,
)
class TestConsolidatorSummarize:
async def test_summarize_appends_to_history(self, consolidator, mock_provider, store):
"""Consolidator should call LLM to summarize, then append to HISTORY.md."""
mock_provider.chat_with_retry.return_value = MagicMock(
content="User fixed a bug in the auth module."
)
messages = [
{"role": "user", "content": "fix the auth bug"},
{"role": "assistant", "content": "Done, fixed the race condition."},
]
result = await consolidator.archive(messages)
assert result is True
entries = store.read_unprocessed_history(since_cursor=0)
assert len(entries) == 1
async def test_summarize_raw_dumps_on_llm_failure(self, consolidator, mock_provider, store):
"""On LLM failure, raw-dump messages to HISTORY.md."""
mock_provider.chat_with_retry.side_effect = Exception("API error")
messages = [{"role": "user", "content": "hello"}]
result = await consolidator.archive(messages)
assert result is True # always succeeds
entries = store.read_unprocessed_history(since_cursor=0)
assert len(entries) == 1
assert "[RAW]" in entries[0]["content"]
async def test_summarize_skips_empty_messages(self, consolidator):
result = await consolidator.archive([])
assert result is False
class TestConsolidatorTokenBudget:
async def test_prompt_below_threshold_does_not_consolidate(self, consolidator):
"""No consolidation when tokens are within budget."""
session = MagicMock()
session.last_consolidated = 0
session.messages = [{"role": "user", "content": "hi"}]
session.key = "test:key"
consolidator.estimate_session_prompt_tokens = MagicMock(return_value=(100, "tiktoken"))
consolidator.archive = AsyncMock(return_value=True)
await consolidator.maybe_consolidate_by_tokens(session)
consolidator.archive.assert_not_called()

97
tests/agent/test_dream.py Normal file
View File

@ -0,0 +1,97 @@
"""Tests for the Dream class — two-phase memory consolidation via AgentRunner."""
import pytest
from unittest.mock import AsyncMock, MagicMock
from nanobot.agent.memory import Dream, MemoryStore
from nanobot.agent.runner import AgentRunResult
@pytest.fixture
def store(tmp_path):
s = MemoryStore(tmp_path)
s.write_soul("# Soul\n- Helpful")
s.write_user("# User\n- Developer")
s.write_memory("# Memory\n- Project X active")
return s
@pytest.fixture
def mock_provider():
p = MagicMock()
p.chat_with_retry = AsyncMock()
return p
@pytest.fixture
def mock_runner():
return MagicMock()
@pytest.fixture
def dream(store, mock_provider, mock_runner):
d = Dream(store=store, provider=mock_provider, model="test-model", max_batch_size=5)
d._runner = mock_runner
return d
def _make_run_result(
stop_reason="completed",
final_content=None,
tool_events=None,
usage=None,
):
return AgentRunResult(
final_content=final_content or stop_reason,
stop_reason=stop_reason,
messages=[],
tools_used=[],
usage={},
tool_events=tool_events or [],
)
class TestDreamRun:
async def test_noop_when_no_unprocessed_history(self, dream, mock_provider, mock_runner, store):
"""Dream should not call LLM when there's nothing to process."""
result = await dream.run()
assert result is False
mock_provider.chat_with_retry.assert_not_called()
mock_runner.run.assert_not_called()
async def test_calls_runner_for_unprocessed_entries(self, dream, mock_provider, mock_runner, store):
"""Dream should call AgentRunner when there are unprocessed history entries."""
store.append_history("User prefers dark mode")
mock_provider.chat_with_retry.return_value = MagicMock(content="New fact")
mock_runner.run = AsyncMock(return_value=_make_run_result(
tool_events=[{"name": "edit_file", "status": "ok", "detail": "memory/MEMORY.md"}],
))
result = await dream.run()
assert result is True
mock_runner.run.assert_called_once()
spec = mock_runner.run.call_args[0][0]
assert spec.max_iterations == 10
assert spec.fail_on_tool_error is True
async def test_advances_dream_cursor(self, dream, mock_provider, mock_runner, store):
"""Dream should advance the cursor after processing."""
store.append_history("event 1")
store.append_history("event 2")
mock_provider.chat_with_retry.return_value = MagicMock(content="Nothing new")
mock_runner.run = AsyncMock(return_value=_make_run_result())
await dream.run()
assert store.get_last_dream_cursor() == 2
async def test_compacts_processed_history(self, dream, mock_provider, mock_runner, store):
"""Dream should compact history after processing."""
store.append_history("event 1")
store.append_history("event 2")
store.append_history("event 3")
mock_provider.chat_with_retry.return_value = MagicMock(content="Nothing new")
mock_runner.run = AsyncMock(return_value=_make_run_result())
await dream.run()
# After Dream, cursor is advanced and 3, compact keeps last max_history_entries
entries = store.read_unprocessed_history(since_cursor=0)
assert all(e["cursor"] > 0 for e in entries)

3
tests/agent/test_hook_composite.py
View File

@ -249,7 +249,8 @@ def _make_loop(tmp_path, hooks=None):
with patch("nanobot.agent.loop.ContextBuilder"), \
patch("nanobot.agent.loop.SessionManager"), \
patch("nanobot.agent.loop.SubagentManager") as mock_sub_mgr, \
patch("nanobot.agent.loop.MemoryConsolidator"):
patch("nanobot.agent.loop.Consolidator"), \
patch("nanobot.agent.loop.Dream"):
mock_sub_mgr.return_value.cancel_by_session = AsyncMock(return_value=0)
loop = AgentLoop(
bus=bus, provider=provider, workspace=tmp_path, hooks=hooks,

36
tests/agent/test_loop_consolidation_tokens.py
View File

@ -26,24 +26,24 @@ def _make_loop(tmp_path, *, estimated_tokens: int, context_window_tokens: int) -
context_window_tokens=context_window_tokens,
)
loop.tools.get_definitions = MagicMock(return_value=[])
loop.memory_consolidator._SAFETY_BUFFER = 0
loop.consolidator._SAFETY_BUFFER = 0
return loop
@pytest.mark.asyncio
async def test_prompt_below_threshold_does_not_consolidate(tmp_path) -> None:
loop = _make_loop(tmp_path, estimated_tokens=100, context_window_tokens=200)
loop.memory_consolidator.consolidate_messages = AsyncMock(return_value=True) # type: ignore[method-assign]
loop.consolidator.archive = AsyncMock(return_value=True) # type: ignore[method-assign]
await loop.process_direct("hello", session_key="cli:test")
loop.memory_consolidator.consolidate_messages.assert_not_awaited()
loop.consolidator.archive.assert_not_awaited()
@pytest.mark.asyncio
async def test_prompt_above_threshold_triggers_consolidation(tmp_path, monkeypatch) -> None:
loop = _make_loop(tmp_path, estimated_tokens=1000, context_window_tokens=200)
loop.memory_consolidator.consolidate_messages = AsyncMock(return_value=True) # type: ignore[method-assign]
loop.consolidator.archive = AsyncMock(return_value=True) # type: ignore[method-assign]
session = loop.sessions.get_or_create("cli:test")
session.messages = [
{"role": "user", "content": "u1", "timestamp": "2026-01-01T00:00:00"},
@ -55,13 +55,13 @@ async def test_prompt_above_threshold_triggers_consolidation(tmp_path, monkeypat
await loop.process_direct("hello", session_key="cli:test")
assert loop.memory_consolidator.consolidate_messages.await_count >= 1
assert loop.consolidator.archive.await_count >= 1
@pytest.mark.asyncio
async def test_prompt_above_threshold_archives_until_next_user_boundary(tmp_path, monkeypatch) -> None:
loop = _make_loop(tmp_path, estimated_tokens=1000, context_window_tokens=200)
loop.memory_consolidator.consolidate_messages = AsyncMock(return_value=True) # type: ignore[method-assign]
loop.consolidator.archive = AsyncMock(return_value=True) # type: ignore[method-assign]
session = loop.sessions.get_or_create("cli:test")
session.messages = [
@ -76,9 +76,9 @@ async def test_prompt_above_threshold_archives_until_next_user_boundary(tmp_path
token_map = {"u1": 120, "a1": 120, "u2": 120, "a2": 120, "u3": 120}
monkeypatch.setattr(memory_module, "estimate_message_tokens", lambda message: token_map[message["content"]])
await loop.memory_consolidator.maybe_consolidate_by_tokens(session)
await loop.consolidator.maybe_consolidate_by_tokens(session)
archived_chunk = loop.memory_consolidator.consolidate_messages.await_args.args[0]
archived_chunk = loop.consolidator.archive.await_args.args[0]
assert [message["content"] for message in archived_chunk] == ["u1", "a1", "u2", "a2"]
assert session.last_consolidated == 4
@ -87,7 +87,7 @@ async def test_prompt_above_threshold_archives_until_next_user_boundary(tmp_path
async def test_consolidation_loops_until_target_met(tmp_path, monkeypatch) -> None:
"""Verify maybe_consolidate_by_tokens keeps looping until under threshold."""
loop = _make_loop(tmp_path, estimated_tokens=0, context_window_tokens=200)
loop.memory_consolidator.consolidate_messages = AsyncMock(return_value=True) # type: ignore[method-assign]
loop.consolidator.archive = AsyncMock(return_value=True) # type: ignore[method-assign]
session = loop.sessions.get_or_create("cli:test")
session.messages = [
@ -110,12 +110,12 @@ async def test_consolidation_loops_until_target_met(tmp_path, monkeypatch) -> No
return (300, "test")
return (80, "test")
loop.memory_consolidator.estimate_session_prompt_tokens = mock_estimate # type: ignore[method-assign]
loop.consolidator.estimate_session_prompt_tokens = mock_estimate # type: ignore[method-assign]
monkeypatch.setattr(memory_module, "estimate_message_tokens", lambda _m: 100)
await loop.memory_consolidator.maybe_consolidate_by_tokens(session)
await loop.consolidator.maybe_consolidate_by_tokens(session)
assert loop.memory_consolidator.consolidate_messages.await_count == 2
assert loop.consolidator.archive.await_count == 2
assert session.last_consolidated == 6
@ -123,7 +123,7 @@ async def test_consolidation_loops_until_target_met(tmp_path, monkeypatch) -> No
async def test_consolidation_continues_below_trigger_until_half_target(tmp_path, monkeypatch) -> None:
"""Once triggered, consolidation should continue until it drops below half threshold."""
loop = _make_loop(tmp_path, estimated_tokens=0, context_window_tokens=200)
loop.memory_consolidator.consolidate_messages = AsyncMock(return_value=True) # type: ignore[method-assign]
loop.consolidator.archive = AsyncMock(return_value=True) # type: ignore[method-assign]
session = loop.sessions.get_or_create("cli:test")
session.messages = [
@ -147,12 +147,12 @@ async def test_consolidation_continues_below_trigger_until_half_target(tmp_path,
return (150, "test")
return (80, "test")
loop.memory_consolidator.estimate_session_prompt_tokens = mock_estimate # type: ignore[method-assign]
loop.consolidator.estimate_session_prompt_tokens = mock_estimate # type: ignore[method-assign]
monkeypatch.setattr(memory_module, "estimate_message_tokens", lambda _m: 100)
await loop.memory_consolidator.maybe_consolidate_by_tokens(session)
await loop.consolidator.maybe_consolidate_by_tokens(session)
assert loop.memory_consolidator.consolidate_messages.await_count == 2
assert loop.consolidator.archive.await_count == 2
assert session.last_consolidated == 6
@ -166,7 +166,7 @@ async def test_preflight_consolidation_before_llm_call(tmp_path, monkeypatch) ->
async def track_consolidate(messages):
order.append("consolidate")
return True
loop.memory_consolidator.consolidate_messages = track_consolidate # type: ignore[method-assign]
loop.consolidator.archive = track_consolidate # type: ignore[method-assign]
async def track_llm(*args, **kwargs):
order.append("llm")
@ -187,7 +187,7 @@ async def test_preflight_consolidation_before_llm_call(tmp_path, monkeypatch) ->
def mock_estimate(_session):
call_count[0] += 1
return (1000 if call_count[0] <= 1 else 80, "test")
loop.memory_consolidator.estimate_session_prompt_tokens = mock_estimate # type: ignore[method-assign]
loop.consolidator.estimate_session_prompt_tokens = mock_estimate # type: ignore[method-assign]
await loop.process_direct("hello", session_key="cli:test")

478
tests/agent/test_memory_consolidation_types.py
View File

@ -1,478 +0,0 @@
"""Test MemoryStore.consolidate() handles non-string tool call arguments.
Regression test for https://github.com/HKUDS/nanobot/issues/1042
When memory consolidation receives dict values instead of strings from the LLM
tool call response, it should serialize them to JSON instead of raising TypeError.
"""
import json
from pathlib import Path
from unittest.mock import AsyncMock
import pytest
from nanobot.agent.memory import MemoryStore
from nanobot.providers.base import LLMProvider, LLMResponse, ToolCallRequest
def _make_messages(message_count: int = 30):
"""Create a list of mock messages."""
return [
{"role": "user", "content": f"msg{i}", "timestamp": "2026-01-01 00:00"}
for i in range(message_count)
]
def _make_tool_response(history_entry, memory_update):
"""Create an LLMResponse with a save_memory tool call."""
return LLMResponse(
content=None,
tool_calls=[
ToolCallRequest(
id="call_1",
name="save_memory",
arguments={
"history_entry": history_entry,
"memory_update": memory_update,
},
)
],
)
class ScriptedProvider(LLMProvider):
def __init__(self, responses: list[LLMResponse]):
super().__init__()
self._responses = list(responses)
self.calls = 0
async def chat(self, *args, **kwargs) -> LLMResponse:
self.calls += 1
if self._responses:
return self._responses.pop(0)
return LLMResponse(content="", tool_calls=[])
def get_default_model(self) -> str:
return "test-model"
class TestMemoryConsolidationTypeHandling:
"""Test that consolidation handles various argument types correctly."""
@pytest.mark.asyncio
async def test_string_arguments_work(self, tmp_path: Path) -> None:
"""Normal case: LLM returns string arguments."""
store = MemoryStore(tmp_path)
provider = AsyncMock()
provider.chat = AsyncMock(
return_value=_make_tool_response(
history_entry="[2026-01-01] User discussed testing.",
memory_update="# Memory\nUser likes testing.",
)
)
provider.chat_with_retry = provider.chat
messages = _make_messages(message_count=60)
result = await store.consolidate(messages, provider, "test-model")
assert result is True
assert store.history_file.exists()
assert "[2026-01-01] User discussed testing." in store.history_file.read_text()
assert "User likes testing." in store.memory_file.read_text()
@pytest.mark.asyncio
async def test_dict_arguments_serialized_to_json(self, tmp_path: Path) -> None:
"""Issue #1042: LLM returns dict instead of string — must not raise TypeError."""
store = MemoryStore(tmp_path)
provider = AsyncMock()
provider.chat = AsyncMock(
return_value=_make_tool_response(
history_entry={"timestamp": "2026-01-01", "summary": "User discussed testing."},
memory_update={"facts": ["User likes testing"], "topics": ["testing"]},
)
)
provider.chat_with_retry = provider.chat
messages = _make_messages(message_count=60)
result = await store.consolidate(messages, provider, "test-model")
assert result is True
assert store.history_file.exists()
history_content = store.history_file.read_text()
parsed = json.loads(history_content.strip())
assert parsed["summary"] == "User discussed testing."
memory_content = store.memory_file.read_text()
parsed_mem = json.loads(memory_content)
assert "User likes testing" in parsed_mem["facts"]
@pytest.mark.asyncio
async def test_string_arguments_as_raw_json(self, tmp_path: Path) -> None:
"""Some providers return arguments as a JSON string instead of parsed dict."""
store = MemoryStore(tmp_path)
provider = AsyncMock()
response = LLMResponse(
content=None,
tool_calls=[
ToolCallRequest(
id="call_1",
name="save_memory",
arguments=json.dumps({
"history_entry": "[2026-01-01] User discussed testing.",
"memory_update": "# Memory\nUser likes testing.",
}),
)
],
)
provider.chat = AsyncMock(return_value=response)
provider.chat_with_retry = provider.chat
messages = _make_messages(message_count=60)
result = await store.consolidate(messages, provider, "test-model")
assert result is True
assert "User discussed testing." in store.history_file.read_text()
@pytest.mark.asyncio
async def test_no_tool_call_returns_false(self, tmp_path: Path) -> None:
"""When LLM doesn't use the save_memory tool, return False."""
store = MemoryStore(tmp_path)
provider = AsyncMock()
provider.chat = AsyncMock(
return_value=LLMResponse(content="I summarized the conversation.", tool_calls=[])
)
provider.chat_with_retry = provider.chat
messages = _make_messages(message_count=60)
result = await store.consolidate(messages, provider, "test-model")
assert result is False
assert not store.history_file.exists()
@pytest.mark.asyncio
async def test_skips_when_message_chunk_is_empty(self, tmp_path: Path) -> None:
"""Consolidation should be a no-op when the selected chunk is empty."""
store = MemoryStore(tmp_path)
provider = AsyncMock()
provider.chat_with_retry = provider.chat
messages: list[dict] = []
result = await store.consolidate(messages, provider, "test-model")
assert result is True
provider.chat.assert_not_called()
@pytest.mark.asyncio
async def test_list_arguments_extracts_first_dict(self, tmp_path: Path) -> None:
"""Some providers return arguments as a list - extract first element if it's a dict."""
store = MemoryStore(tmp_path)
provider = AsyncMock()
response = LLMResponse(
content=None,
tool_calls=[
ToolCallRequest(
id="call_1",
name="save_memory",
arguments=[{
"history_entry": "[2026-01-01] User discussed testing.",
"memory_update": "# Memory\nUser likes testing.",
}],
)
],
)
provider.chat = AsyncMock(return_value=response)
provider.chat_with_retry = provider.chat
messages = _make_messages(message_count=60)
result = await store.consolidate(messages, provider, "test-model")
assert result is True
assert "User discussed testing." in store.history_file.read_text()
assert "User likes testing." in store.memory_file.read_text()
@pytest.mark.asyncio
async def test_list_arguments_empty_list_returns_false(self, tmp_path: Path) -> None:
"""Empty list arguments should return False."""
store = MemoryStore(tmp_path)
provider = AsyncMock()
response = LLMResponse(
content=None,
tool_calls=[
ToolCallRequest(
id="call_1",
name="save_memory",
arguments=[],
)
],
)
provider.chat = AsyncMock(return_value=response)
provider.chat_with_retry = provider.chat
messages = _make_messages(message_count=60)
result = await store.consolidate(messages, provider, "test-model")
assert result is False
@pytest.mark.asyncio
async def test_list_arguments_non_dict_content_returns_false(self, tmp_path: Path) -> None:
"""List with non-dict content should return False."""
store = MemoryStore(tmp_path)
provider = AsyncMock()
response = LLMResponse(
content=None,
tool_calls=[
ToolCallRequest(
id="call_1",
name="save_memory",
arguments=["string", "content"],
)
],
)
provider.chat = AsyncMock(return_value=response)
provider.chat_with_retry = provider.chat
messages = _make_messages(message_count=60)
result = await store.consolidate(messages, provider, "test-model")
assert result is False
@pytest.mark.asyncio
async def test_missing_history_entry_returns_false_without_writing(self, tmp_path: Path) -> None:
"""Do not persist partial results when required fields are missing."""
store = MemoryStore(tmp_path)
provider = AsyncMock()
provider.chat_with_retry = AsyncMock(
return_value=LLMResponse(
content=None,
tool_calls=[
ToolCallRequest(
id="call_1",
name="save_memory",
arguments={"memory_update": "# Memory\nOnly memory update"},
)
],
)
)
messages = _make_messages(message_count=60)
result = await store.consolidate(messages, provider, "test-model")
assert result is False
assert not store.history_file.exists()
assert not store.memory_file.exists()
@pytest.mark.asyncio
async def test_missing_memory_update_returns_false_without_writing(self, tmp_path: Path) -> None:
"""Do not append history if memory_update is missing."""
store = MemoryStore(tmp_path)
provider = AsyncMock()
provider.chat_with_retry = AsyncMock(
return_value=LLMResponse(
content=None,
tool_calls=[
ToolCallRequest(
id="call_1",
name="save_memory",
arguments={"history_entry": "[2026-01-01] Partial output."},
)
],
)
)
messages = _make_messages(message_count=60)
result = await store.consolidate(messages, provider, "test-model")
assert result is False
assert not store.history_file.exists()
assert not store.memory_file.exists()
@pytest.mark.asyncio
async def test_null_required_field_returns_false_without_writing(self, tmp_path: Path) -> None:
"""Null required fields should be rejected before persistence."""
store = MemoryStore(tmp_path)
provider = AsyncMock()
provider.chat_with_retry = AsyncMock(
return_value=_make_tool_response(
history_entry=None,
memory_update="# Memory\nUser likes testing.",
)
)
messages = _make_messages(message_count=60)
result = await store.consolidate(messages, provider, "test-model")
assert result is False
assert not store.history_file.exists()
assert not store.memory_file.exists()
@pytest.mark.asyncio
async def test_empty_history_entry_returns_false_without_writing(self, tmp_path: Path) -> None:
"""Empty history entries should be rejected to avoid blank archival records."""
store = MemoryStore(tmp_path)
provider = AsyncMock()
provider.chat_with_retry = AsyncMock(
return_value=_make_tool_response(
history_entry=" ",
memory_update="# Memory\nUser likes testing.",
)
)
messages = _make_messages(message_count=60)
result = await store.consolidate(messages, provider, "test-model")
assert result is False
assert not store.history_file.exists()
assert not store.memory_file.exists()
@pytest.mark.asyncio
async def test_retries_transient_error_then_succeeds(self, tmp_path: Path, monkeypatch) -> None:
store = MemoryStore(tmp_path)
provider = ScriptedProvider([
LLMResponse(content="503 server error", finish_reason="error"),
_make_tool_response(
history_entry="[2026-01-01] User discussed testing.",
memory_update="# Memory\nUser likes testing.",
),
])
messages = _make_messages(message_count=60)
delays: list[int] = []
async def _fake_sleep(delay: int) -> None:
delays.append(delay)
monkeypatch.setattr("nanobot.providers.base.asyncio.sleep", _fake_sleep)
result = await store.consolidate(messages, provider, "test-model")
assert result is True
assert provider.calls == 2
assert delays == [1]
@pytest.mark.asyncio
async def test_consolidation_delegates_to_provider_defaults(self, tmp_path: Path) -> None:
"""Consolidation no longer passes generation params — the provider owns them."""
store = MemoryStore(tmp_path)
provider = AsyncMock()
provider.chat_with_retry = AsyncMock(
return_value=_make_tool_response(
history_entry="[2026-01-01] User discussed testing.",
memory_update="# Memory\nUser likes testing.",
)
)
messages = _make_messages(message_count=60)
result = await store.consolidate(messages, provider, "test-model")
assert result is True
provider.chat_with_retry.assert_awaited_once()
_, kwargs = provider.chat_with_retry.await_args
assert kwargs["model"] == "test-model"
assert "temperature" not in kwargs
assert "max_tokens" not in kwargs
assert "reasoning_effort" not in kwargs
@pytest.mark.asyncio
async def test_tool_choice_fallback_on_unsupported_error(self, tmp_path: Path) -> None:
"""Forced tool_choice rejected by provider -> retry with auto and succeed."""
store = MemoryStore(tmp_path)
error_resp = LLMResponse(
content="Error calling LLM: BadRequestError: "
"The tool_choice parameter does not support being set to required or object",
finish_reason="error",
tool_calls=[],
)
ok_resp = _make_tool_response(
history_entry="[2026-01-01] Fallback worked.",
memory_update="# Memory\nFallback OK.",
)
call_log: list[dict] = []
async def _tracking_chat(**kwargs):
call_log.append(kwargs)
return error_resp if len(call_log) == 1 else ok_resp
provider = AsyncMock()
provider.chat_with_retry = AsyncMock(side_effect=_tracking_chat)
messages = _make_messages(message_count=60)
result = await store.consolidate(messages, provider, "test-model")
assert result is True
assert len(call_log) == 2
assert isinstance(call_log[0]["tool_choice"], dict)
assert call_log[1]["tool_choice"] == "auto"
assert "Fallback worked." in store.history_file.read_text()
@pytest.mark.asyncio
async def test_tool_choice_fallback_auto_no_tool_call(self, tmp_path: Path) -> None:
"""Forced rejected, auto retry also produces no tool call -> return False."""
store = MemoryStore(tmp_path)
error_resp = LLMResponse(
content="Error: tool_choice must be none or auto",
finish_reason="error",
tool_calls=[],
)
no_tool_resp = LLMResponse(
content="Here is a summary.",
finish_reason="stop",
tool_calls=[],
)
provider = AsyncMock()
provider.chat_with_retry = AsyncMock(side_effect=[error_resp, no_tool_resp])
messages = _make_messages(message_count=60)
result = await store.consolidate(messages, provider, "test-model")
assert result is False
assert not store.history_file.exists()
@pytest.mark.asyncio
async def test_raw_archive_after_consecutive_failures(self, tmp_path: Path) -> None:
"""After 3 consecutive failures, raw-archive messages and return True."""
store = MemoryStore(tmp_path)
no_tool = LLMResponse(content="No tool call.", finish_reason="stop", tool_calls=[])
provider = AsyncMock()
provider.chat_with_retry = AsyncMock(return_value=no_tool)
messages = _make_messages(message_count=10)
assert await store.consolidate(messages, provider, "m") is False
assert await store.consolidate(messages, provider, "m") is False
assert await store.consolidate(messages, provider, "m") is True
assert store.history_file.exists()
content = store.history_file.read_text()
assert "[RAW]" in content
assert "10 messages" in content
assert "msg0" in content
assert not store.memory_file.exists()
@pytest.mark.asyncio
async def test_raw_archive_counter_resets_on_success(self, tmp_path: Path) -> None:
"""A successful consolidation resets the failure counter."""
store = MemoryStore(tmp_path)
no_tool = LLMResponse(content="Nope.", finish_reason="stop", tool_calls=[])
ok_resp = _make_tool_response(
history_entry="[2026-01-01] OK.",
memory_update="# Memory\nOK.",
)
messages = _make_messages(message_count=10)
provider = AsyncMock()
provider.chat_with_retry = AsyncMock(return_value=no_tool)
assert await store.consolidate(messages, provider, "m") is False
assert await store.consolidate(messages, provider, "m") is False
assert store._consecutive_failures == 2
provider.chat_with_retry = AsyncMock(return_value=ok_resp)
assert await store.consolidate(messages, provider, "m") is True
assert store._consecutive_failures == 0
provider.chat_with_retry = AsyncMock(return_value=no_tool)
assert await store.consolidate(messages, provider, "m") is False
assert store._consecutive_failures == 1

133
tests/agent/test_memory_store.py Normal file
View File

@ -0,0 +1,133 @@
"""Tests for the restructured MemoryStore — pure file I/O layer."""
import json
import pytest
from pathlib import Path
from nanobot.agent.memory import MemoryStore
@pytest.fixture
def store(tmp_path):
return MemoryStore(tmp_path)
class TestMemoryStoreBasicIO:
def test_read_memory_returns_empty_when_missing(self, store):
assert store.read_memory() == ""
def test_write_and_read_memory(self, store):
store.write_memory("hello")
assert store.read_memory() == "hello"
def test_read_soul_returns_empty_when_missing(self, store):
assert store.read_soul() == ""
def test_write_and_read_soul(self, store):
store.write_soul("soul content")
assert store.read_soul() == "soul content"
def test_read_user_returns_empty_when_missing(self, store):
assert store.read_user() == ""
def test_write_and_read_user(self, store):
store.write_user("user content")
assert store.read_user() == "user content"
def test_get_memory_context_returns_empty_when_missing(self, store):
assert store.get_memory_context() == ""
def test_get_memory_context_returns_formatted_content(self, store):
store.write_memory("important fact")
ctx = store.get_memory_context()
assert "Long-term Memory" in ctx
assert "important fact" in ctx
class TestHistoryWithCursor:
def test_append_history_returns_cursor(self, store):
cursor = store.append_history("event 1")
assert cursor == 1
cursor2 = store.append_history("event 2")
assert cursor2 == 2
def test_append_history_includes_cursor_in_file(self, store):
store.append_history("event 1")
content = store.read_file(store.history_file)
data = json.loads(content)
assert data["cursor"] == 1
def test_cursor_persists_across_appends(self, store):
store.append_history("event 1")
store.append_history("event 2")
cursor = store.append_history("event 3")
assert cursor == 3
def test_read_unprocessed_history(self, store):
store.append_history("event 1")
store.append_history("event 2")
store.append_history("event 3")
entries = store.read_unprocessed_history(since_cursor=1)
assert len(entries) == 2
assert entries[0]["cursor"] == 2
def test_read_unprocessed_history_returns_all_when_cursor_zero(self, store):
store.append_history("event 1")
store.append_history("event 2")
entries = store.read_unprocessed_history(since_cursor=0)
assert len(entries) == 2
def test_compact_history_drops_oldest(self, tmp_path):
store = MemoryStore(tmp_path, max_history_entries=2)
store.append_history("event 1")
store.append_history("event 2")
store.append_history("event 3")
store.append_history("event 4")
store.append_history("event 5")
store.compact_history()
entries = store.read_unprocessed_history(since_cursor=0)
assert len(entries) == 2
assert entries[0]["cursor"] in {4, 5}
class TestDreamCursor:
def test_initial_cursor_is_zero(self, store):
assert store.get_last_dream_cursor() == 0
def test_set_and_get_cursor(self, store):
store.set_last_dream_cursor(5)
assert store.get_last_dream_cursor() == 5
def test_cursor_persists(self, store):
store.set_last_dream_cursor(3)
store2 = MemoryStore(store.workspace)
assert store2.get_last_dream_cursor() == 3
class TestDreamLog:
def test_read_dream_log_returns_empty_when_missing(self, store):
assert store.read_dream_log() == ""
def test_append_dream_log(self, store):
store.append_dream_log("## 2026-03-30\nProcessed entries #1-#5")
log = store.read_dream_log()
assert "Processed entries #1-#5" in log
def test_append_dream_log_is_additive(self, store):
store.append_dream_log("first run")
store.append_dream_log("second run")
log = store.read_dream_log()
assert "first run" in log
assert "second run" in log
class TestLegacyHistoryMigration:
def test_read_unprocessed_history_handles_entries_without_cursor(self, store):
"""JSONL entries with cursor=1 are correctly parsed and returned."""
store.history_file.write_text(
'{"cursor": 1, "timestamp": "2026-03-30 14:30", "content": "Old event"}\n',
encoding="utf-8")
entries = store.read_unprocessed_history(since_cursor=0)
assert len(entries) == 1
assert entries[0]["cursor"] == 1

4
tests/cli/test_restart_command.py
View File

@ -127,7 +127,7 @@ class TestRestartCommand:
loop.sessions.get_or_create.return_value = session
loop._start_time = time.time() - 125
loop._last_usage = {"prompt_tokens": 0, "completion_tokens": 0}
loop.memory_consolidator.estimate_session_prompt_tokens = MagicMock(
loop.consolidator.estimate_session_prompt_tokens = MagicMock(
return_value=(20500, "tiktoken")
)
@ -166,7 +166,7 @@ class TestRestartCommand:
session.get_history.return_value = [{"role": "user"}]
loop.sessions.get_or_create.return_value = session
loop._last_usage = {"prompt_tokens": 1200, "completion_tokens": 34}
loop.memory_consolidator.estimate_session_prompt_tokens = MagicMock(
loop.consolidator.estimate_session_prompt_tokens = MagicMock(
return_value=(0, "none")
)

14
tests/test_openai_api.py
View File

@ -235,15 +235,10 @@ async def test_followup_requests_share_same_session_key(aiohttp_client) -> None:
@pytest.mark.asyncio
async def test_fixed_session_requests_are_serialized(aiohttp_client) -> None:
order: list[str] = []
barrier = asyncio.Event()
async def slow_process(content, session_key="", channel="", chat_id=""):
order.append(f"start:{content}")
if content == "first":
barrier.set()
await asyncio.sleep(0.1)
else:
await barrier.wait()
await asyncio.sleep(0.1)
order.append(f"end:{content}")
return content
@ -264,7 +259,12 @@ async def test_fixed_session_requests_are_serialized(aiohttp_client) -> None:
r1, r2 = await asyncio.gather(send("first"), send("second"))
assert r1.status == 200
assert r2.status == 200
assert order.index("end:first") < order.index("start:second")
# Verify serialization: one process must fully finish before the other starts
assert "end:second" in order or "end:first" in order
if order[0] == "start:first":
assert order.index("end:first") < order.index("start:second")
else:
assert order.index("end:second") < order.index("start:first")
@pytest.mark.skipif(not HAS_AIOHTTP, reason="aiohttp not installed")