feat: add persistent memory system + SQLite session store
Two-part implementation: Part A - Curated Bounded Memory: - New memory tool (tools/memory_tool.py) with MEMORY.md + USER.md stores - Character-limited (2200/1375 chars), § delimited entries - Frozen snapshot injected into system prompt at session start - Model manages pruning via replace/remove with substring matching - Usage indicator shown in system prompt header Part B - SQLite Session Store: - New hermes_state.py with SessionDB class, FTS5 full-text search - Gateway session.py rewritten to dual-write SQLite + legacy JSONL - Compression-triggered session splitting with parent_session_id chains - New session_search tool with Gemini Flash summarization of matched sessions - CLI session lifecycle (create on launch, close on exit) Also: - System prompt now cached per session, only rebuilt on compression (fixes prefix cache invalidation from date/time changes every turn) - Config version bumped to 3, hermes doctor checks for new artifacts - Disabled in batch_runner and RL environments
This commit is contained in:
teknium1
parent
655303f2f1
commit
440c244cac
19 changed files with 2397 additions and 327 deletions
303
run_agent.py
303
run_agent.py
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@ -875,6 +875,24 @@ def _build_tool_preview(tool_name: str, args: dict, max_len: int = 40) -> str:
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else:
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return f"planning {len(todos_arg)} task(s)"
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if tool_name == "session_search":
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query = args.get("query", "")
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return f"recall: \"{query[:25]}{'...' if len(query) > 25 else ''}\""
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if tool_name == "memory":
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action = args.get("action", "")
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target = args.get("target", "")
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if action == "add":
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content = args.get("content", "")
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return f"+{target}: \"{content[:25]}{'...' if len(content) > 25 else ''}\""
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elif action == "replace":
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return f"~{target}: \"{args.get('old_text', '')[:20]}\""
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elif action == "remove":
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return f"-{target}: \"{args.get('old_text', '')[:20]}\""
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elif action == "read":
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return f"read {target}"
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return action
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if tool_name == "send_message":
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target = args.get("target", "?")
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msg = args.get("message", "")
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@ -1061,6 +1079,8 @@ class AIAgent:
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prefill_messages: List[Dict[str, Any]] = None,
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platform: str = None,
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skip_context_files: bool = False,
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skip_memory: bool = False,
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session_db=None,
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):
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"""
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Initialize the AI Agent.
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@ -1269,10 +1289,51 @@ class AIAgent:
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# Track conversation messages for session logging
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self._session_messages: List[Dict[str, Any]] = []
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# Cached system prompt -- built once per session, only rebuilt on compression
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self._cached_system_prompt: Optional[str] = None
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# SQLite session store (optional -- provided by CLI or gateway)
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self._session_db = session_db
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if self._session_db:
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try:
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self._session_db.create_session(
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session_id=self.session_id,
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source=self.platform or "cli",
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model=self.model,
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model_config={
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"max_iterations": self.max_iterations,
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"reasoning_config": reasoning_config,
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"max_tokens": max_tokens,
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},
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user_id=None,
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)
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except Exception:
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pass
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# In-memory todo list for task planning (one per agent/session)
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from tools.todo_tool import TodoStore
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self._todo_store = TodoStore()
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# Persistent memory (MEMORY.md + USER.md) -- loaded from disk
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self._memory_store = None
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self._memory_enabled = False
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self._user_profile_enabled = False
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if not skip_memory:
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try:
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from hermes_cli.config import load_config as _load_mem_config
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mem_config = _load_mem_config().get("memory", {})
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self._memory_enabled = mem_config.get("memory_enabled", False)
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self._user_profile_enabled = mem_config.get("user_profile_enabled", False)
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if self._memory_enabled or self._user_profile_enabled:
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from tools.memory_tool import MemoryStore
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self._memory_store = MemoryStore(
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memory_char_limit=mem_config.get("memory_char_limit", 2200),
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user_char_limit=mem_config.get("user_char_limit", 1375),
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)
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self._memory_store.load_from_disk()
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except Exception:
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pass # Memory is optional -- don't break agent init
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# Initialize context compressor for automatic context management
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# Compresses conversation when approaching model's context limit
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# Configuration via environment variables (can be set in .env or cli-config.yaml)
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@ -1452,6 +1513,32 @@ class AIAgent:
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else:
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return f"┊ 📋 plan {len(todos_arg)} task(s) {dur}"
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# ── Session Search ──
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if tool_name == "session_search":
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query = _trunc(args.get("query", ""), 35)
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return f"┊ 🔍 recall \"{query}\" {dur}"
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# ── Memory ──
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if tool_name == "memory":
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action = args.get("action", "?")
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target = args.get("target", "")
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if action == "add":
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preview = _trunc(args.get("content", ""), 30)
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return f"┊ 🧠 memory +{target}: \"{preview}\" {dur}"
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elif action == "replace":
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snippet = _trunc(args.get("old_text", ""), 20)
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return f"┊ 🧠 memory ~{target}: \"{snippet}\" {dur}"
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elif action == "remove":
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snippet = _trunc(args.get("old_text", ""), 20)
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return f"┊ 🧠 memory -{target}: \"{snippet}\" {dur}"
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elif action == "read":
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return f"┊ 🧠 memory read {target} {dur}"
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elif action == "search_sessions":
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query = _trunc(args.get("content", ""), 30)
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return f"┊ 🧠 recall \"{query}\" {dur}"
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else:
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return f"┊ 🧠 memory {action} {dur}"
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# ── Skills ──
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if tool_name == "skills_list":
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return f"┊ 📚 skills list {args.get('category', 'all')} {dur}"
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@ -2041,6 +2128,70 @@ class AIAgent:
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"""Check if an interrupt has been requested."""
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return self._interrupt_requested
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def _build_system_prompt(self, system_message: str = None) -> str:
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"""
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Assemble the full system prompt from all layers.
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Called once per session (cached on self._cached_system_prompt) and only
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rebuilt after context compression events. This ensures the system prompt
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is stable across all turns in a session, maximizing prefix cache hits.
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"""
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# Layers (in order):
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# 1. Default agent identity (always present)
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# 2. User / gateway system prompt (if provided)
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# 3. Persistent memory (frozen snapshot)
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# 4. Skills guidance (if skills tools are loaded)
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# 5. Context files (SOUL.md, AGENTS.md, .cursorrules)
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# 6. Current date & time (frozen at build time)
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# 7. Platform-specific formatting hint
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prompt_parts = [DEFAULT_AGENT_IDENTITY]
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caller_prompt = system_message if system_message is not None else self.ephemeral_system_prompt
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if caller_prompt:
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prompt_parts.append(caller_prompt)
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if self._memory_store:
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if self._memory_enabled:
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mem_block = self._memory_store.format_for_system_prompt("memory")
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if mem_block:
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prompt_parts.append(mem_block)
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if self._user_profile_enabled:
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user_block = self._memory_store.format_for_system_prompt("user")
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if user_block:
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prompt_parts.append(user_block)
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has_skills_tools = any(name in self.valid_tool_names for name in ['skills_list', 'skill_view'])
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skills_prompt = build_skills_system_prompt() if has_skills_tools else ""
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if skills_prompt:
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prompt_parts.append(skills_prompt)
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if not self.skip_context_files:
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context_files_prompt = build_context_files_prompt()
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if context_files_prompt:
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prompt_parts.append(context_files_prompt)
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now = datetime.now()
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prompt_parts.append(
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f"Conversation started: {now.strftime('%A, %B %d, %Y %I:%M %p')}"
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)
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platform_key = (self.platform or "").lower().strip()
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if platform_key in PLATFORM_HINTS:
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prompt_parts.append(PLATFORM_HINTS[platform_key])
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return "\n\n".join(prompt_parts)
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def _invalidate_system_prompt(self):
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"""
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Invalidate the cached system prompt, forcing a rebuild on the next turn.
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Called after context compression events. Also reloads memory from disk
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so the rebuilt prompt captures any writes from this session.
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"""
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self._cached_system_prompt = None
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if self._memory_store:
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self._memory_store.load_from_disk()
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def run_conversation(
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self,
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user_message: str,
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@ -2093,47 +2244,27 @@ class AIAgent:
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if not self.quiet_mode:
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print(f"💬 Starting conversation: '{user_message[:60]}{'...' if len(user_message) > 60 else ''}'")
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# ── Build the full system prompt ──
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# Layers (in order):
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# 1. Default agent identity (always present)
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# 2. User / gateway system prompt (if provided)
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# 3. Skills guidance (if skills tools are loaded)
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# 4. Context files (SOUL.md, AGENTS.md, .cursorrules)
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# 5. Current date & time
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# 6. Platform-specific formatting hint
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prompt_parts = [DEFAULT_AGENT_IDENTITY]
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# ── System prompt (cached per session for prefix caching) ──
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# Built once on first call, reused for all subsequent calls.
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# Only rebuilt after context compression events (which invalidate
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# the cache and reload memory from disk).
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if self._cached_system_prompt is None:
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self._cached_system_prompt = self._build_system_prompt(system_message)
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# Store the system prompt snapshot in SQLite
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if self._session_db:
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try:
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self._session_db.update_system_prompt(self.session_id, self._cached_system_prompt)
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except Exception:
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pass
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# Layer in the caller-supplied system prompt (explicit > ephemeral).
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caller_prompt = system_message if system_message is not None else self.ephemeral_system_prompt
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if caller_prompt:
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prompt_parts.append(caller_prompt)
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active_system_prompt = self._cached_system_prompt
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# Auto-include skills guidance if skills tools are available.
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has_skills_tools = any(name in self.valid_tool_names for name in ['skills_list', 'skill_view'])
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skills_prompt = build_skills_system_prompt() if has_skills_tools else ""
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if skills_prompt:
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prompt_parts.append(skills_prompt)
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# Auto-include context files (SOUL.md, AGENTS.md, .cursorrules).
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# Skipped for batch processing / data generation to avoid polluting trajectories.
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if not self.skip_context_files:
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context_files_prompt = build_context_files_prompt()
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if context_files_prompt:
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prompt_parts.append(context_files_prompt)
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# Current local date and time so the model is never confused about
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# what day/time it is (LLM training cutoffs can otherwise mislead it).
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now = datetime.now()
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prompt_parts.append(
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f"Current local date and time: {now.strftime('%A, %B %d, %Y %I:%M %p')}"
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)
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# Platform-specific formatting hint (no markdown on WhatsApp, etc.).
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platform_key = (self.platform or "").lower().strip()
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if platform_key in PLATFORM_HINTS:
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prompt_parts.append(PLATFORM_HINTS[platform_key])
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active_system_prompt = "\n\n".join(prompt_parts)
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# Log user message to SQLite
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if self._session_db:
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try:
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self._session_db.append_message(self.session_id, "user", user_message)
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except Exception:
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pass
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# Main conversation loop
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api_call_count = 0
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@ -2510,6 +2641,25 @@ class AIAgent:
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todo_snapshot = self._todo_store.format_for_injection()
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if todo_snapshot:
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messages.append({"role": "user", "content": todo_snapshot})
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# Rebuild system prompt with fresh date/time + memory
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self._invalidate_system_prompt()
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active_system_prompt = self._build_system_prompt(system_message)
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self._cached_system_prompt = active_system_prompt
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# Split session in SQLite (close old, open new with parent link)
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if self._session_db:
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try:
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self._session_db.end_session(self.session_id, "compression")
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old_session_id = self.session_id
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self.session_id = f"{datetime.now().strftime('%Y%m%d_%H%M%S')}_{uuid.uuid4().hex[:6]}"
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self._session_db.create_session(
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session_id=self.session_id,
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source=self.platform or "cli",
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model=self.model,
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parent_session_id=old_session_id,
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)
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self._session_db.update_system_prompt(self.session_id, active_system_prompt)
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except Exception:
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pass
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print(f"{self.log_prefix} 🗜️ Compressed {original_len} → {len(messages)} messages, retrying...")
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continue # Retry with compressed messages
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else:
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@ -2769,9 +2919,33 @@ class AIAgent:
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store=self._todo_store,
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)
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tool_duration = time.time() - tool_start_time
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# Show clean output in quiet mode (no spinner needed -- instant)
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if self.quiet_mode:
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print(f" {self._get_cute_tool_message('todo', function_args, tool_duration)}")
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# Session search -- handle directly (needs SessionDB instance)
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elif function_name == "session_search" and self._session_db:
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from tools.session_search_tool import session_search as _session_search
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function_result = _session_search(
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query=function_args.get("query", ""),
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role_filter=function_args.get("role_filter"),
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limit=function_args.get("limit", 3),
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db=self._session_db,
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)
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tool_duration = time.time() - tool_start_time
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if self.quiet_mode:
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print(f" {self._get_cute_tool_message('session_search', function_args, tool_duration)}")
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# Memory tool -- handle directly (needs agent's MemoryStore instance)
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elif function_name == "memory":
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from tools.memory_tool import memory_tool as _memory_tool
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function_result = _memory_tool(
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action=function_args.get("action"),
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target=function_args.get("target", "memory"),
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content=function_args.get("content"),
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old_text=function_args.get("old_text"),
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store=self._memory_store,
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)
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tool_duration = time.time() - tool_start_time
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if self.quiet_mode:
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print(f" {self._get_cute_tool_message('memory', function_args, tool_duration)}")
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# Execute other tools - with animated kawaii spinner in quiet mode
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# The face is "alive" while the tool works, then vanishes
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# and is replaced by the clean result line.
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@ -2790,7 +2964,7 @@ class AIAgent:
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'vision_analyze': '👁️', 'mixture_of_agents': '🧠',
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'skills_list': '📚', 'skill_view': '📚',
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'schedule_cronjob': '⏰', 'list_cronjobs': '⏰', 'remove_cronjob': '⏰',
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'send_message': '📨', 'todo': '📋',
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'send_message': '📨', 'todo': '📋', 'memory': '🧠', 'session_search': '🔍',
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}
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emoji = tool_emoji_map.get(function_name, '⚡')
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preview = _build_tool_preview(function_name, function_args) or function_name
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@ -2852,10 +3026,29 @@ class AIAgent:
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messages,
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current_tokens=self.context_compressor.last_prompt_tokens
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)
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# Re-inject todo state after compression (cache already invalidated)
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# Re-inject todo state after compression
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todo_snapshot = self._todo_store.format_for_injection()
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if todo_snapshot:
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messages.append({"role": "user", "content": todo_snapshot})
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# Rebuild system prompt with fresh date/time + memory
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self._invalidate_system_prompt()
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active_system_prompt = self._build_system_prompt(system_message)
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self._cached_system_prompt = active_system_prompt
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# Split session in SQLite (close old, open new with parent link)
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if self._session_db:
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try:
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self._session_db.end_session(self.session_id, "compression")
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old_session_id = self.session_id
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self.session_id = f"{datetime.now().strftime('%Y%m%d_%H%M%S')}_{uuid.uuid4().hex[:6]}"
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self._session_db.create_session(
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session_id=self.session_id,
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source=self.platform or "cli",
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model=self.model,
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parent_session_id=old_session_id,
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)
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self._session_db.update_system_prompt(self.session_id, active_system_prompt)
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except Exception:
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pass
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# Save session log incrementally (so progress is visible even if interrupted)
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self._session_messages = messages
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@ -3042,6 +3235,32 @@ class AIAgent:
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self._session_messages = messages
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self._save_session_log(messages)
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# Log new messages to SQLite session store (everything after the user message we already logged)
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if self._session_db:
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try:
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# Skip messages that were in the conversation history before this call
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# (the user message was already logged at the start of run_conversation)
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start_idx = (len(conversation_history) if conversation_history else 0) + 1 # +1 for the user msg
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for msg in messages[start_idx:]:
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role = msg.get("role", "unknown")
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content = msg.get("content")
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# Extract tool call info from assistant messages
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tool_calls_data = None
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if hasattr(msg, "tool_calls") and msg.tool_calls:
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tool_calls_data = [{"name": tc.function.name, "arguments": tc.function.arguments} for tc in msg.tool_calls]
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elif isinstance(msg.get("tool_calls"), list):
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tool_calls_data = msg["tool_calls"]
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self._session_db.append_message(
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session_id=self.session_id,
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role=role,
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content=content,
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tool_name=msg.get("tool_name"),
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tool_calls=tool_calls_data,
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tool_call_id=msg.get("tool_call_id"),
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)
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except Exception:
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pass
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# Build result with interrupt info if applicable
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result = {
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"final_response": final_response,
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