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refactor(run_agent): extract system-prompt builder to agent/system_prompt.py

Browse Source 
Four AIAgent methods move into a dedicated module:

* build_system_prompt_parts — three-tier stable/context/volatile dict
* build_system_prompt        — joiner used at session start
* invalidate_system_prompt   — drop cache + reload memory
* format_tools_for_system_message — trajectory-format tool dump

The extracted helpers look up patch-target names (load_soul_md,
build_skills_system_prompt, get_toolset_for_tool, build_environment_hints,
build_context_files_prompt, build_nous_subscription_prompt) through the
run_agent module via _ra() instead of importing them directly.  That
preserves the patch surface tests rely on
(patch('run_agent.load_soul_md', ...) and friends).

AIAgent keeps thin forwarder methods.

tests/run_agent/ + tests/agent/: 4313 passed (same pre-existing
test_auxiliary_client failure as before).

run_agent.py: 14555 -> 14292 lines (-263).
This commit is contained in:
teknium1
2026-05-16 18:16:20 -07:00
parent 5311d9959e
commit 2d2cd5e904
2 changed files with 345 additions and 258 deletions
Download Patch File Download Diff File Expand all files Collapse all files
agent/system_prompt.py
+333
View File
@@ -0,0 +1,333 @@
"""System-prompt assembly for :class:`AIAgent`.
The agent's system prompt is built once per session and reused across all
turns — only context compression triggers a rebuild. This keeps the
upstream prefix cache warm. See ``hermes-agent-dev``'s
``references/system-prompt-invariant.md`` for the invariants and
``references/self-improvement-loop.md`` for how the background-review
fork inherits the cached prompt verbatim.
Three tiers are joined with ``\\n\\n``:
* ``stable`` — identity (SOUL.md or DEFAULT_AGENT_IDENTITY), tool
guidance, computer-use guidance, nous subscription block, tool-use
enforcement guidance + per-model operational guidance, skills prompt,
alibaba model-name workaround, environment hints, platform hints.
* ``context`` — caller-supplied ``system_message`` plus context files
(AGENTS.md / .cursorrules / etc.) discovered under ``TERMINAL_CWD``.
* ``volatile`` — memory snapshot, USER.md profile, external memory
provider block, timestamp/session/model/provider line.
Pure helpers that read the agent's state. AIAgent keeps thin forwarders.
"""
from __future__ import annotations
import json
import os
from typing import Any, Dict, List, Optional
from agent.prompt_builder import (
DEFAULT_AGENT_IDENTITY,
GOOGLE_MODEL_OPERATIONAL_GUIDANCE,
HERMES_AGENT_HELP_GUIDANCE,
KANBAN_GUIDANCE,
MEMORY_GUIDANCE,
OPENAI_MODEL_EXECUTION_GUIDANCE,
PLATFORM_HINTS,
SESSION_SEARCH_GUIDANCE,
SKILLS_GUIDANCE,
TOOL_USE_ENFORCEMENT_GUIDANCE,
TOOL_USE_ENFORCEMENT_MODELS,
)
def _ra():
"""Lazy reference to the ``run_agent`` module.
Helpers like ``load_soul_md``, ``build_environment_hints``,
``build_context_files_prompt``, ``build_nous_subscription_prompt``,
``build_skills_system_prompt`` and ``get_toolset_for_tool`` are
imported into ``run_agent``'s namespace. Many tests
``patch("run_agent.load_soul_md", ...)``; if we imported them
directly here those patches would not reach us. Looking them up
through ``run_agent`` on every call preserves the patch contract.
"""
import run_agent
return run_agent
def build_system_prompt_parts(agent: Any, system_message: Optional[str] = None) -> Dict[str, str]:
"""Assemble the system prompt as three ordered parts.
Returns a dict with three keys:
* ``stable`` — identity, tool guidance, skills prompt,
environment hints, platform hints, model-family operational
guidance.
* ``context`` — context files (AGENTS.md, .cursorrules, etc.)
and caller-supplied system_message.
* ``volatile`` — memory snapshot, user profile, external
memory provider block, timestamp line.
Joined into a single string by :func:`build_system_prompt` and
cached on ``agent._cached_system_prompt`` for the lifetime of the
AIAgent. Hermes never re-renders parts of this string mid-
session — that's the only way to keep upstream prompt caches
warm across turns.
"""
# Local import to avoid pulling model_tools at module load. Tests
# patch ``run_agent.get_toolset_for_tool`` and similar helpers, so
# we resolve through ``_ra()`` to honor those patches.
_r = _ra()
# ── Stable tier ────────────────────────────────────────────────
stable_parts: List[str] = []
# Try SOUL.md as primary identity unless the caller explicitly skipped it.
# Some execution modes (cron) still want HERMES_HOME persona while keeping
# cwd project instructions disabled.
_soul_loaded = False
if agent.load_soul_identity or not agent.skip_context_files:
_soul_content = _r.load_soul_md()
if _soul_content:
stable_parts.append(_soul_content)
_soul_loaded = True
if not _soul_loaded:
# Fallback to hardcoded identity
stable_parts.append(DEFAULT_AGENT_IDENTITY)
# Pointer to the hermes-agent skill + docs for user questions about Hermes itself.
stable_parts.append(HERMES_AGENT_HELP_GUIDANCE)
# Tool-aware behavioral guidance: only inject when the tools are loaded
tool_guidance = []
if "memory" in agent.valid_tool_names:
tool_guidance.append(MEMORY_GUIDANCE)
if "session_search" in agent.valid_tool_names:
tool_guidance.append(SESSION_SEARCH_GUIDANCE)
if "skill_manage" in agent.valid_tool_names:
tool_guidance.append(SKILLS_GUIDANCE)
# Kanban worker/orchestrator lifecycle — only present when the
# dispatcher spawned this process (kanban_show check_fn gates on
# HERMES_KANBAN_TASK env var). Normal chat sessions never see
# this block.
if "kanban_show" in agent.valid_tool_names:
tool_guidance.append(KANBAN_GUIDANCE)
if tool_guidance:
stable_parts.append(" ".join(tool_guidance))
# Computer-use (macOS) — goes in as its own block rather than being
# merged into tool_guidance because the content is multi-paragraph.
if "computer_use" in agent.valid_tool_names:
from agent.prompt_builder import COMPUTER_USE_GUIDANCE
stable_parts.append(COMPUTER_USE_GUIDANCE)
nous_subscription_prompt = _r.build_nous_subscription_prompt(agent.valid_tool_names)
if nous_subscription_prompt:
stable_parts.append(nous_subscription_prompt)
# Tool-use enforcement: tells the model to actually call tools instead
# of describing intended actions. Controlled by config.yaml
# agent.tool_use_enforcement:
# "auto" (default) — matches TOOL_USE_ENFORCEMENT_MODELS
# true — always inject (all models)
# false — never inject
# list — custom model-name substrings to match
if agent.valid_tool_names:
_enforce = agent._tool_use_enforcement
_inject = False
if _enforce is True or (isinstance(_enforce, str) and _enforce.lower() in {"true", "always", "yes", "on"}):
_inject = True
elif _enforce is False or (isinstance(_enforce, str) and _enforce.lower() in {"false", "never", "no", "off"}):
_inject = False
elif isinstance(_enforce, list):
model_lower = (agent.model or "").lower()
_inject = any(p.lower() in model_lower for p in _enforce if isinstance(p, str))
else:
# "auto" or any unrecognised value — use hardcoded defaults
model_lower = (agent.model or "").lower()
_inject = any(p in model_lower for p in TOOL_USE_ENFORCEMENT_MODELS)
if _inject:
stable_parts.append(TOOL_USE_ENFORCEMENT_GUIDANCE)
_model_lower = (agent.model or "").lower()
# Google model operational guidance (conciseness, absolute
# paths, parallel tool calls, verify-before-edit, etc.)
if "gemini" in _model_lower or "gemma" in _model_lower:
stable_parts.append(GOOGLE_MODEL_OPERATIONAL_GUIDANCE)
# OpenAI GPT/Codex execution discipline (tool persistence,
# prerequisite checks, verification, anti-hallucination).
if "gpt" in _model_lower or "codex" in _model_lower:
stable_parts.append(OPENAI_MODEL_EXECUTION_GUIDANCE)
has_skills_tools = any(name in agent.valid_tool_names for name in ['skills_list', 'skill_view', 'skill_manage'])
if has_skills_tools:
avail_toolsets = {
toolset
for toolset in (
_r.get_toolset_for_tool(tool_name) for tool_name in agent.valid_tool_names
)
if toolset
}
skills_prompt = _r.build_skills_system_prompt(
available_tools=agent.valid_tool_names,
available_toolsets=avail_toolsets,
)
else:
skills_prompt = ""
if skills_prompt:
stable_parts.append(skills_prompt)
# Alibaba Coding Plan API always returns "glm-4.7" as model name regardless
# of the requested model. Inject explicit model identity into the system prompt
# so the agent can correctly report which model it is (workaround for API bug).
# Stable for the lifetime of an agent instance — model and provider are fixed
# at construction time.
if agent.provider == "alibaba":
_model_short = agent.model.split("/")[-1] if "/" in agent.model else agent.model
stable_parts.append(
f"You are powered by the model named {_model_short}. "
f"The exact model ID is {agent.model}. "
f"When asked what model you are, always answer based on this information, "
f"not on any model name returned by the API."
)
# Environment hints (WSL, Termux, etc.) — tell the agent about the
# execution environment so it can translate paths and adapt behavior.
# Stable for the lifetime of the process.
_env_hints = _r.build_environment_hints()
if _env_hints:
stable_parts.append(_env_hints)
platform_key = (agent.platform or "").lower().strip()
if platform_key in PLATFORM_HINTS:
stable_parts.append(PLATFORM_HINTS[platform_key])
elif platform_key:
# Check plugin registry for platform-specific LLM guidance
try:
from gateway.platform_registry import platform_registry
_entry = platform_registry.get(platform_key)
if _entry and _entry.platform_hint:
stable_parts.append(_entry.platform_hint)
except Exception:
pass
# ── Context tier (cwd-dependent, may change between sessions) ─
context_parts: List[str] = []
# Note: ephemeral_system_prompt is NOT included here. It's injected at
# API-call time only so it stays out of the cached/stored system prompt.
if system_message is not None:
context_parts.append(system_message)
if not agent.skip_context_files:
# Use TERMINAL_CWD for context file discovery when set (gateway
# mode). The gateway process runs from the hermes-agent install
# dir, so os.getcwd() would pick up the repo's AGENTS.md and
# other dev files — inflating token usage by ~10k for no benefit.
_context_cwd = os.getenv("TERMINAL_CWD") or None
context_files_prompt = _r.build_context_files_prompt(
cwd=_context_cwd, skip_soul=_soul_loaded)
if context_files_prompt:
context_parts.append(context_files_prompt)
# ── Volatile tier (changes per session/turn — never cached) ───
volatile_parts: List[str] = []
if agent._memory_store:
if agent._memory_enabled:
mem_block = agent._memory_store.format_for_system_prompt("memory")
if mem_block:
volatile_parts.append(mem_block)
# USER.md is always included when enabled.
if agent._user_profile_enabled:
user_block = agent._memory_store.format_for_system_prompt("user")
if user_block:
volatile_parts.append(user_block)
# External memory provider system prompt block (additive to built-in)
if agent._memory_manager:
try:
_ext_mem_block = agent._memory_manager.build_system_prompt()
if _ext_mem_block:
volatile_parts.append(_ext_mem_block)
except Exception:
pass
from hermes_time import now as _hermes_now
now = _hermes_now()
timestamp_line = f"Conversation started: {now.strftime('%A, %B %d, %Y %I:%M %p')}"
if agent.pass_session_id and agent.session_id:
timestamp_line += f"\nSession ID: {agent.session_id}"
if agent.model:
timestamp_line += f"\nModel: {agent.model}"
if agent.provider:
timestamp_line += f"\nProvider: {agent.provider}"
volatile_parts.append(timestamp_line)
return {
"stable": "\n\n".join(p.strip() for p in stable_parts if p and p.strip()),
"context": "\n\n".join(p.strip() for p in context_parts if p and p.strip()),
"volatile": "\n\n".join(p.strip() for p in volatile_parts if p and p.strip()),
}
def build_system_prompt(agent: Any, system_message: Optional[str] = None) -> str:
"""Assemble the full system prompt from all layers.
Called once per session (cached on ``agent._cached_system_prompt``) and
only rebuilt after context compression events. This ensures the system
prompt is stable across all turns in a session, maximizing prefix cache
hits.
Layers are ordered cache-friendly: stable identity/guidance first,
then session-stable context files, then per-call volatile content
(memory, USER profile, timestamp). The whole string is treated as
one cached block — Hermes never rebuilds or reinjects parts of it
mid-session, which is the only way to keep upstream prompt caches
warm across turns.
"""
parts = build_system_prompt_parts(agent, system_message=system_message)
return "\n\n".join(p for p in (parts["stable"], parts["context"], parts["volatile"]) if p)
def invalidate_system_prompt(agent: Any) -> None:
"""Invalidate the cached system prompt, forcing a rebuild on the next turn.
Called after context compression events. Also reloads memory from disk
so the rebuilt prompt captures any writes from this session.
"""
agent._cached_system_prompt = None
if agent._memory_store:
agent._memory_store.load_from_disk()
def format_tools_for_system_message(agent: Any) -> str:
"""Format tool definitions for the system message in the trajectory format.
Returns:
str: JSON string representation of tool definitions
"""
if not agent.tools:
return "[]"
# Convert tool definitions to the format expected in trajectories
formatted_tools = []
for tool in agent.tools:
func = tool["function"]
formatted_tool = {
"name": func["name"],
"description": func.get("description", ""),
"parameters": func.get("parameters", {}),
"required": None # Match the format in the example
}
formatted_tools.append(formatted_tool)
return json.dumps(formatted_tools, ensure_ascii=False)
__all__ = [
"build_system_prompt_parts",
"build_system_prompt",
"invalidate_system_prompt",
"format_tools_for_system_message",
]
run_agent.py
+12 -258
View File
@@ -3499,28 +3499,9 @@ class AIAgent:
return messages[:last_assistant_idx]
def _format_tools_for_system_message(self) -> str:
"""
Format tool definitions for the system message in the trajectory format.
Returns:
str: JSON string representation of tool definitions
"""
if not self.tools:
return "[]"
# Convert tool definitions to the format expected in trajectories
formatted_tools = []
for tool in self.tools:
func = tool["function"]
formatted_tool = {
"name": func["name"],
"description": func.get("description", ""),
"parameters": func.get("parameters", {}),
"required": None # Match the format in the example
}
formatted_tools.append(formatted_tool)
return json.dumps(formatted_tools, ensure_ascii=False)
"""Forwarder — see ``agent.system_prompt.format_tools_for_system_message``."""
from agent.system_prompt import format_tools_for_system_message
return format_tools_for_system_message(self)
def _convert_to_trajectory_format(self, messages: List[Dict[str, Any]], user_query: str, completed: bool) -> List[Dict[str, Any]]:
"""
@@ -4651,235 +4632,14 @@ class AIAgent:
def _build_system_prompt_parts(self, system_message: str = None) -> Dict[str, str]:
"""Assemble the system prompt as three ordered parts.
Returns a dict with three keys:
* ``stable`` — identity, tool guidance, skills prompt,
environment hints, platform hints, model-family operational
guidance.
* ``context`` — context files (AGENTS.md, .cursorrules, etc.)
and caller-supplied system_message.
* ``volatile`` — memory snapshot, user profile, external
memory provider block, timestamp line.
Joined into a single string by ``_build_system_prompt`` and
cached on ``_cached_system_prompt`` for the lifetime of the
AIAgent. Hermes never re-renders parts of this string mid-
session — that's the only way to keep upstream prompt caches
warm across turns.
"""
# ── Stable tier ────────────────────────────────────────────────
stable_parts: List[str] = []
# Try SOUL.md as primary identity unless the caller explicitly skipped it.
# Some execution modes (cron) still want HERMES_HOME persona while keeping
# cwd project instructions disabled.
_soul_loaded = False
if self.load_soul_identity or not self.skip_context_files:
_soul_content = load_soul_md()
if _soul_content:
stable_parts.append(_soul_content)
_soul_loaded = True
if not _soul_loaded:
# Fallback to hardcoded identity
stable_parts.append(DEFAULT_AGENT_IDENTITY)
# Pointer to the hermes-agent skill + docs for user questions about Hermes itself.
stable_parts.append(HERMES_AGENT_HELP_GUIDANCE)
# Tool-aware behavioral guidance: only inject when the tools are loaded
tool_guidance = []
if "memory" in self.valid_tool_names:
tool_guidance.append(MEMORY_GUIDANCE)
if "session_search" in self.valid_tool_names:
tool_guidance.append(SESSION_SEARCH_GUIDANCE)
if "skill_manage" in self.valid_tool_names:
tool_guidance.append(SKILLS_GUIDANCE)
# Kanban worker/orchestrator lifecycle — only present when the
# dispatcher spawned this process (kanban_show check_fn gates on
# HERMES_KANBAN_TASK env var). Normal chat sessions never see
# this block.
if "kanban_show" in self.valid_tool_names:
tool_guidance.append(KANBAN_GUIDANCE)
if tool_guidance:
stable_parts.append(" ".join(tool_guidance))
# Computer-use (macOS) — goes in as its own block rather than being
# merged into tool_guidance because the content is multi-paragraph.
if "computer_use" in self.valid_tool_names:
from agent.prompt_builder import COMPUTER_USE_GUIDANCE
stable_parts.append(COMPUTER_USE_GUIDANCE)
nous_subscription_prompt = build_nous_subscription_prompt(self.valid_tool_names)
if nous_subscription_prompt:
stable_parts.append(nous_subscription_prompt)
# Tool-use enforcement: tells the model to actually call tools instead
# of describing intended actions. Controlled by config.yaml
# agent.tool_use_enforcement:
# "auto" (default) — matches TOOL_USE_ENFORCEMENT_MODELS
# true — always inject (all models)
# false — never inject
# list — custom model-name substrings to match
if self.valid_tool_names:
_enforce = self._tool_use_enforcement
_inject = False
if _enforce is True or (isinstance(_enforce, str) and _enforce.lower() in {"true", "always", "yes", "on"}):
_inject = True
elif _enforce is False or (isinstance(_enforce, str) and _enforce.lower() in {"false", "never", "no", "off"}):
_inject = False
elif isinstance(_enforce, list):
model_lower = (self.model or "").lower()
_inject = any(p.lower() in model_lower for p in _enforce if isinstance(p, str))
else:
# "auto" or any unrecognised value — use hardcoded defaults
model_lower = (self.model or "").lower()
_inject = any(p in model_lower for p in TOOL_USE_ENFORCEMENT_MODELS)
if _inject:
stable_parts.append(TOOL_USE_ENFORCEMENT_GUIDANCE)
_model_lower = (self.model or "").lower()
# Google model operational guidance (conciseness, absolute
# paths, parallel tool calls, verify-before-edit, etc.)
if "gemini" in _model_lower or "gemma" in _model_lower:
stable_parts.append(GOOGLE_MODEL_OPERATIONAL_GUIDANCE)
# OpenAI GPT/Codex execution discipline (tool persistence,
# prerequisite checks, verification, anti-hallucination).
if "gpt" in _model_lower or "codex" in _model_lower:
stable_parts.append(OPENAI_MODEL_EXECUTION_GUIDANCE)
has_skills_tools = any(name in self.valid_tool_names for name in ['skills_list', 'skill_view', 'skill_manage'])
if has_skills_tools:
avail_toolsets = {
toolset
for toolset in (
get_toolset_for_tool(tool_name) for tool_name in self.valid_tool_names
)
if toolset
}
skills_prompt = build_skills_system_prompt(
available_tools=self.valid_tool_names,
available_toolsets=avail_toolsets,
)
else:
skills_prompt = ""
if skills_prompt:
stable_parts.append(skills_prompt)
# Alibaba Coding Plan API always returns "glm-4.7" as model name regardless
# of the requested model. Inject explicit model identity into the system prompt
# so the agent can correctly report which model it is (workaround for API bug).
# Stable for the lifetime of an agent instance — model and provider are fixed
# at construction time.
if self.provider == "alibaba":
_model_short = self.model.split("/")[-1] if "/" in self.model else self.model
stable_parts.append(
f"You are powered by the model named {_model_short}. "
f"The exact model ID is {self.model}. "
f"When asked what model you are, always answer based on this information, "
f"not on any model name returned by the API."
)
# Environment hints (WSL, Termux, etc.) — tell the agent about the
# execution environment so it can translate paths and adapt behavior.
# Stable for the lifetime of the process.
_env_hints = build_environment_hints()
if _env_hints:
stable_parts.append(_env_hints)
platform_key = (self.platform or "").lower().strip()
if platform_key in PLATFORM_HINTS:
stable_parts.append(PLATFORM_HINTS[platform_key])
elif platform_key:
# Check plugin registry for platform-specific LLM guidance
try:
from gateway.platform_registry import platform_registry
_entry = platform_registry.get(platform_key)
if _entry and _entry.platform_hint:
stable_parts.append(_entry.platform_hint)
except Exception:
pass
# ── Context tier (cwd-dependent, may change between sessions) ─
context_parts: List[str] = []
# Note: ephemeral_system_prompt is NOT included here. It's injected at
# API-call time only so it stays out of the cached/stored system prompt.
if system_message is not None:
context_parts.append(system_message)
if not self.skip_context_files:
# Use TERMINAL_CWD for context file discovery when set (gateway
# mode). The gateway process runs from the hermes-agent install
# dir, so os.getcwd() would pick up the repo's AGENTS.md and
# other dev files — inflating token usage by ~10k for no benefit.
_context_cwd = os.getenv("TERMINAL_CWD") or None
context_files_prompt = build_context_files_prompt(
cwd=_context_cwd, skip_soul=_soul_loaded)
if context_files_prompt:
context_parts.append(context_files_prompt)
# ── Volatile tier (changes per session/turn — never cached) ───
volatile_parts: List[str] = []
if self._memory_store:
if self._memory_enabled:
mem_block = self._memory_store.format_for_system_prompt("memory")
if mem_block:
volatile_parts.append(mem_block)
# USER.md is always included when enabled.
if self._user_profile_enabled:
user_block = self._memory_store.format_for_system_prompt("user")
if user_block:
volatile_parts.append(user_block)
# External memory provider system prompt block (additive to built-in)
if self._memory_manager:
try:
_ext_mem_block = self._memory_manager.build_system_prompt()
if _ext_mem_block:
volatile_parts.append(_ext_mem_block)
except Exception:
pass
from hermes_time import now as _hermes_now
now = _hermes_now()
timestamp_line = f"Conversation started: {now.strftime('%A, %B %d, %Y %I:%M %p')}"
if self.pass_session_id and self.session_id:
timestamp_line += f"\nSession ID: {self.session_id}"
if self.model:
timestamp_line += f"\nModel: {self.model}"
if self.provider:
timestamp_line += f"\nProvider: {self.provider}"
volatile_parts.append(timestamp_line)
return {
"stable": "\n\n".join(p.strip() for p in stable_parts if p and p.strip()),
"context": "\n\n".join(p.strip() for p in context_parts if p and p.strip()),
"volatile": "\n\n".join(p.strip() for p in volatile_parts if p and p.strip()),
}
"""Forwarder — see ``agent.system_prompt.build_system_prompt_parts``."""
from agent.system_prompt import build_system_prompt_parts
return build_system_prompt_parts(self, system_message=system_message)
def _build_system_prompt(self, system_message: str = None) -> str:
"""
Assemble the full system prompt from all layers.
Called once per session (cached on self._cached_system_prompt) and only
rebuilt after context compression events. This ensures the system prompt
is stable across all turns in a session, maximizing prefix cache hits.
Layers are ordered cache-friendly: stable identity/guidance first,
then session-stable context files, then per-call volatile content
(memory, USER profile, timestamp). The whole string is treated as
one cached block — Hermes never rebuilds or reinjects parts of it
mid-session, which is the only way to keep upstream prompt caches
warm across turns.
"""
parts = self._build_system_prompt_parts(system_message=system_message)
joined = "\n\n".join(p for p in (parts["stable"], parts["context"], parts["volatile"]) if p)
return joined
# =========================================================================
# Pre/post-call guardrails (inspired by PR #1321 — @alireza78a)
# =========================================================================
"""Forwarder — see ``agent.system_prompt.build_system_prompt``."""
from agent.system_prompt import build_system_prompt
return build_system_prompt(self, system_message=system_message)
@staticmethod
def _get_tool_call_id_static(tc) -> str:
@@ -5239,15 +4999,9 @@ class AIAgent:
return None
def _invalidate_system_prompt(self):
"""
Invalidate the cached system prompt, forcing a rebuild on the next turn.
Called after context compression events. Also reloads memory from disk
so the rebuilt prompt captures any writes from this session.
"""
self._cached_system_prompt = None
if self._memory_store:
self._memory_store.load_from_disk()
"""Forwarder — see ``agent.system_prompt.invalidate_system_prompt``."""
from agent.system_prompt import invalidate_system_prompt
invalidate_system_prompt(self)
@staticmethod
def _deterministic_call_id(fn_name: str, arguments: str, index: int = 0) -> str: