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odysseus/src/tool_execution.py

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"""
tool_execution.py
Tool dispatcher and result formatter for the agent loop.
Routes tool blocks to MCP servers or native implementations.
Extracted from agent_tools.py.
"""
import asyncio
import collections
import json
import logging
import os
import pathlib
fix(images): render agent-generated images in chat (#2809) * fix(images): render agent-generated images in chat When a chat model calls generate_image mid-conversation (agentic flow), the image does not display — it survives only as a URL the model echoes in prose. generate_image runs as a text-only MCP server, so result['image_url'] is never populated and the existing buildImageBubble render path never fires. Promote the image URL out of the tool's stdout in tool_execution so the agent loop's existing forwarding renders it via buildImageBubble — deterministically, no dependence on the model echoing the URL. Backend-only; reuses dev's image bubble, forwarding, and the tool's existing parseable output. Co-Authored-By: Claude Opus 4.8 (1M context) <noreply@anthropic.com> * feat(images): fully-qualified, valid generated-image links The chat model often mangled the generated-image URL it echoed in prose (relative path, or copying the 'image_url:' label into the link href). Build a fully-qualified link by prefixing the existing app_public_url setting (empty default keeps relative paths), and present it as a clean 'Direct link:' the model can echo verbatim (the frontend auto-links bare https URLs). One file; independent of how the image is rendered. Co-Authored-By: Claude Opus 4.8 (1M context) <noreply@anthropic.com> * test(images): cover _promote_image_fields; make exit-code guard self-contained Adds the unit tests requested in review on #2809: absolute URL, relative URL, no URL (result unchanged), and non-zero exit_code (not promoted). Moves the dict/exit_code==0 guard from the call site into _promote_image_fields so the function is self-contained and the failure case is unit-testable; call-site behavior is unchanged. Co-Authored-By: Claude Opus 4.8 (1M context) <noreply@anthropic.com> --------- Co-authored-by: Claude Opus 4.8 (1M context) <noreply@anthropic.com>
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import re
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import sys
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import time
from typing import Any, Awaitable, Callable, Dict, Optional, Tuple
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from src.tool_security import is_public_blocked_tool, owner_is_admin_or_single_user
from src.tool_policy import ToolPolicy
from src.constants import MAX_OUTPUT_CHARS, MAX_READ_CHARS, MAX_DIFF_LINES, DATA_DIR
from src.tool_utils import _truncate, get_mcp_manager
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# Persistent working directory for agent subprocesses.
# Resolves to <repo_root>/data, which is the bind-mounted volume in Docker
# (/app/data) and the local data directory for manual installs.
# Using this as cwd and HOME prevents the agent from silently creating files
# in ephemeral container layers that are lost on the next rebuild.
_AGENT_WORKDIR = DATA_DIR
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# ---------------------------------------------------------------------------
# Path confinement for read_file / write_file
# ---------------------------------------------------------------------------
# read_file + write_file are admin-only tools, but the path the agent
# supplies is model-controlled. Prompt-injection in an admin's chat can
# weaponise "read /etc/shadow" or "write ~/.ssh/authorized_keys" without
# the admin noticing.
#
# Policy:
# 1. Sensitive-subpath deny list — checked FIRST. Blocks .ssh,
# .gnupg, shell rc files, token/env files even if the root above
# them is on the allowlist.
# 2. Allowlist — only the directories the agent legitimately needs
# (project data/, system tmp). $HOME is NOT on the default list.
# 3. Opt-in extra roots — admin can add broader roots via the
# "tool_path_extra_roots" setting (list of path strings).
# ---------------------------------------------------------------------------
_SENSITIVE_BASENAMES: set[str] = {
".ssh", ".gnupg", ".gitconfig",
".bashrc", ".bash_profile", ".bash_logout",
".zshrc", ".zprofile", ".zshenv",
".profile", ".tcshrc", ".cshrc",
".env", ".netrc",
}
_SENSITIVE_FILE_PATTERNS: tuple[str, ...] = (
"authorized_keys", "id_rsa", "id_ed25519", "id_ecdsa",
"known_hosts",
)
def _is_sensitive_path(resolved: str) -> bool:
"""Return True if *resolved* falls under a sensitive directory or
matches a sensitive filename — regardless of what root it sits under.
"""
parts = resolved.split(os.sep)
filenames: set[str] = {parts[-1]} if parts else set()
# Check if any path component is a sensitive directory.
for part in parts:
if part in _SENSITIVE_BASENAMES:
return True
# Check filename against known sensitive files.
for pat in _SENSITIVE_FILE_PATTERNS:
if pat in filenames:
return True
return False
def _tool_path_roots() -> list[str]:
"""Return the list of directory roots that read_file / write_file
may touch. Default: project data/ + system temp dirs. Extra roots
are loaded from the ``tool_path_extra_roots`` setting.
"""
roots: list[str] = []
# Project data directory — the agent's primary workspace.
from src.constants import DATA_DIR
roots.append(DATA_DIR)
# /tmp (and its macOS realpath /private/tmp).
roots.append("/tmp")
try:
private_tmp = os.path.realpath("/tmp")
if private_tmp != "/tmp":
roots.append(private_tmp)
except OSError:
pass
# $TMPDIR — per-user temp root on macOS (e.g. /var/folders/.../T/).
tmpdir = os.environ.get("TMPDIR")
if tmpdir:
roots.append(tmpdir)
# Opt-in extra roots from settings.
try:
from src.settings import get_setting
extra = get_setting("tool_path_extra_roots")
if isinstance(extra, list):
roots.extend(str(r) for r in extra if r)
except Exception:
pass
# Deduplicate; resolve symlinks so containment is unambiguous.
seen: set[str] = set()
out: list[str] = []
for r in roots:
try:
real = os.path.realpath(r)
except OSError:
continue
if real in seen:
continue
seen.add(real)
out.append(real)
return out
def _resolve_tool_path(raw_path: str) -> str:
"""Resolve and confine a model-supplied path.
Order of checks:
1. Non-empty path.
2. Sensitive-subpath deny list (blocks .ssh, .gnupg, etc.
even when the root is on the allowlist).
3. Allowlist containment (must land under one of the roots).
Returns the realpath on success. Raises ValueError on rejection.
Symlinks are resolved before comparison.
"""
if raw_path is None or not str(raw_path).strip():
raise ValueError("path is required")
expanded = os.path.expanduser(str(raw_path).strip())
resolved = os.path.realpath(expanded)
if _is_sensitive_path(resolved):
raise ValueError(
f"path '{raw_path}' is inside a sensitive directory "
f"(e.g. .ssh, .gnupg) or matches a sensitive filename"
)
for root in _tool_path_roots():
if resolved == root:
return resolved
try:
common = os.path.commonpath([resolved, root])
except ValueError:
continue
if common == root:
return resolved
raise ValueError(
f"path '{raw_path}' is outside the allowed roots"
)
feat: Add workspace: confine agent tools to a folder (#1103) * feat: Add workspace: confine agent tools to a folder Pick a server folder as the agent's workspace so its file/shell tools work there and don't touch files outside it. File tools are hard-confined; bash/ python run with cwd set to the folder. Includes a slash command: `/workspace` (alias `/ws`) — show / `set <path>` / `clear` / `pick` (open the directory browser). - routes/workspace_routes.py: GET /api/workspace/browse (admin-only). - src/tool_execution.py: hard path confinement for read_file/write_file; bash/python cwd. Threaded route → stream_agent_loop → execute_tool_block. - src/agent_loop.py: workspace note prepended to the system prompt. - static/: overflow menu item, input-bar pill, directory-browser modal, and the /workspace slash command. - tests/test_workspace_confine.py. * Wire workspace confinement into tools that landed after this PR edit_file (#1239) and grep/glob/ls (#1670) merged after workspace-confine was written, so they bypassed the workspace boundary. Thread the workspace through: - edit_file: _do_edit_file resolves via _resolve_tool_path_in_workspace - grep/glob/ls: _resolve_search_root confines to the workspace (root + paths) - bash/python/bg cwd: workspace or _AGENT_WORKDIR (keep the #2586 data-dir default when no workspace is set) Tests cover edit_file + grep/ls confinement (inside ok, outside rejected). * Workspace picker: editable path bar + modal style cohesion + cross-platform hardening - Make the current-folder strip an editable address bar: type/paste a full path and press Enter to navigate (also reaches other Windows drives and hidden dirs the up-only browser cannot). - Reuse shared modal CSS: drop bespoke .workspace-modal-content/.workspace-btn* in favour of base .modal-content/.modal-body and the .confirm-btn button family; separators/hover use var(--border). Net -31 CSS lines. - Fix the path field overflowing the modal right edge (flex stretch + margin vs an overflow:auto scrollbar-feedback loop): full-bleed, no h-margin. - Cross-platform confinement: normcase the workspace commonpath check so containment holds on case-insensitive filesystems (Windows/macOS). - Make tests OS-portable: sibling temp dirs instead of /etc, python os.getcwd() instead of pwd. 5 pass.
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def _resolve_tool_path_in_workspace(workspace: str, raw_path: str) -> str:
"""Confine a model-supplied path to the active workspace.
Layered on top of upstream's path policy: the workspace is the allowed
root (relative paths resolve under it; paths that escape it are rejected),
and the sensitive-file deny list (.ssh, .gnupg, id_rsa, …) still applies
inside it. When no workspace is set, callers use _resolve_tool_path (the
default data/tmp allowlist) instead.
"""
if raw_path is None or not str(raw_path).strip():
raise ValueError("path is required")
base = os.path.realpath(workspace)
expanded = os.path.expanduser(str(raw_path).strip())
candidate = expanded if os.path.isabs(expanded) else os.path.join(base, expanded)
resolved = os.path.realpath(candidate)
if _is_sensitive_path(resolved):
raise ValueError(
f"path '{raw_path}' is inside a sensitive directory "
f"(e.g. .ssh, .gnupg) or matches a sensitive filename"
)
if resolved != base:
# normcase so containment holds on case-insensitive filesystems
# (Windows, default macOS): it lowercases on Windows and is a no-op on
# POSIX. commonpath raises ValueError across Windows drives (C: vs D:)
# or mixed abs/rel — both mean "outside", so the except rejects them.
nbase = os.path.normcase(base)
try:
if os.path.commonpath([os.path.normcase(resolved), nbase]) != nbase:
raise ValueError
except ValueError:
raise ValueError(f"path '{raw_path}' is outside the workspace ({workspace})")
return resolved
def get_mcp_manager():
from src import agent_tools
return agent_tools.get_mcp_manager()
def _resolve_search_root(raw_path: str) -> str:
"""Resolve + confine a code-nav path (grep/glob/ls).
An empty path defaults to the agent's primary root (project data dir) and a
supplied path is confined by the global allowlist + sensitive-file policy.
"""
raw = (raw_path or "").strip()
if not raw:
roots = _tool_path_roots()
return roots[0] if roots else os.path.realpath(".")
return _resolve_tool_path(raw)
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logger = logging.getLogger(__name__)
_ADMIN_TOOLS = {
"app_api",
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"manage_endpoints",
"manage_mcp",
"manage_webhooks",
"manage_tokens",
"manage_settings",
"download_model",
"serve_model",
"serve_preset",
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"stop_served_model",
"cancel_download",
}
def _owner_is_admin(owner: Optional[str]) -> bool:
"""Mirror route-level admin behavior for agent tool execution."""
return owner_is_admin_or_single_user(owner)
# ---------------------------------------------------------------------------
# MCP-backed tool helpers
# ---------------------------------------------------------------------------
# Map legacy tool names -> (MCP server_id, MCP tool_name)
_MCP_TOOL_MAP = {
"bash": ("bash", "bash"),
"python": ("python", "python"),
"read_file": ("filesystem", "read_file"),
"write_file": ("filesystem", "write_file"),
"web_search": ("web_search", "web_search"),
"web_fetch": ("web_fetch", "web_fetch"),
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"generate_image": ("image_gen", "generate_image"),
}
def _parse_generate_image(content: str) -> Dict:
lines = content.strip().split("\n")
args = {"prompt": lines[0].strip() if lines else ""}
for i, key in enumerate(["model", "size", "quality"], 1):
if len(lines) > i and lines[i].strip():
args[key] = lines[i].strip()
return args
def _parse_manage_memory(content: str) -> Dict:
lines = content.strip().split("\n")
action = lines[0].strip().lower() if lines else ""
args = {"action": action}
if action == "add":
args["text"] = lines[1].strip() if len(lines) > 1 else ""
if len(lines) > 2 and lines[2].strip():
args["category"] = lines[2].strip().lower()
elif action == "edit":
args["memory_id"] = lines[1].strip() if len(lines) > 1 else ""
args["text"] = lines[2].strip() if len(lines) > 2 else ""
elif action == "delete":
args["memory_id"] = lines[1].strip() if len(lines) > 1 else ""
elif action == "search":
args["text"] = lines[1].strip() if len(lines) > 1 else ""
elif action == "list":
if len(lines) > 1 and lines[1].strip():
args["category"] = lines[1].strip().lower()
return args
def _parse_write_file(content: str) -> Dict:
lines = content.split("\n", 1)
return {"path": lines[0].strip(), "content": lines[1] if len(lines) > 1 else ""}
_MCP_ARG_PARSERS: Dict[str, Callable[[str], Dict[str, str]]] = {
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"bash": lambda c: {"command": c},
"python": lambda c: {"code": c},
"web_search": lambda c: {"query": c.split("\n")[0].strip()},
"web_fetch": lambda c: {"url": c.split("\n")[0].strip()},
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"read_file": lambda c: {"path": c.split("\n")[0].strip()},
"write_file": _parse_write_file,
"generate_image": _parse_generate_image,
"manage_memory": _parse_manage_memory,
}
def _build_mcp_args(tool: str, content: str) -> Dict:
"""Convert fenced-block text content to structured MCP arguments."""
parser = _MCP_ARG_PARSERS.get(tool)
return parser(content) if parser else {}
async def _call_mcp_tool(
tool: str,
content: str,
progress_cb: Optional[Callable[[Dict], Awaitable[None]]] = None,
) -> Dict:
"""Route a legacy tool call through the MCP manager, with direct fallbacks."""
mcp = get_mcp_manager()
if not mcp:
return await _direct_fallback(tool, content, progress_cb=progress_cb) or {"error": f"MCP manager not available for tool '{tool}'", "exit_code": 1}
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server_id, tool_name = _MCP_TOOL_MAP[tool]
qualified = f"mcp__{server_id}__{tool_name}"
args = _build_mcp_args(tool, content)
result = await mcp.call_tool(qualified, args)
# If MCP server not connected, try direct fallback
if isinstance(result, dict) and result.get("exit_code") == 1 and "not connected" in result.get("error", ""):
fallback = await _direct_fallback(tool, content, progress_cb=progress_cb)
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if fallback:
return fallback
fix(images): render agent-generated images in chat (#2809) * fix(images): render agent-generated images in chat When a chat model calls generate_image mid-conversation (agentic flow), the image does not display — it survives only as a URL the model echoes in prose. generate_image runs as a text-only MCP server, so result['image_url'] is never populated and the existing buildImageBubble render path never fires. Promote the image URL out of the tool's stdout in tool_execution so the agent loop's existing forwarding renders it via buildImageBubble — deterministically, no dependence on the model echoing the URL. Backend-only; reuses dev's image bubble, forwarding, and the tool's existing parseable output. Co-Authored-By: Claude Opus 4.8 (1M context) <noreply@anthropic.com> * feat(images): fully-qualified, valid generated-image links The chat model often mangled the generated-image URL it echoed in prose (relative path, or copying the 'image_url:' label into the link href). Build a fully-qualified link by prefixing the existing app_public_url setting (empty default keeps relative paths), and present it as a clean 'Direct link:' the model can echo verbatim (the frontend auto-links bare https URLs). One file; independent of how the image is rendered. Co-Authored-By: Claude Opus 4.8 (1M context) <noreply@anthropic.com> * test(images): cover _promote_image_fields; make exit-code guard self-contained Adds the unit tests requested in review on #2809: absolute URL, relative URL, no URL (result unchanged), and non-zero exit_code (not promoted). Moves the dict/exit_code==0 guard from the call site into _promote_image_fields so the function is self-contained and the failure case is unit-testable; call-site behavior is unchanged. Co-Authored-By: Claude Opus 4.8 (1M context) <noreply@anthropic.com> --------- Co-authored-by: Claude Opus 4.8 (1M context) <noreply@anthropic.com>
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# generate_image runs as a text-only MCP tool, so the saved image URL never
# reaches the agent loop's structured forwarding (which renders the image via
# buildImageBubble on result["image_url"]). Lift it out of the tool's stdout so
# the image renders deterministically — no dependence on the model echoing the
# URL into its prose (which it mangles/hallucinates).
if tool == "generate_image":
_promote_image_fields(result)
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return result
fix(images): render agent-generated images in chat (#2809) * fix(images): render agent-generated images in chat When a chat model calls generate_image mid-conversation (agentic flow), the image does not display — it survives only as a URL the model echoes in prose. generate_image runs as a text-only MCP server, so result['image_url'] is never populated and the existing buildImageBubble render path never fires. Promote the image URL out of the tool's stdout in tool_execution so the agent loop's existing forwarding renders it via buildImageBubble — deterministically, no dependence on the model echoing the URL. Backend-only; reuses dev's image bubble, forwarding, and the tool's existing parseable output. Co-Authored-By: Claude Opus 4.8 (1M context) <noreply@anthropic.com> * feat(images): fully-qualified, valid generated-image links The chat model often mangled the generated-image URL it echoed in prose (relative path, or copying the 'image_url:' label into the link href). Build a fully-qualified link by prefixing the existing app_public_url setting (empty default keeps relative paths), and present it as a clean 'Direct link:' the model can echo verbatim (the frontend auto-links bare https URLs). One file; independent of how the image is rendered. Co-Authored-By: Claude Opus 4.8 (1M context) <noreply@anthropic.com> * test(images): cover _promote_image_fields; make exit-code guard self-contained Adds the unit tests requested in review on #2809: absolute URL, relative URL, no URL (result unchanged), and non-zero exit_code (not promoted). Moves the dict/exit_code==0 guard from the call site into _promote_image_fields so the function is self-contained and the failure case is unit-testable; call-site behavior is unchanged. Co-Authored-By: Claude Opus 4.8 (1M context) <noreply@anthropic.com> --------- Co-authored-by: Claude Opus 4.8 (1M context) <noreply@anthropic.com>
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def _promote_image_fields(result: Dict) -> None:
"""Lift the image URL (+ prompt/model/size) from a successful generate_image MCP
text result into structured fields the agent loop already forwards to
buildImageBubble. Only acts on a dict result with exit_code 0; matches the
generated-image URL by pattern (absolute or relative) so it's robust to the
result's wording."""
if not isinstance(result, dict) or result.get("exit_code") != 0:
return
out = result.get("stdout") or ""
m = re.search(r'(?:https?://[^\s)\]]+)?/api/generated-image/[A-Za-z0-9._-]+', out)
if not m:
return
result["image_url"] = m.group(0).strip()
for field, pat in (
("image_prompt", r'^Generated image for:\s*(.+)$'),
("image_model", r'^model:\s*(.+)$'),
("image_size", r'^size:\s*(.+)$'),
):
fm = re.search(pat, out, re.M)
if fm:
result[field] = fm.group(1).strip()
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_BG_MARKERS = {"#!bg", "#bg", "# bg", "#background", "# background", "@background", "# @background"}
def _split_bg_marker(content: str):
"""If the bash content's first non-empty line is a background marker
(e.g. `#!bg`), return (True, command_without_marker); else (False, content)."""
lines = content.split("\n")
i = 0
while i < len(lines) and not lines[i].strip():
i += 1
if i < len(lines) and lines[i].strip().lower() in _BG_MARKERS:
del lines[i]
return True, "\n".join(lines).strip()
return False, content
async def _direct_fallback(
tool: str,
content: str,
progress_cb: Optional[Callable[[Dict], Awaitable[None]]] = None,
workspace: Optional[str] = None,
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) -> Optional[Dict]:
_subproc_env = {
**os.environ,
"TERM": "xterm-256color",
"COLUMNS": "120",
"LINES": "40",
"HOME": _AGENT_WORKDIR,
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}
try:
ctx = {
"progress_cb": progress_cb,
"workspace": workspace,
"subproc_env": _subproc_env,
}
from src.agent_tools import TOOL_HANDLERS
if tool in TOOL_HANDLERS:
return await TOOL_HANDLERS[tool](content, ctx)
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except Exception as e:
return {"error": f"{tool}: {e}", "exit_code": 1}
return None
# ---------------------------------------------------------------------------
# Dispatcher
# ---------------------------------------------------------------------------
async def execute_tool_block(
block: Any,
session_id: Optional[str] = None,
disabled_tools: Optional[set] = None,
owner: Optional[str] = None,
progress_cb: Optional[Callable[[Dict], Awaitable[None]]] = None,
workspace: Optional[str] = None,
tool_policy: Optional[Any] = None,
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) -> Tuple[str, Dict]:
"""Execute a single tool block. Returns (description, result_dict).
`progress_cb` is forwarded to long-running subprocess tools
(bash, python) so the agent loop can emit `tool_progress` SSE
events while the command is in flight. Ignored by other tools.
"""
from src.tool_implementations import (
do_create_document, do_update_document, do_edit_document,
do_suggest_document, do_search_chats, do_manage_tasks,
do_manage_skills, do_api_call, do_manage_endpoints,
do_manage_mcp, do_manage_webhooks, do_manage_tokens,
do_manage_documents, do_manage_settings, do_manage_notes,
do_manage_calendar,
do_download_model, do_serve_model, do_list_served_models, do_stop_served_model,
cookbook agent debug loop: persistent log files, auto-adopt orphan tmux, Codex/Claude skill parity Three converging fixes so the chat agent + external Codex/Claude skills can actually debug a crashed serve instead of staring at a post-crash neofetch banner: * Serves now `tee` to /tmp/odysseus-tmux/SESSION.log on the host running them. Runner saves fds 3/4 before the tee and restores them right before `exec ${SHELL}`, so the post-crash interactive zsh banner does NOT pollute the log file. * `tail_serve_output` (chat agent) and `/api/codex/cookbook/output/{sid}` (Codex+Claude skills) both prefer the persistent log file over the tmux pane. Pane is fallback for sessions predating the tee runner. Default tail bumped 150 -> 400. * `list_served_models` "recent log" snippet seeks to the Traceback line instead of showing the last 6 lines (which was always the bash prompt). Cookbook auto-adoption sweep on `/api/cookbook/tasks/status`: every 20s (rate-limited) the cookbook SSHes each configured server, finds `serve-*` / `cookbook-*` tmux sessions running an actual model process (vllm/python/llama-server/etc., filtered via `pane_current_command`), and writes them into state.tasks. So when the agent falls back to raw ssh+tmux, the session appears in the Cookbook UI on the next poll. `serve_model` error path now reads `data["detail"]` in addition to `data["error"]` so the FastAPI HTTPException message ("Invalid characters in cmd") actually reaches the agent instead of being swallowed as a generic "Serve failed". Tool description updated to warn against `cd …`/`source …`/`&&` prefixes. Intent-without-action supervisor in agent_loop: when the model writes "Let me tail the output" / "I'll check the logs" / "Let me investigate" and ends the turn without emitting a tool call, the loop injects a sharp system nudge ("You said you would X — DO IT NOW") and continues. Capped at 2 nudges per chat so a model that genuinely cannot use the tool does not pin the loop. Codex/Claude skill parity: adds `/cookbook/cached`, `/cookbook/presets`, `/cookbook/preset/{name}`, `/cookbook/adopt` so external agents have the same surface as the chat agent. SKILL.md docs + odysseus_api.py wrapper updated for both bundles. `adopt_served_model` promoted to the always-on tool set so the agent has a documented fallback when serve_model rejects a cmd. Also various cookbook UI tweaks accumulated alongside the above (cookbook.js, cookbookRunning.js, cookbookServe.js, cookbook-diagnosis.js, settings.js, style.css).
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do_tail_serve_output,
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do_list_downloads, do_cancel_download, do_search_hf_models, do_list_cached_models,
do_list_serve_presets, do_serve_preset, do_adopt_served_model,
do_list_cookbook_servers,
do_edit_image, do_trigger_research, do_manage_research, do_resolve_contact,
do_manage_contact,
do_vault_search, do_vault_get, do_vault_unlock,
do_app_api,
)
tool = block.tool_type
content = block.content
# Misformatted tool call detection: model put JSON inside ```python``` (or
# similar) without naming the tool. Common with MiniMax-style outputs.
# Return a helpful error so the model retries with the correct format.
if tool in ("python", "json", "xml") and content.strip().startswith("{") and content.strip().endswith("}"):
try:
parsed = json.loads(content.strip())
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if isinstance(parsed, dict):
desc = f"{tool}: misformatted tool call"
result = {
"error": (
f"You wrote a JSON object inside a ```{tool}``` block, but that's not a tool call.\n"
"To call a tool, use the tool name as the fence tag, e.g.\n"
"```resolve_contact\n"
"{\"name\": \"...\"}\n"
"```\n"
"or\n"
"```send_email\n"
"{\"to\": \"...\", \"subject\": \"...\", \"body\": \"...\"}\n"
"```"
),
"exit_code": 1,
}
return desc, result
except (ValueError, TypeError):
pass
# Reject tools that the user has disabled for this request
if disabled_tools and tool in disabled_tools:
desc = f"{tool}: BLOCKED"
result = {"error": f"Tool '{tool}' is disabled by user.", "exit_code": 1}
logger.info(f"Tool blocked by user: {tool}")
return desc, result
if tool_policy and tool_policy.blocks(tool):
desc = f"{tool}: BLOCKED"
result = {
"error": f"Execution of tool '{tool}' is forbade by the active guide-only policy.",
"exit_code": 1,
}
logger.warning("Tool policy blocked tool=%s", tool)
return desc, result
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if tool in _ADMIN_TOOLS and not _owner_is_admin(owner):
desc = f"{tool}: BLOCKED"
result = {"error": f"Tool '{tool}' requires an admin user.", "exit_code": 1}
logger.warning("Admin tool blocked for non-admin owner=%r tool=%s", owner, tool)
return desc, result
if is_public_blocked_tool(tool) and not _owner_is_admin(owner):
desc = f"{tool}: BLOCKED"
result = {
"error": (
f"Tool '{tool}' is restricted to admin users on this deployment. "
"Ask an admin to perform this action or grant the needed permission."
),
"exit_code": 1,
}
logger.warning("Public tool policy blocked owner=%r tool=%s", owner, tool)
return desc, result
Add ask_user tool: agent-posed multiple-choice questions (#2111) Let the agent pause and ask the user a multiple-choice question when a task is genuinely ambiguous and the answer changes what it does next — choosing between approaches, confirming an assumption, picking a target — instead of guessing. Modeled on the existing `ui_control` marker pattern: the `ask_user` tool returns an `ask_user` payload that the agent loop emits as an SSE event and then ends the turn. The frontend renders the question with clickable option buttons, a free-text "Other" input, and an x to dismiss; the user's choice is sent as the next message and the agent resumes with it in context. - src/tool_execution.py: `ask_user` handler — pure UI marker, no I/O. Validates a non-empty question + 2..6 options, normalizes string/object options, returns the payload. - src/agent_loop.py: emit the `ask_user` event and break the round loop so the turn ends and waits for the user's selection. Stream the question as assistant text so it persists/replays (prevents a re-ask loop). - Registration: TOOL_TAGS, ALWAYS_AVAILABLE, BUILTIN_TOOL_DESCRIPTIONS, FUNCTION_TOOL_SCHEMAS, the system-prompt blurb. Not admin-gated (any user can be asked); the structured args serialize via the default json.dumps path. - routes/chat_routes.py: relay the `ask_user` event to the client. - static/js/chat.js + static/style.css: render the question card (options + free-text Other + dismiss x; removed once answered). Reuses CSS vars and the .modal-close button; emoji go through the monochrome-SVG pipeline. Bump chat.js cache pin. - tests/test_ask_user_tool.py: payload, multi flag, string options, option cap, validation errors, serializer round-trip, registration.
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# ask_user: the agent poses a multiple-choice question to the user to get a
# decision/clarification. This is a pure UI-control marker — no subprocess,
# no filesystem. It returns an `ask_user` payload that the agent loop turns
# into an `ask_user` SSE event and then ENDS the turn, so the chat waits for
# the user's selection (their choice arrives as the next message).
if tool == "ask_user":
question, options, multi = "", [], False
raw = (content or "").strip()
try:
parsed = json.loads(raw) if raw else {}
Add ask_user tool: agent-posed multiple-choice questions (#2111) Let the agent pause and ask the user a multiple-choice question when a task is genuinely ambiguous and the answer changes what it does next — choosing between approaches, confirming an assumption, picking a target — instead of guessing. Modeled on the existing `ui_control` marker pattern: the `ask_user` tool returns an `ask_user` payload that the agent loop emits as an SSE event and then ends the turn. The frontend renders the question with clickable option buttons, a free-text "Other" input, and an x to dismiss; the user's choice is sent as the next message and the agent resumes with it in context. - src/tool_execution.py: `ask_user` handler — pure UI marker, no I/O. Validates a non-empty question + 2..6 options, normalizes string/object options, returns the payload. - src/agent_loop.py: emit the `ask_user` event and break the round loop so the turn ends and waits for the user's selection. Stream the question as assistant text so it persists/replays (prevents a re-ask loop). - Registration: TOOL_TAGS, ALWAYS_AVAILABLE, BUILTIN_TOOL_DESCRIPTIONS, FUNCTION_TOOL_SCHEMAS, the system-prompt blurb. Not admin-gated (any user can be asked); the structured args serialize via the default json.dumps path. - routes/chat_routes.py: relay the `ask_user` event to the client. - static/js/chat.js + static/style.css: render the question card (options + free-text Other + dismiss x; removed once answered). Reuses CSS vars and the .modal-close button; emoji go through the monochrome-SVG pipeline. Bump chat.js cache pin. - tests/test_ask_user_tool.py: payload, multi flag, string options, option cap, validation errors, serializer round-trip, registration.
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except (ValueError, TypeError):
parsed = {}
if isinstance(parsed, dict):
question = str(parsed.get("question", "")).strip()
multi = bool(parsed.get("multi") or parsed.get("multiSelect"))
for opt in (parsed.get("options") or []):
if isinstance(opt, dict):
label = str(opt.get("label", "")).strip()
descr = str(opt.get("description", "")).strip()
elif isinstance(opt, str):
label, descr = opt.strip(), ""
else:
continue
if label:
options.append({"label": label, "description": descr})
else:
question = raw
if not question or len(options) < 2:
return "ask_user: invalid", {
"error": (
"ask_user needs a non-empty `question` and at least 2 `options` "
"(each an object with a `label`, optional `description`)."
),
"exit_code": 1,
}
options = options[:6] # keep the choice list sane
desc = f"ask_user: {question[:80]}"
labels = ", ".join(o["label"] for o in options)
result = {
"ask_user": {"question": question, "options": options, "multi": multi},
"output": f"Asked the user: {question}\nOptions: {labels}\nAwaiting their selection.",
"exit_code": 0,
}
logger.info("Tool executed: %s (%d options, multi=%s)", desc, len(options), multi)
return desc, result
feat: Add plan mode to the chat agent (#638) * feat: Add plan mode to the chat agent Adds a plan mode: the agent investigates read-only, proposes a checklist, and waits for approval before changing anything. On approval it runs with full tools and checks items off as it goes. Enforcement reuses the existing disabled_tools gate. Includes a slash command: `/plan [on|off]` (and `/toggle plan`) to flip the plan toggle from the chat input. - src/tool_security.py, src/mcp_manager.py: read-only allowlist (tools + MCP). - src/agent_loop.py, routes/chat_routes.py: union the disabled set, prepend the plan directive, force agent mode. - static/: plan toggle pill, Approve & Run, dockable plan window, task-list checkboxes, and the /plan slash command. - tests/test_plan_mode.py. * Plan mode: persistent re-referenceable plan + agent write-back Three improvements so a long plan survives a weak model and stays in reach: 1. Re-reference the plan (out-of-context fix). On the execution turn the frontend sends the approved checklist back (`approved_plan`); the backend pins it as a top-of-context `## ACTIVE PLAN` system note (kept by the context trimmer), so the agent can always re-read the plan instead of losing the thread on a long run. New `build_active_plan_note()` (unit-tested). 2. Re-open / dock the plan anytime. The plan checklist is stored per-session (localStorage). When a plan exists, the plan-mode button opens a small menu ("Show plan" / "Plan mode: On/Off") that re-opens the side-dockable plan window — so it can stay docked while the agent works. The window live-refreshes as the plan changes. 3. Agent write-back: new `update_plan` tool. The agent calls it to tick steps `- [x]` after finishing them, or to revise steps when the user asks. Marker tool (no I/O) → `plan_update` SSE event → the stored plan + docked window update live. The ACTIVE PLAN note instructs the agent to use it. Backend: src/agent_loop.py (param + pin + note builder + emit + prompt blurb), src/tool_execution.py (update_plan handler), routes/chat_routes.py (parse `approved_plan`, relay `plan_update`), registration in tool_schemas / agent_tools / tool_index (always-available, not admin-gated). Frontend: static/js/chat.js (plan store, send `approved_plan`, handle `plan_update`, capture restated checklists), static/app.js (plan-button menu), static/js/planWindow.js (`isPlanWindowOpen`), static/js/storage.js (PLAN key). Tests: tests/test_plan_mode.py (plan-note), tests/test_update_plan_tool.py. * Plan mode: drop bash/python, rely on read-only discovery tools Shell can mutate (write files, hit the network) and can't be constrained to read-only at the tool layer, so plan mode no longer relies on a prompt to keep it well-behaved — bash/python are removed from the read-only allowlist and added to the fail-closed block set. Discovery is covered by the dedicated read-only tools (read_file, grep, glob, ls) instead. Rewrites the plan-mode directive to state shell is disabled and lists the available read-only tools positively. Addresses review feedback on #638. Co-Authored-By: Claude Opus 4.8 <noreply@anthropic.com> * Comment: note _MCP_READONLY_VERBS are prefixes not whole words Clarifies that entries like "summar" are intentional stems matched via startswith (covers summarise/summarize/summary), not typos. Addresses review feedback on #638. Co-Authored-By: Claude Opus 4.8 <noreply@anthropic.com> * Plan mode: clarify why gating inverts the allowlist into a denylist Rename _PLAN_MODE_FALLBACK_BLOCK -> _PLAN_MODE_KNOWN_MUTATORS and rewrite the comments. The tool gate is a denylist (disabled_tools); plan mode's policy is an allowlist, so it returns the inverse (all known tool names minus the allowlist). The static mutator set is a backstop for the schema-derived name list, which misses XML-only tools and can fail to import. Addresses review feedback on #638. Co-Authored-By: Claude Opus 4.8 <noreply@anthropic.com> * Plan mode: stop hardcoding the read-only tool list in the directive The model is already shown its available (read-only) tools by _assemble_prompt, which removes every disabled tool. Enumerating them again in the directive only duplicated that list and would drift as tools change. Point at the tools listed below instead. Addresses review feedback on #638.
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# update_plan: the agent writes back to the active plan — tick an item done
# or revise steps (e.g. when the user asks to change something). Pure UI
# marker: returns a `plan_update` payload the agent loop turns into a
# `plan_update` SSE event; the frontend replaces the stored plan and refreshes
# the docked plan window. Does NOT end the turn.
if tool == "update_plan":
import json as _json
raw = (content or "").strip()
plan = ""
try:
parsed = _json.loads(raw) if raw else {}
except (ValueError, TypeError):
parsed = {}
if isinstance(parsed, dict) and parsed.get("plan"):
plan = str(parsed.get("plan", "")).strip()
else:
# Plain-string call (raw checklist) or JSON without a usable `plan`.
plan = raw
if not plan:
return "update_plan: invalid", {
"error": "update_plan needs a non-empty `plan` (the full updated checklist as markdown).",
"exit_code": 1,
}
plan = plan[:8192]
done = plan.count("- [x]") + plan.count("- [X]")
total = done + plan.count("- [ ]")
desc = f"update_plan: {done}/{total} done" if total else "update_plan"
result = {
"plan_update": {"plan": plan},
"output": f"Plan updated ({done}/{total} steps complete)." if total else "Plan updated.",
"exit_code": 0,
}
logger.info("Tool executed: %s", desc)
return desc, result
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# Background execution: a `bash` block whose first line is the `#!bg`
# marker runs DETACHED — returns a job id immediately so the chat stream
# isn't held open for a multi-minute install/ffmpeg/download. The always-on
# monitor re-invokes the agent with the full output when the job finishes.
if tool == "bash" and session_id:
_is_bg, _bg_cmd = _split_bg_marker(content)
if _is_bg and _bg_cmd:
from src import bg_jobs
rec = bg_jobs.launch(_bg_cmd, session_id=session_id, cwd=_AGENT_WORKDIR)
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short = _bg_cmd.strip().split(chr(10))[0][:80]
desc = f"bash (background): {short}"
result = {
"output": (
f"Started background job `{rec['id']}`. It is running detached — "
f"do NOT wait for it or poll it. You will be automatically re-invoked "
f"with its full output when it finishes. Continue with other work, or "
f"end your turn now and resume when the result arrives."
),
"exit_code": 0,
"bg_job_id": rec["id"],
}
logger.info(f"Tool executed: {desc} -> bg job {rec['id']}")
return desc, result
# Route MCP-extracted tools through the MCP manager. Forward
# the progress callback so long-running subprocess tools
# (bash, python) can stream `tool_progress` events to the UI.
if tool in _MCP_TOOL_MAP:
first_line = content.split(chr(10))[0][:80]
desc = f"{tool}: {first_line}"
result = await _call_mcp_tool(tool, content, progress_cb=progress_cb)
elif tool in ("grep", "glob", "ls"):
# Code-navigation tools — no MCP server; run the direct implementation.
first_line = content.split(chr(10))[0][:80]
desc = f"{tool}: {first_line}"
result = await _direct_fallback(tool, content, progress_cb=progress_cb) \
or {"error": f"{tool}: execution failed", "exit_code": 1}
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elif tool == "create_document":
title = content.split("\n")[0].strip()[:60]
desc = f"create_document: {title}"
result = await do_create_document(content, session_id=session_id, owner=owner)
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elif tool == "update_document":
desc = f"update_document: {content.split(chr(10))[0][:60]}"
result = await do_update_document(content, owner=owner)
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elif tool == "edit_document":
result = await do_edit_document(content, owner=owner)
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desc = f"edit_document: {result.get('title', '')}"
elif tool == "suggest_document":
result = await do_suggest_document(content, owner=owner)
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desc = f"suggest_document: {result.get('count', 0)} suggestions"
elif tool == "search_chats":
query = content.split("\n")[0].strip()
desc = f"search_chats: {query[:80]}"
result = await do_search_chats(query, owner=owner)
elif tool in ("chat_with_model", "create_session", "list_sessions",
"send_to_session", "pipeline",
"manage_session", "manage_memory", "list_models",
"ui_control", "ask_teacher"):
from src.ai_interaction import dispatch_ai_tool
desc, result = await dispatch_ai_tool(tool, content, session_id, owner=owner)
elif tool == "manage_tasks":
desc = "manage_tasks"
result = await do_manage_tasks(content, owner=owner)
elif tool == "manage_skills":
desc = "manage_skills"
result = await do_manage_skills(content, owner=owner)
elif tool == "api_call":
first_line = content.split("\n")[0].strip()[:60]
desc = f"api_call: {first_line}"
result = await do_api_call(content)
elif tool == "manage_endpoints":
desc = "manage_endpoints"
result = await do_manage_endpoints(content, owner=owner)
elif tool == "manage_mcp":
desc = "manage_mcp"
result = await do_manage_mcp(content, owner=owner)
elif tool == "manage_webhooks":
desc = "manage_webhooks"
result = await do_manage_webhooks(content, owner=owner)
elif tool == "manage_tokens":
desc = "manage_tokens"
result = await do_manage_tokens(content, owner=owner)
elif tool == "manage_documents":
desc = "manage_documents"
result = await do_manage_documents(content, owner=owner)
elif tool == "manage_settings":
desc = "manage_settings"
result = await do_manage_settings(content, owner=owner)
elif tool == "manage_notes":
desc = "manage_notes"
result = await do_manage_notes(content, owner=owner)
elif tool == "manage_calendar":
desc = "manage_calendar"
result = await do_manage_calendar(content, owner=owner)
elif tool == "download_model":
desc = "download_model"
result = await do_download_model(content, owner=owner)
elif tool == "serve_model":
desc = "serve_model"
result = await do_serve_model(content, owner=owner)
elif tool == "list_served_models":
desc = "list_served_models"
result = await do_list_served_models(content, owner=owner)
elif tool == "stop_served_model":
desc = "stop_served_model"
result = await do_stop_served_model(content, owner=owner)
cookbook agent debug loop: persistent log files, auto-adopt orphan tmux, Codex/Claude skill parity Three converging fixes so the chat agent + external Codex/Claude skills can actually debug a crashed serve instead of staring at a post-crash neofetch banner: * Serves now `tee` to /tmp/odysseus-tmux/SESSION.log on the host running them. Runner saves fds 3/4 before the tee and restores them right before `exec ${SHELL}`, so the post-crash interactive zsh banner does NOT pollute the log file. * `tail_serve_output` (chat agent) and `/api/codex/cookbook/output/{sid}` (Codex+Claude skills) both prefer the persistent log file over the tmux pane. Pane is fallback for sessions predating the tee runner. Default tail bumped 150 -> 400. * `list_served_models` "recent log" snippet seeks to the Traceback line instead of showing the last 6 lines (which was always the bash prompt). Cookbook auto-adoption sweep on `/api/cookbook/tasks/status`: every 20s (rate-limited) the cookbook SSHes each configured server, finds `serve-*` / `cookbook-*` tmux sessions running an actual model process (vllm/python/llama-server/etc., filtered via `pane_current_command`), and writes them into state.tasks. So when the agent falls back to raw ssh+tmux, the session appears in the Cookbook UI on the next poll. `serve_model` error path now reads `data["detail"]` in addition to `data["error"]` so the FastAPI HTTPException message ("Invalid characters in cmd") actually reaches the agent instead of being swallowed as a generic "Serve failed". Tool description updated to warn against `cd …`/`source …`/`&&` prefixes. Intent-without-action supervisor in agent_loop: when the model writes "Let me tail the output" / "I'll check the logs" / "Let me investigate" and ends the turn without emitting a tool call, the loop injects a sharp system nudge ("You said you would X — DO IT NOW") and continues. Capped at 2 nudges per chat so a model that genuinely cannot use the tool does not pin the loop. Codex/Claude skill parity: adds `/cookbook/cached`, `/cookbook/presets`, `/cookbook/preset/{name}`, `/cookbook/adopt` so external agents have the same surface as the chat agent. SKILL.md docs + odysseus_api.py wrapper updated for both bundles. `adopt_served_model` promoted to the always-on tool set so the agent has a documented fallback when serve_model rejects a cmd. Also various cookbook UI tweaks accumulated alongside the above (cookbook.js, cookbookRunning.js, cookbookServe.js, cookbook-diagnosis.js, settings.js, style.css).
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elif tool == "tail_serve_output":
desc = "tail_serve_output"
result = await do_tail_serve_output(content, owner=owner)
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elif tool == "list_downloads":
desc = "list_downloads"
result = await do_list_downloads(content, owner=owner)
elif tool == "cancel_download":
desc = "cancel_download"
result = await do_cancel_download(content, owner=owner)
elif tool == "search_hf_models":
desc = "search_hf_models"
result = await do_search_hf_models(content, owner=owner)
elif tool == "list_cached_models":
desc = "list_cached_models"
result = await do_list_cached_models(content, owner=owner)
elif tool == "app_api":
desc = "app_api"
result = await do_app_api(content, owner=owner)
elif tool == "list_serve_presets":
desc = "list_serve_presets"
result = await do_list_serve_presets(content, owner=owner)
elif tool == "serve_preset":
desc = "serve_preset"
result = await do_serve_preset(content, owner=owner)
elif tool == "adopt_served_model":
desc = "adopt_served_model"
result = await do_adopt_served_model(content, owner=owner)
elif tool == "list_cookbook_servers":
desc = "list_cookbook_servers"
result = await do_list_cookbook_servers(content, owner=owner)
elif tool == "edit_image":
desc = "edit_image"
result = await do_edit_image(content, owner=owner)
elif tool == "edit_file":
result = await _direct_fallback(tool, content, workspace=workspace) or {"error": "edit failed", "exit_code": 1}
desc = result.get("output") or result.get("error") or "edit_file"
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elif tool == "trigger_research":
desc = "trigger_research"
result = await do_trigger_research(content, owner=owner)
elif tool == "manage_research":
desc = "manage_research"
result = await do_manage_research(content, owner=owner)
elif tool == "resolve_contact":
desc = "resolve_contact"
result = await do_resolve_contact(content, owner=owner)
elif tool == "manage_contact":
desc = "manage_contact"
result = await do_manage_contact(content, owner=owner)
elif tool == "vault_search":
desc = "vault_search"
result = await do_vault_search(content, owner=owner)
elif tool == "vault_get":
desc = "vault_get"
result = await do_vault_get(content, owner=owner)
elif tool == "vault_unlock":
desc = "vault_unlock"
result = await do_vault_unlock(content, owner=owner)
elif tool.startswith("mcp__"):
# MCP tool dispatch
mcp = get_mcp_manager()
if mcp:
try:
args = json.loads(content) if content.strip().startswith("{") else {}
except (json.JSONDecodeError, TypeError):
args = {}
desc = f"mcp: {tool}"
result = await mcp.call_tool(tool, args)
else:
desc = f"mcp: {tool}"
result = {"error": "MCP manager not available", "exit_code": 1}
else:
desc = f"unknown: {tool}"
result = {"error": f"Unknown tool type: {tool}", "exit_code": 1}
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logger.info(f"Tool executed: {desc} -> exit_code={result.get('exit_code', 'n/a')}")
return desc, result
# ---------------------------------------------------------------------------
# Result formatting
# ---------------------------------------------------------------------------
# Keys handled by the dedicated branches below — never echo them as raw JSON.
_FORMATTER_HANDLED_KEYS = {
"stdout", "stderr", "exit_code", "content", "size",
"response", "results", "session_id", "name", "model", "session_name",
"success", "path", "action", "title", "doc_id", "version", "applied",
"error", "output",
}
def format_tool_result(description: str, result: Dict) -> str:
"""Format a tool result into text for feeding back to the LLM."""
parts = [f"### {description}"]
if "stdout" in result:
if result["stdout"]:
parts.append(f"**stdout:**\n```\n{result['stdout']}\n```")
if result["stderr"]:
parts.append(f"**stderr:**\n```\n{result['stderr']}\n```")
parts.append(f"**exit_code:** {result.get('exit_code', 'unknown')}")
elif "output" in result:
# bash / python canonical result shape: {"output": ..., "exit_code": ...}
parts.append(f"```\n{result['output']}\n```")
if result.get("exit_code") not in (0, None):
parts.append(f"**exit_code:** {result['exit_code']}")
elif "content" in result:
parts.append(f"**content ({result.get('size', '?')} chars):**\n```\n{result['content']}\n```")
elif "response" in result:
model = result.get("model", result.get("session_name", ""))
if model:
parts.append(f"**{model} responded:**\n{result['response']}")
else:
parts.append(result["response"])
elif "results" in result:
parts.append(result["results"])
elif "session_id" in result and "name" in result:
parts.append(f"Session created: **{result['name']}** (id: `{result['session_id']}`, model: {result.get('model', 'unknown')})")
elif "success" in result:
if result["success"]:
parts.append(f"File written: {result['path']} ({result['size']} bytes)")
else:
parts.append(f"Error: {result.get('error', 'unknown')}")
elif "action" in result:
action = result["action"]
if action == "create":
parts.append(f"Document created: \"{result.get('title', '')}\" (id: {result['doc_id']}, v{result['version']})")
elif action == "update":
parts.append(f"Document updated: \"{result.get('title', '')}\" (v{result['version']})")
elif action == "edit":
parts.append(f'Document edited: "{result.get("title", "")}" (v{result.get("version", "?")}, {result.get("applied", 0)} edit(s) applied)')
elif "error" in result:
parts.append(f"**Error:** {result['error']}")
# Surface any additional structured payload (events, tasks, notes, calendars,
# documents, attachments, etc.) that the dedicated branches above don't show.
# Without this, tools that return {"response": "...", "events": [...]} would
# silently drop the events list and the model would only see the summary line.
extra = {k: v for k, v in result.items() if k not in _FORMATTER_HANDLED_KEYS}
if extra:
try:
extra_json = json.dumps(extra, indent=2, default=str, ensure_ascii=False)
# Cap to avoid blowing the context window on huge payloads.
if len(extra_json) > 8000:
extra_json = extra_json[:8000] + f"\n... (truncated, {len(extra_json)} chars total)"
parts.append(f"**data:**\n```json\n{extra_json}\n```")
except (TypeError, ValueError):
pass
return "\n".join(parts)