host
f9a77577c5
- RoutingPolicy.fallback_models: move the cleaning loop into __init__ (it was unreachable after tier_mode's return, so the attribute never existed); still parsed but not applied. - Capability fallback: RoutingDecision gains capability_fallback; the worker audits worker_capability_fallback when it fires, so a tier-model rejection followed by a worker-default fallback is no longer invisible (source stays worker_default, no model_routing event, so shadow samples are unaffected). - Registry: resolve_registry_agent checks skills directly against the loaded definition instead of re-parsing a hand-built dict, so future fields cannot be silently dropped the way tool_approvals was in v9. - Shadow: evaluate_shadow sorts rows earliest-first by created_at (id tiebreak) when timestamps are present, so dedup no longer depends on caller row order; the server path (which strips created_at) is unchanged. - Gateway: when sub2api fills the default model, the worker now checks it against the tier's capability requirements with the same mode semantics as v8 -- shadow only audits, active fails fast with worker_capability_gate_rejected and never calls the runner. No worker-default fallback here: the gateway model is the task's gateway model, falling back to a worker default would run against the wrong gateway.
522 lines
24 KiB
Python
522 lines
24 KiB
Python
"""Declarative agent definitions (harness alignment, v1).
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Zero third-party dependencies; standard library only.
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A definition is a versioned JSON document that bundles the fields a
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task already understands -- model, effort, approval, executor, plan --
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plus a human-written instruction block and a list of required skills.
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It is the Foundry-style "agent defined declaratively, harness runs
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the loop" idea, cut down to what this codebase can actually honour
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today, with every boundary written down instead of implied:
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* Resolution happens at task creation (``POST /tasks`` with an inline
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``agent`` object). Per field, an explicit body field wins over the
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definition; a field absent from both stays NULL and follows the
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existing worker-default chain. A definition that sets ``model``
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therefore makes the task explicit for rule routing v0, and a
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definition that leaves ``model`` empty leaves routing to the worker.
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* Instructions reach the model only as a marked header prepended to
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the stored prompt (``[Agent: <name>]``), because the dispatch path
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(``cloud/sandbox.py`` -> ``host.py dispatch``) has no
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developerInstructions channel today. This is NOT claimed to be
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``developerInstructions``: it is visible in the stored prompt, in
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the creation response, and in every later prompt read. Instructions
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are rejected with goal tasks and coordinator tasks, where the
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prompt is a goal or is never run as a model turn, so the header
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cannot silently pollute a goal text or a coordinator parent.
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* Skills are validated against the ``cloud/skills/`` pack at parse
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time (an unknown skill is a 400, never a silent drop). Seeding
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itself stays worker-global (``--seed-skills`` seeds the whole pack
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into the tenant codex-home); v1 does NOT claim per-agent seeding.
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When seeding is off, a listed skill is recorded in the creation
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response but is not guaranteed present at run time -- that is the
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documented v1 boundary, not a bug to be discovered later.
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* Routing policy is deliberately NOT part of a definition in v1:
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routing stays a worker-global policy (see ``cloud/models.py``).
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Putting a routing object inside a definition would either be
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silently dropped (a lie) or need a per-task policy column that does
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not exist. A definition that contains ``routing`` / ``routing_policy``
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is rejected, so a typo or a v2 idea cannot masquerade as v1 behaviour.
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* Unknown top-level or defaults keys are rejected for the same
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reason: a declarative file must fail on typos, not silently ignore
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them.
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v2 (this file, plus server/worker/sandbox/host changes) closes the
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three v1 boundaries that were explicitly left open:
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* Registry: definitions can live as files under repo-root ``agents/``
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(``default_agents_dir()``) and be referenced by name
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(``agent_name``) instead of only inline. The server never loads a
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definition from a caller-supplied path; it only loads
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``<name>.json`` under the registry dir, with the traversal guard
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in ``load_agent_definition``.
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* Persistence: ``tasks`` gains ``agent_name`` / ``agent_version`` /
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``agent_instructions`` columns (see ``migrate_tasks_table``), so
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``GET /tasks`` shows the definition identity and instructions, and
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the worker no longer has to reconstruct them from a prompt header.
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* Real channel: for local/docker executors, instructions travel as
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``--developer-instructions`` on the host.py top-level command,
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through ``sandbox`` into ``host.py dispatch`` ->
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``create_thread(developerInstructions=...)``. The stored prompt
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stays clean. For the generic cloud executor, which has no such
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channel, the worker falls back to the v1 marked header in the
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in-memory dispatch prompt only (DB prompt stays clean) -- that
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fallback is the documented cloud boundary, not a claim that the
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cloud runner honours developerInstructions. Goal and coordinator
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tasks still reject instructions in v2: the goal loop and the
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coordinator parent do not pass instructions through, and silently
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dropping them there would be worse than rejecting them.
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v3 (tools, this file plus server/worker changes) adds a ``tools``
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field, but deliberately as REQUIRED tools, not an allowlist:
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* A definition may declare ``tools`` from the MCP servers this
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codebase actually seeds: ``websearch``, ``imagegen``,
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``playwright``. An unknown tool is a 400, never a silent drop.
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* The worker does NOT claim to block other tools. Blocking would
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need a tool allowlist in ``create_thread`` (which does not exist)
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or per-task codex-homes (tenant homes are per-project and shared,
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and MCP seeding is worker-global), so an allowlist claim would be
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false. What v3 does instead is fail fast on the opposite, silent
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failure: a task that declares a required tool but whose tenant
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codex-home config.toml has no ``[mcp_servers.<tool>]`` section
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at dispatch time is marked failed before any model call, with the
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missing tools named. The check runs only for tenant-isolated
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Docker workers, where the tenant config.toml is the actual
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availability record (worker flags alone would be wrong: a section
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seeded earlier by another worker persists in the shared tenant
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home). Local/cloud workers skip the check and are documented as
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such, not as "tools verified".
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v9 (tool approvals, this file plus server/worker changes) adds a
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``tool_approvals`` field, and it is deliberately narrower than
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"per-tool approval enforcement" -- the execution layer cannot
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honour that today, and this track says so instead of claiming it:
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* Declaration: ``tool_approvals`` maps a known tool to one of
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``ask`` / ``auto`` / ``deny``. Unknown tools and unknown
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policies are 400s. A tool declared both required (``tools``)
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and ``deny`` is a 400: a task that must use a tool it must
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never be allowed to use is a contradiction, not a policy.
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* What is NOT claimed: per-tool interception in host.py.
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``host.py _answer_server_request`` answers every elicitation
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with one global switch (``approval == "auto"`` accepts,
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anything else declines) and the elicitation message carries
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no tool/server identity this codebase can rely on, so host
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cannot tell which tool is asking. The approvals queue
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(``cloud/approvals.py``) is record-only today: host runs in
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a worker-spawned subprocess with no control-plane channel,
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so there is no "ask the user" path at runtime. Credential
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scope is NOT part of this field either: MCP credentials live
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in the tenant codex-home config / env, per-project, not
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per-task, and declaring a scope here without a channel to
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enforce it would be the same kind of lie.
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* What IS executed: the declaration is validated at creation,
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persisted (tasks.agent_tool_approvals, JSON object, NULL =
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undeclared), echoed in GET and the creation response, and
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at dispatch the worker audits the declaration together with
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its effective outcome under today's host behaviour
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(``effective_tool_approvals``): ``auto`` + host-auto accepts,
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everything else declines, and ``ask`` is reported as
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"decline (no ask channel)" -- never as "will ask". The
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worker also re-checks the required+deny contradiction at
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dispatch (defence in depth for rows written before v9 or
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outside the API) and fails fast before any model call, in
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the v3 tools-gate shape. Goal and coordinator tasks reject
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tool_approvals at creation, like instructions: their turns
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do not pass the declaration through, so accepting it there
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would be a silent drop.
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"""
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import json
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import os
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import re
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from pathlib import Path
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MAX_INSTRUCTIONS_CHARS = 8000
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MAX_DESCRIPTION_CHARS = 1000
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MAX_SKILLS = 20
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NAME_RE = re.compile(r"^[a-z0-9][a-z0-9_-]{0,63}$")
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EFFORTS = ("low", "medium", "high", "xhigh", "max")
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APPROVALS = ("ask", "auto", "full")
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EXECUTORS = ("local", "docker", "cloud")
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_TOP_LEVEL_KEYS = ("name", "version", "description", "instructions",
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"skills", "tools", "tool_approvals", "defaults")
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KNOWN_TOOLS = ("websearch", "imagegen", "playwright")
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TOOL_APPROVAL_POLICIES = ("ask", "auto", "deny")
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_DEFAULTS_KEYS = ("model", "effort", "approval", "executor", "plan")
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SKILLS_DIR = Path(__file__).resolve().parent / "skills"
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AGENTS_DIR = Path(__file__).resolve().parent.parent / "agents"
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def default_agents_dir():
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"""Repo-root ``agents/`` directory (definitions live with code)."""
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return AGENTS_DIR
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def list_known_skills(skills_dir=None):
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"""Sorted skill names present in the pack (dirs with SKILL.md)."""
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base = Path(skills_dir) if skills_dir else SKILLS_DIR
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names = []
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try:
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entries = sorted(base.iterdir())
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except OSError:
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return names
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for entry in entries:
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if entry.is_dir() and (entry / "SKILL.md").is_file():
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names.append(entry.name)
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return names
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def _clean_str(value, field):
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if not isinstance(value, str) or not value.strip():
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raise ValueError("%s must be a non-empty string" % field)
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return value.strip()
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class AgentDefinition:
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"""A parsed, validated agent definition."""
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def __init__(self, name, version=None, description=None,
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instructions=None, skills=None, tools=None,
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tool_approvals=None, defaults=None):
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self.name = name
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self.version = version
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self.description = description
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self.instructions = instructions
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self.skills = list(skills or [])
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self.tools = list(tools or [])
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self.tool_approvals = dict(tool_approvals or {})
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self.defaults = dict(defaults or {})
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def to_summary(self):
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"""The identity echoed in the task-creation response."""
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return {
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"name": self.name,
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"version": self.version,
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"skills": list(self.skills),
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"tools": list(self.tools),
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"tool_approvals": dict(self.tool_approvals),
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}
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def effective_tool_approvals(tool_approvals, host_approval):
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"""What host.py will actually answer per declared tool, today.
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``host_approval`` is the host subprocess's global approval
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(``"auto"`` or anything else, default ``"deny"``): host
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accepts an elicitation only when it is exactly ``"auto"``.
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Returns {tool: {"declared": policy, "effective": "accept" /
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"decline", "ask_honoured": bool}} -- ``ask_honoured`` is
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always False, because there is no ask channel at runtime:
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a declared ``ask`` currently declines, it does not ask.
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Reporting that plainly is the point of this function; a
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caller that presented ``ask`` as "will ask" would be lying.
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Unknown tools in the input are ignored (parse already
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rejects them at creation; this function is defensive).
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"""
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out = {}
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host_auto = host_approval == "auto"
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for tool, policy in (tool_approvals or {}).items():
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if tool not in KNOWN_TOOLS or policy not in TOOL_APPROVAL_POLICIES:
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continue
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if policy == "auto" and host_auto:
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out[tool] = {"declared": policy, "effective": "accept",
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"ask_honoured": False}
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else:
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out[tool] = {"declared": policy, "effective": "decline",
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"ask_honoured": False}
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return out
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def missing_required_tools(tools, config_text):
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"""Tools declared but with no [mcp_servers.<tool>] section.
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Pure function over the tenant config.toml text. An empty or
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missing config means every declared tool is missing -- a task
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that needs a tool must not run on a home that never seeded it.
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"""
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text = config_text or ""
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return [t for t in (tools or [])
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if ("[mcp_servers.%s]" % t) not in text]
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def parse_agent_definition(data, known_skills=None):
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"""Parse and validate one definition, or raise ValueError.
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``known_skills`` is a list of pack skill names; when it is not
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None, every listed skill must be in it. Passing None skips the
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existence check (pure-function tests), it never invents a skill.
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"""
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if not isinstance(data, dict):
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raise ValueError("agent must be an object")
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unknown = sorted(set(data) - set(_TOP_LEVEL_KEYS))
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if unknown:
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raise ValueError("agent has unknown field(s): %s"
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% ", ".join(unknown))
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name = data.get("name")
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if not isinstance(name, str) or not NAME_RE.match(name):
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raise ValueError(
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"agent.name must match [a-z0-9][a-z0-9_-]{0,63}")
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version = None
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if data.get("version") is not None:
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version = _clean_str(data.get("version"), "agent.version")
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if len(version) > 64:
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raise ValueError("agent.version is too long")
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description = None
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if data.get("description") is not None:
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if not isinstance(data.get("description"), str):
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raise ValueError("agent.description must be a string")
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description = data["description"]
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if len(description) > MAX_DESCRIPTION_CHARS:
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raise ValueError("agent.description is too long")
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instructions = None
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if data.get("instructions") is not None:
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if not isinstance(data.get("instructions"), str):
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raise ValueError("agent.instructions must be a string")
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instructions = data["instructions"].strip()
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if not instructions:
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instructions = None
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elif len(instructions) > MAX_INSTRUCTIONS_CHARS:
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raise ValueError(
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"agent.instructions exceeds %d characters"
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% MAX_INSTRUCTIONS_CHARS)
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skills = []
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if data.get("skills") is not None:
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raw_skills = data.get("skills")
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if not isinstance(raw_skills, list):
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raise ValueError("agent.skills must be a list")
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if len(raw_skills) > MAX_SKILLS:
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raise ValueError("agent.skills has too many entries")
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for skill in raw_skills:
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skill = _clean_str(skill, "agent.skills entry")
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if skill not in skills:
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skills.append(skill)
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if known_skills is not None:
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known = set(known_skills)
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missing = [s for s in skills if s not in known]
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if missing:
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raise ValueError("agent.skills unknown skill(s): %s"
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% ", ".join(missing))
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tools = []
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if data.get("tools") is not None:
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raw_tools = data.get("tools")
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if not isinstance(raw_tools, list):
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raise ValueError("agent.tools must be a list")
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for tool in raw_tools:
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tool = _clean_str(tool, "agent.tools entry")
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if tool not in tools:
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tools.append(tool)
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unknown_tools = [t for t in tools if t not in KNOWN_TOOLS]
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if unknown_tools:
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raise ValueError("agent.tools unknown tool(s): %s"
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% ", ".join(unknown_tools))
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tool_approvals = {}
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if data.get("tool_approvals") is not None:
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raw_approvals = data.get("tool_approvals")
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if not isinstance(raw_approvals, dict):
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raise ValueError("agent.tool_approvals must be an object")
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for tool, policy in raw_approvals.items():
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tool = _clean_str(tool, "agent.tool_approvals tool")
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if tool not in KNOWN_TOOLS:
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raise ValueError(
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"agent.tool_approvals unknown tool(s): %s" % tool)
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if policy not in TOOL_APPROVAL_POLICIES:
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raise ValueError(
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"agent.tool_approvals[%s] must be ask, auto or deny"
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% tool)
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tool_approvals[tool] = policy
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denied_required = [t for t in tools
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if tool_approvals.get(t) == "deny"]
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if denied_required:
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raise ValueError(
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"agent.tool_approvals denies required tool(s): %s"
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% ", ".join(denied_required))
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defaults = {}
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if data.get("defaults") is not None:
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raw_defaults = data.get("defaults")
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if not isinstance(raw_defaults, dict):
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raise ValueError("agent.defaults must be an object")
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unknown_defaults = sorted(set(raw_defaults) - set(_DEFAULTS_KEYS))
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if unknown_defaults:
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raise ValueError("agent.defaults has unknown field(s): %s"
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% ", ".join(unknown_defaults))
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if raw_defaults.get("model") is not None:
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defaults["model"] = _clean_str(raw_defaults["model"],
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"agent.defaults.model")
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if raw_defaults.get("effort") is not None:
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if raw_defaults["effort"] not in EFFORTS:
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raise ValueError(
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"agent.defaults.effort must be low, medium, high,"
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" xhigh or max")
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defaults["effort"] = raw_defaults["effort"]
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if raw_defaults.get("approval") is not None:
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if raw_defaults["approval"] not in APPROVALS:
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raise ValueError(
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"agent.defaults.approval must be ask, auto or full")
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defaults["approval"] = raw_defaults["approval"]
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if raw_defaults.get("executor") is not None:
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if raw_defaults["executor"] not in EXECUTORS:
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raise ValueError(
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"agent.defaults.executor must be local, docker"
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" or cloud")
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defaults["executor"] = raw_defaults["executor"]
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if raw_defaults.get("plan") is not None:
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plan = raw_defaults["plan"]
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if plan not in (True, False, 0, 1):
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raise ValueError("agent.defaults.plan must be true/false")
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defaults["plan"] = 1 if plan in (True, 1) else 0
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if defaults.get("plan") and defaults.get("approval") == "full":
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raise ValueError(
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"plan 模式与 approval=full 冲突（只读 vs 完全访问）")
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return AgentDefinition(name=name, version=version,
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description=description,
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instructions=instructions, skills=skills,
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tools=tools, tool_approvals=tool_approvals,
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defaults=defaults)
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def load_agent_definition(path, agents_dir=None):
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"""Load one definition from a JSON file (operator/local use only).
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This is NOT an API path: the server never loads a definition from
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a caller-supplied path (that would be a file-read primitive). When
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``agents_dir`` is given, the resolved path must stay under it, so
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``../`` cannot escape the definitions directory.
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"""
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candidate = Path(path)
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if agents_dir is not None and not candidate.is_absolute():
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candidate = Path(agents_dir) / candidate
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resolved = candidate.resolve()
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if agents_dir is not None:
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base = Path(agents_dir).resolve()
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if base not in resolved.parents and resolved != base:
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raise ValueError("agent definition path escapes agents dir")
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try:
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with open(resolved, encoding="utf-8") as f:
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data = json.load(f)
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except OSError as exc:
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raise ValueError("cannot read agent definition: %s" % exc)
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except ValueError as exc:
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raise ValueError("agent definition is not valid JSON: %s" % exc)
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return parse_agent_definition(data)
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VERSION_RE = re.compile(r"^[A-Za-z0-9][A-Za-z0-9._-]{0,63}$")
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def resolve_registry_agent(name, version=None, agents_dir=None,
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known_skills=None):
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"""Resolve a registry definition, with optional version pinning.
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``name`` may be ``<name>`` or ``<name>@<version>``; ``version``
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may also be given separately. If both are given they must agree,
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otherwise this is a 400 (ambiguity is never guessed). Without a
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version, this loads ``<name>.json`` (the default/latest file).
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With a version, it loads ``<name>.<version>.json`` and requires
|
|
the file's own ``version`` field to equal the requested version:
|
|
a misnamed snapshot (file says v1, filename says v2) is rejected
|
|
instead of being used as the wrong version, and a missing snapshot
|
|
is an error, never a silent fallback to the default file (that
|
|
fallback would defeat pinning entirely). A version string is
|
|
restricted to alphanumerics, ``.``, ``_``, ``-`` so it cannot
|
|
carry a path separator or ``..``; the agents-dir traversal guard
|
|
in load_agent_definition is a second line of defence, not the only
|
|
one. Skill existence is checked only when ``known_skills`` is
|
|
given (the server passes the pack; pure-function callers may omit
|
|
it, exactly as with parse_agent_definition).
|
|
"""
|
|
if not isinstance(name, str) or not name.strip():
|
|
raise ValueError("agent_name must be a non-empty string")
|
|
name = name.strip()
|
|
pinned_from_name = None
|
|
if "@" in name:
|
|
parts = name.split("@")
|
|
if len(parts) != 2 or not parts[0] or not parts[1]:
|
|
raise ValueError(
|
|
"agent_name with @ must be <name>@<version>")
|
|
name, pinned_from_name = parts
|
|
if version is not None:
|
|
if not isinstance(version, str) or not version.strip():
|
|
raise ValueError("agent_version must be a non-empty string")
|
|
version = version.strip()
|
|
if pinned_from_name is not None:
|
|
if version is not None and version != pinned_from_name:
|
|
raise ValueError(
|
|
"agent_version %r does not match agent_name version "
|
|
"%r" % (version, pinned_from_name))
|
|
version = pinned_from_name
|
|
if not NAME_RE.match(name):
|
|
raise ValueError(
|
|
"agent_name must match [a-z0-9][a-z0-9_-]{0,63}")
|
|
if version is not None and not VERSION_RE.match(version):
|
|
raise ValueError(
|
|
"agent_version must match [A-Za-z0-9][A-Za-z0-9._-]{0,63}")
|
|
filename = "%s.json" % name if version is None else \
|
|
"%s.%s.json" % (name, version)
|
|
definition = load_agent_definition(filename, agents_dir=agents_dir)
|
|
if version is not None and definition.version != version:
|
|
raise ValueError(
|
|
"registry file %s says version %r, not requested %r" %
|
|
(filename, definition.version, version))
|
|
if known_skills is not None:
|
|
# Audit fix: skill existence is now checked directly
|
|
# against the loaded definition, instead of re-parsing
|
|
# a hand-built dict. The old re-parse silently dropped
|
|
# any field not copied into that dict (tool_approvals
|
|
# was lost exactly this way in v9, and the next new
|
|
# field would have been lost the same way). The
|
|
# original definition is returned unchanged, so future
|
|
# fields survive automatically. The error message is
|
|
# kept identical to parse_agent_definition's.
|
|
known = set(known_skills)
|
|
missing = [s for s in definition.skills if s not in known]
|
|
if missing:
|
|
raise ValueError("agent.skills unknown skill(s): %s"
|
|
% ", ".join(missing))
|
|
return definition
|
|
|
|
|
|
def resolve_task_fields(definition, body):
|
|
"""Resolve task fields: explicit body field wins, else definition.
|
|
|
|
``body`` is the task-creation body dict. Returns a dict with
|
|
``model``, ``effort``, ``approval``, ``executor`` (None when
|
|
absent from both) and ``plan`` (int 0/1). It never mutates either
|
|
input, and it never validates the resolved values -- the caller
|
|
(server task creation) runs its existing validation on them, so
|
|
a bad definition value is caught by the same 400 path as a bad
|
|
body value.
|
|
"""
|
|
body = body if isinstance(body, dict) else {}
|
|
defaults = definition.defaults if definition else {}
|
|
resolved = {}
|
|
for field in ("model", "effort", "approval", "executor"):
|
|
value = body.get(field)
|
|
if value is None:
|
|
value = defaults.get(field)
|
|
resolved[field] = value
|
|
if "plan" in body:
|
|
resolved["plan"] = 1 if body.get("plan") in (True, 1) else 0
|
|
elif defaults.get("plan") is not None:
|
|
resolved["plan"] = defaults["plan"]
|
|
else:
|
|
resolved["plan"] = 0
|
|
return resolved
|
|
|
|
|
|
def format_instructions_header(definition, prompt):
|
|
"""Prepend a definition's instructions to a prompt, marked.
|
|
|
|
Returns the original prompt unchanged when there is no definition
|
|
or no instructions, so callers that do not use definitions pay
|
|
nothing and see no header.
|
|
"""
|
|
if definition is None or not definition.instructions:
|
|
return prompt
|
|
header = "[Agent: %s" % definition.name
|
|
if definition.version:
|
|
header += " %s" % definition.version
|
|
header += "]"
|
|
return "%s\n%s\n\n%s" % (header, definition.instructions, prompt)
|