mirror of
https://github.com/langchain-ai/deepagents.git
synced 2026-07-24 20:35:31 -04:00
Mason Daugherty
4600365ea0
Approval mode now has a live control path for remote `dcode` sessions instead of relying only on the run-context snapshot captured at stream start. That lets mid-run toggles take effect before the next gated tool call, while failing closed to manual approval when the live state cannot be synced or read. ## Changes - **Live approval state:** Added `approval_mode_key`, `approval_mode_payload`, `read_approval_mode_from_store`, and `awrite_approval_mode` to store per-thread auto-approve state in the LangGraph Store under a deterministic hashed thread key. - **Interrupt decisions:** Updated `_should_interrupt_tool_call` to prefer the live store value over stale context, so toggling from auto-approve back to manual can interrupt subsequent gated tool calls in the same active run. - **TUI synchronization:** Updated `DeepAgentsApp.action_toggle_auto_approve` and `_on_auto_approve_enabled` to write the live approval mode whenever the user toggles approval behavior. - **Fail-closed behavior:** If manual approval cannot sync while an agent is running, the app warns the user and cancels the active run rather than allowing stale auto-approval to continue. - **Remote support:** Added `RemoteAgent.aput_store_item` so remote sessions can write unindexed LangGraph Store records used by the server-side approval predicate. - **Context reconstruction:** Preserved `auto_approve` and `approval_mode_key` when dict-based runtime context is reconstructed into `CLIContextSchema`. - **Stream propagation:** Updated `execute_task_textual` to write live approval state before each stream iteration and await async auto-approve callbacks triggered from approval prompts.
90 lines
3.4 KiB
Python
90 lines
3.4 KiB
Python
"""Lightweight runtime context types for the CLI agent graph.
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Carries per-run overrides (model swap/params, approval mode) passed via
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`context=`. Extracted from `configurable_model` so hot-path modules (`app`,
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`textual_adapter`) can import `CLIContext` without pulling in the langchain
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middleware stack.
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"""
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from __future__ import annotations
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from dataclasses import dataclass, field
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from typing import Any, TypedDict
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@dataclass
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class CLIContextSchema:
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"""Declared `context_schema` for the agent graph.
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Registered via `context_schema=` when the graph is built, so LangGraph
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coerces each run's `context=` payload into this dataclass — in-process,
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`runtime.context` is a `CLIContextSchema` instance.
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It exists alongside `CLIContext` (below) because the payload is shaped
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differently on each side of the API boundary: in-process it is coerced to
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this dataclass, but over the LangGraph API server (RemoteGraph) it is
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serialized to JSON and arrives as a plain dict. Consumers
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(`configurable_model._get_context`, `_should_interrupt_tool_call`)
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therefore accept both shapes. `CLIContext` is the client-facing builder for
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constructing that payload.
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Fields mirror `CLIContext`; see its per-field docstrings for semantics.
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"""
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model: str | None = None
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model_params: dict[str, Any] = field(default_factory=dict)
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effective_model: str | None = None
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auto_approve: bool = False
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approval_mode_key: str | None = None
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class CLIContext(TypedDict, total=False):
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"""Client-facing builder for the per-run graph context payload.
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Callers populate this and pass it via `context=` to `astream`/`ainvoke`.
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`ConfigurableModelMiddleware` and the `interrupt_on` `when` predicate read
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it from `request.runtime.context`. In-process LangGraph coerces it into
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`CLIContextSchema` (the registered `context_schema`); over the API it stays
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a plain dict — which is why consumers handle both shapes.
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"""
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model: str | None
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"""Model spec to swap at runtime (e.g. `'provider:model'`)."""
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model_params: dict[str, Any]
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"""Invocation params (e.g. `temperature`, `max_tokens`) to merge
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into `model_settings`."""
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effective_model: str | None
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"""Resolved `provider:model` spec actually in use for this invocation.
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Unlike `model` (a swap *instruction* that is `None` when the base model is
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used), this carries the model in effect — whether from a `/model` override
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or the startup default — so `ResumeStateMiddleware` can record it to
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checkpoint state for restore on resume. `None` when no usable spec is
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resolved yet (e.g. credentials not configured), in which case nothing is
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recorded rather than a malformed spec.
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"""
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auto_approve: bool
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"""Whether gated tool calls should skip the human-approval interrupt.
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Sourced from the client session (not graph state) so the model cannot
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self-approve by writing state. The `interrupt_on` `when` predicate reads
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this to suppress interrupts at the source when "approve always" is on,
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avoiding the interrupt-then-auto-resolve round-trip.
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"""
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approval_mode_key: str | None
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"""Store key for the live approval-mode control record.
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The TUI updates this record when the user toggles approval mode mid-run.
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The server-side interrupt predicate reads it from the LangGraph Store on
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each gated tool call so auto-to-manual changes can take effect before the
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current stream returns.
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"""
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