chore: merge main into fix-windows-click-glob-expansion

Author: Abdullah-k0de

.agents/skills/adk-agent-builder/SKILL.md

@@ -5,27 +5,37 @@ description: Central hub for building, testing, and iterating on ADK agents. Tri
 
 # ADK Agent Builder
 
-This file serves as a directory of specialized reference guides for developing agents with ADK. To avoid context pollution, read only the relevant reference file based on your current task.
+This file serves as a directory of specialized reference guides for developing
+agents with ADK. To avoid context pollution, read only the relevant reference
+file based on your current task.
 
 ## Core Concepts Directory
 
 Refer to these files for foundational knowledge:
+
 - **Getting Started & Basic Agents**: [getting-started.md](references/getting-started.md)
   - Environment setup, API key configuration, and minimal agent definitions.
 - **Tool Catalog**: [tool-catalog.md](references/tool-catalog.md)
   - How to bind function tools, MCP tools, OpenAPI specs, and Google API tools.
 - **Agent Modes (Task / Single-Turn)**: [task-mode.md](references/task-mode.md)
   - Multi-turn structured delegation and autonomous single-turn execution patterns.
+-   **Import Paths**: [import-paths.md](references/import-paths.md)
+    -   Canonical and verbose import paths for core components, tools, and
+        events.
 
 ## Workflow & Graph Orchestration
 
 Refer to these files when building complex graphs:
+
 - **Function Nodes**: [function-nodes.md](references/function-nodes.md)
   - How to use functions as nodes, type resolution, and generators.
 - **Routing & Conditions**: [routing-and-conditions.md](references/routing-and-conditions.md)
   - Edge patterns, dict-based routing, self-loops, and conditional execution.
 - **LLM Agent Nodes**: [llm-agent-nodes.md](references/llm-agent-nodes.md)
   - How to use LLM agents as workflow nodes, task wrappers, and handling output schemas.
+-   **Advanced Patterns**:
+    [advanced-patterns.md](references/advanced-patterns.md)
+    -   Nested workflows, custom node types, and graph validation rules.
 
 ## Advanced Orchestration Patterns
 
@@ -40,6 +50,14 @@ Refer to these files when building complex graphs:
 
 - **State & Events**: [state-and-events.md](references/state-and-events.md)
   - Using context API, sharing global state, and yield event structures.
+-   **Session & Memory**:
+    [session-and-state.md](references/session-and-state.md)
+    -   Session state mutation, scope conventions, and database session
+        services.
+-   **Callbacks & Plugins**:
+    [callbacks-and-plugins.md](references/callbacks-and-plugins.md)
+    -   Implementing callbacks, plugin manager integration, and override
+        behavior.
 - **Multi-Agent Systems**: [multi-agent.md](references/multi-agent.md)
   - Hierarchical execution (e.g., `SequentialAgent`, `LoopAgent`, `ParallelAgent`).
 - **Testing Strategies**: [testing.md](references/testing.md)

.agents/skills/adk-architecture/SKILL.md

@@ -11,7 +11,7 @@ description: ADK architectural knowledge — graph orchestration, resumption, ex
 - [Runner](references/interfaces/runner.md) — The public interface for executing workflows and agents. Documents entrance methods `run` and `run_async`.
 - [Agent](references/interfaces/agent.md) — Blueprint defining identity, instructions, and tools. Documents that `run` is the preferred entrance method.
 - [BaseAgent](references/interfaces/base-agent.md) — Base class for all agents. Defines the contract for subclassing with `_run_impl` as the primary override point.
-- [Event](references/interfaces/event.md) — Core data structure for state reconstruction and communication. Represents a conversation turn or action.
+- [Event](references/interfaces/event.md) — Core data structure for state reconstruction and communication. Represents a conversation turn, action, and state lifecycle immutability.
 
 ## Key Principles (references/principles/)
 - [API Principles](references/principles/api-principles.md) — stability, backward compatibility, and self-containment. Use when making design choices that affect the public API surface.

.agents/skills/adk-architecture/references/architecture/observability.md

@@ -30,7 +30,7 @@ execution flow).
 implicit "current span" context. In a concurrent asyncio.Task
 runtime, implicit context can be unreliable across concurrent
 nodes. All tracing operations (attributes, logs, child spans)
-should go through `ctx._span`.
+should go through `ctx._span`. When attaching or detaching OTel context explicitly (e.g., using `context.attach()` and `context.detach()`), **always pair them inside a `try...finally` block** to prevent context leaks across requests.
 
 **Span lifecycle:**
 

.agents/skills/adk-architecture/references/architecture/workflow-resumability.md

@@ -0,0 +1,125 @@
+# Workflow Resumability: Model and Direction
+
+This note describes how a `Workflow` node preserves and restores execution state
+across a human-in-the-loop pause, how that compares to peer agent frameworks,
+and the direction we are moving in. It complements `checkpoint-resume.md`, which
+covers the interrupt/resume lifecycle for a single node.
+
+The first thing to be clear about: a `Workflow` reconstructs its progress from
+the session event stream on every run, so it resumes whether or not resumability
+is configured. The `is_resumable` flag does not switch resume on or off. What it
+switches on is **durability** — persisting loadable checkpoints and letting an
+invocation be continued across separate runner calls.
+
+## Current model
+
+### Resume is always on: reconstruction by event replay
+
+On every run, the workflow scans the current invocation's session events and
+rebuilds its in-memory loop state (which nodes completed, their outputs, which
+are still waiting on interrupts). Completed nodes are fast-forwarded from their
+recovered output rather than re-executed; the interrupted node re-runs with the
+supplied responses.
+
+This path (`_run_impl` -> `ReplayManager.scan_workflow_events`) has no
+`is_resumable` guard — the scan matches events purely by invocation id. The loop
+state is not persisted and there is no separate workflow checkpoint to load; the
+session event log is the source of truth. So within an invocation, a workflow is
+inherently replay-resumable, flag or no flag. This is exactly what deanchen
+means by "still resumable even when resumability is not set."
+
+### What `is_resumable` actually adds: durability
+
+`ResumabilityConfig.is_resumable` is a durability switch. When it is set:
+
+1.  **Cross-call resume.** The runner will continue an existing invocation
+    without a fresh user message and set up a "resumed invocation" context,
+    rehydrating the recorded agent state and end-of-agent markers from history.
+    With the flag off, the runner requires a new message and starts a fresh
+    invocation instead.
+2.  **Checkpoint markers in the log.** Composite agents (`LlmAgent`,
+    `SequentialAgent`, `LoopAgent`, `ParallelAgent`, and `LlmAgent`s wrapped as
+    workflow nodes) write `agent_state` / `end_of_agent` events into the session
+    only when the invocation is resumable. These make progress loadable across a
+    process boundary.
+3.  **Function-response routing.** Routing an incoming function response back to
+    its originating invocation is enabled only when resumable.
+
+The config's own definition is durability-shaped: pause an invocation on a
+long-running call, and resume it from the last event if it paused or failed
+midway, best-effort and at-least-once, with in-memory state lost. So the
+accurate statement is: the flag decides whether progress is persisted as
+loadable checkpoints and whether an invocation can be resumed across runner
+calls — not whether the workflow can resume. Resumability here is really
+durability.
+
+### The `Workflow` node emits no checkpoint of its own
+
+Today the `Workflow` node does not persist a node-status checkpoint (a `nodes`
+payload of statuses/outputs). It relies solely on event replay. The `nodes`
+shape exists only as an input to graph visualization, not as something the
+runtime writes during a run. The only checkpoint events on the workflow path
+come from wrapped composite agents emitting their own `agent_state`, and those
+are gated on the flag as above.
+
+## How peer frameworks do it
+
+Every mainstream agent framework persists a **state snapshot with a position
+cursor** and, on resume, **loads that snapshot** — none reconstruct by replaying
+the entire history.
+
+Framework                | Durable unit                                                        | Position cursor                                                                    | Resume
+------------------------ | ------------------------------------------------------------------- | ---------------------------------------------------------------------------------- | ------
+LangGraph (graph)        | `StateSnapshot` per super-step in a pluggable checkpointer          | `next` nodes + parent-pointer chain + per-task pending writes                      | re-invoke same thread id; load latest checkpoint, re-run only the interrupted node
+pydantic-graph (graph)   | `NodeSnapshot{state, node, status}` via a state-persistence backend | the snapshot's `node` = next node to run; `status` created/pending/running/success | `iter_from_persistence()` loads the next `created` snapshot
+OpenAI Agents SDK (loop) | serialized `RunState` blob                                          | run cursor inside the state; correlate by tool-call id                             | deserialize the state, apply approvals, resume the run
+Pydantic AI (loop)       | message history + deferred-tool results                             | implicit in the transcript; correlate by tool-call id                              | new run over the prior message history
+
+ADK's durable unit today is the event log itself, and resume is by replay over
+it. Two patterns from the peers are worth copying, both consistent across them:
+
+-   **A snapshot is the source of truth for resume.** The runtime writes a
+    snapshot as it advances and reads the latest one to continue. Resume cost is
+    bounded by the snapshot size, not by history length.
+-   **Resume re-runs the paused unit from its start** (LangGraph and
+    pydantic-graph both re-execute the whole node, not a saved program counter),
+    which keeps the durable state small and pushes an idempotency contract onto
+    the node author — the same at-least-once contract ADK already documents.
+
+## Direction: persist a workflow checkpoint as the durable source of truth
+
+Even with durability on, the `Workflow` node reloads by replaying the event
+history rather than loading a compact checkpoint. The direction — peer-aligned,
+and the one ADK's own composite agents already follow — is to persist a workflow
+checkpoint and load the latest one on resume:
+
+-   As the workflow advances, persist node statuses and outputs as a checkpoint
+    (an `agent_state` payload), the way composite agents already persist theirs.
+-   On resume, seed the loop state from the most recent checkpoint, then
+    continue: re-run only the interrupted node and dispatch newly-ready
+    successors.
+-   This makes resume cost independent of history length and unifies the
+    `Workflow` node with composite agents and with LangGraph / pydantic-graph /
+    the OpenAI SDK.
+
+This only applies when durability is on. Without `is_resumable` there is nothing
+to persist, and the workflow continues to resume within an invocation by replay
+as it does today.
+
+## Open considerations
+
+-   **Payload completeness.** A workflow checkpoint must carry (or be able to
+    recover) each completed node's output, run id, and branch — the equivalent
+    of LangGraph's per-task pending writes — to fully replace event replay.
+-   **Partial interrupt resolution.** A node with several interrupts may receive
+    only some responses on a resume. The re-run-vs-wait behavior differs between
+    an orchestrating node (re-run to dispatch the resolved branch) and a leaf
+    node (wait for all), keyed on `rerun_on_resume`. This is decided in the
+    shared replay-interception logic and should be settled before a load path
+    relies on it.
+-   **Versioning.** Long-lived paused runs can outlive a code change. A version
+    marker on the checkpoint lets a resume route to a compatible code path (the
+    OpenAI SDK makes this an explicit recommendation).
+-   **Serialization.** Keep payloads JSON-serializable (Pydantic `model_dump`),
+    so any persistence backend works and no code objects are serialized; node
+    objects are rebound from the in-memory graph definition on resume.

.agents/skills/adk-architecture/references/interfaces/event.md

@@ -23,3 +23,8 @@ The `Event` class represents a single event in the conversation history or workf
 - `get_function_calls()`: Returns function calls in the event.
 - `get_function_responses()`: Returns function responses in the event.
 - `is_final_response()`: Returns whether the event is the final response of an agent.
+
+## State Lifecycle & Immutability
+- **Event Immutability**: Event history is immutable. Never assume that events are mutated or cleared after they are saved to a session.
+- **Signal & Action Persistence**: When checking if a signal or action is "pending" versus "resolved", do not rely on events being modified in place.
+- **Compaction Side-Effects**: Be aware that storing stateful flags on events (such as requested actions or transient status) can have permanent unintended effects on background compaction when those events age but remain in history.

.agents/skills/adk-sample-creator/SKILL.md

@@ -89,7 +89,7 @@ Each sample should have a `README.md` with the following structure:
 
 - **Overview**: What the sample does.
 - **Sample Inputs**: Examples of inputs to test with. Each prompt must be wrapped in backticks. If a prompt has an explanation, always add a blank line between the prompt and the explanation, and indent the explanation by two spaces.
-- **Graph**: Visualization of the graph flow (Mermaid recommended for workflows).
+- **Graph**: Visualization of the graph flow (Mermaid recommended). For Workflow root agents, visualize the graph flow of nodes. For agents that orchestrate tools or sub-agents (e.g., `LlmAgent`, `ManagedAgent`), visualize the topology of the agent and its tools/sub-agents instead of internal workflow nodes. Keep it a simple topology diagram (a few nodes and edges). Do **not** draw a request/response data-flow sequence (e.g., `user -> agent -> API -> tool -> ... -> user`); those are noisy and add little value over the topology.
 - **How To**: Explanation of key techniques used (e.g., `ctx.run_node`).
 - **Related Guides**: Links to relevant developer guides in `docs/guides/` that explain the concepts or classes used.
 
@@ -112,11 +112,18 @@ Brief description.
 
 ## Graph
 
+For Workflow root agents:
 ```mermaid
 graph TD
     START --> MyNode
 ```
 
+For agents that orchestrate tools or sub-agents (`LlmAgent`, `ManagedAgent`, ...):
+```mermaid
+graph TD
+    MyAgent[my_agent] -->|calls| MyTool(my_tool)
+```
+
 ## How To
 
 Explain the details.

.agents/skills/adk-style/references/typing.md

@@ -48,3 +48,7 @@ Use `isinstance()` for runtime type discrimination when handling polymorphic inp
 - Always include an `else` branch that raises `TypeError` or handles the unknown case.
 - Prefer `isinstance(x, SomeType)` over `type(x) is SomeType` — it handles subclasses correctly.
 - For checking multiple types: `isinstance(x, (TypeA, TypeB))`.
+
+## No Asserts in Production Code
+
+**Never use `assert` statements in production code.** They can be optimized away when Python runs with `-O` flags and provide poor error messages. Use specific exceptions like `ValueError`, `TypeError`, or `RuntimeError` instead.