vv-agent

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A lightweight agent framework extracted from VectorVein's production runtime. Cycle-based execution with pluggable LLM backends, tool dispatch, memory compression, and distributed scheduling.

Architecture

Agent / RunConfig / ModelSettings
└── Runner
    └── AgentRuntime
        ├── CycleRunner          # single LLM turn: context -> completion -> tool calls
        ├── ToolCallRunner       # tool dispatch, directive convergence
        ├── RuntimeHookManager   # before/after hooks
        ├── MemoryManager        # automatic history compression
        └── ExecutionBackend     # inline, thread, or Celery scheduling

The public SDK entry points are exported from vv_agent: Agent, Runner, RunConfig, RunHandle, ModelSettings, function_tool, Session, typed RunEvent objects, ApprovalProvider, ContextProvider, RunEventStore, and the interactive session API for desktop/runtime integrations. Extension points that live in package modules include vv_agent.memory.MemoryProvider and vv_agent.tools.ToolExecutor. Lower-level runtime implementation details include AgentTask, AgentResult, Message, CycleRecord, and ToolCall.

Task completion is explicit: tool directives are the default, while a declared no-tool policy can finish or pause on a normal assistant response. No implicit "last message = answer" heuristic is used.

Setup

cp local_settings.example.py local_settings.py
# Fill in your API keys and endpoints in local_settings.py

uv sync --dev
uv run pytest

Quick Start

CLI

uv run vv-agent --prompt "Summarize this framework" --backend moonshot --model kimi-k3

# With per-cycle logging
uv run vv-agent --prompt "Summarize this framework" --backend moonshot --model kimi-k3 --verbose

CLI flags: --settings-file, --backend, --model, --verbose.

Programmatic SDK

from vv_agent import Agent, RunConfig, Runner, function_tool

@function_tool
def read_order(order_id: str) -> str:
    """Read order information."""
    return "order details"

agent = Agent(
    name="ops",
    instructions="Check facts first, then answer.",
    model="kimi-k3",
    tools=[read_order],
)

result = Runner.run_sync(agent, "Analyze order 123", run_config=RunConfig(
    default_backend="moonshot",
))
print(result.status, result.final_output)

For defaults shared by several runs, create a configured Runner instead of repeating the same RunConfig:

runner = Runner.configured(RunConfig(
    model_provider=provider,
    model="kimi-k3",
    workspace="./workspace",
))
result = runner.run_sync(agent, "Analyze order 123")

Provider resolution is per-run then Runner. Model resolution is per-run, Agent, Runner, then the selected provider default. Model settings merge in the opposite layering direction: provider, Runner, Agent, then per-run, with each later layer overriding earlier fields.

Agent.output_type can coerce JSON final output into dict, list, dataclasses, or Pydantic-style models. Decorated tools may accept a leading ToolContext parameter; it is passed at invocation time and omitted from the tool JSON schema.

Streaming And Sessions

RunConfig.workspace controls the workspace for a run. RunConfig.session accepts MemorySession, SQLiteSession, or RedisSession to persist message history across runs.

from vv_agent import Agent, MemorySession, RunConfig, Runner

agent = Agent(name="assistant", instructions="Remember context.", model="kimi-k3")
session = MemorySession("thread-001")
config = RunConfig(
    default_backend="moonshot",
    workspace="./workspace/thread-001",
    session=session,
)

Runner.run_sync(agent, "Inspect the project", run_config=config)
for event in Runner.stream_sync(agent, "Continue and report progress", run_config=config):
    if event.type == "assistant_delta":
        print(event.delta, end="")

Use Runner.start() when the host needs a live handle instead of blocking for the final result. RunHandle.events() yields the same typed RunEvent stream as Runner.stream_sync(), RunHandle.result() waits for the final RunResult, RunHandle.cancel() cancels the run, and RunHandle.approve() resolves pending approval requests. When the handle is attached to an AgentSession, RunHandle.steer() queues context for the active run and RunHandle.follow_up() queues the next session turn. Plain one-shot Runner.start() handles do not own session queues, so those methods require an interactive session controller.

RunConfig.event_store can persist every typed event. JsonlRunEventStore stores event dictionaries and replays events by run_id, including child runs whose parent_run_id points at the requested run. Typed RunEvent is the only public runtime event boundary; task-neutral observations use DiagnosticEvent.

For a normalized tool call, the execution lifecycle is tool_call_planned, optional approval events, tool_call_started immediately before effects may begin, and tool_call_completed after a result exists. Argument parse failures emit none of these events. Policy, approval, and unknown-tool short-circuits emit planned plus completed without started; completed events report directive, nullable error_code, execution_started, and nullable monotonic duration_ms. A started event may remain unmatched after cancellation or process loss, so checkpoint v2's operation journal remains the recovery authority.

The lower-level AgentRuntime API remains available for backend integrations that need direct cycle-loop control.

Install Redis support with uv sync --extra redis or inject a Redis-compatible client when constructing RedisSession.

App Server

Use the App Server when a desktop app, worker, IDE, or other host process needs to drive vv-agent through a stable protocol instead of embedding the Python SDK directly. It runs JSONL over stdio, exposes Thread / Turn / Item lifecycle events, routes tool approval as server-to-client requests, supports thread/read and thread/resume replay, and exports typed JSON Schema and self-contained TypeScript bindings.

uv run vv-agent app-server --listen stdio --settings local_settings.py --backend moonshot --model kimi-k3
uv run vv-agent app-server schema --out ./app-server-schema
uv run vv-agent app-server generate-ts --out ./app-server-schema/typescript
uv run vv-agent debug app-server send-message "hello"

Product hosts implement AppServerHost to map product profiles, workspace context, tools, approval UI, memory, and model settings into framework Agent and RunConfig objects. The App Server remains a runtime boundary; it does not import product UI, account, billing, browser, or IM modules.

See docs/app-server.md for protocol details and docs/app-server-host-integration.md for the current host boundary and rollout checks.

Interactive Sessions

Use Runner for one-shot runs, streamed runs, and conversation history managed by RunConfig.session. Use InteractiveAgentClient when the host application needs a stateful, bidirectional runtime session with stable session ids, runtime listeners, queued steering prompts, follow-up turns, cancellation, and shared tool state. During a running session, session.active_run_handle exposes the unified RunHandle control surface for approval, cancellation, steering, and follow-up.

Pass an existing MemorySession, SQLiteSession, or RedisSession through AgentSessionOptions.session (or create_session(session=...)) to hydrate a facade from durable history and let Runner append each turn to the same store. When both are provided, the requested session_id must match the backing Session id. Do not also pass that history as initial messages.

from pathlib import Path

from vv_agent import (
    AgentSessionOptions,
    InteractiveAgentClient,
    InteractiveAgentDefinition,
    SQLiteSession,
)
from vv_agent.runtime.backends import ThreadBackend

client = InteractiveAgentClient(
    options=AgentSessionOptions(
        settings_file=Path("local_settings.py"),
        default_backend="moonshot",
        workspace=Path("./workspace/thread-001"),
        execution_backend=ThreadBackend(max_workers=4),
        session=SQLiteSession("thread-001", db_path=Path("./sessions.sqlite3")),
    )
)

session = client.create_session(
    session_id="thread-001",
    agent=InteractiveAgentDefinition(
        description="Operate in the user's workspace and report progress.",
        model="kimi-k3",
        no_tool_policy="finish",
    ),
)
unsubscribe = session.subscribe(lambda event, payload: print(event, payload))
try:
    run = session.prompt("Inspect the workspace")
    print(run.result.status, run.result.final_answer)
finally:
    unsubscribe()

Interactive sessions are additive to the normal SDK facade; they do not reintroduce the old 0.1 AgentSDKClient or AgentSDKOptions names.

Agent As Tool, Handoff, And Policy

Use agent.as_tool() when a child agent should return a result to the parent agent and let the parent continue. Use handoff() when control should transfer to the target agent and the target output should finish the run.

from vv_agent import Agent, RunConfig, Runner, ToolPolicy, handoff
from vv_agent.constants import TASK_FINISH_TOOL_NAME

researcher = Agent(name="researcher", instructions="Collect facts.", model="kimi-k3")
writer = Agent(
    name="writer",
    instructions="Write from research.",
    model="kimi-k3",
    tools=[researcher.as_tool(name="research", description="Collect facts.")],
)
triage = Agent(
    name="triage",
    instructions="Transfer writing tasks.",
    model="kimi-k3",
    handoffs=[handoff(agent=writer, description="Use for writing.")],
)

result = Runner.run_sync(
    triage,
    "Write a short report.",
    run_config=RunConfig(
        default_backend="moonshot",
        max_handoffs=4,
        tool_policy=ToolPolicy(allowed_tools=[TASK_FINISH_TOOL_NAME, "transfer_to_writer"]),
    ),
)

A handoff is an outer Runner control transfer, not an agent-as-tool call. The target Agent resolves its own model and model settings, while the active session, cancellation token, and mutated shared state continue across the transition. max_handoffs defaults to 10 and limits control transfers independently from max_cycles. Approval resume preserves the same behavior.

No-tool completion is an explicit control, not a task or answer classifier. Set Agent(no_tool_policy="finish") when a normal assistant response should finish the run without task_finish, or override it for one call with RunConfig(no_tool_policy="continue" | "wait_user" | "finish"). Per-run configuration wins over a configured Runner default, which wins over the Agent value; omitting every layer uses continue. Inspect result.completion_reason, result.completion_tool_name, and result.partial_output to distinguish natural completion, tool-driven completion, waits, cancellation, failure, and max-cycle exhaustion.

RunConfig.budget_limits can independently limit total tokens, uncached input tokens, total or exact-name tool calls, active wall time, and host-metered cost. Limits are optional and task-neutral. Inspect result.budget_usage and result.budget_exhaustion; a budget stop is a typed failed result, not a successful answer. See Run Budgets.

Tools can request approval with @function_tool(needs_approval=True). By default the run enters WAIT_USER before the tool body is called and emits a ApprovalRequestedEvent. ToolPolicy(approval="never") disables that approval gate for trusted runs. The four policy modes are default, always, never, and on_request: default inherits the next configured policy, whereas explicit on_request follows each tool's static or dynamic approval declaration.

Custom tools may also attach an optional host-visible capability declaration:

from vv_agent import (
    ToolIdempotency,
    ToolMetadata,
    ToolPolicy,
    ToolSideEffect,
    function_tool,
)

@function_tool(
    tool_metadata=ToolMetadata(
        side_effect=ToolSideEffect.EXTERNAL,
        idempotency=ToolIdempotency.UNSUPPORTED,
        terminal=False,
        capability_tags=["ticket.write"],
        cost_dimensions=["support_api.request"],
    )
)
def create_ticket(title: str) -> dict[str, str]:
    return {"ticket_id": "TCK-1001", "title": title}

policy = ToolPolicy(
    denied_side_effects=[ToolSideEffect.EXECUTE],
    denied_capability_tags=["filesystem.delete"],
    deny_terminal_tools=True,
    denied_cost_dimensions=["gpu.second"],
)

side_effect is one coarse declaration with no inferred hierarchy; capability_tags and cost_dimensions are opaque exact-match labels, and cost dimensions are not measurements or prices. terminal=True only declares that a tool may return finish or wait_user; it never ends a run by itself. The four new policy fields are cumulative denials across Agent, configured Runner, per-run, and delegated-child layers, and a matching denial returns tool_not_allowed. They cannot grant a capability or remove an existing name, argument, approval, budget, or runtime restriction.

Typed metadata is separate from generic FunctionTool.metadata and is not added to the model-visible function schema. ToolMetadata.idempotency is the only idempotency declaration used by execution, telemetry, and checkpointing.

Guardrails And Tracing

Input guardrails run before the model provider is called. Output guardrails run after a final output is available. Trace processors receive lightweight run and tool spans.

from vv_agent import Agent, GuardrailResult, RunConfig, Runner, input_guardrail

@input_guardrail
def reject_empty(ctx, input_text: str) -> GuardrailResult:
    del ctx
    if not input_text.strip():
        return GuardrailResult.block("input is required")
    return GuardrailResult.allow()

agent = Agent(
    name="assistant",
    instructions="Answer carefully.",
    model="kimi-k3",
    input_guardrails=[reject_empty],
)

result = Runner.run_sync(
    agent,
    "Summarize this project.",
    run_config=RunConfig(default_backend="moonshot", tracing={"workflow_name": "summary"}),
)

Shell Runtime Configuration (Windows)

bash runtime defaults are a startup/session configuration, not tool-call arguments.

• Run defaults: pass bash_shell, windows_shell_priority, and bash_env through RunConfig.metadata.
• Per-agent defaults: put the same keys in Agent.metadata.
• Recommended Windows priority: ["git-bash", "powershell", "cmd"]
• On Windows, bash-tool child processes default PYTHONUTF8=1 and PYTHONIOENCODING=utf-8 unless already overridden via the parent environment or bash_env.
• On Windows, bash-tool child processes are launched with hidden-console flags so GUI hosts can run bash / powershell commands without flashing a terminal window.
• Runner.run_sync(...) and Runner.stream_sync(...) both inherit compiled shell metadata.
• The bash tool schema description includes a runtime shell hint (resolved shell kind + invocation prefix), so the model sees which shell command style is expected before calling the tool.
• The runtime shell hint is frozen per task/session-run to keep tool schemas stable across cycles and preserve LLM prompt cache efficiency.
• Runner/CLI-generated runtime tasks attach structured system_prompt_sections metadata to the system message when prompt sections are available, so Anthropic prompt-cache breakpoints can keep the stable prompt prefix hot while treating current time and session-memory blocks as volatile.

from vv_agent import Agent, RunConfig, Runner

agent = Agent(
    name="desktop",
    instructions="Desktop helper",
    model="kimi-k3",
    metadata={"bash_env": {"HTTP_PROXY": "http://127.0.0.1:7890"}},
)
result = Runner.run_sync(
    agent,
    "Check the workspace.",
    run_config=RunConfig(
        default_backend="moonshot",
        metadata={
            "windows_shell_priority": ["git-bash", "powershell", "cmd"],
            "bash_env": {"PIP_INDEX_URL": "https://pypi.tuna.tsinghua.edu.cn/simple"},
        },
    ),
)

Execution Backends

The cycle loop is delegated to a pluggable ExecutionBackend.

CeleryBackend

Each cycle is a Celery task. Workers rebuild the AgentRuntime from a required RuntimeRecipe and resolve the declared shared CheckpointStore capability.

from vv_agent import CheckpointConfig, RunConfig
from vv_agent.runtime.backends.celery import CeleryBackend, RuntimeRecipe, register_cycle_task
from vv_agent.runtime.backends.distributed import (
    CapabilityRef,
    DistributedCapabilities,
    DistributedCapabilityRegistry,
)
from vv_agent.runtime.stores.sqlite import SqliteCheckpointStore

checkpoint_ref = CapabilityRef("checkpoint.production", "1")
checkpoint_store = SqliteCheckpointStore(".vv-agent-state/checkpoints.db")
worker_capabilities = DistributedCapabilityRegistry()
worker_capabilities.register("checkpoint_store", checkpoint_ref, checkpoint_store)
register_cycle_task(celery_app, capability_registry=worker_capabilities)

recipe = RuntimeRecipe(
    settings_file="local_settings.py",
    backend="moonshot",
    model="kimi-k3",
    workspace="./workspace",
    capabilities=DistributedCapabilities(checkpoint_store_ref=checkpoint_ref),
)
backend = CeleryBackend(celery_app=celery_app, runtime_recipe=recipe)
run_config = RunConfig(
    execution_backend=backend,
    checkpoint_config=CheckpointConfig(
        key="tenant-7/task-42",
        store=checkpoint_store,
    ),
)

Install celery extras: uv sync --extra celery.

Cancellation and Streaming

from vv_agent.events import AssistantDeltaEvent, RunEvent
from vv_agent.runtime import CancellationToken, ExecutionContext

# Cancel from another thread
token = CancellationToken()
ctx = ExecutionContext(cancellation_token=token)
result = runtime.run(task, ctx=ctx)

def on_event(event: RunEvent) -> None:
    if isinstance(event, AssistantDeltaEvent):
        print(event.delta, end="")


# Stream LLM output events, including assistant deltas and tool progress
ctx = ExecutionContext(event_handler=on_event)
result = runtime.run(task, ctx=ctx)

Runtime Log Payloads

tool_result runtime events carry full tool output in content and any structured tool payload in metadata (no implicit truncation of content). content_preview and assistant_preview are still emitted for UI convenience.

If you need shorter previews for logs/transport, configure an explicit preview limit:

from vv_agent import RunConfig

config = RunConfig(
    log_preview_chars=220,  # optional: enable preview truncation explicitly
)

Workspace Backends

Workspace file I/O is delegated to a pluggable WorkspaceBackend protocol. All built-in file tools (read_file, write_file, find_files, etc.) go through this abstraction.

find_files includes built-in safety defaults for large workspaces:

• Returns at most 100 paths per call by default (max_results can tune this, with hard cap).
• Uses ripgrep (rg) for fast local traversal when available, with automatic fallback to Python walk.
• search_files also uses rg for local workspaces (with Python fallback), defaults to smart-case matching (lowercase patterns are case-insensitive; patterns with uppercase stay case-sensitive), and skips hidden/common dependency roots unless explicitly included.
• search_files returns model-facing search text in ToolExecutionResult.content, while structured files/matches/counts live in ToolExecutionResult.metadata.
• Sensitive files such as .env and private keys are omitted by default; set include_sensitive=true to opt in.
• When listing from workspace root, common dependency/cache roots (for example node_modules, .venv, .git) are summarized instead of expanded.
• You can still inspect those paths explicitly by setting path to that directory (or by setting include_ignored=true).
• Supports scan_limit to stop early on very large trees; when triggered, response sets count_is_estimate=true.

from vv_agent.workspace import LocalWorkspaceBackend, MemoryWorkspaceBackend

# Explicit local backend
runtime = AgentRuntime(
    llm_client=llm,
    tool_registry=registry,
    workspace_backend=LocalWorkspaceBackend(Path("./workspace")),
)

# In-memory backend for testing
runtime = AgentRuntime(
    llm_client=llm,
    tool_registry=registry,
    workspace_backend=MemoryWorkspaceBackend(),
)

S3WorkspaceBackend

Install the optional S3 dependency: uv pip install 'vv-agent[s3]'.

from vv_agent.workspace import S3WorkspaceBackend

backend = S3WorkspaceBackend(
    bucket="my-bucket",
    prefix="agent-workspace",
    endpoint_url="https://oss-cn-hangzhou.aliyuncs.com",  # or None for AWS
    aws_access_key_id="...",
    aws_secret_access_key="...",
    addressing_style="virtual",  # "path" for MinIO
)

Custom Backend

Implement the WorkspaceBackend protocol declared in src/vv_agent/workspace/base.py to plug in any storage backend. A custom backend provides file enumeration, text/binary reads, writes, metadata, existence checks, file checks, and directory creation.

from vv_agent.workspace import WorkspaceBackend

class MyBackend(WorkspaceBackend):
    ...

Modules

Runtime Boundary

vv-agent owns the portable agent runtime: prompt assembly, model calls, tool planning, tool execution, memory compaction, typed events, cancellation, approval interruption, and replayable run history. Host products own product UI, user and workspace resolution, product storage, browser or IM integration, and the product-specific tools exposed to the model.

Host products should implement providers instead of patching vv-agent internals:

• AppServerHost maps product profiles, workspaces, tools, approval UI, memory, context, and model settings into App Server Agent and RunConfig objects when the host uses JSONL process integration.
• ApprovalProvider decides whether a tool call needs approval and returns the allow, deny, session-allow, or timeout decision from product UI or rules.
• ContextProvider contributes product prompt fragments such as profile, workspace, policy, or feature context before each run is compiled.
• vv_agent.memory.MemoryProvider connects product memory stores to memory search/save hooks and compaction lifecycle events.
• vv_agent.tools.ToolExecutor exposes product tools with schema, approval, timeout, error, and execution behavior. FunctionTool and @function_tool cover normal Python functions; custom executors are routed by ToolOrchestrator.
• RunEventStore persists typed RunEvent history so app views can replay completed runs and parent/child run graphs.

This boundary keeps Agent, Runner, RunConfig, RunHandle, and RunEvent stable while allowing each host to keep its own account model, workspace model, storage backend, and UI workflow outside the framework.

Memory Compaction

MemoryManager measures context size in tokens and compacts history when the resolved auto-compaction threshold is exceeded.

• Task-level knobs:
  • memory_compact_threshold (default 250000; configured ceiling for full compaction)
  • memory_threshold_percentage (warning threshold percentage, default 90)
• Compile mapping:
  • AgentCompiler forwards stable agent/run metadata into AgentTask.
  • Resolved model limits are recorded as model_context_window and model_max_output_tokens; output capability is not copied into reserved_output_tokens.
  • Current durable task/checkpoint records carry the exact configured threshold and capacity metadata used by resume.
  • Runtime-only compaction knobs remain metadata-backed until promoted into stable public fields.
• Token budget model:
  • Context precedence is explicit model_context_window, resolved model capability, then a derived planning context. The default is 250000 + 16000 + 13000 = 279000.
  • Output reserve precedence is effective ModelSettings.max_tokens, explicit reserved_output_tokens, then the 16000 framework fallback.
  • Only the framework fallback reserve may be capped downward by a smaller model_max_output_tokens; capability never overrides an explicit request or host reserve.
  • derived_prompt_capacity = max(model_context_window - reserved_output_tokens - autocompact_buffer_tokens, 0)
  • autocompact_threshold = min(memory_compact_threshold, derived_prompt_capacity); a configured threshold of zero selects the derived capacity, and a known derived capacity of zero stays zero.
  • The default autocompact buffer is 13000; the default microcompact trigger is 75% of the effective full threshold.
• Effective-length strategy (backend-aligned):
  • If previous cycle token usage exists:
    • effective_length = previous_prompt_tokens + token_count(recent_tool_messages)
  • Otherwise fallback to:
    • vv_llm.chat_clients.utils.get_message_token_counts(...)
    • If tokenizer resolution fails, use a local CJK-aware estimate
• Compaction pipeline:
  1. Preemptive microcompact: clear old large tool results when usage crosses microcompact_trigger_ratio
  2. Session Memory extraction: persist key facts before full summarization so they survive later compactions
  3. Structural cleanup (stale tool calls, orphan tool messages, assistant-no-tool collapse, old tool result artifactization)
  4. If still over threshold, generate a compressed memory summary that preserves original user messages, file operations, current work state, and resolved errors
  5. If the provider still returns prompt-too-long, retry with forced compaction once, then progressively stronger emergency tail-dropping
  6. After full compaction, re-inject relevant workspace files into <Post-Compaction File Context> under a bounded token budget
• Compaction events:
  • New memory_compact_started producers include the typed trigger and the complete resolved capacity snapshot.
  • New memory_compact_completed producers include the strongest actual mode (none, micro, structural, summary, or emergency) and a content-aware changed flag.
  • Every current event includes the complete typed capacity and result fields; missing or unknown fields are rejected.
• Session Memory behavior:
  • Stored in workspace/.memory/session/<session-or-task-scope>/session_memory.json by default
  • Scoped to the current session when metadata.session_id is present; otherwise scoped to the current task_id
  • New sessions/tasks start without inherited Session Memory from previous sessions/tasks
  • Injected into the first system message on every cycle as <Session Memory>
  • Extraction reuses the configured memory summary backend/model
  • Full compaction resets transcript tracking but preserves persisted memory entries
  • Sub-tasks disable Session Memory by default to avoid parent/child memory-file contamination

Runtime metadata keys

Pass these via Agent.metadata or RunConfig.metadata; the compiler forwards them into AgentTask.metadata:

• memory_keep_recent_messages
• model_context_window
• model_max_output_tokens (resolved model capability; not an implicit request limit)
• reserved_output_tokens
• autocompact_buffer_tokens
• microcompact_trigger_ratio
• microcompact_keep_recent_cycles
• microcompact_min_result_length
• microcompact_compactable_tools
• include_memory_warning
• session_memory_enabled
• session_memory_min_tokens
• session_memory_max_tokens
• session_memory_min_text_messages
• session_memory_storage_dir
• tool_result_compact_threshold
• tool_result_keep_last
• tool_result_excerpt_head
• tool_result_excerpt_tail
• tool_calls_keep_last
• assistant_no_tool_keep_last
• tool_result_artifact_dir
• summary_event_limit

Memory summary model selection priority

Priority is strict:

1. AgentTask.metadata.memory_summary_model, with optional memory_summary_backend.
2. The current task model through the run's ModelProvider.

Built-in Tools

find_files, file_info, read_file, write_file, edit_file, search_files, compress_memory, todo_write, task_finish, ask_user, bash, read_image, create_sub_task, sub_task_status.

Custom tools can be registered via ToolRegistry.register().

The bash tool supports two background paths:

• Explicit background: pass run_in_background=true, receive a session_id immediately, then poll with check_background_command.
• Timeout handoff: if a foreground command reaches timeout, it is moved into a background session instead of failing immediately. The tool returns a session_id, and the session emits terminal background-command events when that process completes, fails, or times out.

Sub-agents

Use Agent.as_tool() when the parent agent should call a child agent and then continue. Use handoff() when the child agent should take over and finish the run. Use create_sub_task and sub_task_status when the model needs explicit background or parallel task management.

Each delegated sub-task runs in a real AgentSession whose session id defaults to the sub-task id. Child RunEvent values preserve their run, trace, parent, task, and session identities so hosts can subscribe, persist, and replay them without an untyped event translation.

Batch mode in create_sub_task dispatches valid sub-task items through the runtime execution backend's parallel_map, so synchronous batches run concurrently when the backend supports parallel execution.

Use sub_task_status to query runtime sub-task states, inspect lightweight progress snapshots (detail_level=snapshot), or send follow-up messages to running/completed sub-tasks.

When the parent task cannot make useful progress until background sub-tasks finish, call sub_task_status with wait_for_completion=true. The runtime waits inside that tool call and returns when queried tasks finish or max_wait_seconds is reached, avoiding repeated status-polling cycles in the agent context.

Before a completed sub-task is resumed, the runtime now sanitizes the saved session transcript: empty assistant turns, thinking-only turns, orphaned tool results, and unresolved tail tool calls are removed so the next follow-up prompt resumes from a coherent history.

Sub-task runtime metadata now includes task_id, session_id, and browser_scope_key for each sub-agent run, so session-scoped tools (for example, browser controllers) stay isolated across parallel sub-tasks.

Host apps can interrupt a currently running sub-agent by calling vv_agent.runtime.engine.steer_sub_agent_session(session_id=..., prompt=...).

Configured child runs inherit the same explicit ModelProvider as the parent and resolve their own model. No settings path or backend fallback is rebuilt inside the child runtime.

Examples

The examples/ directory now contains public SDK cookbook scripts plus a small set of lower-level runtime integration examples. See examples/README.md for the full list.

uv run python examples/01_quick_start.py
uv run python examples/24_workspace_backends.py

Testing

uv run pytest                              # unit tests (no network)
uv run ruff check .                        # lint
uv run ty check                            # type check

VV_AGENT_RUN_LIVE_TESTS=1 uv run pytest -m live   # integration tests (needs real LLM)

Environment variables for live tests: