feat(sdk/agent): Parallel Tool Call Execution

examples/01_standalone_sdk/45_parallel_tool_execution.py

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+"""Example: Parallel tool execution with tool_concurrency_limit.
+
+Demonstrates how setting tool_concurrency_limit on an Agent enables
+concurrent tool execution within a single step. The orchestrator agent
+delegates to multiple sub-agents in parallel, and each sub-agent itself
+runs tools concurrently. This stress-tests the parallel execution system
+end-to-end.
+"""
+
+import json
+import os
+import tempfile
+from collections import defaultdict
+from pathlib import Path
+
+from openhands.sdk import (
+    LLM,
+    Agent,
+    AgentContext,
+    Conversation,
+    Tool,
+    register_agent,
+)
+from openhands.sdk.context import Skill
+from openhands.tools.delegate import DelegationVisualizer
+from openhands.tools.file_editor import FileEditorTool
+from openhands.tools.task import TaskToolSet
+from openhands.tools.terminal import TerminalTool
+
+
+llm = LLM(
+    model=os.getenv("LLM_MODEL", "anthropic/claude-sonnet-4-5-20250929"),
+    api_key=os.getenv("LLM_API_KEY"),
+    base_url=os.getenv("LLM_BASE_URL"),
+    usage_id="parallel-tools-demo",
+)
+
+
+# --- Sub-agents ---
+
+
+def create_code_analyst(llm: LLM) -> Agent:
+    """Sub-agent that analyzes code structure."""
+    return Agent(
+        llm=llm,
+        tools=[
+            Tool(name=TerminalTool.name),
+            Tool(name=FileEditorTool.name),
+        ],
+        tool_concurrency_limit=4,
+        agent_context=AgentContext(
+            skills=[
+                Skill(
+                    name="code_analysis",
+                    content=(
+                        "You analyze code structure. Use the terminal to count files, "
+                        "lines of code, and list directory structure. Use the file "
+                        "editor to read key files. Run multiple commands at once."
+                    ),
+                    trigger=None,
+                )
+            ],
+            system_message_suffix="Be concise. Report findings in bullet points.",
+        ),
+    )
+
+
+def create_doc_reviewer(llm: LLM) -> Agent:
+    """Sub-agent that reviews documentation."""
+    return Agent(
+        llm=llm,
+        tools=[
+            Tool(name=TerminalTool.name),
+            Tool(name=FileEditorTool.name),
+        ],
+        tool_concurrency_limit=4,
+        agent_context=AgentContext(
+            skills=[
+                Skill(
+                    name="doc_review",
+                    content=(
+                        "You review project documentation. Check README files, "
+                        "docstrings, and inline comments. Use the terminal and "
+                        "file editor to inspect files. Run multiple commands at once."
+                    ),
+                    trigger=None,
+                )
+            ],
+            system_message_suffix="Be concise. Report findings in bullet points.",
+        ),
+    )
+
+
+def create_dependency_checker(llm: LLM) -> Agent:
+    """Sub-agent that checks project dependencies."""
+    return Agent(
+        llm=llm,
+        tools=[
+            Tool(name=TerminalTool.name),
+            Tool(name=FileEditorTool.name),
+        ],
+        tool_concurrency_limit=4,
+        agent_context=AgentContext(
+            skills=[
+                Skill(
+                    name="dependency_check",
+                    content=(
+                        "You analyze project dependencies. Read pyproject.toml, "
+                        "requirements files, and package configs. Summarize key "
+                        "dependencies, their purposes, and any version constraints. "
+                        "Run multiple commands at once."
+                    ),
+                    trigger=None,
+                )
+            ],
+            system_message_suffix="Be concise. Report findings in bullet points.",
+        ),
+    )
+
+
+# Register sub-agents
+register_agent(
+    name="code_analyst",
+    factory_func=create_code_analyst,
+    description="Analyzes code structure, file counts, and directory layout.",
+)
+register_agent(
+    name="doc_reviewer",
+    factory_func=create_doc_reviewer,
+    description="Reviews documentation quality and completeness.",
+)
+register_agent(
+    name="dependency_checker",
+    factory_func=create_dependency_checker,
+    description="Checks and summarizes project dependencies.",
+)
+# --- Orchestrator agent with parallel execution ---
+main_agent = Agent(
+    llm=llm,
+    tools=[
+        Tool(name=TaskToolSet.name),
+        Tool(name=TerminalTool.name),
+        Tool(name=FileEditorTool.name),
+    ],
+    tool_concurrency_limit=8,
+)
+
+persistence_dir = Path(tempfile.mkdtemp(prefix="parallel_example_"))
+
+conversation = Conversation(
+    agent=main_agent,
+    workspace=Path.cwd(),
+    visualizer=DelegationVisualizer(name="Orchestrator"),
+    persistence_dir=persistence_dir,
+)
+
+print("=" * 80)
+print("Parallel Tool Execution Stress Test")
+print("=" * 80)
+
+conversation.send_message("""
+Analyze the current project by delegating to ALL THREE sub-agents IN PARALLEL:
+
+1. code_analyst: Analyze the project structure (file counts, key directories)
+2. doc_reviewer: Review documentation quality (README, docstrings)
+3. dependency_checker: Check dependencies (pyproject.toml, requirements)
+
+IMPORTANT: Delegate to all three agents at the same time using parallel tool calls.
+Do NOT delegate one at a time - call all three delegate tools in a single response.
+
+Once all three have reported back, write a consolidated summary to
+project_analysis_report.txt in the working directory. The report should have
+three sections (Code Structure, Documentation, Dependencies) with the key
+findings from each sub-agent.
+""")
+conversation.run()
+
+# --- Analyze persisted events for parallelism ---
+#
+# Walk the persistence directory to find all conversations (main + sub-agents).
+# Each conversation stores events as event-*.json files under an events/ dir.
+# We parse ActionEvent entries and group by llm_response_id — batches with 2+
+# actions sharing the same response ID prove the LLM requested parallel calls
+# and the executor handled them concurrently.
+
+print("\n" + "=" * 80)
+print("Parallelism Report")
+print("=" * 80)
+
+
+def _analyze_conversation(events_dir: Path) -> dict[str, list[str]]:
+    """Return {llm_response_id: [tool_name, ...]} for multi-tool batches."""
+    batches: dict[str, list[str]] = defaultdict(list)
+    for event_file in sorted(events_dir.glob("event-*.json")):
+        data = json.loads(event_file.read_text())
+        if data.get("kind") == "ActionEvent" and "llm_response_id" in data:
+            batches[data["llm_response_id"]].append(data.get("tool_name", "?"))
+    return {rid: tools for rid, tools in batches.items() if len(tools) >= 2}
+
+
+for events_dir in sorted(persistence_dir.rglob("events")):
+    if not events_dir.is_dir():
+        continue
+    # Derive a label from the path (main conv vs sub-agent)
+    rel = events_dir.parent.relative_to(persistence_dir)
+    is_subagent = "subagents" in rel.parts
+    label = "sub-agent" if is_subagent else "main agent"
+
+    multi_batches = _analyze_conversation(events_dir)
+    if multi_batches:
+        for resp_id, tools in multi_batches.items():
+            print(f"\n  {label} batch ({resp_id[:16]}...):")
+            print(f"    Parallel tools: {tools}")
+    else:
+        print(f"\n  {label}: no parallel batches")
+
+cost = conversation.conversation_stats.get_combined_metrics().accumulated_cost
+print(f"\nTotal cost: ${cost:.4f}")
+print(f"EXAMPLE_COST: {cost:.4f}")