orchestrating-agents

name	orchestrating-agents
description	Orchestrates parallel API instances, delegated sub-tasks, and multi-agent workflows with streaming and tool-enabled delegation patterns. Use for parallel analysis, multi-perspective reviews, or complex task decomposition.
metadata	{"version":"0.4.0"}

SURFACE ROUTING — read first

This skill hand-rolls subagent orchestration via raw Anthropic API calls. A managed runtime now does the same job. Which one to use depends on your surface:

• In Claude Code (incl. CCotw): use the native runtime, NOT this skill. If you can invoke /deep-research, trigger a run with the workflow keyword, set /effort ultracode, or spawn Task subagents — do that instead. The runtime gives 16-concurrent / 1000-agent ceilings, an approval gate, adversarial cross-review, and in-session resume that this skill would otherwise reimplement badly. Dynamic workflows shipped in research preview (Claude Code v2.1.154+, 2026).
• In claude.ai chat or the bare API (no workflow runtime): use this skill. Parallel API instances over httpx is the only fan-out path here. Proceed below.

Discriminator: do you have a native subagent/Task tool or a workflow command? Yes → native. No → this skill. Never reimplement the runtime where it already exists.

Orchestrating Agents

This skill enables programmatic API invocations for advanced workflows including parallel processing, task delegation, and multi-agent analysis using the Anthropic API.

When to Use This Skill

Primary use cases:

• Parallel sub-tasks: Break complex analysis into simultaneous independent streams
• Multi-perspective analysis: Get 3-5 different expert viewpoints concurrently
• Delegation: Offload specific subtasks to specialized API instances
• Recursive workflows: Orchestrator coordinating multiple API instances
• High-volume processing: Batch process multiple items concurrently

Trigger patterns:

• "Parallel analysis", "multi-perspective review", "concurrent processing"
• "Delegate subtasks", "coordinate multiple agents"
• "Run analyses from different perspectives"
• "Get expert opinions from multiple angles"

Quick Start

Single Invocation

import sys
sys.path.append('/home/user/claude-skills/orchestrating-agents/scripts')
from claude_client import invoke_claude

response = invoke_claude(
    prompt="Analyze this code for security vulnerabilities: ...",
    model="claude-sonnet-4-6"
)
print(response)

Parallel Multi-Perspective Analysis

from claude_client import invoke_parallel

prompts = [
    {
        "prompt": "Analyze from security perspective: ...",
        "system": "You are a security expert"
    },
    {
        "prompt": "Analyze from performance perspective: ...",
        "system": "You are a performance optimization expert"
    },
    {
        "prompt": "Analyze from maintainability perspective: ...",
        "system": "You are a software architecture expert"
    }
]

results = invoke_parallel(prompts, model="claude-sonnet-4-6")

for i, result in enumerate(results):
    print(f"\n=== Perspective {i+1} ===")
    print(result)

Parallel with Shared Cached Context (Recommended)

For parallel operations with shared base context, use caching to reduce costs by up to 90%:

from claude_client import invoke_parallel

# Large context shared across all sub-agents (e.g., codebase, documentation)
base_context = """
<codebase>
...large codebase or documentation (1000+ tokens)...
</codebase>
"""

prompts = [
    {"prompt": "Find security vulnerabilities in the authentication module"},
    {"prompt": "Identify performance bottlenecks in the API layer"},
    {"prompt": "Suggest refactoring opportunities in the database layer"}
]

# First sub-agent creates cache, subsequent ones reuse it
results = invoke_parallel(
    prompts,
    shared_system=base_context,
    cache_shared_system=True  # 90% cost reduction for cached content
)

Multi-Turn Conversation with Auto-Caching

For sub-agents that need multiple rounds of conversation:

from claude_client import ConversationThread

# Create a conversation thread (auto-caches history)
agent = ConversationThread(
    system="You are a code refactoring expert with access to the codebase",
    cache_system=True
)

# Turn 1: Initial analysis
response1 = agent.send("Analyze the UserAuth class for issues")
print(response1)

# Turn 2: Follow-up (reuses cached system + turn 1)
response2 = agent.send("How would you refactor the login method?")
print(response2)

# Turn 3: Implementation (reuses all previous context)
response3 = agent.send("Show me the refactored code")
print(response3)

Streaming Responses

For real-time feedback from sub-agents:

from claude_client import invoke_claude_streaming

def show_progress(chunk):
    print(chunk, end='', flush=True)

response = invoke_claude_streaming(
    "Write a comprehensive security analysis...",
    callback=show_progress
)

Parallel Streaming

Monitor multiple sub-agents simultaneously:

from claude_client import invoke_parallel_streaming

def agent1_callback(chunk):
    print(f"[Security] {chunk}", end='', flush=True)

def agent2_callback(chunk):
    print(f"[Performance] {chunk}", end='', flush=True)

results = invoke_parallel_streaming(
    [
        {"prompt": "Security review: ..."},
        {"prompt": "Performance review: ..."}
    ],
    callbacks=[agent1_callback, agent2_callback]
)

Interruptible Operations

Cancel long-running parallel operations:

from claude_client import invoke_parallel_interruptible, InterruptToken
import threading
import time

token = InterruptToken()

# Run in background
def run_analysis():
    results = invoke_parallel_interruptible(
        prompts=[...],
        interrupt_token=token
    )
    return results

thread = threading.Thread(target=run_analysis)
thread.start()

# Interrupt after 5 seconds
time.sleep(5)
token.interrupt()

Core Functions

invoke_claude()

Single synchronous invocation with full control:

invoke_claude(
    prompt: str | list[dict],
    model: str = "claude-sonnet-4-6",
    system: str | list[dict] | None = None,
    max_tokens: int = 4096,
    temperature: float = 1.0,
    streaming: bool = False,
    cache_system: bool = False,
    cache_prompt: bool = False,
    messages: list[dict] | None = None,
    **kwargs
) -> str

Parameters:

• prompt: The user message (string or list of content blocks)
• model: Claude model to use (default: claude-sonnet-4-6)
• system: Optional system prompt (string or list of content blocks)
• max_tokens: Maximum tokens in response (default: 4096)
• temperature: Randomness 0-1 (default: 1.0)
• streaming: Enable streaming response (default: False)
• cache_system: Add cache_control to system prompt (requires 1024+ tokens, default: False)
• cache_prompt: Add cache_control to user prompt (requires 1024+ tokens, default: False)
• messages: Pre-built messages list for multi-turn (overrides prompt)
• **kwargs: Additional API parameters (top_p, top_k, etc.)

Returns: Response text as string

Note: Caching requires minimum 1,024 tokens per cache breakpoint. Cache lifetime is 5 minutes (refreshed on use).

invoke_parallel()

Concurrent invocations using lightweight workflow pattern:

invoke_parallel(
    prompts: list[dict],
    model: str = "claude-sonnet-4-6",
    max_tokens: int = 4096,
    max_workers: int = 5,
    shared_system: str | list[dict] | None = None,
    cache_shared_system: bool = False
) -> list[str]

Parameters:

• prompts: List of dicts with 'prompt' (required) and optional 'system', 'temperature', 'cache_system', 'cache_prompt', etc.
• model: Claude model for all invocations
• max_tokens: Max tokens per response
• max_workers: Max concurrent API calls (default: 5, max: 10)
• shared_system: System context shared across ALL invocations (for cache efficiency)
• cache_shared_system: Add cache_control to shared_system (default: False)

Returns: List of response strings in same order as prompts

Note: For optimal cost savings, put large common context (1024+ tokens) in shared_system with cache_shared_system=True. First invocation creates cache, subsequent ones reuse it (90% cost reduction).

invoke_claude_streaming()

Stream responses in real-time with optional callbacks:

invoke_claude_streaming(
    prompt: str | list[dict],
    callback: callable = None,
    model: str = "claude-sonnet-4-6",
    system: str | list[dict] | None = None,
    max_tokens: int = 4096,
    temperature: float = 1.0,
    cache_system: bool = False,
    cache_prompt: bool = False,
    **kwargs
) -> str

Parameters:

• callback: Optional function called with each text chunk (str) as it arrives
• (other parameters same as invoke_claude)

Returns: Complete accumulated response text

invoke_parallel_streaming()

Parallel invocations with per-agent streaming callbacks:

invoke_parallel_streaming(
    prompts: list[dict],
    callbacks: list[callable] = None,
    model: str = "claude-sonnet-4-6",
    max_tokens: int = 4096,
    max_workers: int = 5,
    shared_system: str | list[dict] | None = None,
    cache_shared_system: bool = False
) -> list[str]

Parameters:

• callbacks: Optional list of callback functions, one per prompt
• (other parameters same as invoke_parallel)

invoke_parallel_interruptible()

Parallel invocations with cancellation support:

invoke_parallel_interruptible(
    prompts: list[dict],
    interrupt_token: InterruptToken = None,
    # ... same other parameters as invoke_parallel
) -> list[str]

Parameters:

• interrupt_token: Optional InterruptToken to signal cancellation
• (other parameters same as invoke_parallel)

Returns: List of response strings (None for interrupted tasks)

ConversationThread

Manages multi-turn conversations with automatic caching:

thread = ConversationThread(
    system: str | list[dict] | None = None,
    model: str = "claude-sonnet-4-6",
    max_tokens: int = 4096,
    temperature: float = 1.0,
    cache_system: bool = True
)

response = thread.send(
    user_message: str | list[dict],
    cache_history: bool = True
) -> str

Methods:

• send(message, cache_history=True): Send message and get response
• get_messages(): Get conversation history
• clear(): Clear conversation history
• __len__(): Get number of turns

New in 0.3.0:

• turn_count property: Number of completed turn pairs
• send_continuation(guidance, cache_history): Lightweight continuation turn (requires prior send())
• max_turns constructor parameter: Optional turn limit
• continuation_prompt constructor parameter: Default continuation guidance

StallDetector

Monitors activity timestamps and detects unresponsive operations:

from claude_client import StallDetector

def handle_stall(task_id, idle_seconds):
    print(f"Task {task_id} stalled for {idle_seconds:.1f}s")

detector = StallDetector(timeout=60.0, on_stall=handle_stall)
detector.register("task-1")
detector.start_monitoring(poll_interval=5.0)

# Call heartbeat() during streaming/progress
detector.heartbeat("task-1")

# When done
detector.unregister("task-1")
detector.stop_monitoring()

TaskTracker (task_state module)

Formal task lifecycle state machine with enforced transitions:

from task_state import TaskTracker, TaskState

tracker = TaskTracker(max_retries=3)
tracker.add("task-1", category="security")

tracker.claim("task-1")    # UNCLAIMED → CLAIMED
tracker.start("task-1")    # CLAIMED → RUNNING (increments attempt)
tracker.complete("task-1")  # RUNNING → COMPLETED

# On failure with retry:
tracker.fail("task-2", error="timeout")
tracker.retry("task-2")     # FAILED → RETRY_QUEUED (if under max_retries)
tracker.claim("task-2")     # RETRY_QUEUED → CLAIMED

# Query state
tracker.active_count(category="security")
tracker.get_by_state(TaskState.RUNNING)
tracker.summary()  # {"completed": 1, "running": 1, ...}

invoke_with_retry() (orchestration module)

Single invocation with exponential backoff:

from orchestration import invoke_with_retry

response = invoke_with_retry(
    "Analyze this code...",
    max_retries=3,
    base_delay_ms=1000,   # 1s, 2s, 4s backoff
    max_delay_ms=10000,   # capped at 10s
)

invoke_parallel_managed() (orchestration module)

Full-featured parallel invocations with all Symphony patterns:

from orchestration import invoke_parallel_managed, ConcurrencyLimiter

limiter = ConcurrencyLimiter(
    global_limit=10,
    category_limits={"security": 3, "perf": 3}
)

def reconcile(prompts, tracker):
    # Filter out invalid/duplicate work before dispatch
    return [p for p in prompts if should_run(p)]

results = invoke_parallel_managed(
    prompts=[
        {"prompt": "Security review...", "task_id": "sec-1", "category": "security"},
        {"prompt": "Perf review...", "task_id": "perf-1", "category": "perf"},
    ],
    reconcile=reconcile,
    concurrency_limiter=limiter,
    max_retries=3,
    stall_timeout=60.0,
    on_stall=lambda tid, idle: print(f"{tid} stalled"),
)

Example Workflows

See references/workflows.md for detailed examples including:

• Multi-expert code review
• Parallel document analysis
• Recursive task delegation
• Advanced Agent SDK delegation patterns
• Prompt caching workflows

Execute Mode (Default Sub-Agent Prompt)

For autonomous sub-agents that should execute without asking questions:

from claude_client import invoke_claude, EXECUTE_MODE

response = invoke_claude(
    prompt="Review auth.py for SQL injection vulnerabilities",
    system=f"You are a security expert.\n\n{EXECUTE_MODE}"
)

EXECUTE_MODE encodes these principles (adapted from OpenAI Codex):

• Make assumptions instead of asking questions; state them briefly
• Think ahead: what else might be needed?
• Report failures with what you tried and what you'll do next
• Summarize deliverables and how to validate them

Agent Pool (Named Agents with Messaging)

For workflows where multiple agents need to communicate:

from agent_pool import AgentPool

pool = AgentPool(
    shared_system="You are reviewing the auth module of a web app.",
    max_depth=3,    # prevent recursive spawn explosion
    max_agents=10,
)

# Spawn named agents with roles
pool.spawn("security", system=f"Focus on vulnerabilities.\n\n{pool.EXECUTE_MODE}")
pool.spawn("perf", system=f"Focus on performance.\n\n{pool.EXECUTE_MODE}")

# Run turns (pending inter-agent messages auto-injected)
sec_result = pool.run("security", "Review the login flow")

# Agent-to-agent messaging
pool.send("security", to="perf",
          content="Auth does N+1 queries in the session check loop",
          trigger_turn=True)  # auto-runs perf with this context

# Broadcast to all agents
pool.broadcast("security", "Auth uses bcrypt cost=12, 200ms per hash")

# Query pool state
pool.agents()           # ["security", "perf"]
pool.agent_info("perf") # {name, depth, children, pending_messages, turns}

Spawn Reservation (Atomic Agent Creation)

For complex workflows where agent creation might fail:

from agent_pool import AgentPool

pool = AgentPool(shared_system="Code review team")

# Reservation pattern: name is reserved, rolled back on exception
with pool.reserve("analyst", parent="lead") as res:
    res.configure(system="You analyze code complexity.", model="claude-opus-4-6")
    # If configure or any other work raises, the name is released
# Agent "analyst" is now live

# Depth limits prevent unbounded recursion
pool.spawn("sub-analyst", parent="analyst")  # depth=2, OK
pool.spawn("sub-sub", parent="sub-analyst")  # depth=3, raises ValueError

When to Use AgentPool vs invoke_parallel

Pattern	Use When
invoke_parallel()	Independent tasks, no inter-agent communication needed
AgentPool	Agents need to share findings, build on each other's work, or have parent/child relationships
invoke_parallel_managed()	Independent tasks with retry, stall detection, concurrency limits

Setup

Prerequisites:

1. Install anthropic library:

   uv pip install anthropic
   

2. Configure API key via project knowledge file:

   Option 1 (recommended): Individual file

   • Create document: ANTHROPIC_API_KEY.txt
   • Content: Your API key (e.g., sk-ant-api03-...)

   Option 2: Combined file

   • Create document: API_CREDENTIALS.json
   • Content:

     {
       "anthropic_api_key": "sk-ant-api03-..."
     }
     

   Get your API key: https://console.anthropic.com/settings/keys

Installation check:

python3 -c "import anthropic; print(f'✓ anthropic {anthropic.__version__}')"

Error Handling

The module provides comprehensive error handling:

from claude_client import invoke_claude, ClaudeInvocationError

try:
    response = invoke_claude("Your prompt here")
except ClaudeInvocationError as e:
    print(f"API Error: {e}")
    print(f"Status: {e.status_code}")
    print(f"Details: {e.details}")
except ValueError as e:
    print(f"Configuration Error: {e}")

Common errors:

• API key missing: Add ANTHROPIC_API_KEY.txt to project knowledge (see Setup above)
• Rate limits: Reduce max_workers or add delays
• Token limits: Reduce prompt size or max_tokens
• Network errors: Automatic retry with exponential backoff

Prompt Caching

For detailed caching workflows and best practices, see references/workflows.md.

Performance Considerations

Token efficiency:

• Parallel calls use more tokens but save wall-clock time
• Use prompt caching for shared context (90% cost reduction)
• Use concise system prompts to reduce overhead
• Consider token budgets when setting max_tokens

Rate limits:

• Anthropic API has per-minute rate limits
• Default max_workers=5 is safe for most tiers
• Adjust based on your API tier and rate limits

Cost management:

• Each invocation consumes API credits
• Monitor usage in Anthropic Console
• Use smaller models (haiku) for simple tasks
• Use prompt caching for repeated context (90% savings)
• Cache lifetime: 5 minutes, refreshed on each use

Best Practices

1. Use parallel invocations for independent tasks only

   • Don't parallelize sequential dependencies
   • Each parallel task should be self-contained

2. Set appropriate system prompts

   • Define clear roles/expertise for each instance
   • Keeps responses focused and relevant

3. Handle errors gracefully

   • Always wrap invocations in try-except
   • Provide fallback behavior for failures

4. Test with small batches first

   • Verify prompts work before scaling
   • Check token usage and costs

5. Consider alternatives

   • Not all tasks benefit from multiple instances
   • Sometimes sequential with context is better

Token Efficiency

This skill uses ~800 tokens when loaded but enables powerful multi-agent patterns that can dramatically improve complex analysis quality and speed.

See Also

• references/api-reference.md - Detailed API documentation
• Anthropic API Docs - Official documentation