أنماط التفويض والعمل المتوازي

Hermes can spawn isolated child agents to work on tasks in parallel. Each subagent gets its own conversation, terminal session, and toolset. Only the final summary comes back — intermediate tool calls never enter your context window.

For the full feature reference, see Subagent Delegation.

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Good candidates for delegation:

• Reasoning-heavy subtasks (debugging, code review, research synthesis)
• Tasks that would flood your context with intermediate data
• Parallel independent workstreams (research A and B simultaneously)
• Fresh-context tasks where you want the agent to approach without bias

Use something else:

• Single tool call → just use the tool directly
• Mechanical multi-step work with logic between steps → execute_code
• Tasks needing user interaction → subagents can't use clarify
• Quick file edits → do them directly
• Durable long-running work that must survive session closure or process restart → cronjob or terminal(background=True, notify_on_complete=True). Top-level delegation is asynchronous but still process-local.

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Research three topics simultaneously and get structured summaries back:

Research these three topics in parallel:
1. Current state of WebAssembly outside the browser
2. RISC-V server chip adoption in 2025
3. Practical quantum computing applications

Focus on recent developments and key players.

Behind the scenes, Hermes uses:

delegate_task(tasks=[
    {
        "goal": "Research WebAssembly outside the browser in 2025",
        "context": "Focus on: runtimes (Wasmtime, Wasmer), cloud/edge use cases, WASI progress"
    },
    {
        "goal": "Research RISC-V server chip adoption",
        "context": "Focus on: server chips shipping, cloud providers adopting, software ecosystem"
    },
    {
        "goal": "Research practical quantum computing applications",
        "context": "Focus on: error correction breakthroughs, real-world use cases, key companies"
    }
])

All three run concurrently. Each subagent searches the web independently and returns a summary. The parent agent then synthesizes them into a coherent briefing.

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Delegate a security review to a fresh-context subagent that approaches the code without preconceptions:

Review the authentication module at src/auth/ for security issues.
Check for SQL injection, JWT validation problems, password handling,
and session management. Fix anything you find and run the tests.

The key is the context field — it must include everything the subagent needs:

delegate_task(
    goal="Review src/auth/ for security issues and fix any found",
    context="""Project at /home/user/webapp. Python 3.11, Flask, PyJWT, bcrypt.
    Auth files: src/auth/login.py, src/auth/jwt.py, src/auth/middleware.py
    Test command: pytest tests/auth/ -v
    Focus on: SQL injection, JWT validation, password hashing, session management.
    Fix issues found and verify tests pass."""
)

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Evaluate multiple approaches to the same problem in parallel, then pick the best:

I need to add full-text search to our Django app. Evaluate three approaches
in parallel:
1. PostgreSQL tsvector (built-in)
2. Elasticsearch via django-elasticsearch-dsl
3. Meilisearch via meilisearch-python

For each: setup complexity, query capabilities, resource requirements,
and maintenance overhead. Compare them and recommend one.

Each subagent researches one option independently. Because they're isolated, there's no cross-contamination — each evaluation stands on its own merits. The parent agent gets all three summaries and makes the comparison.

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Split a large refactoring task across parallel subagents, each handling a different part of the codebase:

delegate_task(tasks=[
    {
        "goal": "Refactor all API endpoint handlers to use the new response format",
        "context": """Project at /home/user/api-server.
        Files: src/handlers/users.py, src/handlers/auth.py, src/handlers/billing.py
        Old format: return {"data": result, "status": "ok"}
        New format: return APIResponse(data=result, status=200).to_dict()
        Import: from src.responses import APIResponse
        Run tests after: pytest tests/handlers/ -v"""
    },
    {
        "goal": "Update all client SDK methods to handle the new response format",
        "context": """Project at /home/user/api-server.
        Files: sdk/python/client.py, sdk/python/models.py
        Old parsing: result = response.json()["data"]
        New parsing: result = response.json()["data"] (same key, but add status code checking)
        Also update sdk/python/tests/test_client.py"""
    },
    {
        "goal": "Update API documentation to reflect the new response format",
        "context": """Project at /home/user/api-server.
        Docs at: docs/api/. Format: Markdown with code examples.
        Update all response examples from old format to new format.
        Add a 'Response Format' section to docs/api/overview.md explaining the schema."""
    }
])

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Use execute_code for mechanical data gathering, then delegate the reasoning-heavy analysis:

# Step 1: Mechanical gathering (execute_code is better here — no reasoning needed)
execute_code("""
from hermes_tools import web_search, web_extract

results = []
for query in ["AI funding Q1 2026", "AI startup acquisitions 2026", "AI IPOs 2026"]:
    r = web_search(query, limit=5)
    for item in r["data"]["web"]:
        results.append({"title": item["title"], "url": item["url"], "desc": item["description"]})

# Extract full content from top 5 most relevant
urls = [r["url"] for r in results[:5]]
content = web_extract(urls)

# Save for the analysis step

with open("/tmp/ai-funding-data.json", "w") as f:
    json.dump({"search_results": results, "extracted": content["results"]}, f)
print(f"Collected {len(results)} results, extracted {len(content['results'])} pages")
""")

# Step 2: Reasoning-heavy analysis (delegation is better here)
delegate_task(
    goal="Analyze AI funding data and write a market report",
    context="""Raw data at /tmp/ai-funding-data.json contains search results and
    extracted web pages about AI funding, acquisitions, and IPOs in Q1 2026.
    Write a structured market report: key deals, trends, notable players,
    and outlook. Focus on deals over $100M."""
)

This is often the most efficient pattern: execute_code handles the 10+ sequential tool calls cheaply, then a subagent does the single expensive reasoning task with a clean context.

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Subagents inherit the parent's enabled toolsets. delegate_task does not accept a model-facing toolsets parameter, so delegated work cannot grant itself capabilities that the parent does not have. Configure the parent's tools before starting the conversation when a delegated task needs web, terminal, file, or other access. Hermes still strips child-blocked tools such as clarify, memory, and send_message; children keep execute_code for programmatic tool calling.

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• Default 3 parallel tasks: batches default to 3 concurrent subagents (configurable via delegation.max_concurrent_children in config.yaml, no hard ceiling, only a floor of 1)
• Nested delegation is opt-in: leaf subagents (default) cannot call delegate_task, clarify, memory, or execute_code. Orchestrator subagents (role="orchestrator") retain delegate_task for further delegation, but only when delegation.max_spawn_depth is raised above the default of 1 (floor 1, no ceiling); the other three remain blocked. Disable globally via delegation.orchestrator_enabled: false.

Tuning Concurrency and Depth

Example: running 30 parallel workers with nested subagents:

delegation:
  max_concurrent_children: 30
  max_spawn_depth: 2

• Separate terminals — each subagent gets its own terminal session with separate working directory and state
• No conversation history — subagents see only the goal and context the parent agent passes when calling delegate_task
• Default 50 iterations — set max_iterations lower for simple tasks to save cost
• Not durable — top-level delegation runs in the background and posts its result back later, but it remains tied to the owning session and Hermes process. Session closure, /stop, /new, or a process restart can cancel or strand in-progress work. Use cronjob or terminal(background=True, notify_on_complete=True) for work that must survive those boundaries.

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Be specific in goals. "Fix the bug" is too vague. "Fix the TypeError in api/handlers.py line 47 where process_request() receives None from parse_body()" gives the subagent enough to work with.

Include file paths. Subagents don't know your project structure. Always include absolute paths to relevant files, the project root, and the test command.

Use delegation for context isolation. Sometimes you want a fresh perspective. Delegating forces you to articulate the problem clearly, and the subagent approaches it without the assumptions that built up in your conversation.

Check results. Subagent summaries are just that — summaries. If a subagent says "fixed the bug and tests pass," verify by running the tests yourself or reading the diff.

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For the complete delegation reference — all parameters, ACP integration, and advanced configuration — see Subagent Delegation.