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Delegation & Parallel Work

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.

When to Delegate

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

Pattern: Parallel Research

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",
        "toolsets": ["web"]
    },
    {
        "goal": "Research RISC-V server chip adoption",
        "context": "Focus on: server chips shipping, cloud providers adopting, software ecosystem",
        "toolsets": ["web"]
    },
    {
        "goal": "Research practical quantum computing applications",
        "context": "Focus on: error correction breakthroughs, real-world use cases, key companies",
        "toolsets": ["web"]
    }
])

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

Pattern: Code Review

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.""",
    toolsets=["terminal", "file"]
)

The Context Problem

Subagents know absolutely nothing about your conversation. They start completely fresh. If you delegate "fix the bug we were discussing," the subagent has no idea what bug you mean. Always pass file paths, error messages, project structure, and constraints explicitly.

Pattern: Compare Alternatives

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.

Pattern: Multi-File Refactoring

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""",
        "toolsets": ["terminal", "file"]
    },
    {
        "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""",
        "toolsets": ["terminal", "file"]
    },
    {
        "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.""",
        "toolsets": ["terminal", "file"]
    }
])

提示

Each subagent gets its own terminal session. They can work on the same project directory without stepping on each other — as long as they're editing different files. If two subagents might touch the same file, handle that file yourself after the parallel work completes.

Pattern: Gather Then Analyze

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
import json
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.""",
    toolsets=["terminal", "file"]
)

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.

Toolset Selection

Choose toolsets based on what the subagent needs:

Task type	Toolsets	Why
Web research	`["web"]`	web_search + web_extract only
Code work	`["terminal", "file"]`	Shell access + file operations
Full-stack	`["terminal", "file", "web"]`	Everything except messaging
Read-only analysis	`["file"]`	Can only read files, no shell

Restricting toolsets keeps the subagent focused and prevents accidental side effects (like a research subagent running shell commands).

Constraints

Max 3 parallel tasks — batches are capped at 3 concurrent subagents
No nesting — subagents cannot call delegate_task, clarify, memory, send_message, or execute_code
Separate terminals — each subagent gets its own terminal session with separate working directory and state
No conversation history — subagents see only what you put in goal and context
Default 50 iterations — set max_iterations lower for simple tasks to save cost

Tips

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.

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

When to Delegate​

Pattern: Parallel Research​

Pattern: Code Review​

Pattern: Compare Alternatives​

Pattern: Multi-File Refactoring​

Pattern: Gather Then Analyze​

Toolset Selection​

Constraints​

Tips​