agentmint-hermes-runner 0.10.0

Route Hermes delegate_task(background=True) to named, persistent AgentMint subagents.

agentmint-hermes-runner

Python adapter that bridges Hermes' delegate_task(background=True) to named, persistent AgentMint subagents — specialists that accumulate /workspace/MEMORY.md across calls.

One install path: monkey-patch delegate_task so every async delegation can route to AgentMint. The LLM picks the target subagent via either default_agent_name (set at install) or by including "agentmint-<name>" in the toolsets list (per-call routing).

The Hermes-installable skill that drives this adapter lives in a separate catalog repo: mesutcelik/agentmint-skills — hermes skills install mesutcelik/agentmint-skills/hermes-delegate-task. The skill references this package by its PyPI name (pip install agentmint-hermes-runner).

Status

v0.10.0 — alpha. Auth backends: BearerAuth (any rail — Stripe-Link / x402 / Tempo MPP), TempoAuth (Tempo USDC.e — Tier 1 direct only; the delegate_task patches require Bearer).

New in 0.10.0: auto-wiring at Hermes boot. pip install + one-time agentmint-hermes-init + restart = adapter active. No edits to Hermes' startup script needed.

Routing model

There are exactly two ways the runner picks the target subagent. They serve different intents — don't conflate them.

default_agent_name	Role
"general-worker" (or similar generic name)	Catch-all for unrouted delegations. Use when Hermes wants to offload arbitrary subtasks to a single persistent worker whose MEMORY.md accumulates as the session breadcrumb trail. The default should always be a generic worker — never a specialist. Naming a specialist as the default breaks the moment you add a second specialist.
None (no default)	Unrouted delegations fall through to Hermes-native delegate_task. Use when AgentMint involvement is opt-in per call.

toolsets=["agentmint-<name>"] directive	Role
Per-call specialist routing. LLM dispatches to a specific named expert (pr-reviewer, data-analyst, slack-bot, …). Each specialist has its own MEMORY.md. Always use this for specialists — not the default slot.

Setup — three commands

# 1. Install the runner inside Hermes' Python environment
pip install agentmint-hermes-runner

# 2. One-time interactive bootstrap — picks a rail, tops up the wallet,
#    caches the JWT to ~/.agentmint/credentials.json, mints `general-worker`
agentmint-hermes-init

# 3. Restart Hermes
#    The runner's `hermes_agent.plugins` entry-point fires at boot,
#    reads the cached JWT, and auto-wires `delegate_task`.

That's it. Every unrouted delegate_task(background=True) now lands in general-worker — its MEMORY.md grows across every delegation that doesn't carry a specialist directive.

If you'd rather wire the adapter by hand (e.g. you're injecting the JWT from a secret manager, not a file on disk), the lower-level API still works:

import os
from agentmint_hermes_runner import AgentMintDispatcher, BearerAuth, install_delegate_task_wrapper

dispatcher = AgentMintDispatcher(auth=BearerAuth(jwt=os.environ["AGENTMINT_JWT"]))
install_delegate_task_wrapper(dispatcher, default_agent_name="general-worker")

The autoload entry-point becomes a no-op if AGENTMINT_JWT is unset AND ~/.agentmint/credentials.json is absent — safe to leave installed even in setups that bring their own wiring.

Setup — per-call specialist routing

The LLM picks the target specialist on each call via the toolsets list:

# Operator setup is identical — generic default + LLM-driven overrides.
install_delegate_task_wrapper(dispatcher, default_agent_name="general-worker")

# The LLM then dispatches like this:
delegate_task(
    background=True,
    goal="Review PR 42 in mesutcelik/agentmint-mono",
    toolsets=["terminal", "file", "agentmint-pr-reviewer"],
)

The adapter parses agentmint-pr-reviewer from toolsets, routes that call to that subagent (overriding default_agent_name), and strips the entry from the toolset list before composing the prompt the subagent receives.

This is a workaround for Hermes' delegate_task not accepting a dispatcher-target argument. A formal proposal is in docs/hermes-feature-request.md — when an upstream extension lands, this hack will be deprecated in favor of a first-class dispatcher or metadata parameter.

Pattern discipline

• default_agent_name → generic worker only (general-worker, default-worker, etc.)
• Specialists → only via toolsets=["agentmint-<name>"]
• Never name a specialist as the default. Specialists scale; defaults are catch-all.

See examples/persistent.py for a complete operator setup snippet.

Install

pip install agentmint-hermes-runner

Test

pip install -e ".[dev]"
pytest
ruff check .

Lower-level surface

If you want to drive AgentMint directly without the delegate_task patch:

result = dispatcher.dispatch(
    agent_name="reviewer-myrepo",
    goal="Review the diff at /workspace/pr-42.diff and flag risks.",
    context="Project at /workspace, Python 3.11, uses Flask + PyJWT.",
    toolsets=["terminal", "file"],     # "web" raises UnsupportedToolset
    role="leaf",                        # or "orchestrator"
    max_iterations=50,
    child_timeout_seconds=600,
    workspace_files=[                   # ship inputs into the sandbox before the run
        {"path": "/workspace/pr-42.diff", "content": "diff --git a/foo ..."},
    ],
    cleanup_paths=["/workspace/pr-42.diff"],  # wipe them after the run
)

# Batch dispatch (Hermes tasks=[…] analog):
results = dispatcher.dispatch_batch(
    tasks=[
        Task(agent_name="researcher-wasm", goal="WASM 2026 survey"),
        Task(agent_name="researcher-riscv", goal="RISC-V 2026 survey"),
    ],
    max_concurrent_children=3,
    child_timeout_seconds=900,
)

Known unsupported

• toolsets=["web"] — no canonical AgentMint web-fetch skill yet. Raises UnsupportedToolset.
• max_spawn_depth — AgentMint sandboxes aren't structurally bounded by depth.
• Tempo + the delegate_task patches — polling against agent.run.status is Bearer-only. Tempo customers can use Tier 1 (direct curl) but not the install/plugin paths above.

License

MIT