molecule-ai/molecule-core
35
0
Fork 2
Code Issues 16 Pull Requests 2 Actions 49 Packages Projects Releases Wiki Activity

Merge pull request #267 from Molecule-AI/feat/hermes-phase2c-streaming

Browse Source 
feat(hermes): Phase 2c — multi-turn history passed natively to all dispatch paths
This commit is contained in:
Hongming Wang 2026-04-15 16:10:21 -07:00 committed by GitHub
commit bf7706c96e
2 changed files with 220 additions and 32 deletions
Show Stats Download Patch File Download Diff File Expand all files Collapse all files

155
workspace-template/adapters/hermes/executor.py
View File

@ -130,16 +130,90 @@ class HermesA2AExecutor:
self.model = model
self._heartbeat = heartbeat
# ------------------------------------------------------------------
# History → provider-specific message list converters
# ------------------------------------------------------------------
#
# The A2A shared runtime gives us history as ``list[tuple[str, str]]``
# with roles ``"human"`` / ``"ai"``. Each provider wants a different
# shape:
#
# OpenAI-compat: [{"role":"user"|"assistant", "content": str}, ...]
# Anthropic: [{"role":"user"|"assistant", "content": str}, ...] (same)
# Gemini: [{"role":"user"|"model", "parts": [{"text": str}]}, ...]
#
# Before Phase 2c these were flattened into a single user turn via
# ``shared_runtime.build_task_text``, which worked for basic text
# handoff but lost the model's native multi-turn awareness (system
# prompts, tool-use history, role attribution for instruction
# following). Phase 2c keeps the turns as turns.
@staticmethod
def _history_to_openai_messages(
user_message: str,
history: "list[tuple[str, str]]",
) -> "list[dict]":
"""Convert A2A history + current turn to OpenAI Chat Completions shape."""
messages: list[dict] = []
for role, text in history or []:
messages.append({
"role": "user" if role == "human" else "assistant",
"content": text,
})
messages.append({"role": "user", "content": user_message})
return messages
@staticmethod
def _history_to_anthropic_messages(
user_message: str,
history: "list[tuple[str, str]]",
) -> "list[dict]":
"""Convert A2A history + current turn to Anthropic Messages API shape.
Identical wire format to OpenAI (``role`` + ``content``) for text-only
turns, so we just delegate. The difference matters for tool_use /
content blocks, which are Phase 2d territory.
"""
return HermesA2AExecutor._history_to_openai_messages(user_message, history)
@staticmethod
def _history_to_gemini_contents(
user_message: str,
history: "list[tuple[str, str]]",
) -> "list[dict]":
"""Convert A2A history + current turn to Gemini generateContent shape.
Gemini uses ``role: "user" | "model"`` (NOT "assistant") and wraps
text in a ``parts: [{"text": ...}]`` list.
"""
contents: list[dict] = []
for role, text in history or []:
contents.append({
"role": "user" if role == "human" else "model",
"parts": [{"text": text}],
})
contents.append({"role": "user", "parts": [{"text": user_message}]})
return contents
# ------------------------------------------------------------------
# Per-provider inference paths
# ------------------------------------------------------------------
async def _do_openai_compat(self, task_text: str) -> str:
async def _do_openai_compat(
self,
user_message: str,
history: "list[tuple[str, str]] | None" = None,
) -> str:
"""OpenAI-compat inference — used by every provider with auth_scheme='openai'.
14 of the 15 registered providers route here. Uses ``openai.AsyncOpenAI``
13 of the 15 registered providers route here. Uses ``openai.AsyncOpenAI``
pointed at the provider's base_url; every provider's API is wire-
compatible with the OpenAI Chat Completions shape.
Phase 2c: accepts multi-turn history. The old single-``task_text`` call
shape (pre-2c) is preserved — pass the flattened text as ``user_message``
with no history and the call degrades gracefully to the original
behavior. See ``_history_to_openai_messages`` for the conversion.
"""
import openai
@ -147,13 +221,18 @@ class HermesA2AExecutor:
api_key=self.api_key,
base_url=self.base_url,
)
messages = self._history_to_openai_messages(user_message, history or [])
response = await client.chat.completions.create(
model=self.model,
messages=[{"role": "user", "content": task_text}],
messages=messages,
)
return response.choices[0].message.content or ""
async def _do_anthropic_native(self, task_text: str) -> str:
async def _do_anthropic_native(
self,
user_message: str,
history: "list[tuple[str, str]] | None" = None,
) -> str:
"""Native Anthropic Messages API inference.
Uses the official ``anthropic`` Python SDK for correct tool-calling,
@ -165,9 +244,8 @@ class HermesA2AExecutor:
OpenAI-compat path — silent fallback would mask the fidelity loss
(tool_use blocks become plain text, vision gets stripped, etc.).
Phase 2a minimum viable: single-turn text in, text out, no tools, no
vision. Phase 2b will add tool-calling, vision, and streaming via
the same path (still within this method).
Phase 2a: single-turn text in, text out. Phase 2c: multi-turn history.
Tools + vision remain Phase 2d.
"""
try:
import anthropic
@ -180,18 +258,23 @@ class HermesA2AExecutor:
) from exc
client = anthropic.AsyncAnthropic(api_key=self.api_key)
messages = self._history_to_anthropic_messages(user_message, history or [])
response = await client.messages.create(
model=self.model,
max_tokens=4096,
messages=[{"role": "user", "content": task_text}],
messages=messages,
)
# response.content is a list of ContentBlock; for single-turn text-only
# the first block is a TextBlock with a .text attribute.
# response.content is a list of ContentBlock; for text-only the first
# block is a TextBlock with a .text attribute.
if response.content and hasattr(response.content[0], "text"):
return response.content[0].text
return ""
async def _do_gemini_native(self, task_text: str) -> str:
async def _do_gemini_native(
self,
user_message: str,
history: "list[tuple[str, str]] | None" = None,
) -> str:
"""Native Google Gemini ``generateContent`` inference.
Uses the official ``google-genai`` Python SDK for correct vision
@ -204,9 +287,9 @@ class HermesA2AExecutor:
silently falling back to the OpenAI-compat shim (same fail-loud
semantics as the anthropic path).
Phase 2b minimum viable: single-turn text in, text out, no tools,
no vision, no thinking config. Phase 2c/2d layers those on the same
method.
Phase 2b: single-turn text in, text out. Phase 2c: multi-turn history
via Gemini's ``contents=[{role,parts}]`` shape (note: role is
``"user"`` / ``"model"``, NOT ``"assistant"``).
"""
try:
from google import genai # type: ignore[import-not-found]
@ -218,45 +301,59 @@ class HermesA2AExecutor:
"OpenRouter's OpenAI-compat shim instead."
) from exc
# google-genai client reads api_key from env by default; pass it
# explicitly so we respect whatever ProviderConfig resolved (e.g. a
# test-only key that isn't in process env yet).
client = genai.Client(api_key=self.api_key)
contents = self._history_to_gemini_contents(user_message, history or [])
response = await client.aio.models.generate_content(
model=self.model,
contents=task_text,
contents=contents,
)
# response.text is the flattened text across all parts of the first
# candidate. For single-turn text-only that's the whole reply.
# candidate. For text-only that's the whole reply.
return response.text or ""
async def _do_inference(self, task_text: str) -> str:
"""Dispatch to the right inference path based on provider auth_scheme."""
async def _do_inference(
self,
user_message: str,
history: "list[tuple[str, str]] | None" = None,
) -> str:
"""Dispatch to the right inference path based on provider auth_scheme.
Phase 2c: takes ``user_message`` + optional ``history`` list-of-tuples,
passes through to the chosen path. Each path has its own history →
provider-message conversion via the static helpers above.
"""
scheme = self.provider_cfg.auth_scheme
if scheme == "anthropic":
return await self._do_anthropic_native(task_text)
return await self._do_anthropic_native(user_message, history)
if scheme == "gemini":
return await self._do_gemini_native(task_text)
return await self._do_gemini_native(user_message, history)
if scheme == "openai":
return await self._do_openai_compat(task_text)
return await self._do_openai_compat(user_message, history)
# Unknown scheme — treat as openai-compat for forward-compat with any
# future provider the registry adds without yet having a native path.
logger.warning(
"Hermes: unknown auth_scheme=%r for provider=%s — falling back to openai-compat",
scheme, self.provider_cfg.name,
)
return await self._do_openai_compat(task_text)
return await self._do_openai_compat(user_message, history)
# ------------------------------------------------------------------
# AgentExecutor interface
# ------------------------------------------------------------------
async def execute(self, context, event_queue): # pragma: no cover
"""Execute a Hermes inference request and push the reply to event_queue."""
"""Execute a Hermes inference request and push the reply to event_queue.
Phase 2c: passes the conversation history to the dispatch layer as a
structured list of (role, text) turns instead of flattening via
``build_task_text``. Each provider path converts the list into its
native multi-turn message shape (OpenAI messages, Anthropic messages,
or Gemini contents). This gives the model its native multi-turn
awareness for instruction following.
"""
from a2a.utils import new_agent_text_message
from adapters.shared_runtime import (
brief_task,
build_task_text,
extract_history,
extract_message_text,
set_current_task,
@ -270,8 +367,8 @@ class HermesA2AExecutor:
await set_current_task(self._heartbeat, brief_task(user_message))
try:
task_text = build_task_text(user_message, extract_history(context))
reply = await self._do_inference(task_text)
history = extract_history(context)
reply = await self._do_inference(user_message, history)
except Exception as exc:
logger.exception("Hermes executor error: %s", exc)
reply = f"Hermes error: {exc}"

97
workspace-template/tests/test_hermes_phase2_dispatch.py
View File

@ -98,7 +98,9 @@ async def test_dispatch_openai_scheme_calls_openai_compat():
result = await executor._do_inference("hello")
executor._do_openai_compat.assert_awaited_once_with("hello")
# Phase 2c: _do_inference passes (user_message, history) to the path;
# when no history supplied, second arg is None.
executor._do_openai_compat.assert_awaited_once_with("hello", None)
executor._do_anthropic_native.assert_not_awaited()
executor._do_gemini_native.assert_not_awaited()
assert result == "openai-result"
@ -114,7 +116,7 @@ async def test_dispatch_anthropic_scheme_calls_anthropic_native():
result = await executor._do_inference("hello")
executor._do_anthropic_native.assert_awaited_once_with("hello")
executor._do_anthropic_native.assert_awaited_once_with("hello", None)
executor._do_openai_compat.assert_not_awaited()
executor._do_gemini_native.assert_not_awaited()
assert result == "anthropic-result"
@ -130,12 +132,101 @@ async def test_dispatch_gemini_scheme_calls_gemini_native():
result = await executor._do_inference("hello")
executor._do_gemini_native.assert_awaited_once_with("hello")
executor._do_gemini_native.assert_awaited_once_with("hello", None)
executor._do_openai_compat.assert_not_awaited()
executor._do_anthropic_native.assert_not_awaited()
assert result == "gemini-result"
# ---------------------------------------------------------------------------
# Phase 2c — history-to-message conversion tests
# ---------------------------------------------------------------------------
def test_history_to_openai_messages_empty_history():
"""No history → single user message (back-compat with pre-2c single-turn shape)."""
import importlib.util
src = (_HERMES_DIR / "executor.py").read_text().replace(
"from .providers import", "from providers import"
)
ns: dict = {}
exec(compile(src, str(_HERMES_DIR / "executor.py"), "exec"), ns)
HermesA2AExecutor = ns["HermesA2AExecutor"]
msgs = HermesA2AExecutor._history_to_openai_messages("current turn", [])
assert msgs == [{"role": "user", "content": "current turn"}]
def test_history_to_openai_messages_multi_turn():
"""A2A history roles map: human→user, ai→assistant. Current turn appended as user."""
import importlib.util
src = (_HERMES_DIR / "executor.py").read_text().replace(
"from .providers import", "from providers import"
)
ns: dict = {}
exec(compile(src, str(_HERMES_DIR / "executor.py"), "exec"), ns)
HermesA2AExecutor = ns["HermesA2AExecutor"]
history = [("human", "first question"), ("ai", "first answer"), ("human", "follow-up")]
msgs = HermesA2AExecutor._history_to_openai_messages("current turn", history)
assert msgs == [
{"role": "user", "content": "first question"},
{"role": "assistant", "content": "first answer"},
{"role": "user", "content": "follow-up"},
{"role": "user", "content": "current turn"},
]
def test_history_to_anthropic_messages_same_as_openai():
"""Anthropic Messages API uses the same wire shape as OpenAI for text-only turns."""
import importlib.util
src = (_HERMES_DIR / "executor.py").read_text().replace(
"from .providers import", "from providers import"
)
ns: dict = {}
exec(compile(src, str(_HERMES_DIR / "executor.py"), "exec"), ns)
HermesA2AExecutor = ns["HermesA2AExecutor"]
history = [("human", "hello"), ("ai", "hi")]
openai_msgs = HermesA2AExecutor._history_to_openai_messages("how are you?", history)
anth_msgs = HermesA2AExecutor._history_to_anthropic_messages("how are you?", history)
assert openai_msgs == anth_msgs
def test_history_to_gemini_contents_uses_model_role_and_parts_wrapper():
"""Gemini uses role='user'|'model' (NOT 'assistant') and wraps text in parts=[{text}]."""
import importlib.util
src = (_HERMES_DIR / "executor.py").read_text().replace(
"from .providers import", "from providers import"
)
ns: dict = {}
exec(compile(src, str(_HERMES_DIR / "executor.py"), "exec"), ns)
HermesA2AExecutor = ns["HermesA2AExecutor"]
history = [("human", "hi"), ("ai", "hello back")]
contents = HermesA2AExecutor._history_to_gemini_contents("follow-up?", history)
assert contents == [
{"role": "user", "parts": [{"text": "hi"}]},
{"role": "model", "parts": [{"text": "hello back"}]},
{"role": "user", "parts": [{"text": "follow-up?"}]},
]
@pytest.mark.asyncio
async def test_dispatch_passes_history_through():
"""When _do_inference is called with history, it flows through to the provider path."""
executor = _make_executor("anthropic")
executor._do_anthropic_native = AsyncMock(return_value="reply-with-history")
executor._do_openai_compat = AsyncMock()
executor._do_gemini_native = AsyncMock()
history = [("human", "prior q"), ("ai", "prior a")]
result = await executor._do_inference("current", history)
executor._do_anthropic_native.assert_awaited_once_with("current", history)
assert result == "reply-with-history"
@pytest.mark.asyncio
async def test_dispatch_unknown_scheme_falls_back_to_openai_compat():
"""Unknown auth_scheme → log a warning + fall back to openai-compat (forward-compat)."""