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https://github.com/NousResearch/hermes-agent.git
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4 Commits
fix/plugin
...
hermes/her
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8c5c598a58 | ||
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66d475eabe | ||
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0c96b54ef8 | ||
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77d432a545 |
@@ -564,8 +564,10 @@ class APIServerAdapter(BasePlatformAdapter):
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if delta is not None:
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_stream_q.put(delta)
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def _on_tool_progress(name, preview, args):
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def _on_tool_progress(event_type, name, preview, args, **kwargs):
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"""Inject tool progress into the SSE stream for Open WebUI."""
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if event_type != "tool.started":
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return # Only show tool start events in chat stream
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if name.startswith("_"):
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return # Skip internal events (_thinking)
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from agent.display import get_tool_emoji
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@@ -816,9 +818,29 @@ class APIServerAdapter(BasePlatformAdapter):
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else:
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return web.json_response(_openai_error("'input' must be a string or array"), status=400)
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# Reconstruct conversation history from previous_response_id
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# Accept explicit conversation_history from the request body.
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# This lets stateless clients supply their own history instead of
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# relying on server-side response chaining via previous_response_id.
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# Precedence: explicit conversation_history > previous_response_id.
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conversation_history: List[Dict[str, str]] = []
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if previous_response_id:
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raw_history = body.get("conversation_history")
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if raw_history:
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if not isinstance(raw_history, list):
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return web.json_response(
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_openai_error("'conversation_history' must be an array of message objects"),
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status=400,
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)
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for i, entry in enumerate(raw_history):
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if not isinstance(entry, dict) or "role" not in entry or "content" not in entry:
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return web.json_response(
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_openai_error(f"conversation_history[{i}] must have 'role' and 'content' fields"),
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status=400,
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)
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conversation_history.append({"role": str(entry["role"]), "content": str(entry["content"])})
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if previous_response_id:
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logger.debug("Both conversation_history and previous_response_id provided; using conversation_history")
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if not conversation_history and previous_response_id:
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stored = self._response_store.get(previous_response_id)
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if stored is None:
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return web.json_response(_openai_error(f"Previous response not found: {previous_response_id}"), status=404)
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@@ -1404,14 +1426,49 @@ class APIServerAdapter(BasePlatformAdapter):
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instructions = body.get("instructions")
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previous_response_id = body.get("previous_response_id")
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# Accept explicit conversation_history from the request body.
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# Precedence: explicit conversation_history > previous_response_id.
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conversation_history: List[Dict[str, str]] = []
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if previous_response_id:
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raw_history = body.get("conversation_history")
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if raw_history:
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if not isinstance(raw_history, list):
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return web.json_response(
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_openai_error("'conversation_history' must be an array of message objects"),
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status=400,
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)
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for i, entry in enumerate(raw_history):
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if not isinstance(entry, dict) or "role" not in entry or "content" not in entry:
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return web.json_response(
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_openai_error(f"conversation_history[{i}] must have 'role' and 'content' fields"),
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status=400,
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)
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conversation_history.append({"role": str(entry["role"]), "content": str(entry["content"])})
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if previous_response_id:
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logger.debug("Both conversation_history and previous_response_id provided; using conversation_history")
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if not conversation_history and previous_response_id:
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stored = self._response_store.get(previous_response_id)
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if stored:
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conversation_history = list(stored.get("conversation_history", []))
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if instructions is None:
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instructions = stored.get("instructions")
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# When input is a multi-message array, extract all but the last
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# message as conversation history (the last becomes user_message).
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# Only fires when no explicit history was provided.
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if not conversation_history and isinstance(raw_input, list) and len(raw_input) > 1:
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for msg in raw_input[:-1]:
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if isinstance(msg, dict) and msg.get("role") and msg.get("content"):
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content = msg["content"]
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if isinstance(content, list):
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# Flatten multi-part content blocks to text
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content = " ".join(
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part.get("text", "") for part in content
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if isinstance(part, dict) and part.get("type") == "text"
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)
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conversation_history.append({"role": msg["role"], "content": str(content)})
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session_id = body.get("session_id") or run_id
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ephemeral_system_prompt = instructions
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@@ -439,7 +439,7 @@ class TestChatCompletionsEndpoint:
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tp_cb = kwargs.get("tool_progress_callback")
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# Simulate tool progress before streaming content
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if tp_cb:
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tp_cb("terminal", "ls -la", {"command": "ls -la"})
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tp_cb("tool.started", "terminal", "ls -la", {"command": "ls -la"})
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if cb:
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await asyncio.sleep(0.05)
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cb("Here are the files.")
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@@ -476,8 +476,8 @@ class TestChatCompletionsEndpoint:
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cb = kwargs.get("stream_delta_callback")
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tp_cb = kwargs.get("tool_progress_callback")
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if tp_cb:
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tp_cb("_thinking", "some internal state", {})
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tp_cb("web_search", "Python docs", {"query": "Python docs"})
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tp_cb("tool.started", "_thinking", "some internal state", {})
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tp_cb("tool.started", "web_search", "Python docs", {"query": "Python docs"})
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if cb:
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await asyncio.sleep(0.05)
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cb("Found it.")
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