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from typing import Any, Generator
from openai import OpenAI
from .tools import Tool, _TOOL_RESULTS_CACHE
from .mcp import MCPClient, load_mcp_tools, load_all_mcp_tools
# ---------------------------------------------------------------------------
# Streaming event contract
# ---------------------------------------------------------------------------
# Each yield from Agent.stream() is a dict with a "type" key:
#
# {"type": "text", "content": "partial text"}
# {"type": "reasoning","content": "model thinking"}
# {"type": "tool_call", "name": str, "arguments": '{"url":"..."}'}
# {"type": "tool_output","name": str, "arguments": str, "content": "result", "partial": bool}
# {"type": "done", "content": "full assistant response"}
# {"type": "error", "content": "error message"}
# ---------------------------------------------------------------------------
class Agent:
"""OpenAI-compatible tool-calling agent with streaming.
When ``register_final_message_tool()`` is used the model **must** call
``final_message`` to signal completion — plain text responses without
the tool will keep the conversation loop alive.
"""
def __init__(
self,
base_url: str,
api_key: str,
model: str,
max_iterations: int = 150,
system_prompt: str | None = None,
) -> None:
self.client = OpenAI(base_url=base_url, api_key=api_key)
self.model = model
self._tools: list[Tool] = []
self._final_tool_name: str | None = None
self._max_iterations = max_iterations
self.system_prompt = system_prompt
# ------------------------------------------------------------------
# Tool registration
# ------------------------------------------------------------------
def register_tool(self, *tools: Tool) -> None:
self._tools.extend(tools)
def register_mcp(self, url: str, headers: dict[str, str] | None = None) -> list[Tool]:
"""Connect to an MCP server and register all its tools.
Returns the list of registered Tool instances.
"""
tools = load_mcp_tools(url=url, headers=headers)
self._tools.extend(tools)
return tools
def register_all_mcp(self) -> dict[str, list[Tool]]:
"""Load tools from all pre-configured MCP servers.
Returns ``{server_name: [Tool, ...]}`` and registers them.
"""
all_tools = load_all_mcp_tools()
for server_tools in all_tools.values():
self._tools.extend(server_tools)
return all_tools
def set_final_message_tool(self, tool_name: str = "final_message") -> None:
"""Set the name of the final message tool for internal handling."""
self._final_tool_name = tool_name
# ------------------------------------------------------------------
# Streaming loop
# ------------------------------------------------------------------
def stream(self, messages: list[dict]) -> Generator[dict, None, None]:
"""Yield streaming events until the model calls ``final_message``.
*messages* is mutated in-place — after the generator completes it
contains the full conversation history.
"""
iteration = 0
# Inject system prompt once at the front if set
if self.system_prompt and (
not messages or messages[0].get("role") != "system"
):
messages.insert(0, {"role": "system", "content": self.system_prompt})
while True:
iteration += 1
if iteration > self._max_iterations:
yield {
"type": "error",
"content": f"Agent did not call final_message after "
f"{self._max_iterations} iterations",
}
return
specs = [t.to_openai_spec() for t in self._tools] if self._tools else None
collected_content = ""
collected_tool_calls: dict[int, dict] = {}
kwargs: dict[str, Any] = dict(
model=self.model,
messages=messages,
stream=True,
extra_body={"thinking_token_budget": 2000}
)
if specs:
kwargs["tools"] = specs
try:
stream = self.client.chat.completions.create(**kwargs)
except Exception as exc:
yield {"type": "error", "content": str(exc)}
return
for chunk in stream:
cd = chunk.to_dict()
choices = cd.get("choices", [])
if not choices:
continue
delta = choices[0].get("delta", {})
reasoning = delta.get("reasoning_content") or delta.get("reasoning")
if reasoning:
yield {"type": "reasoning", "content": reasoning}
# Text content
content = delta.get("content", "")
if content:
collected_content += content
yield {"type": "text", "content": content}
# Tool call fragments
for tc in delta.get("tool_calls", []):
idx = tc.get("index", 0)
if idx not in collected_tool_calls:
collected_tool_calls[idx] = {
"id": tc.get("id", ""),
"function": {"name": "", "arguments": ""},
}
if tc.get("id"):
collected_tool_calls[idx]["id"] = tc["id"]
fn = tc.get("function", {})
if fn.get("name"):
collected_tool_calls[idx]["function"]["name"] += fn["name"]
if fn.get("arguments"):
collected_tool_calls[idx]["function"]["arguments"] += fn[
"arguments"
]
# --- Handle tool calls ---
if collected_tool_calls:
tool_call_list: list[dict[str, Any]] = []
for idx in sorted(collected_tool_calls.keys()):
tc = collected_tool_calls[idx]
tool_call_list.append(
{
"id": tc["id"],
"type": "function",
"function": {
"name": tc["function"]["name"],
"arguments": tc["function"]["arguments"],
},
}
)
# Assistant message with tool_calls (appended before we decide
# whether to continue or stop so the conversation is coherent)
assistant_msg: dict[str, Any] = {
"role": "assistant",
"content": collected_content or None,
}
assistant_msg["tool_calls"] = tool_call_list
messages.append(assistant_msg)
# --- final_message check (handled internally) ---
if self._final_tool_name:
for tc_spec in tool_call_list:
if tc_spec["function"]["name"] == self._final_tool_name:
# Dummy tool result so history stays well-formed
messages.append(
{
"role": "tool",
"tool_call_id": tc_spec["id"],
"content": "",
}
)
messages.append(
{"role": "assistant", "content": collected_content}
)
yield {"type": "done", "content": collected_content}
return
# --- Execute real tools ---
for tc_spec in tool_call_list:
tname = tc_spec["function"]["name"]
try:
targs = json.loads(tc_spec["function"]["arguments"])
except json.JSONDecodeError:
targs = {}
yield {
"type": "tool_call",
"name": tname,
"arguments": tc_spec["function"]["arguments"],
}
tool_obj = next(
(t for t in self._tools if t.name == tname), None
)
if tool_obj is None:
result_str = f"Error: Tool '{tname}' not found"
_TOOL_RESULTS_CACHE[tc_spec["id"]] = result_str
yield {
"type": "tool_output",
"name": tname,
"arguments": tc_spec["function"]["arguments"],
"content": result_str,
}
messages.append({
"role": "tool",
"tool_call_id": tc_spec["id"],
"content": result_str,
})
continue
# Streamable tool — yield partial results
if tool_obj.streamable:
accumulated = ""
last_chunk = ""
try:
for chunk in tool_obj.stream(**targs):
accumulated += chunk
last_chunk = chunk
# Truncate display but keep full for read_tool_response
display = accumulated
if len(display) > 5_000:
lines = display.split("\n")
char_count = 0
cut_line = 0
for i, line in enumerate(lines):
char_count += len(line) + 1
if char_count > 5_000:
cut_line = i
break
display = "\n".join(lines[:cut_line])
yield {
"type": "tool_output",
"name": tname,
"arguments": tc_spec["function"]["arguments"],
"content": display,
"partial": True,
}
# Mark final yield as non-partial
display = accumulated
if len(display) > 5_000:
lines = display.split("\n")
char_count = 0
cut_line = 0
for i, line in enumerate(lines):
char_count += len(line) + 1
if char_count > 5_000:
cut_line = i
break
display = "\n".join(lines[:cut_line])
yield {
"type": "tool_output",
"name": tname,
"arguments": tc_spec["function"]["arguments"],
"content": display,
"partial": False,
}
result_str = accumulated
except Exception as e:
result_str = f"Error executing {tname}: {e}"
else:
# Regular tool — single result
try:
result_str = str(tool_obj.run(**targs))
except Exception as e:
result_str = f"Error executing {tname}: {e}"
# Store full result and truncate for message history
_TOOL_RESULTS_CACHE[tc_spec["id"]] = result_str
lines = result_str.split("\n")
if len(result_str) > 5_000:
char_count = 0
cut_line = 0
for i, line in enumerate(lines):
char_count += len(line) + 1
if char_count > 5_000:
cut_line = i
break
truncated = "\n".join(lines[:cut_line])
remaining = len(lines) - cut_line
result_str = f"{truncated}\n\n...[{remaining} lines truncated — use read_tool_response tool_call_id=\"{tc_spec['id']}\" start_line={cut_line} to read more]"
# Final tool_output (non-partial) for history
if not tool_obj.streamable:
yield {
"type": "tool_output",
"name": tname,
"arguments": tc_spec["function"]["arguments"],
"content": result_str,
}
messages.append({
"role": "tool",
"tool_call_id": tc_spec["id"],
"content": result_str,
})
continue # Loop back — model can call more tools or final_message
# --- No tool calls ---
if self._final_tool_name:
# final_message is expected but wasn't called — keep the
# conversation loop alive so the model gets another chance
messages.append(
{"role": "assistant", "content": collected_content}
)
continue # Loop back
# No final_message tool registered — normal end
messages.append({"role": "assistant", "content": collected_content})
yield {"type": "done", "content": collected_content}
break
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