import asyncio
import json
import logging
import re
from abc import ABCMeta
from typing import AsyncGenerator, Optional, Tuple
from core.base import AsyncSyncMeta, LLMChatCompletion, Message, syncable
from core.base.agent import Agent, Conversation
from core.utils import (
CitationTracker,
SearchResultsCollector,
SSEFormatter,
convert_nonserializable_objects,
dump_obj,
find_new_citation_spans,
)
logger = logging.getLogger()
class CombinedMeta(AsyncSyncMeta, ABCMeta):
pass
def sync_wrapper(async_gen):
loop = asyncio.get_event_loop()
def wrapper():
try:
while True:
try:
yield loop.run_until_complete(async_gen.__anext__())
except StopAsyncIteration:
break
finally:
loop.run_until_complete(async_gen.aclose())
return wrapper()
class R2RAgent(Agent, metaclass=CombinedMeta):
def __init__(self, *args, **kwargs):
self.search_results_collector = SearchResultsCollector()
super().__init__(*args, **kwargs)
self._reset()
async def _generate_llm_summary(self, iterations_count: int) -> str:
"""
Generate a summary of the conversation using the LLM when max iterations are exceeded.
Args:
iterations_count: The number of iterations that were completed
Returns:
A string containing the LLM-generated summary
"""
try:
# Get all messages in the conversation
all_messages = await self.conversation.get_messages()
# Create a prompt for the LLM to summarize
summary_prompt = {
"role": "user",
"content": (
f"The conversation has reached the maximum limit of {iterations_count} iterations "
f"without completing the task. Please provide a concise summary of: "
f"1) The key information you've gathered that's relevant to the original query, "
f"2) What you've attempted so far and why it's incomplete, and "
f"3) A specific recommendation for how to proceed. "
f"Keep your summary brief (3-4 sentences total) and focused on the most valuable insights. If it is possible to answer the original user query, then do so now instead."
f"Start with '⚠️ **Maximum iterations exceeded**'"
),
}
# Create a new message list with just the conversation history and summary request
summary_messages = all_messages + [summary_prompt]
# Get a completion for the summary
generation_config = self.get_generation_config(summary_prompt)
response = await self.llm_provider.aget_completion(
summary_messages,
generation_config,
)
return response.choices[0].message.content
except Exception as e:
logger.error(f"Error generating LLM summary: {str(e)}")
# Fall back to basic summary if LLM generation fails
return (
"⚠️ **Maximum iterations exceeded**\n\n"
"The agent reached the maximum iteration limit without completing the task. "
"Consider breaking your request into smaller steps or refining your query."
)
def _reset(self):
self._completed = False
self.conversation = Conversation()
@syncable
async def arun(
self,
messages: list[Message],
system_instruction: Optional[str] = None,
*args,
**kwargs,
) -> list[dict]:
self._reset()
await self._setup(system_instruction)
if messages:
for message in messages:
await self.conversation.add_message(message)
iterations_count = 0
while (
not self._completed
and iterations_count < self.config.max_iterations
):
iterations_count += 1
messages_list = await self.conversation.get_messages()
generation_config = self.get_generation_config(messages_list[-1])
response = await self.llm_provider.aget_completion(
messages_list,
generation_config,
)
logger.debug(f"R2RAgent response: {response}")
await self.process_llm_response(response, *args, **kwargs)
if not self._completed:
# Generate a summary of the conversation using the LLM
summary = await self._generate_llm_summary(iterations_count)
await self.conversation.add_message(
Message(role="assistant", content=summary)
)
# Return final content
all_messages: list[dict] = await self.conversation.get_messages()
all_messages.reverse()
output_messages = []
for message_2 in all_messages:
if (
# message_2.get("content")
message_2.get("content") != messages[-1].content
):
output_messages.append(message_2)
else:
break
output_messages.reverse()
return output_messages
async def process_llm_response(
self, response: LLMChatCompletion, *args, **kwargs
) -> None:
if not self._completed:
message = response.choices[0].message
finish_reason = response.choices[0].finish_reason
if finish_reason == "stop":
self._completed = True
# Determine which provider we're using
using_anthropic = (
"anthropic" in self.rag_generation_config.model.lower()
)
# OPENAI HANDLING
if not using_anthropic:
if message.tool_calls:
assistant_msg = Message(
role="assistant",
content="",
tool_calls=[msg.dict() for msg in message.tool_calls],
)
await self.conversation.add_message(assistant_msg)
# If there are multiple tool_calls, call them sequentially here
for tool_call in message.tool_calls:
await self.handle_function_or_tool_call(
tool_call.function.name,
tool_call.function.arguments,
tool_id=tool_call.id,
*args,
**kwargs,
)
else:
await self.conversation.add_message(
Message(role="assistant", content=message.content)
)
self._completed = True
else:
# First handle thinking blocks if present
if (
hasattr(message, "structured_content")
and message.structured_content
):
# Check if structured_content contains any tool_use blocks
has_tool_use = any(
block.get("type") == "tool_use"
for block in message.structured_content
)
if not has_tool_use and message.tool_calls:
# If it has thinking but no tool_use, add a separate message with structured_content
assistant_msg = Message(
role="assistant",
structured_content=message.structured_content, # Use structured_content field
)
await self.conversation.add_message(assistant_msg)
# Add explicit tool_use blocks in a separate message
tool_uses = []
for tool_call in message.tool_calls:
# Safely parse arguments if they're a string
try:
if isinstance(
tool_call.function.arguments, str
):
input_args = json.loads(
tool_call.function.arguments
)
else:
input_args = tool_call.function.arguments
except json.JSONDecodeError:
logger.error(
f"Failed to parse tool arguments: {tool_call.function.arguments}"
)
input_args = {
"_raw": tool_call.function.arguments
}
tool_uses.append(
{
"type": "tool_use",
"id": tool_call.id,
"name": tool_call.function.name,
"input": input_args,
}
)
# Add tool_use blocks as a separate assistant message with structured content
if tool_uses:
await self.conversation.add_message(
Message(
role="assistant",
structured_content=tool_uses,
content="",
)
)
else:
# If it already has tool_use or no tool_calls, preserve original structure
assistant_msg = Message(
role="assistant",
structured_content=message.structured_content,
)
await self.conversation.add_message(assistant_msg)
elif message.content:
# For regular text content
await self.conversation.add_message(
Message(role="assistant", content=message.content)
)
# If there are tool calls, add them as structured content
if message.tool_calls:
tool_uses = []
for tool_call in message.tool_calls:
# Same safe parsing as above
try:
if isinstance(
tool_call.function.arguments, str
):
input_args = json.loads(
tool_call.function.arguments
)
else:
input_args = tool_call.function.arguments
except json.JSONDecodeError:
logger.error(
f"Failed to parse tool arguments: {tool_call.function.arguments}"
)
input_args = {
"_raw": tool_call.function.arguments
}
tool_uses.append(
{
"type": "tool_use",
"id": tool_call.id,
"name": tool_call.function.name,
"input": input_args,
}
)
await self.conversation.add_message(
Message(
role="assistant", structured_content=tool_uses
)
)
# NEW CASE: Handle tool_calls with no content or structured_content
elif message.tool_calls:
# Create tool_uses for the message with only tool_calls
tool_uses = []
for tool_call in message.tool_calls:
try:
if isinstance(tool_call.function.arguments, str):
input_args = json.loads(
tool_call.function.arguments
)
else:
input_args = tool_call.function.arguments
except json.JSONDecodeError:
logger.error(
f"Failed to parse tool arguments: {tool_call.function.arguments}"
)
input_args = {"_raw": tool_call.function.arguments}
tool_uses.append(
{
"type": "tool_use",
"id": tool_call.id,
"name": tool_call.function.name,
"input": input_args,
}
)
# Add tool_use blocks as a message before processing tools
if tool_uses:
await self.conversation.add_message(
Message(
role="assistant",
structured_content=tool_uses,
)
)
# Process the tool calls
if message.tool_calls:
for tool_call in message.tool_calls:
await self.handle_function_or_tool_call(
tool_call.function.name,
tool_call.function.arguments,
tool_id=tool_call.id,
*args,
**kwargs,
)
class R2RStreamingAgent(R2RAgent):
"""
Base class for all streaming agents with core streaming functionality.
Supports emitting messages, tool calls, and results as SSE events.
"""
# These two regexes will detect bracket references and then find short IDs.
BRACKET_PATTERN = re.compile(r"\[([^\]]+)\]")
SHORT_ID_PATTERN = re.compile(
r"[A-Za-z0-9]{7,8}"
) # 7-8 chars, for example
def __init__(self, *args, **kwargs):
# Force streaming on
if hasattr(kwargs.get("config", {}), "stream"):
kwargs["config"].stream = True
super().__init__(*args, **kwargs)
async def arun(
self,
system_instruction: str | None = None,
messages: list[Message] | None = None,
*args,
**kwargs,
) -> AsyncGenerator[str, None]:
"""
Main streaming entrypoint: returns an async generator of SSE lines.
"""
self._reset()
await self._setup(system_instruction)
if messages:
for m in messages:
await self.conversation.add_message(m)
# Initialize citation tracker for this run
citation_tracker = CitationTracker()
# Dictionary to store citation payloads by ID
citation_payloads = {}
# Track all citations emitted during streaming for final persistence
self.streaming_citations: list[dict] = []
async def sse_generator() -> AsyncGenerator[str, None]:
pending_tool_calls = {}
partial_text_buffer = ""
iterations_count = 0
try:
# Keep streaming until we complete
while (
not self._completed
and iterations_count < self.config.max_iterations
):
iterations_count += 1
# 1) Get current messages
msg_list = await self.conversation.get_messages()
gen_cfg = self.get_generation_config(
msg_list[-1], stream=True
)
accumulated_thinking = ""
thinking_signatures = {} # Map thinking content to signatures
# 2) Start streaming from LLM
llm_stream = self.llm_provider.aget_completion_stream(
msg_list, gen_cfg
)
async for chunk in llm_stream:
delta = chunk.choices[0].delta
finish_reason = chunk.choices[0].finish_reason
if hasattr(delta, "thinking") and delta.thinking:
# Accumulate thinking for later use in messages
accumulated_thinking += delta.thinking
# Emit SSE "thinking" event
async for (
line
) in SSEFormatter.yield_thinking_event(
delta.thinking
):
yield line
# Add this new handler for thinking signatures
if hasattr(delta, "thinking_signature"):
thinking_signatures[accumulated_thinking] = (
delta.thinking_signature
)
accumulated_thinking = ""
# 3) If new text, accumulate it
if delta.content:
partial_text_buffer += delta.content
# (a) Now emit the newly streamed text as a "message" event
async for line in SSEFormatter.yield_message_event(
delta.content
):
yield line
# (b) Find new citation spans in the accumulated text
new_citation_spans = find_new_citation_spans(
partial_text_buffer, citation_tracker
)
# Process each new citation span
for cid, spans in new_citation_spans.items():
for span in spans:
# Check if this is the first time we've seen this citation ID
is_new_citation = (
citation_tracker.is_new_citation(cid)
)
# Get payload if it's a new citation
payload = None
if is_new_citation:
source_obj = self.search_results_collector.find_by_short_id(
cid
)
if source_obj:
# Store payload for reuse
payload = dump_obj(source_obj)
citation_payloads[cid] = payload
# Create citation event payload
citation_data = {
"id": cid,
"object": "citation",
"is_new": is_new_citation,
"span": {
"start": span[0],
"end": span[1],
},
}
# Only include full payload for new citations
if is_new_citation and payload:
citation_data["payload"] = payload
# Add to streaming citations for final answer
self.streaming_citations.append(
citation_data
)
# Emit the citation event
async for (
line
) in SSEFormatter.yield_citation_event(
citation_data
):
yield line
if delta.tool_calls:
for tc in delta.tool_calls:
idx = tc.index
if idx not in pending_tool_calls:
pending_tool_calls[idx] = {
"id": tc.id,
"name": tc.function.name or "",
"arguments": tc.function.arguments
or "",
}
else:
# Accumulate partial name/arguments
if tc.function.name:
pending_tool_calls[idx]["name"] = (
tc.function.name
)
if tc.function.arguments:
pending_tool_calls[idx][
"arguments"
] += tc.function.arguments
# 5) If the stream signals we should handle "tool_calls"
if finish_reason == "tool_calls":
# Handle thinking if present
await self._handle_thinking(
thinking_signatures, accumulated_thinking
)
calls_list = []
for idx in sorted(pending_tool_calls.keys()):
cinfo = pending_tool_calls[idx]
calls_list.append(
{
"tool_call_id": cinfo["id"]
or f"call_{idx}",
"name": cinfo["name"],
"arguments": cinfo["arguments"],
}
)
# (a) Emit SSE "tool_call" events
for c in calls_list:
tc_data = self._create_tool_call_data(c)
async for (
line
) in SSEFormatter.yield_tool_call_event(
tc_data
):
yield line
# (b) Add an assistant message capturing these calls
await self._add_tool_calls_message(
calls_list, partial_text_buffer
)
# (c) Execute each tool call in parallel
await asyncio.gather(
*[
self.handle_function_or_tool_call(
c["name"],
c["arguments"],
tool_id=c["tool_call_id"],
)
for c in calls_list
]
)
# Reset buffer & calls
pending_tool_calls.clear()
partial_text_buffer = ""
elif finish_reason == "stop":
# Handle thinking if present
await self._handle_thinking(
thinking_signatures, accumulated_thinking
)
# 6) The LLM is done. If we have any leftover partial text,
# finalize it in the conversation
if partial_text_buffer:
# Create the final message with metadata including citations
final_message = Message(
role="assistant",
content=partial_text_buffer,
metadata={
"citations": self.streaming_citations
},
)
# Add it to the conversation
await self.conversation.add_message(
final_message
)
# (a) Prepare final answer with optimized citations
consolidated_citations = []
# Group citations by ID with all their spans
for (
cid,
spans,
) in citation_tracker.get_all_spans().items():
if cid in citation_payloads:
consolidated_citations.append(
{
"id": cid,
"object": "citation",
"spans": [
{"start": s[0], "end": s[1]}
for s in spans
],
"payload": citation_payloads[cid],
}
)
# Create final answer payload
final_evt_payload = {
"id": "msg_final",
"object": "agent.final_answer",
"generated_answer": partial_text_buffer,
"citations": consolidated_citations,
}
# Emit final answer event
async for (
line
) in SSEFormatter.yield_final_answer_event(
final_evt_payload
):
yield line
# (b) Signal the end of the SSE stream
yield SSEFormatter.yield_done_event()
self._completed = True
break
# If we exit the while loop due to hitting max iterations
if not self._completed:
# Generate a summary using the LLM
summary = await self._generate_llm_summary(
iterations_count
)
# Send the summary as a message event
async for line in SSEFormatter.yield_message_event(
summary
):
yield line
# Add summary to conversation with citations metadata
await self.conversation.add_message(
Message(
role="assistant",
content=summary,
metadata={"citations": self.streaming_citations},
)
)
# Create and emit a final answer payload with the summary
final_evt_payload = {
"id": "msg_final",
"object": "agent.final_answer",
"generated_answer": summary,
"citations": consolidated_citations,
}
async for line in SSEFormatter.yield_final_answer_event(
final_evt_payload
):
yield line
# Signal the end of the SSE stream
yield SSEFormatter.yield_done_event()
self._completed = True
except Exception as e:
logger.error(f"Error in streaming agent: {str(e)}")
# Emit error event for client
async for line in SSEFormatter.yield_error_event(
f"Agent error: {str(e)}"
):
yield line
# Send done event to close the stream
yield SSEFormatter.yield_done_event()
# Finally, we return the async generator
async for line in sse_generator():
yield line
async def _handle_thinking(
self, thinking_signatures, accumulated_thinking
):
"""Process any accumulated thinking content"""
if accumulated_thinking:
structured_content = [
{
"type": "thinking",
"thinking": accumulated_thinking,
# Anthropic will validate this in their API
"signature": "placeholder_signature",
}
]
assistant_msg = Message(
role="assistant",
structured_content=structured_content,
)
await self.conversation.add_message(assistant_msg)
elif thinking_signatures:
for (
accumulated_thinking,
thinking_signature,
) in thinking_signatures.items():
structured_content = [
{
"type": "thinking",
"thinking": accumulated_thinking,
# Anthropic will validate this in their API
"signature": thinking_signature,
}
]
assistant_msg = Message(
role="assistant",
structured_content=structured_content,
)
await self.conversation.add_message(assistant_msg)
async def _add_tool_calls_message(self, calls_list, partial_text_buffer):
"""Add a message with tool calls to the conversation"""
assistant_msg = Message(
role="assistant",
content=partial_text_buffer or "",
tool_calls=[
{
"id": c["tool_call_id"],
"type": "function",
"function": {
"name": c["name"],
"arguments": c["arguments"],
},
}
for c in calls_list
],
)
await self.conversation.add_message(assistant_msg)
def _create_tool_call_data(self, call_info):
"""Create tool call data structure from call info"""
return {
"tool_call_id": call_info["tool_call_id"],
"name": call_info["name"],
"arguments": call_info["arguments"],
}
def _create_citation_payload(self, short_id, payload):
"""Create citation payload for a short ID"""
# This will be overridden in RAG subclasses
# check if as_dict is on payload
if hasattr(payload, "as_dict"):
payload = payload.as_dict()
if hasattr(payload, "dict"):
payload = payload.dict
if hasattr(payload, "to_dict"):
payload = payload.to_dict()
return {
"id": f"{short_id}",
"object": "citation",
"payload": dump_obj(payload), # Will be populated in RAG agents
}
def _create_final_answer_payload(self, answer_text, citations):
"""Create the final answer payload"""
# This will be extended in RAG subclasses
return {
"id": "msg_final",
"object": "agent.final_answer",
"generated_answer": answer_text,
"citations": citations,
}
class R2RXMLStreamingAgent(R2RStreamingAgent):
"""
A streaming agent that parses XML-formatted responses with special handling for:
- or blocks for chain-of-thought reasoning
- , , blocks for tool execution
"""
# We treat or as the same token boundaries
THOUGHT_OPEN = re.compile(r"<(Thought|think)>", re.IGNORECASE)
THOUGHT_CLOSE = re.compile(r"", re.IGNORECASE)
# Regexes to parse out , , , , ,
ACTION_PATTERN = re.compile(
r"(.*?)", re.IGNORECASE | re.DOTALL
)
TOOLCALLS_PATTERN = re.compile(
r"(.*?)", re.IGNORECASE | re.DOTALL
)
TOOLCALL_PATTERN = re.compile(
r"(.*?)", re.IGNORECASE | re.DOTALL
)
NAME_PATTERN = re.compile(r"(.*?)", re.IGNORECASE | re.DOTALL)
PARAMS_PATTERN = re.compile(
r"(.*?)", re.IGNORECASE | re.DOTALL
)
RESPONSE_PATTERN = re.compile(
r"(.*?)", re.IGNORECASE | re.DOTALL
)
async def arun(
self,
system_instruction: str | None = None,
messages: list[Message] | None = None,
*args,
**kwargs,
) -> AsyncGenerator[str, None]:
"""
Main streaming entrypoint: returns an async generator of SSE lines.
"""
self._reset()
await self._setup(system_instruction)
if messages:
for m in messages:
await self.conversation.add_message(m)
# Initialize citation tracker for this run
citation_tracker = CitationTracker()
# Dictionary to store citation payloads by ID
citation_payloads = {}
# Track all citations emitted during streaming for final persistence
self.streaming_citations: list[dict] = []
async def sse_generator() -> AsyncGenerator[str, None]:
iterations_count = 0
try:
# Keep streaming until we complete
while (
not self._completed
and iterations_count < self.config.max_iterations
):
iterations_count += 1
# 1) Get current messages
msg_list = await self.conversation.get_messages()
gen_cfg = self.get_generation_config(
msg_list[-1], stream=True
)
# 2) Start streaming from LLM
llm_stream = self.llm_provider.aget_completion_stream(
msg_list, gen_cfg
)
# Create state variables for each iteration
iteration_buffer = ""
yielded_first_event = False
in_action_block = False
is_thinking = False
accumulated_thinking = ""
thinking_signatures = {}
async for chunk in llm_stream:
delta = chunk.choices[0].delta
finish_reason = chunk.choices[0].finish_reason
# Handle thinking if present
if hasattr(delta, "thinking") and delta.thinking:
# Accumulate thinking for later use in messages
accumulated_thinking += delta.thinking
# Emit SSE "thinking" event
async for (
line
) in SSEFormatter.yield_thinking_event(
delta.thinking
):
yield line
# Add this new handler for thinking signatures
if hasattr(delta, "thinking_signature"):
thinking_signatures[accumulated_thinking] = (
delta.thinking_signature
)
accumulated_thinking = ""
# 3) If new text, accumulate it
if delta.content:
iteration_buffer += delta.content
# Check if we have accumulated enough text for a `` block
if len(iteration_buffer) < len(""):
continue
# Check if we have yielded the first event
if not yielded_first_event:
# Emit the first chunk
if self.THOUGHT_OPEN.findall(iteration_buffer):
is_thinking = True
async for (
line
) in SSEFormatter.yield_thinking_event(
iteration_buffer
):
yield line
else:
async for (
line
) in SSEFormatter.yield_message_event(
iteration_buffer
):
yield line
# Mark as yielded
yielded_first_event = True
continue
# Check if we are in a thinking block
if is_thinking:
# Still thinking, so keep yielding thinking events
if not self.THOUGHT_CLOSE.findall(
iteration_buffer
):
# Emit SSE "thinking" event
async for (
line
) in SSEFormatter.yield_thinking_event(
delta.content
):
yield line
continue
# Done thinking, so emit the last thinking event
else:
is_thinking = False
thought_text = delta.content.split(
""
)[0].split("")[0]
async for (
line
) in SSEFormatter.yield_thinking_event(
thought_text
):
yield line
post_thought_text = delta.content.split(
""
)[-1].split("")[-1]
delta.content = post_thought_text
# (b) Find new citation spans in the accumulated text
new_citation_spans = find_new_citation_spans(
iteration_buffer, citation_tracker
)
# Process each new citation span
for cid, spans in new_citation_spans.items():
for span in spans:
# Check if this is the first time we've seen this citation ID
is_new_citation = (
citation_tracker.is_new_citation(cid)
)
# Get payload if it's a new citation
payload = None
if is_new_citation:
source_obj = self.search_results_collector.find_by_short_id(
cid
)
if source_obj:
# Store payload for reuse
payload = dump_obj(source_obj)
citation_payloads[cid] = payload
# Create citation event payload
citation_data = {
"id": cid,
"object": "citation",
"is_new": is_new_citation,
"span": {
"start": span[0],
"end": span[1],
},
}
# Only include full payload for new citations
if is_new_citation and payload:
citation_data["payload"] = payload
# Add to streaming citations for final answer
self.streaming_citations.append(
citation_data
)
# Emit the citation event
async for (
line
) in SSEFormatter.yield_citation_event(
citation_data
):
yield line
# Now prepare to emit the newly streamed text as a "message" event
if (
iteration_buffer.count("<")
and not in_action_block
):
in_action_block = True
if (
in_action_block
and len(
self.ACTION_PATTERN.findall(
iteration_buffer
)
)
< 2
):
continue
elif in_action_block:
in_action_block = False
# Emit the post action block text, if it is there
post_action_text = iteration_buffer.split(
""
)[-1]
if post_action_text:
async for (
line
) in SSEFormatter.yield_message_event(
post_action_text
):
yield line
else:
async for (
line
) in SSEFormatter.yield_message_event(
delta.content
):
yield line
elif finish_reason == "stop":
break
# Process any accumulated thinking
await self._handle_thinking(
thinking_signatures, accumulated_thinking
)
# 6) The LLM is done. If we have any leftover partial text,
# finalize it in the conversation
if iteration_buffer:
# Create the final message with metadata including citations
final_message = Message(
role="assistant",
content=iteration_buffer,
metadata={"citations": self.streaming_citations},
)
# Add it to the conversation
await self.conversation.add_message(final_message)
# --- 4) Process any / blocks, or mark completed
action_matches = self.ACTION_PATTERN.findall(
iteration_buffer
)
if len(action_matches) > 0:
# Process each ToolCall
xml_toolcalls = ""
for action_block in action_matches:
tool_calls_text = []
# Look for ToolCalls wrapper, or use the raw action block
calls_wrapper = self.TOOLCALLS_PATTERN.findall(
action_block
)
if calls_wrapper:
for tw in calls_wrapper:
tool_calls_text.append(tw)
else:
tool_calls_text.append(action_block)
for calls_region in tool_calls_text:
calls_found = self.TOOLCALL_PATTERN.findall(
calls_region
)
for tc_block in calls_found:
tool_name, tool_params = (
self._parse_single_tool_call(tc_block)
)
if tool_name:
# Emit SSE event for tool call
tool_call_id = (
f"call_{abs(hash(tc_block))}"
)
call_evt_data = {
"tool_call_id": tool_call_id,
"name": tool_name,
"arguments": json.dumps(
tool_params
),
}
async for line in (
SSEFormatter.yield_tool_call_event(
call_evt_data
)
):
yield line
try:
tool_result = await self.handle_function_or_tool_call(
tool_name,
json.dumps(tool_params),
tool_id=tool_call_id,
save_messages=False,
)
result_content = tool_result.llm_formatted_result
except Exception as e:
result_content = f"Error in tool '{tool_name}': {str(e)}"
xml_toolcalls += (
f""
f"{tool_name}"
f"{json.dumps(tool_params)}"
f"{result_content}"
f""
)
# Emit SSE tool result for non-result tools
result_data = {
"tool_call_id": tool_call_id,
"role": "tool",
"content": json.dumps(
convert_nonserializable_objects(
result_content
)
),
}
async for line in SSEFormatter.yield_tool_result_event(
result_data
):
yield line
xml_toolcalls += ""
pre_action_text = iteration_buffer[
: iteration_buffer.find(action_block)
]
post_action_text = iteration_buffer[
iteration_buffer.find(action_block)
+ len(action_block) :
]
iteration_text = (
pre_action_text + xml_toolcalls + post_action_text
)
# Update the conversation with tool results
await self.conversation.add_message(
Message(
role="assistant",
content=iteration_text,
metadata={
"citations": self.streaming_citations
},
)
)
else:
# (a) Prepare final answer with optimized citations
consolidated_citations = []
# Group citations by ID with all their spans
for (
cid,
spans,
) in citation_tracker.get_all_spans().items():
if cid in citation_payloads:
consolidated_citations.append(
{
"id": cid,
"object": "citation",
"spans": [
{"start": s[0], "end": s[1]}
for s in spans
],
"payload": citation_payloads[cid],
}
)
# Create final answer payload
final_evt_payload = {
"id": "msg_final",
"object": "agent.final_answer",
"generated_answer": iteration_buffer,
"citations": consolidated_citations,
}
# Emit final answer event
async for (
line
) in SSEFormatter.yield_final_answer_event(
final_evt_payload
):
yield line
# (b) Signal the end of the SSE stream
yield SSEFormatter.yield_done_event()
self._completed = True
# If we exit the while loop due to hitting max iterations
if not self._completed:
# Generate a summary using the LLM
summary = await self._generate_llm_summary(
iterations_count
)
# Send the summary as a message event
async for line in SSEFormatter.yield_message_event(
summary
):
yield line
# Add summary to conversation with citations metadata
await self.conversation.add_message(
Message(
role="assistant",
content=summary,
metadata={"citations": self.streaming_citations},
)
)
# Create and emit a final answer payload with the summary
final_evt_payload = {
"id": "msg_final",
"object": "agent.final_answer",
"generated_answer": summary,
"citations": consolidated_citations,
}
async for line in SSEFormatter.yield_final_answer_event(
final_evt_payload
):
yield line
# Signal the end of the SSE stream
yield SSEFormatter.yield_done_event()
self._completed = True
except Exception as e:
logger.error(f"Error in streaming agent: {str(e)}")
# Emit error event for client
async for line in SSEFormatter.yield_error_event(
f"Agent error: {str(e)}"
):
yield line
# Send done event to close the stream
yield SSEFormatter.yield_done_event()
# Finally, we return the async generator
async for line in sse_generator():
yield line
def _parse_single_tool_call(
self, toolcall_text: str
) -> Tuple[Optional[str], dict]:
"""
Parse a ToolCall block to extract the name and parameters.
Args:
toolcall_text: The text content of a ToolCall block
Returns:
Tuple of (tool_name, tool_parameters)
"""
name_match = self.NAME_PATTERN.search(toolcall_text)
if not name_match:
return None, {}
tool_name = name_match.group(1).strip()
params_match = self.PARAMS_PATTERN.search(toolcall_text)
if not params_match:
return tool_name, {}
raw_params = params_match.group(1).strip()
try:
# Handle potential JSON parsing issues
# First try direct parsing
tool_params = json.loads(raw_params)
except json.JSONDecodeError:
# If that fails, try to clean up the JSON string
try:
# Replace escaped quotes that might cause issues
cleaned_params = raw_params.replace('\\"', '"')
# Try again with the cleaned string
tool_params = json.loads(cleaned_params)
except json.JSONDecodeError:
# If all else fails, treat as a plain string value
tool_params = {"value": raw_params}
return tool_name, tool_params
class R2RXMLToolsAgent(R2RAgent):
"""
A non-streaming agent that:
- parses or blocks as chain-of-thought
- filters out XML tags related to tool calls and actions
- processes blocks
- properly extracts citations when they appear in the text
"""
# We treat or as the same token boundaries
THOUGHT_OPEN = re.compile(r"<(Thought|think)>", re.IGNORECASE)
THOUGHT_CLOSE = re.compile(r"", re.IGNORECASE)
# Regexes to parse out , , , , ,
ACTION_PATTERN = re.compile(
r"(.*?)", re.IGNORECASE | re.DOTALL
)
TOOLCALLS_PATTERN = re.compile(
r"(.*?)", re.IGNORECASE | re.DOTALL
)
TOOLCALL_PATTERN = re.compile(
r"(.*?)", re.IGNORECASE | re.DOTALL
)
NAME_PATTERN = re.compile(r"(.*?)", re.IGNORECASE | re.DOTALL)
PARAMS_PATTERN = re.compile(
r"(.*?)", re.IGNORECASE | re.DOTALL
)
RESPONSE_PATTERN = re.compile(
r"(.*?)", re.IGNORECASE | re.DOTALL
)
async def process_llm_response(self, response, *args, **kwargs):
"""
Override the base process_llm_response to handle XML structured responses
including thoughts and tool calls.
"""
if self._completed:
return
message = response.choices[0].message
finish_reason = response.choices[0].finish_reason
if not message.content:
# If there's no content, let the parent class handle the normal tool_calls flow
return await super().process_llm_response(
response, *args, **kwargs
)
# Get the response content
content = message.content
# HACK for gemini
content = content.replace("```action", "")
content = content.replace("```tool_code", "")
content = content.replace("```", "")
if (
not content.startswith("<")
and "deepseek" in self.rag_generation_config.model
): # HACK - fix issues with adding `` to the beginning
content = "" + content
# Process any tool calls in the content
action_matches = self.ACTION_PATTERN.findall(content)
if action_matches:
xml_toolcalls = ""
for action_block in action_matches:
tool_calls_text = []
# Look for ToolCalls wrapper, or use the raw action block
calls_wrapper = self.TOOLCALLS_PATTERN.findall(action_block)
if calls_wrapper:
for tw in calls_wrapper:
tool_calls_text.append(tw)
else:
tool_calls_text.append(action_block)
# Process each ToolCall
for calls_region in tool_calls_text:
calls_found = self.TOOLCALL_PATTERN.findall(calls_region)
for tc_block in calls_found:
tool_name, tool_params = self._parse_single_tool_call(
tc_block
)
if tool_name:
tool_call_id = f"call_{abs(hash(tc_block))}"
try:
tool_result = (
await self.handle_function_or_tool_call(
tool_name,
json.dumps(tool_params),
tool_id=tool_call_id,
save_messages=False,
)
)
# Add tool result to XML
xml_toolcalls += (
f""
f"{tool_name}"
f"{json.dumps(tool_params)}"
f"{tool_result.llm_formatted_result}"
f""
)
except Exception as e:
logger.error(f"Error in tool call: {str(e)}")
# Add error to XML
xml_toolcalls += (
f""
f"{tool_name}"
f"{json.dumps(tool_params)}"
f"Error: {str(e)}"
f""
)
xml_toolcalls += ""
pre_action_text = content[: content.find(action_block)]
post_action_text = content[
content.find(action_block) + len(action_block) :
]
iteration_text = pre_action_text + xml_toolcalls + post_action_text
# Create the assistant message
await self.conversation.add_message(
Message(role="assistant", content=iteration_text)
)
else:
# Create an assistant message with the content as-is
await self.conversation.add_message(
Message(role="assistant", content=content)
)
# Only mark as completed if the finish_reason is "stop" or there are no action calls
# This allows the agent to continue the conversation when tool calls are processed
if finish_reason == "stop":
self._completed = True
def _parse_single_tool_call(
self, toolcall_text: str
) -> Tuple[Optional[str], dict]:
"""
Parse a ToolCall block to extract the name and parameters.
Args:
toolcall_text: The text content of a ToolCall block
Returns:
Tuple of (tool_name, tool_parameters)
"""
name_match = self.NAME_PATTERN.search(toolcall_text)
if not name_match:
return None, {}
tool_name = name_match.group(1).strip()
params_match = self.PARAMS_PATTERN.search(toolcall_text)
if not params_match:
return tool_name, {}
raw_params = params_match.group(1).strip()
try:
# Handle potential JSON parsing issues
# First try direct parsing
tool_params = json.loads(raw_params)
except json.JSONDecodeError:
# If that fails, try to clean up the JSON string
try:
# Replace escaped quotes that might cause issues
cleaned_params = raw_params.replace('\\"', '"')
# Try again with the cleaned string
tool_params = json.loads(cleaned_params)
except json.JSONDecodeError:
# If all else fails, treat as a plain string value
tool_params = {"value": raw_params}
return tool_name, tool_params