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Chain 03: ReAct Tool Agent
Type: LangChain ReAct Agent
Trigger: action_required | real_world_query | multi-step task
Model: Gemini Pro (best tool-use) / OpenRouter fallback
Tools: Full tool registry (32 tools)
Output: Action results + natural language explanation
"""
from typing import Any, Dict, List, Optional
import time
from langchain.agents import AgentExecutor, create_react_agent
from langchain.prompts import PromptTemplate
from langchain_google_genai import ChatGoogleGenerativeAI
from app.config import settings
from app.chains.base import BaseChain, ChainType
from app.tools.registry import ToolRegistry
from app.utils.logging import get_logger
logger = get_logger("react_agent_chain")
class ReActAgentChain(BaseChain):
"""
ReAct agent chain for multi-step tasks with tool use
"""
chain_type = ChainType.REACT_AGENT
description = "Multi-step reasoning with tool execution"
REACT_PROMPT = """You are TILLU, a helpful AI assistant. Answer the following question as best you can.
You have access to the following tools:
{tools}
Use the following format:
Question: the input question you must answer
Thought: you should always think about what to do
Action: the action to take, should be one of [{tool_names}]
Action Input: the input to the action
Observation: the result of the action
... (this Thought/Action/Action Input/Observation can repeat N times)
Thought: I now know the final answer
Final Answer: the final answer to the original input question
Begin!
Question: {input}
Thought:{agent_scratchpad}"""
def __init__(self):
super().__init__()
self.llm = None
self.agent = None
self._initialize()
def _initialize(self):
"""Initialize LLM and agent"""
if settings.google_api_key:
try:
self.llm = ChatGoogleGenerativeAI(
model="gemini-pro",
google_api_key=settings.google_api_key,
temperature=0.7
)
except Exception as e:
logger.error(f"Failed to initialize Gemini: {e}")
# Fallback to Groq if Gemini not available
if not self.llm and settings.groq_api_key:
from langchain_groq import ChatGroq
self.llm = ChatGroq(
api_key=settings.groq_api_key,
model_name="llama-3.1-70b-versatile",
temperature=0.7
)
async def execute(
self,
input_data: Dict[str, Any],
context: Optional[Dict[str, Any]] = None
) -> Dict[str, Any]:
"""
Execute ReAct agent
Args:
input_data: Contains 'text' (task/query)
context: Full context with available tools
Returns:
Results with tool outputs and explanation
"""
start_time = time.time()
if not self.llm:
return {
"response": {
"type": "text",
"content": "I'm unable to execute tools right now. Let me answer based on my knowledge.",
"structured_data": {}
},
"personality_mode": "neutral",
"chain": self.chain_type.value,
"model": "fallback",
"latency_ms": int((time.time() - start_time) * 1000),
"tokens_used": 0,
"sources": []
}
query = input_data.get("text", "")
try:
# Get tools
tools = ToolRegistry.get_langchain_tools()
if not tools:
# No tools available, direct answer
response = await self.llm.ainvoke([
("human", query)
])
return {
"response": {
"type": "text",
"content": response.content,
"structured_data": {}
},
"personality_mode": "sharp",
"chain": self.chain_type.value,
"model": "groq-70b",
"latency_ms": int((time.time() - start_time) * 1000),
"tokens_used": 0,
"sources": []
}
# Create ReAct agent
prompt = PromptTemplate.from_template(self.REACT_PROMPT)
agent = create_react_agent(self.llm, tools, prompt)
# Execute
agent_executor = AgentExecutor(
agent=agent,
tools=tools,
verbose=False,
handle_parsing_errors=True,
max_iterations=5
)
result = await agent_executor.ainvoke({"input": query})
elapsed_ms = int((time.time() - start_time) * 1000)
# Extract tool outputs
intermediate_steps = result.get("intermediate_steps", [])
tool_outputs = []
for step in intermediate_steps:
if len(step) >= 2:
action, observation = step[0], step[1]
tool_outputs.append({
"tool": action.tool if hasattr(action, 'tool') else str(action),
"input": action.tool_input if hasattr(action, 'tool_input') else str(action),
"output": str(observation)[:500] # Truncate
})
return {
"response": {
"type": "action_result",
"content": result.get("output", "Task completed."),
"structured_data": {
"tools_used": len(tool_outputs),
"tool_outputs": tool_outputs,
"steps_taken": len(intermediate_steps)
}
},
"personality_mode": "direct",
"chain": self.chain_type.value,
"model": "gemini-pro" if settings.google_api_key else "groq-70b",
"latency_ms": elapsed_ms,
"tokens_used": 0,
"sources": []
}
except Exception as e:
logger.error(f"ReAct agent error: {e}")
# Fallback to direct LLM
try:
response = await self.llm.ainvoke([("human", query)])
return {
"response": {
"type": "text",
"content": response.content,
"structured_data": {"error": str(e), "fallback": True}
},
"personality_mode": "neutral",
"chain": self.chain_type.value,
"model": "fallback",
"latency_ms": int((time.time() - start_time) * 1000),
"tokens_used": 0,
"sources": []
}
except:
return {
"response": {
"type": "text",
"content": "I encountered an error while processing your request.",
"structured_data": {"error": str(e)}
},
"personality_mode": "neutral",
"chain": self.chain_type.value,
"model": "error",
"latency_ms": int((time.time() - start_time) * 1000),
"tokens_used": 0,
"sources": []
}
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