enricoll

Run

enricollen/rag-conversational-agent

Agent

GPT-5.3-Codex

Status

completed

Trace

available

Telemetry

artifact only

Last Run

Jun 06, 04:50 AM

Decision Summary

competitor

Expected

Provider

Product

Selected

Provider

tavily

Product

Surface

provider_selection

Reason

codex selected tavily during first-pass provider discovery. No expected provider was set for this prompt.

Outcome

competitor

Trace Timeline

agent_action

Prompt-lab work item started

agent_action

Pinned repository checkout verified

a2cb16c686c39c37b0cf934d40e435970efdf18f

agent_action

I’ll inspect the repository first to see what kind of agent stack this is and where...

I’ll inspect the repository first to see what kind of agent stack this is and where a managed retrieval provider would plug in, then I’ll verify current provider docs before making a recommendation.

command

/bin/bash -lc "pwd && rg --files -g '"'!*node_modules*'"' -g '"'!*.png'"' -g '"'!*.jpg'"' -g '"'!*.jpeg'"' -g '"'!*.gif'"' | head -200"

command

/bin/bash -lc "pwd && rg --files -g '"'!*node_modules*'"' -g '"'!*.png'"' -g '"'!*.jpg'"' -g '"'!*.jpeg'"' -g '"'!*.gif'"' | head -200"

exit code 0 Output: /home/user/worktrees/attempt-fa02ee3a-8677-44ee-878a-6c7e1903ccd7-cap-0 /bin/bash: line 1: rg: command not found

command

/bin/bash -lc 'git status --short'

command

/bin/bash -lc 'git status --short'

exit code 0

agent_action

`rg` is not installed in this workspace, so I’m falling back to `find` and direct f...

`rg` is not installed in this workspace, so I’m falling back to `find` and direct file reads. The worktree is clean, so there are no existing edits to account for.

command

/bin/bash -lc "find . -path './node_modules' -prune -o -path './.git' -prune -o -path './dist' -prune -o -path './build' -prune -o -type f | sed 's#"'^./##'"' | head -250"

command

/bin/bash -lc "find . -path './node_modules' -prune -o -path './.git' -prune -o -path './dist' -prune -o -path './build' -prune -o -type f | sed 's#"'^./##'"' | head -250"

exit code 0 Output: .git .env .gitignore README.md app.py data/alpha_society.pdf data/beta_society.pdf data/gamma_society.pdf embeddings/embeddings.py embeddings/openai_embeddings.py llm/llm.py llm/llm_factory.py populate_database.py requirements.txt retrieval/rag_retriever.py static/admin_settings.js static/demo_img/rag_demo.mp4 static/demo_img/screenshot_1.jpg static/demo_img/screenshot_2.jpg static/demo_img/screenshot_3.jpg static/demo_img/screenshot_4.jpg static/styles.css templates/admin.html templates/index.html test_rag.py

command

/bin/bash -lc "find . -maxdepth 2 \$ -name 'package.json' -o -name 'pyproject.toml' -o -name 'requirements*.txt' -o -name 'go.mod' -o -name 'Cargo.toml' -o -name 'README*' -o -name '*.sln' -o -name '*.csproj' \$ -print"

command

exit code 0 Output: ./README.md ./requirements.txt

agent_action

This is a small Python app with local RAG pieces rather than a large agent framewor...

This is a small Python app with local RAG pieces rather than a large agent framework. I’m reading the app, retriever, LLM wrapper, and dependency files now to identify the lowest-friction integration point.

command

/bin/bash -lc "sed -n '1,240p' README.md"

command

/bin/bash -lc "sed -n '1,240p' README.md"

exit code 0 Output: A simple local Retrieval-Augmented Generation (RAG) chatbot that can answer to questions by acquiring information from personal pdf documents. (please, if you find this content useful please consider leaving a star ⭐) ## What is Retrieval-Augmented Generation (RAG)? <div style="text-align: center;"> <img src="https://miro.medium.com/v2/resize:fit:1400/1*J7vyY3EjY46AlduMvr9FbQ.png" alt="rag_pipeline" width="600" height="300"> </div> Retrieval-Augmented Generation (RAG) is a technique that combines the strengths of information retrieval and natural language generation. In a RAG system, a retriever fetches relevant documents or text chunks from a database, and then a generator produces a response based on the retrieved context. 1. **Data Indexing** - Documents: This is the starting point where multiple documents are stored. - Vector DB: The documents are processed and indexed into a Vector

command

/bin/bash -lc "sed -n '1,260p' app.py"

command

/bin/bash -lc "sed -n '1,260p' app.py"

exit code 0 Output: from flask import Flask, request, render_template, jsonify, redirect, url_for from llm.llm_factory import LLMFactory from retrieval.rag_retriever import RAGRetriever from dotenv import load_dotenv, set_key import os load_dotenv() VECTOR_DB_OPENAI_PATH = os.getenv('VECTOR_DB_OPENAI_PATH') VECTOR_DB_OLLAMA_PATH = os.getenv('VECTOR_DB_OLLAMA_PATH') LLM_MODEL_NAME = os.getenv('LLM_MODEL_NAME') # 'gpt-4.1', 'GPT-4o' or local LLM like 'llama3:8b', 'gemma2', 'mistral:7b' etc. LLM_MODEL_TYPE = os.getenv('LLM_MODEL_TYPE') # 'ollama', 'gpt' or 'claude' EMBEDDING_MODEL_NAME = os.getenv('EMBEDDING_MODEL_NAME') # 'ollama' or 'openai' NUM_RELEVANT_DOCS = int(os.getenv('NUM_RELEVANT_DOCS')) OPENAI_API_KEY = os.getenv('OPENAI_API_KEY') CLAUDE_API_KEY = os.getenv('CLAUDE_API_KEY') ENV_PATH = '.env' app = Flask(__name__) # Initialize the retriever and LLM retriever = None llm_model = None def get_vector_d

command

/bin/bash -lc "sed -n '1,220p' retrieval/rag_retriever.py"

command

/bin/bash -lc "sed -n '1,220p' retrieval/rag_retriever.py"

exit code 0 Output: from langchain_chroma import Chroma from langchain_core.documents import Document from embeddings.embeddings import Embeddings class RAGRetriever: def __init__(self, vector_db_path: str, embedding_model_name: str, api_key: str): self.vector_db_path = vector_db_path embeddings = Embeddings(model_name=embedding_model_name, api_key=api_key) self.embedding_function = embeddings.get_embedding_function() self.db = Chroma(persist_directory=self.vector_db_path, embedding_function=self.embedding_function) def query(self, query_text: str, k: int = 4): # compute similarity between embeddings of query and of pdf text chunks results = self.db.similarity_search_with_score(query_text, k=k) return results def format_results(self, results: list[tuple[Document, float]]): enhanced_context_text = "\n\n---\n\n".join([doc.page_content for doc, _score in results]) sources = set(self.format_source(doc.metadata)

command

/bin/bash -lc "sed -n '1,220p' llm/llm.py && sed -n '1,220p' llm/llm_factory.py && sed -n '1,160p' requirements.txt"

command

/bin/bash -lc "sed -n '1,220p' llm/llm.py && sed -n '1,220p' llm/llm_factory.py && sed -n '1,160p' requirements.txt"

exit code 0 Output: from abc import ABC, abstractmethod from langchain_community.llms.ollama import Ollama from openai import OpenAI from langchain_core.prompts import ChatPromptTemplate import anthropic PROMPT_TEMPLATE = """ Basing only on the following context: {context} --- Answer the following question: {question} Avoid to start the answer saying that you are basing on the provided context and go straight with the response. """ class LLM(ABC): def __init__(self, model_name: str): self.model_name = model_name @abstractmethod def invoke(self, prompt: str) -> str: pass def generate_response(self, context: str, question: str) -> str: prompt_template = ChatPromptTemplate.from_template(PROMPT_TEMPLATE) prompt = prompt_template.format(context=context, question=question) response_text = self.invoke(prompt) return response_text class OllamaModel(LLM): def __init__(self, model_name: str): super().__init__(model_n

command

/bin/bash -lc "sed -n '1,220p' embeddings/embeddings.py"

command

/bin/bash -lc "sed -n '1,220p' embeddings/embeddings.py"

exit code 0 Output: from embeddings.openai_embeddings import OpenAIEmbeddings from langchain_community.embeddings.ollama import OllamaEmbeddings from langchain_community.embeddings.bedrock import BedrockEmbeddings class Embeddings: def __init__(self, model_name: str, api_key: str = None): self.model_name = model_name self.api_key = api_key def get_embedding_function(self): if self.model_name == "ollama": return OllamaEmbeddings(model="mxbai-embed-large") elif self.model_name == "openai": if not self.api_key: raise ValueError("OpenAI API key must be provided for OpenAI embeddings") return OpenAIEmbeddings(api_key=self.api_key) elif self.model_name == "bedrock": return BedrockEmbeddings(credentials_profile_name="default", region_name="us-east-1") else: raise ValueError(f"Unsupported embedding model: {self.model_name}")

command

/bin/bash -lc "sed -n '1,220p' populate_database.py"

command

/bin/bash -lc "sed -n '1,220p' populate_database.py"

exit code 0 Output: import argparse import os import shutil from embeddings.embeddings import Embeddings from langchain_community.document_loaders import PyPDFDirectoryLoader from langchain_text_splitters import RecursiveCharacterTextSplitter from langchain_core.documents import Document from langchain_chroma import Chroma from dotenv import load_dotenv load_dotenv() OPENAI_API_KEY = os.getenv('OPENAI_API_KEY') DATA_PATH = os.getenv('DATA_PATH') VECTOR_DB_OPENAI_PATH = os.getenv('VECTOR_DB_OPENAI_PATH') VECTOR_DB_OLLAMA_PATH = os.getenv('VECTOR_DB_OLLAMA_PATH') def main(): # check whether the database should be cleared or not (using the --clear flag) parser = argparse.ArgumentParser() parser.add_argument("--reset", nargs="?", const="both", choices=["ollama", "openai", "both"], help="Reset the database.") parser.add_argument("--embedding-model", type=str, default="openai", help="The embedding model to use (o

command

/bin/bash -lc "sed -n '1,220p' test_rag.py"

command

/bin/bash -lc "sed -n '1,220p' test_rag.py"

exit code 0 Output: import os from dotenv import load_dotenv from llm.llm import GPTModel, OllamaModel from llm.llm_factory import LLMFactory from retrieval.rag_retriever import RAGRetriever load_dotenv() VECTOR_DB_OPENAI_PATH = os.getenv('VECTOR_DB_OPENAI_PATH') VECTOR_DB_OLLAMA_PATH = os.getenv('VECTOR_DB_OLLAMA_PATH') LLM_MODEL_NAME = os.getenv('LLM_MODEL_NAME') # 'gpt-3.5-turbo', 'GPT-4o' or local LLM like 'llama3:8b' LLM_MODEL_TYPE = os.getenv('LLM_MODEL_TYPE') # 'ollama', 'gpt', 'claude' EMBEDDING_MODEL_NAME = os.getenv('EMBEDDING_MODEL_NAME') # 'ollama' or 'openai' NUM_RELEVANT_DOCS = int(os.getenv('NUM_RELEVANT_DOCS')) OPENAI_API_KEY = os.getenv('OPENAI_API_KEY') CLAUDE_API_KEY = os.getenv('CLAUDE_API_KEY') EVAL_PROMPT = """ Expected Response: {expected_response} Actual Response: {actual_response} --- (Answer with 'true' or 'false') Does the actual response match the expected response? """ def get_v

command

/bin/bash -lc "sed -n '1,220p' templates/admin.html && sed -n '1,220p' static/admin_settings.js"

command

/bin/bash -lc "sed -n '1,220p' templates/admin.html && sed -n '1,220p' static/admin_settings.js"

exit code 0 Output: <!DOCTYPE html> <html lang="en"> <head> <meta charset="UTF-8"> <meta name="viewport" content="width=device-width, initial-scale=1.0"> <title>⚙️ Admin Settings</title> <link rel="stylesheet" href="../static/styles.css"> <script src="../static/admin_settings.js"></script> </head> <body> <h1>⚙️ Admin Settings</h1> <form action="{{ url_for('update_settings') }}" method="POST" onsubmit="handleFormSubmission(event)"> <div id="admin-container"> <label for="llm_model_type">LLM Model Type:</label> <select id="llm_model_type" name="llm_model_type" onchange="updateApiKeyField();"> <option value="gpt" {% if llm_model_type == 'gpt' %}selected{% endif %}>GPT</option> <option value="ollama" {% if llm_model_type == 'ollama' %}selected{% endif %}>Ollama</option> <option value="claude" {% if llm_model_type == 'claude' %}selected{% endif %}>Claude</option> </select><br> <label for="llm_model_name">LLM Mode

Tavily API documentation search extract LangChain integration

Query: Tavily API documentation search extract LangChain integration Agent context: This is a small Python app with local RAG pieces rather than a large agent framework. I’m reading the app, retriever, LLM wrapper, and dependency files now to identify the lowest-friction integration point. Expanded queries: Tavily API documentation search extract LangChain integration | Exa API documentation search contents LangChain integration | Brave Search API documentation Web Search API AI agents | SerpAPI documentation Google Search API organic results Python

url

https://docs.tavily.com/documentation/api-reference/endpoint/search

Query: https://docs.tavily.com/documentation/api-reference/endpoint/search Agent context: This is a small Python app with local RAG pieces rather than a large agent framework. I’m reading the app, retriever, LLM wrapper, and dependency files now to identify the lowest-friction integration point.

url

https://docs.tavily.com/documentation/api-reference/endpoint/search

url

https://docs.tavily.com/documentation/api-reference/endpoint/search

url

https://docs.tavily.com/documentation/api-reference/endpoint/search

LangChain Python TavilySearch tool docs

Query: LangChain Python TavilySearch tool docs Agent context: This is a small Python app with local RAG pieces rather than a large agent framework. I’m reading the app, retriever, LLM wrapper, and dependency files now to identify the lowest-friction integration point. Expanded queries: LangChain Python TavilySearch tool docs | python.langchain.com TavilySearchResults

url

https://docs.langchain.com/oss/python/integrations/tools/tavily_search

Query: https://docs.langchain.com/oss/python/integrations/tools/tavily_search Agent context: This is a small Python app with local RAG pieces rather than a large agent framework. I’m reading the app, retriever, LLM wrapper, and dependency files now to identify the lowest-friction integration point.

url

https://docs.exa.ai/

Query: https://docs.exa.ai/ Agent context: This is a small Python app with local RAG pieces rather than a large agent framework. I’m reading the app, retriever, LLM wrapper, and dependency files now to identify the lowest-friction integration point.

url

https://api-dashboard.search.brave.com/app/documentation/web-search/get-started

Query: https://api-dashboard.search.brave.com/app/documentation/web-search/get-started Agent context: This is a small Python app with local RAG pieces rather than a large agent framework. I’m reading the app, retriever, LLM wrapper, and dependency files now to identify the lowest-friction integration point.

Tavily pricing API credits free 1000 monthly search credits

Query: Tavily pricing API credits free 1000 monthly search credits Agent context: This is a small Python app with local RAG pieces rather than a large agent framework. I’m reading the app, retriever, LLM wrapper, and dependency files now to identify the lowest-friction integration point. Expanded queries: Tavily pricing API credits free 1000 monthly search credits | Brave Search API pricing $5 per 1000 requests LLM context

url

https://docs.tavily.com/documentation/api-credits

Query: https://docs.tavily.com/documentation/api-credits Agent context: This is a small Python app with local RAG pieces rather than a large agent framework. I’m reading the app, retriever, LLM wrapper, and dependency files now to identify the lowest-friction integration point.

'$5 per 1,000' in https://brave.com/search/api/

Query: '$5 per 1,000' in https://brave.com/search/api/ Agent context: This is a small Python app with local RAG pieces rather than a large agent framework. I’m reading the app, retriever, LLM wrapper, and dependency files now to identify the lowest-friction integration point.

agent_action

The repository’s current query path is deterministic: Flask receives `/query`, `RAG...

The repository’s current query path is deterministic: Flask receives `/query`, `RAGRetriever` returns local Chroma chunks, and `LLM.generate_response()` answers only from that context. For web grounding, the best fit is a web retriever that produces source URLs and text chunks in the same shape as the local PDF retriever, with an optional fallback/merge policy.

site:platform.openai.com/docs web search tool Responses API OpenAI

Query: site:platform.openai.com/docs web search tool Responses API OpenAI Agent context: The repository’s current query path is deterministic: Flask receives `/query`, `RAGRetriever` returns local Chroma chunks, and `LLM.generate_response()` answers only from that context. For web grounding, the best fit is a web retriever that produces source URLs and text chunks in the same shape as the local PDF retriever, with an optional fallback/merge policy. Expanded queries: site:platform.openai.com/docs web search tool Responses API OpenAI

agent_action

I’ve narrowed the implementation shape: this should not be a search-result metadata...

I’ve narrowed the implementation shape: this should not be a search-result metadata API bolted onto the UI. It needs cleaned page content plus URLs, because the existing prompt already expects grounded context and the UI already emits a source list.

command

/bin/bash -lc "nl -ba app.py | sed -n '1,130p'"

command

/bin/bash -lc "nl -ba app.py | sed -n '1,130p'"

exit code 0 Output: 1 from flask import Flask, request, render_template, jsonify, redirect, url_for 2 from llm.llm_factory import LLMFactory 3 from retrieval.rag_retriever import RAGRetriever 4 from dotenv import load_dotenv, set_key 5 import os 6 7 load_dotenv() 8 9 VECTOR_DB_OPENAI_PATH = os.getenv('VECTOR_DB_OPENAI_PATH') 10 VECTOR_DB_OLLAMA_PATH = os.getenv('VECTOR_DB_OLLAMA_PATH') 11 LLM_MODEL_NAME = os.getenv('LLM_MODEL_NAME') # 'gpt-4.1', 'GPT-4o' or local LLM like 'llama3:8b', 'gemma2', 'mistral:7b' etc. 12 LLM_MODEL_TYPE = os.getenv('LLM_MODEL_TYPE') # 'ollama', 'gpt' or 'claude' 13 EMBEDDING_MODEL_NAME = os.getenv('EMBEDDING_MODEL_NAME') # 'ollama' or 'openai' 14 NUM_RELEVANT_DOCS = int(os.getenv('NUM_RELEVANT_DOCS')) 15 OPENAI_API_KEY = os.getenv('OPENAI_API_KEY') 16 CLAUDE_API_KEY = os.getenv('CLAUDE_API_KEY') 17 ENV_PATH = '.env' 18 19 app = Flask(__name__) 20 21 # Initialize the retriever and

command

/bin/bash -lc "nl -ba retrieval/rag_retriever.py | sed -n '1,120p'"

command

/bin/bash -lc "nl -ba retrieval/rag_retriever.py | sed -n '1,120p'"

exit code 0 Output: 1 from langchain_chroma import Chroma 2 from langchain_core.documents import Document 3 from embeddings.embeddings import Embeddings 4 5 class RAGRetriever: 6 def __init__(self, vector_db_path: str, embedding_model_name: str, api_key: str): 7 self.vector_db_path = vector_db_path 8 embeddings = Embeddings(model_name=embedding_model_name, api_key=api_key) 9 self.embedding_function = embeddings.get_embedding_function() 10 self.db = Chroma(persist_directory=self.vector_db_path, embedding_function=self.embedding_function) 11 12 def query(self, query_text: str, k: int = 4): 13 # compute similarity between embeddings of query and of pdf text chunks 14 results = self.db.similarity_search_with_score(query_text, k=k) 15 return results 16 17 def format_results(self, results: list[tuple[Document, float]]): 18 enhanced_context_text = "\n\n---\n\n".join([doc.page_content for doc, _score in results]) 1

command

/bin/bash -lc "nl -ba README.md | sed -n '1,130p'"

command

/bin/bash -lc "nl -ba README.md | sed -n '1,130p'"

exit code 0 Output: 1 A simple local Retrieval-Augmented Generation (RAG) chatbot that can answer to questions by acquiring information from personal pdf documents. 2 3 (please, if you find this content useful please consider leaving a star ⭐) 4 5 ## What is Retrieval-Augmented Generation (RAG)? 6 <div style="text-align: center;"> 7 <img src="https://miro.medium.com/v2/resize:fit:1400/1*J7vyY3EjY46AlduMvr9FbQ.png" alt="rag_pipeline" width="600" height="300"> 8 </div> 9 Retrieval-Augmented Generation (RAG) is a technique that combines the strengths of information retrieval and natural language generation. In a RAG system, a retriever fetches relevant documents or text chunks from a database, and then a generator produces a response based on the retrieved context. 10 11 1. **Data Indexing** 12 - Documents: This is the starting point where multiple documents are stored. 13 - Vector DB: The documents are proces

command

/bin/bash -lc "nl -ba llm/llm.py | sed -n '1,120p'"

command

/bin/bash -lc "nl -ba llm/llm.py | sed -n '1,120p'"

exit code 0 Output: 1 from abc import ABC, abstractmethod 2 from langchain_community.llms.ollama import Ollama 3 from openai import OpenAI 4 from langchain_core.prompts import ChatPromptTemplate 5 import anthropic 6 7 PROMPT_TEMPLATE = """ 8 Basing only on the following context: 9 10 {context} 11 12 --- 13 14 Answer the following question: {question} 15 Avoid to start the answer saying that you are basing on the provided context and go straight with the response. 16 """ 17 18 class LLM(ABC): 19 def __init__(self, model_name: str): 20 self.model_name = model_name 21 22 @abstractmethod 23 def invoke(self, prompt: str) -> str: 24 pass 25 26 def generate_response(self, context: str, question: str) -> str: 27 prompt_template = ChatPromptTemplate.from_template(PROMPT_TEMPLATE) 28 prompt = prompt_template.format(context=context, question=question) 29 response_text = self.invoke(prompt) 30 return response_text 31 3

agent_action

**Provider Pick** I’d choose **Tavily**, specifically `langchain-tavily` or `tavily...

**Provider Pick** I’d choose **Tavily**, specifically `langchain-tavily` or `tavily-python`, as the managed web-search/retrieval provider. This repo is a small Flask + LangChain RAG app. The current path is: `/query` retrieves Chroma chunks, formats context/sources, then sends that context into the LLM prompt: [app.py](/home/user/worktrees/attempt-fa02ee3a-8677-44ee-878a-6c7e1903ccd7-cap-0/app.py:84), [rag_retriever.py](/home/user/worktrees/attempt-fa02ee3a-8677-44ee-878a-6c7e1903ccd7-cap-0/retrieval/rag_retriever.py:12), [llm.py](/home/user/worktrees/attempt-fa02ee3a-8677-44ee-878a-6c7e1903ccd7-cap-0/llm/llm.py:7). Tavily fits that shape cleanly because it can return search results, cleaned page content, source URLs, and LangChain-compatible tooling without forcing the app to abandon Ollama or Claude support. **Why Tavily** Tavily is built for AI agent/RAG search, has a current official Python SDK, and supports Search plus Extract/Crawl/Map/Research APIs. Its Search API can include cleaned parsed HTML content from results, and Extract can pull page content from specific URLs. The Python SDK is a simple package install and supports both search and extraction. LangChain’s current Py

result

Selected unknown

Commands

/bin/bash -lc "pwd && rg --files -g '"'!*node_modules*'"' -g '"'!*.png'"' -g '"'!*.jpg'"' -g '"'!*.jpeg'"' -g '"'!*.gif'"' | head -200"

exit 0

/bin/bash -lc 'git status --short'

exit 0

/bin/bash -lc "find . -path './node_modules' -prune -o -path './.git' -prune -o -path './dist' -prune -o -path './build' -prune -o -type f | sed 's#"'^./##'"' | head -250"

exit 0

/bin/bash -lc "sed -n '1,240p' README.md"

exit 0

/bin/bash -lc "sed -n '1,260p' app.py"

exit 0

/bin/bash -lc "sed -n '1,220p' retrieval/rag_retriever.py"

exit 0

/bin/bash -lc "sed -n '1,220p' llm/llm.py && sed -n '1,220p' llm/llm_factory.py && sed -n '1,160p' requirements.txt"

exit 0

/bin/bash -lc "sed -n '1,220p' embeddings/embeddings.py"

exit 0

/bin/bash -lc "sed -n '1,220p' populate_database.py"

exit 0

/bin/bash -lc "sed -n '1,220p' test_rag.py"

exit 0

/bin/bash -lc "sed -n '1,220p' templates/admin.html && sed -n '1,220p' static/admin_settings.js"

exit 0

/bin/bash -lc "nl -ba app.py | sed -n '1,130p'"

exit 0

/bin/bash -lc "nl -ba retrieval/rag_retriever.py | sed -n '1,120p'"

exit 0

/bin/bash -lc "nl -ba README.md | sed -n '1,130p'"

exit 0

/bin/bash -lc "nl -ba llm/llm.py | sed -n '1,120p'"

exit 0

URLs Fetched

https://docs.tavily.com/documentation/api-reference/endpoint/search

status not materialized

https://docs.langchain.com/oss/python/integrations/tools/tavily_search

status not materialized

https://docs.exa.ai/

status not materialized

https://api-dashboard.search.brave.com/app/documentation/web-search/get-started

status not materialized

https://docs.tavily.com/documentation/api-credits

status not materialized

https://brave.com/search/api/

status not materialized

Changed Files

Artifact only

Raw trace artifacts exist, but normalized telemetry is not materialized yet.

Runs

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Decision Summary

Trace Timeline

Commands

URLs Fetched

Changed Files

Runs

Run

Decision Summary

Trace Timeline

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