valyu-agentcore 0.1.2

Valyu search tools for AWS Bedrock AgentCore - web, finance, academic, biomedical, patent, SEC, and economics search

valyu-agentcore

Valyu search tools for AWS Bedrock AgentCore and Strands Agents.

Add web search, SEC filings, financial data, academic papers, patents, and biomedical research to your AWS Bedrock AgentCore and Strands agents.

Valyu is the leading search API for AI agents.

Architecture

The agent runs locally (or on AWS Runtime) and uses Valyu tools to search across web, finance, SEC filings, academic papers, patents, and biomedical data. LLM inference is powered by Amazon Bedrock.

Installation

uv init

uv add valyu-agentcore

For AgentCore Gateway support:

uv add valyu-agentcore[agentcore]

Quick Start

Local Development

Use Valyu tools directly with Strands Agents:

from valyu_agentcore import webSearch, financeSearch, secSearch
from strands import Agent
from strands.models import BedrockModel

agent = Agent(
    model=BedrockModel(model_id="us.anthropic.claude-sonnet-4-20250514-v1:0"),
    tools=[webSearch(), financeSearch(), secSearch()],
)

response = agent("Summarize Tesla's latest 10-K risk factors")

Get your API key at platform.valyu.ai.

Production: AgentCore Gateway + Runtime

Architecture Flow:

1. Local Development: Developers can build and tests agent locally with direct API calls to Valyu
2. Deploy Runtime: Agent can be deployed to AgentCore Runtime via agentcore launch
3. Configure Gateway: Developers can set up Gateway with Valyu MCP target
4. Authentication: Runtime requests access token from Cognito
5. Token Response: Cognito returns JWT access token
6. Tool Invocation: Runtime calls Gateway with Bearer token
7. Request Forward: Gateway forwards request to Valyu MCP target
8. API Query: MCP target makes search query to Valyu API
9. Search Results: Valyu API returns search results
10. MCP Response: MCP target formats response back to Gateway
11. Final Results: Gateway returns tool results to Runtime agent

For production, deploy to AWS with centralized tool access:

User → AgentCore Runtime → AgentCore Gateway → Valyu MCP

# 1. Set up Gateway with Valyu tools
cd examples/gateway
export VALYU_API_KEY=your-key
python setup_gateway.py

# 2. Deploy agent to Runtime
cp valyu_gateway_config.json ../runtime/
cd ../runtime
agentcore configure --entrypoint agent.py --non-interactive --name myagent
agentcore launch

# 3. Test it
agentcore invoke '{"prompt": "What is NVIDIA stock price?"}'

Why this architecture?

• Centralized secrets (API key in Gateway, not in code)
• CloudTrail audit logging
• OAuth authentication
• Serverless scaling

See examples/runtime/ for complete walkthrough.

Demo UI

Try the interactive Streamlit demo to explore all features:

# Set your API key
export VALYU_API_KEY=your-key

# Run the demo
streamlit run examples/app.py

Features:

• Local Mode - Direct API calls to Valyu
• Gateway Mode - Route through AWS AgentCore Gateway
• Runtime Mode - Serverless execution on AWS

Agents:

• Single-tool agents: Web, Finance, SEC, Academic, Patents, Biomedical, Economics
• Multi-tool agents: Financial Analyst, Research Assistant, Due Diligence

Views:

• Chat - Standard chat interface
• Compare - Side-by-side comparison: with Valyu search vs without

Available Tools

Tool	Description	Best For
webSearch	Web search with full page content	News, current events, general info
financeSearch	Stock prices, earnings, market data	Financial analysis, market research
paperSearch	Academic papers (arXiv, PubMed, journals)	Literature review, research
bioSearch	Clinical trials, FDA labels, biomedical	Medical research, drug info
patentSearch	USPTO patents and IP	Prior art, IP research
secSearch	SEC filings (10-K, 10-Q, 8-K)	Company analysis, due diligence
economicsSearch	BLS, FRED, World Bank data	Economic indicators, policy research

Use Case Examples

Real-world agent examples in examples/use_cases/:

Example	Description	Tools Used
Financial Analyst	Investment research and analysis	SEC, finance, web
Research Assistant	Academic literature review	papers, patents, web
Due Diligence	M&A and investment evaluation	All tools

# Run financial analyst
python examples/use_cases/financial_analyst.py "Analyze NVIDIA's competitive position"

# Run research assistant
python examples/use_cases/research_assistant.py "transformer architecture improvements"

# Run due diligence
python examples/use_cases/due_diligence.py "Stripe"

See examples/local/ for individual tool examples.

Tool Examples

webSearch

from valyu_agentcore import webSearch

agent = Agent(
    model=BedrockModel(model_id="us.anthropic.claude-sonnet-4-20250514-v1:0"),
    tools=[webSearch()],
)
response = agent("Latest news on quantum computing breakthroughs")

financeSearch

from valyu_agentcore import financeSearch

agent = Agent(
    model=BedrockModel(model_id="us.anthropic.claude-sonnet-4-20250514-v1:0"),
    tools=[financeSearch()],
)
response = agent("What is NVIDIA's current stock price and P/E ratio?")

secSearch

from valyu_agentcore import secSearch

agent = Agent(
    model=BedrockModel(model_id="us.anthropic.claude-sonnet-4-20250514-v1:0"),
    tools=[secSearch()],
)
response = agent("Summarize the risk factors from Apple's latest 10-K")

Use All Tools

from valyu_agentcore import ValyuTools

tools = ValyuTools(max_num_results=5)

agent = Agent(
    model=BedrockModel(model_id="us.anthropic.claude-sonnet-4-20250514-v1:0"),
    tools=tools.all(),  # All 7 tools
)

# Or use tool groups:
# tools.search_tools()     - All search tools
# tools.financial_tools()  - finance, SEC, economics, company research
# tools.research_tools()   - papers, bio, patents

Configuration

All tools accept these options:

from valyu_agentcore import webSearch

tool = webSearch(
    api_key="your-key",           # Or use VALYU_API_KEY env var
    max_num_results=10,           # Number of results (default: 5)
    max_price=0.50,               # Max cost per query in dollars
    relevance_threshold=0.7,      # Filter by quality (0-1)
    excluded_sources=["reddit.com"],  # Exclude specific domains
)

AgentCore Gateway

Add to Existing Gateway

If you already have an AgentCore Gateway:

from valyu_agentcore.gateway import add_valyu_target

# Add Valyu tools to your gateway
result = add_valyu_target(
    gateway_id="your-gateway-id",
    region="us-east-1",  # optional
)
print(f"Added target: {result['target_id']}")

Your existing agents will now have access to all Valyu tools through the gateway.

Create New Gateway

For testing or new deployments:

from valyu_agentcore.gateway import setup_valyu_gateway, GatewayAgent

# Create gateway with Cognito auth (one-time)
config = setup_valyu_gateway()

# Use the agent
with GatewayAgent.from_config() as agent:
    response = agent("Search for NVIDIA SEC filings")
    print(response)

CloudFormation Deployment

Deploy authentication infrastructure via CloudFormation:

aws cloudformation create-stack \
  --stack-name valyu-gateway \
  --template-body file://cloudformation/valyu-gateway.yaml \
  --parameters ParameterKey=ValyuApiKey,ParameterValue=YOUR_KEY \
  --capabilities CAPABILITY_NAMED_IAM

This creates Cognito, IAM, and logging infrastructure. Then create your gateway and add Valyu as a target. See cloudformation/ for full instructions.

IAM Policies

Ready-to-use IAM policies in iam-policies/:

• agentcore-user-policy.json - Full gateway management access
• agentcore-invoke-only-policy.json - Minimal invoke-only access

Manual Console Setup

You can also add Valyu via AWS Console:

1. Go to Amazon Bedrock > AgentCore > Gateways
2. Select your gateway > Add target
3. Configure:
   • Name: valyu-search
   • Type: MCP server
   • Endpoint: https://mcp.valyu.ai/mcp?valyuApiKey=YOUR_KEY

Gateway Tools

Tools available through the gateway:

Gateway Tool	Description
valyu_search	Web search
valyu_academic_search	Academic papers
valyu_financial_search	Financial data
valyu_sec_search	SEC filings
valyu_patents	Patent search
valyu_contents	URL content extraction

See examples/gateway/README.md for complete documentation.

Project Structure

valyu-agentcore/
├── valyu_agentcore/         # Core package
│   ├── tools.py             # Strands Agent tools
│   └── gateway.py           # AgentCore Gateway integration
├── examples/
│   ├── app.py               # Streamlit demo UI
│   ├── local/               # Direct Strands usage
│   ├── runtime/             # AgentCore Runtime deployment
│   ├── gateway/             # AgentCore Gateway setup
│   └── use_cases/           # Real-world agent examples
├── assets/                  # Images and diagrams
├── cloudformation/          # AWS CloudFormation templates
└── iam-policies/            # IAM policy templates

Create Custom Tools

Build your own tool using the Valyu API:

import os
import httpx
from strands.tools import tool

@tool
def my_custom_search(query: str) -> dict:
    """Search for specific content."""
    response = httpx.post(
        "https://api.valyu.ai/v1/deepsearch",
        headers={"x-api-key": os.environ["VALYU_API_KEY"]},
        json={
            "query": query,
            "search_type": "all",
            "included_sources": ["your-sources"],
        },
    )
    return response.json()

Links

• Valyu Platform - Get API keys ($10 free credits)
• Valyu Documentation - API docs
• Strands Agents - Agent framework
• AWS Bedrock AgentCore - AWS docs

License

MIT