# FMP Data Client
[](https://github.com/MehdiZare/fmp-data/actions/workflows/test.yml)
[](https://github.com/MehdiZare/fmp-data/actions/workflows/ci-cd.yml)[](https://codecov.io/gh/MehdiZare/fmp-data)
[](https://pypi.org/project/fmp-data/)
[](https://python-poetry.org/)
[](https://opensource.org/licenses/MIT)
A Python client for the Financial Modeling Prep (FMP) API with comprehensive logging, rate limiting, and error handling.
## Features
- 🚀 Simple and intuitive interface
- 🔒 Built-in rate limiting
- 📝 Comprehensive logging
- ⚡ Async support
- 🏷️ Type hints and validation with Pydantic
- 🔄 Automatic retries with exponential backoff
- 🎯 100% test coverage (excluding predefined endpoints)
- 🛡️ Secure API key handling
- 📊 Support for all major FMP endpoints
- 🔍 Detailed error messages
- 🚦 Configurable retry strategies
- 🤖 **NEW: Langchain Integration**
## Getting an API Key
To use this library, you'll need an API key from Financial Modeling Prep (FMP). You can:
- Get a [free API key from FMP](https://site.financialmodelingprep.com/pricing-plans?couponCode=mehdi)
- All paid plans come with a 10% discount.
## Installation
```bash
# Using pip
pip install fmp-data
# Using poetry
poetry add fmp-data
# For Langchain integration
pip install fmp-data[langchain]
# or
poetry add fmp-data --extras langchain
```
## Langchain Integration
### Prerequisites
- FMP API Key (`FMP_API_KEY`) - [Get one here](https://site.financialmodelingprep.com/pricing-plans?couponCode=mehdi)
- OpenAI API Key (`OPENAI_API_KEY`) - Required for embeddings
### Quick Start with Vector Store
```python
from fmp_data import create_vector_store
# Initialize the vector store
vector_store = create_vector_store(
fmp_api_key="YOUR_FMP_API_KEY", # pragma: allowlist secret
openai_api_key="YOUR_OPENAI_API_KEY" # pragma: allowlist secret
)
# Example queries
queries = [
"what is the price of Apple stock?",
"what was the revenue of Tesla last year?",
"what's new in the market?"
]
# Search for relevant endpoints and tools
for query in queries:
print(f"\nQuery: {query}")
# Get tools formatted for OpenAI
tools = vector_store.get_tools(query, provider="openai")
print("\nMatching Tools:")
for tool in tools:
print(f"Name: {tool.get('name')}")
print(f"Description: {tool.get('description')}")
print("Parameters:", tool.get('parameters'))
print()
# You can also search endpoints directly
results = vector_store.search(query)
print("\nRelevant Endpoints:")
for result in results:
print(f"Endpoint: {result.name}")
print(f"Score: {result.score:.2f}")
print()
```
### Alternative Setup: Using Configuration
```python
from fmp_data import FMPDataClient, ClientConfig
from fmp_data.lc.config import LangChainConfig
from fmp_data.lc.embedding import EmbeddingProvider
# Configure with LangChain support
config = LangChainConfig(
api_key="YOUR_FMP_API_KEY", # pragma: allowlist secret
embedding_provider=EmbeddingProvider.OPENAI,
embedding_api_key="YOUR_OPENAI_API_KEY", # pragma: allowlist secret
embedding_model="text-embedding-3-small"
)
# Create client with LangChain config
client = FMPDataClient(config=config)
# Create vector store using the client
vector_store = client.create_vector_store()
# Search for relevant endpoints
results = vector_store.search("show me Tesla's financial metrics")
for result in results:
print(f"Found endpoint: {result.name}")
print(f"Relevance score: {result.score:.2f}")
```
### Interactive Example
Try out the LangChain integration in our interactive Colab notebook:
[](link-to-your-colab-notebook)
This notebook demonstrates how to:
- Build an intelligent financial agent using fmp-data and LangChain
- Access real-time market data through natural language queries
- Use semantic search to select relevant financial tools
- Create multi-turn conversations about financial data
-
### Environment Variables
You can also configure the integration using environment variables:
```bash
# Required
export FMP_API_KEY=your_fmp_api_key_here
export OPENAI_API_KEY=your_openai_api_key_here
# Optional
export FMP_EMBEDDING_PROVIDER=openai
export FMP_EMBEDDING_MODEL=text-embedding-3-small
```
### Features
- 🔍 Semantic search across all FMP endpoints
- 🤖 Auto-conversion to LangChain tools
- 📊 Query endpoints using natural language
- 🎯 Relevance scoring for search results
- 🔄 Automatic caching of embeddings
- 💾 Persistent vector store for faster lookups
## Quick Start
```python
from fmp_data import FMPDataClient, ClientConfig, LoggingConfig
from fmp_data.exceptions import FMPError, RateLimitError, AuthenticationError
# Method 1: Initialize with direct API key
client = FMPDataClient(api_key="your_api_key_here") # pragma: allowlist secret
# Method 2: Initialize from environment variable (FMP_API_KEY)
client = FMPDataClient.from_env()
# Method 3: Initialize with custom configuration
config = ClientConfig(
api_key="your_api_key_here", #pragma: allowlist secret
timeout=30,
max_retries=3,
base_url="https://financialmodelingprep.com/api",
logging=LoggingConfig(level="INFO")
)
client = FMPDataClient(config=config)
# Using with context manager (recommended)
with FMPDataClient(api_key="your_api_key_here") as client: # pragma: allowlist secret
try:
# Get company profile
profile = client.company.get_profile("AAPL")
print(f"Company: {profile.company_name}")
print(f"Industry: {profile.industry}")
print(f"Market Cap: ${profile.mkt_cap:,.2f}")
# Search companies
results = client.company.search("Tesla", limit=5)
for company in results:
print(f"{company.symbol}: {company.name}")
except RateLimitError as e:
print(f"Rate limit exceeded. Wait {e.retry_after} seconds")
except AuthenticationError:
print("Invalid API key")
except FMPError as e:
print(f"API error: {e}")
# Client is automatically closed after the with block
```
## Key Components
### 1. Company Information
```python
from fmp_data import FMPDataClient
with FMPDataClient.from_env() as client:
# Get company profile
profile = client.company.get_profile("AAPL")
# Get company executives
executives = client.company.get_executives("AAPL")
# Search companies
results = client.company.search("Tesla", limit=5)
# Get employee count history
employees = client.company.get_employee_count("AAPL")
```
### 2. Financial Statements
```python
from fmp_data import FMPDataClient
with FMPDataClient.from_env() as client:
# Get income statements
income_stmt = client.fundamental.get_income_statement(
"AAPL",
period="quarter", # or "annual"
limit=4
)
# Get balance sheets
balance_sheet = client.fundamental.get_balance_sheet(
"AAPL",
period="annual"
)
# Get cash flow statements
cash_flow = client.fundamental.get_cash_flow_statement("AAPL")
```
### 3. Market Data
```python
from fmp_data import FMPDataClient
with FMPDataClient.from_env() as client:
# Get real-time quote
quote = client.market.get_quote("TSLA")
# Get historical prices
history = client.market.get_historical_price(
"TSLA",
from_date="2023-01-01",
to_date="2023-12-31"
)
```
### 4. Async Support
```python
import asyncio
from fmp_data import FMPDataClient
async def get_multiple_profiles(symbols):
async with FMPDataClient.from_env() as client:
tasks = [client.company.get_profile_async(symbol)
for symbol in symbols]
return await asyncio.gather(*tasks)
# Run async function
symbols = ["AAPL", "MSFT", "GOOGL"]
profiles = asyncio.run(get_multiple_profiles(symbols))
```
## Configuration
### Environment Variables
```bash
# Required
FMP_API_KEY=your_api_key_here
# Optional
FMP_BASE_URL=https://financialmodelingprep.com/api
FMP_TIMEOUT=30
FMP_MAX_RETRIES=3
# Rate Limiting
FMP_DAILY_LIMIT=250
FMP_REQUESTS_PER_SECOND=10
FMP_REQUESTS_PER_MINUTE=300
# Logging
FMP_LOG_LEVEL=INFO
FMP_LOG_PATH=/path/to/logs
FMP_LOG_MAX_BYTES=10485760
FMP_LOG_BACKUP_COUNT=5
```
### Custom Configuration
```python
from fmp_data import FMPDataClient, ClientConfig, LoggingConfig, RateLimitConfig, LogHandlerConfig
config = ClientConfig(
api_key="your_api_key_here", # pragma: allowlist secret
timeout=30,
max_retries=3,
base_url="https://financialmodelingprep.com/api",
rate_limit=RateLimitConfig(
daily_limit=250,
requests_per_second=10,
requests_per_minute=300
),
logging=LoggingConfig(
level="DEBUG",
handlers={
"console": LogHandlerConfig(
class_name="StreamHandler",
level="INFO",
format="%(asctime)s - %(name)s - %(levelname)s - %(message)s"
),
"file": LogHandlerConfig(
class_name="RotatingFileHandler",
level="DEBUG",
format="%(asctime)s - %(name)s - %(levelname)s - %(message)s",
handler_kwargs={
"filename": "fmp.log",
"maxBytes": 10485760,
"backupCount": 5
}
)
}
)
)
client = FMPDataClient(config=config)
```
## Error Handling
```python
from fmp_data import FMPDataClient
from fmp_data.exceptions import (
FMPError,
RateLimitError,
AuthenticationError,
ValidationError,
ConfigError
)
try:
with FMPDataClient.from_env() as client:
profile = client.company.get_profile("INVALID")
except RateLimitError as e:
print(f"Rate limit exceeded. Wait {e.retry_after} seconds")
print(f"Status code: {e.status_code}")
print(f"Response: {e.response}")
except AuthenticationError as e:
print("Invalid API key or authentication failed")
print(f"Status code: {e.status_code}")
except ValidationError as e:
print(f"Invalid parameters: {e.message}")
except ConfigError as e:
print(f"Configuration error: {e.message}")
except FMPError as e:
print(f"General API error: {e.message}")
```
## Development Setup
1. Clone the repository:
```bash
git clone https://github.com/MehdiZare/fmp-data.git
cd fmp-data
```
2. Install dependencies using Poetry:
```bash
poetry install
```
3. Set up pre-commit hooks:
```bash
poetry run pre-commit install
```
## Running Tests
```bash
# Run all tests with coverage
poetry run pytest --cov=fmp_data
# Run specific test file
poetry run pytest tests/test_client.py
# Run integration tests (requires API key)
FMP_TEST_API_KEY=your_test_api_key poetry run pytest tests/integration/
```
View the latest test coverage report [here](https://codecov.io/gh/MehdiZare/fmp-data).
## Contributing
1. Fork the repository
2. Create a feature branch: `git checkout -b feature-name`
3. Make your changes
4. Run tests: `poetry run pytest`
5. Create a pull request
Please ensure:
- Tests pass
- Code is formatted with black
- Type hints are included
- Documentation is updated
- Commit messages follow conventional commits
## License
This project is licensed under the MIT License - see the [LICENSE](./LICENSE) file for details.
## Acknowledgments
- Financial Modeling Prep for providing the API
- Contributors to the project
- Open source packages used in this project
## Support
- GitHub Issues: [Create an issue](https://github.com/MehdiZare/fmp-data/issues)
- Documentation: [Read the docs](./docs)
## Examples
### Interactive Notebooks
- [Financial Agent Tutorial](https://colab.research.google.com/drive/1cSyLX-j9XhyrXyVJ2HwMZJvPy1Lf2CuA?usp=sharing): Build an intelligent financial agent with LangChain integration
- [Basic Usage Examples](./examples): Simple code examples demonstrating key features
### Code Examples
```python
# Basic usage example
from fmp_data import FMPDataClient
with FMPDataClient.from_env() as client:
# Get company profile
profile = client.company.get_profile("AAPL")
print(f"Company: {profile.company_name}")
```
## Release Notes
See [CHANGELOG.md](./CHANGELOG.md) for a list of changes in each release.
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"description": "# FMP Data Client\n\n[](https://github.com/MehdiZare/fmp-data/actions/workflows/test.yml)\n[](https://github.com/MehdiZare/fmp-data/actions/workflows/ci-cd.yml)[](https://codecov.io/gh/MehdiZare/fmp-data)\n[](https://pypi.org/project/fmp-data/)\n[](https://python-poetry.org/)\n[](https://opensource.org/licenses/MIT)\n\nA Python client for the Financial Modeling Prep (FMP) API with comprehensive logging, rate limiting, and error handling.\n\n## Features\n\n- \ud83d\ude80 Simple and intuitive interface\n- \ud83d\udd12 Built-in rate limiting\n- \ud83d\udcdd Comprehensive logging\n- \u26a1 Async support\n- \ud83c\udff7\ufe0f Type hints and validation with Pydantic\n- \ud83d\udd04 Automatic retries with exponential backoff\n- \ud83c\udfaf 100% test coverage (excluding predefined endpoints)\n- \ud83d\udee1\ufe0f Secure API key handling\n- \ud83d\udcca Support for all major FMP endpoints\n- \ud83d\udd0d Detailed error messages\n- \ud83d\udea6 Configurable retry strategies\n- \ud83e\udd16 **NEW: Langchain Integration**\n\n## Getting an API Key\n\nTo use this library, you'll need an API key from Financial Modeling Prep (FMP). You can:\n- Get a [free API key from FMP](https://site.financialmodelingprep.com/pricing-plans?couponCode=mehdi)\n- All paid plans come with a 10% discount.\n\n## Installation\n\n```bash\n# Using pip\npip install fmp-data\n\n# Using poetry\npoetry add fmp-data\n\n# For Langchain integration\npip install fmp-data[langchain]\n# or\npoetry add fmp-data --extras langchain\n```\n\n## Langchain Integration\n\n### Prerequisites\n- FMP API Key (`FMP_API_KEY`) - [Get one here](https://site.financialmodelingprep.com/pricing-plans?couponCode=mehdi)\n- OpenAI API Key (`OPENAI_API_KEY`) - Required for embeddings\n\n### Quick Start with Vector Store\n\n```python\nfrom fmp_data import create_vector_store\n\n# Initialize the vector store\nvector_store = create_vector_store(\n fmp_api_key=\"YOUR_FMP_API_KEY\", # pragma: allowlist secret\n openai_api_key=\"YOUR_OPENAI_API_KEY\" # pragma: allowlist secret\n)\n\n# Example queries\nqueries = [\n \"what is the price of Apple stock?\",\n \"what was the revenue of Tesla last year?\",\n \"what's new in the market?\"\n]\n\n# Search for relevant endpoints and tools\nfor query in queries:\n print(f\"\\nQuery: {query}\")\n\n # Get tools formatted for OpenAI\n tools = vector_store.get_tools(query, provider=\"openai\")\n\n print(\"\\nMatching Tools:\")\n for tool in tools:\n print(f\"Name: {tool.get('name')}\")\n print(f\"Description: {tool.get('description')}\")\n print(\"Parameters:\", tool.get('parameters'))\n print()\n\n # You can also search endpoints directly\n results = vector_store.search(query)\n print(\"\\nRelevant Endpoints:\")\n for result in results:\n print(f\"Endpoint: {result.name}\")\n print(f\"Score: {result.score:.2f}\")\n print()\n```\n\n### Alternative Setup: Using Configuration\n\n```python\nfrom fmp_data import FMPDataClient, ClientConfig\nfrom fmp_data.lc.config import LangChainConfig\nfrom fmp_data.lc.embedding import EmbeddingProvider\n\n# Configure with LangChain support\nconfig = LangChainConfig(\n api_key=\"YOUR_FMP_API_KEY\", # pragma: allowlist secret\n embedding_provider=EmbeddingProvider.OPENAI,\n embedding_api_key=\"YOUR_OPENAI_API_KEY\", # pragma: allowlist secret\n embedding_model=\"text-embedding-3-small\"\n)\n\n# Create client with LangChain config\nclient = FMPDataClient(config=config)\n\n# Create vector store using the client\nvector_store = client.create_vector_store()\n\n# Search for relevant endpoints\nresults = vector_store.search(\"show me Tesla's financial metrics\")\nfor result in results:\n print(f\"Found endpoint: {result.name}\")\n print(f\"Relevance score: {result.score:.2f}\")\n```\n### Interactive Example\nTry out the LangChain integration in our interactive Colab notebook:\n[](link-to-your-colab-notebook)\n\nThis notebook demonstrates how to:\n- Build an intelligent financial agent using fmp-data and LangChain\n- Access real-time market data through natural language queries\n- Use semantic search to select relevant financial tools\n- Create multi-turn conversations about financial data\n-\n### Environment Variables\nYou can also configure the integration using environment variables:\n```bash\n# Required\nexport FMP_API_KEY=your_fmp_api_key_here\nexport OPENAI_API_KEY=your_openai_api_key_here\n\n# Optional\nexport FMP_EMBEDDING_PROVIDER=openai\nexport FMP_EMBEDDING_MODEL=text-embedding-3-small\n```\n\n### Features\n- \ud83d\udd0d Semantic search across all FMP endpoints\n- \ud83e\udd16 Auto-conversion to LangChain tools\n- \ud83d\udcca Query endpoints using natural language\n- \ud83c\udfaf Relevance scoring for search results\n- \ud83d\udd04 Automatic caching of embeddings\n- \ud83d\udcbe Persistent vector store for faster lookups\n\n## Quick Start\n\n```python\nfrom fmp_data import FMPDataClient, ClientConfig, LoggingConfig\nfrom fmp_data.exceptions import FMPError, RateLimitError, AuthenticationError\n\n# Method 1: Initialize with direct API key\nclient = FMPDataClient(api_key=\"your_api_key_here\") # pragma: allowlist secret\n\n# Method 2: Initialize from environment variable (FMP_API_KEY)\nclient = FMPDataClient.from_env()\n\n# Method 3: Initialize with custom configuration\nconfig = ClientConfig(\n api_key=\"your_api_key_here\", #pragma: allowlist secret\n timeout=30,\n max_retries=3,\n base_url=\"https://financialmodelingprep.com/api\",\n logging=LoggingConfig(level=\"INFO\")\n)\nclient = FMPDataClient(config=config)\n\n# Using with context manager (recommended)\nwith FMPDataClient(api_key=\"your_api_key_here\") as client: # pragma: allowlist secret\n try:\n # Get company profile\n profile = client.company.get_profile(\"AAPL\")\n print(f\"Company: {profile.company_name}\")\n print(f\"Industry: {profile.industry}\")\n print(f\"Market Cap: ${profile.mkt_cap:,.2f}\")\n\n # Search companies\n results = client.company.search(\"Tesla\", limit=5)\n for company in results:\n print(f\"{company.symbol}: {company.name}\")\n\n except RateLimitError as e:\n print(f\"Rate limit exceeded. Wait {e.retry_after} seconds\")\n except AuthenticationError:\n print(\"Invalid API key\")\n except FMPError as e:\n print(f\"API error: {e}\")\n\n# Client is automatically closed after the with block\n```\n\n## Key Components\n\n### 1. Company Information\n```python\nfrom fmp_data import FMPDataClient\n\nwith FMPDataClient.from_env() as client:\n # Get company profile\n profile = client.company.get_profile(\"AAPL\")\n\n # Get company executives\n executives = client.company.get_executives(\"AAPL\")\n\n # Search companies\n results = client.company.search(\"Tesla\", limit=5)\n\n # Get employee count history\n employees = client.company.get_employee_count(\"AAPL\")\n```\n\n### 2. Financial Statements\n```python\nfrom fmp_data import FMPDataClient\n\nwith FMPDataClient.from_env() as client:\n # Get income statements\n income_stmt = client.fundamental.get_income_statement(\n \"AAPL\",\n period=\"quarter\", # or \"annual\"\n limit=4\n )\n\n # Get balance sheets\n balance_sheet = client.fundamental.get_balance_sheet(\n \"AAPL\",\n period=\"annual\"\n )\n\n # Get cash flow statements\n cash_flow = client.fundamental.get_cash_flow_statement(\"AAPL\")\n```\n\n### 3. Market Data\n```python\nfrom fmp_data import FMPDataClient\n\nwith FMPDataClient.from_env() as client:\n # Get real-time quote\n quote = client.market.get_quote(\"TSLA\")\n\n # Get historical prices\n history = client.market.get_historical_price(\n \"TSLA\",\n from_date=\"2023-01-01\",\n to_date=\"2023-12-31\"\n )\n```\n\n### 4. Async Support\n```python\nimport asyncio\nfrom fmp_data import FMPDataClient\n\nasync def get_multiple_profiles(symbols):\n async with FMPDataClient.from_env() as client:\n tasks = [client.company.get_profile_async(symbol)\n for symbol in symbols]\n return await asyncio.gather(*tasks)\n\n# Run async function\nsymbols = [\"AAPL\", \"MSFT\", \"GOOGL\"]\nprofiles = asyncio.run(get_multiple_profiles(symbols))\n```\n\n## Configuration\n\n### Environment Variables\n```bash\n# Required\nFMP_API_KEY=your_api_key_here\n\n# Optional\nFMP_BASE_URL=https://financialmodelingprep.com/api\nFMP_TIMEOUT=30\nFMP_MAX_RETRIES=3\n\n# Rate Limiting\nFMP_DAILY_LIMIT=250\nFMP_REQUESTS_PER_SECOND=10\nFMP_REQUESTS_PER_MINUTE=300\n\n# Logging\nFMP_LOG_LEVEL=INFO\nFMP_LOG_PATH=/path/to/logs\nFMP_LOG_MAX_BYTES=10485760\nFMP_LOG_BACKUP_COUNT=5\n```\n\n### Custom Configuration\n```python\nfrom fmp_data import FMPDataClient, ClientConfig, LoggingConfig, RateLimitConfig, LogHandlerConfig\n\nconfig = ClientConfig(\n api_key=\"your_api_key_here\", # pragma: allowlist secret\n timeout=30,\n max_retries=3,\n base_url=\"https://financialmodelingprep.com/api\",\n rate_limit=RateLimitConfig(\n daily_limit=250,\n requests_per_second=10,\n requests_per_minute=300\n ),\n logging=LoggingConfig(\n level=\"DEBUG\",\n handlers={\n \"console\": LogHandlerConfig(\n class_name=\"StreamHandler\",\n level=\"INFO\",\n format=\"%(asctime)s - %(name)s - %(levelname)s - %(message)s\"\n ),\n \"file\": LogHandlerConfig(\n class_name=\"RotatingFileHandler\",\n level=\"DEBUG\",\n format=\"%(asctime)s - %(name)s - %(levelname)s - %(message)s\",\n handler_kwargs={\n \"filename\": \"fmp.log\",\n \"maxBytes\": 10485760,\n \"backupCount\": 5\n }\n )\n }\n )\n)\n\nclient = FMPDataClient(config=config)\n```\n\n## Error Handling\n\n```python\nfrom fmp_data import FMPDataClient\nfrom fmp_data.exceptions import (\n FMPError,\n RateLimitError,\n AuthenticationError,\n ValidationError,\n ConfigError\n)\ntry:\n with FMPDataClient.from_env() as client:\n profile = client.company.get_profile(\"INVALID\")\n\nexcept RateLimitError as e:\n print(f\"Rate limit exceeded. Wait {e.retry_after} seconds\")\n print(f\"Status code: {e.status_code}\")\n print(f\"Response: {e.response}\")\n\nexcept AuthenticationError as e:\n print(\"Invalid API key or authentication failed\")\n print(f\"Status code: {e.status_code}\")\n\nexcept ValidationError as e:\n print(f\"Invalid parameters: {e.message}\")\n\nexcept ConfigError as e:\n print(f\"Configuration error: {e.message}\")\n\nexcept FMPError as e:\n print(f\"General API error: {e.message}\")\n```\n\n## Development Setup\n\n1. Clone the repository:\n```bash\ngit clone https://github.com/MehdiZare/fmp-data.git\ncd fmp-data\n```\n\n2. Install dependencies using Poetry:\n```bash\npoetry install\n```\n\n3. Set up pre-commit hooks:\n```bash\npoetry run pre-commit install\n```\n\n## Running Tests\n\n```bash\n# Run all tests with coverage\npoetry run pytest --cov=fmp_data\n\n# Run specific test file\npoetry run pytest tests/test_client.py\n\n# Run integration tests (requires API key)\nFMP_TEST_API_KEY=your_test_api_key poetry run pytest tests/integration/\n```\n\nView the latest test coverage report [here](https://codecov.io/gh/MehdiZare/fmp-data).\n\n## Contributing\n\n1. Fork the repository\n2. Create a feature branch: `git checkout -b feature-name`\n3. Make your changes\n4. Run tests: `poetry run pytest`\n5. Create a pull request\n\nPlease ensure:\n- Tests pass\n- Code is formatted with black\n- Type hints are included\n- Documentation is updated\n- Commit messages follow conventional commits\n\n## License\n\nThis project is licensed under the MIT License - see the [LICENSE](./LICENSE) file for details.\n\n## Acknowledgments\n\n- Financial Modeling Prep for providing the API\n- Contributors to the project\n- Open source packages used in this project\n\n## Support\n\n- GitHub Issues: [Create an issue](https://github.com/MehdiZare/fmp-data/issues)\n- Documentation: [Read the docs](./docs)\n\n## Examples\n\n### Interactive Notebooks\n- [Financial Agent Tutorial](https://colab.research.google.com/drive/1cSyLX-j9XhyrXyVJ2HwMZJvPy1Lf2CuA?usp=sharing): Build an intelligent financial agent with LangChain integration\n- [Basic Usage Examples](./examples): Simple code examples demonstrating key features\n\n### Code Examples\n\n```python\n# Basic usage example\nfrom fmp_data import FMPDataClient\n\nwith FMPDataClient.from_env() as client:\n # Get company profile\n profile = client.company.get_profile(\"AAPL\")\n print(f\"Company: {profile.company_name}\")\n```\n\n## Release Notes\n\nSee [CHANGELOG.md](./CHANGELOG.md) for a list of changes in each release.",
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