OpenBB Open Data Platform
The open-source data infrastructure for analysts, quants, and AI agents — connect once, consume everywhere.
Evidence shows a security policy (SECURITY.md) and security scanning in CI workflows (bandit), but no specific implementation or documentation for least privilege, user confirmation, data flow transparency, sensitive data handling, dependency security, external effects, rollback, or source attribution. Therefore all trust criteria score 0.
Self-consistency: README, LICENSE, SECURITY.md, and CI workflows are broadly consistent in description, but code consistency not deeply verified, score 2. Dependency availability: README provides installation instructions and PyPI link, but no dependency locking or availability guarantees, score 2. Failure messages: Test files show error handling tests, but no user-facing failure message documentation, score 1.
Audience and scenarios: README clearly targets analysts, quants, and AI agents, and describes multiple use cases, score 2. Capability boundaries: No explicit statement of platform boundaries, score 1. Trigger precision: CLI and API documented, but trigger conditions not detailed, score 1. Environment fit: Multiple installation methods (pip, CLI, Dev Containers, Colab), score 2.
Information architecture: README well-structured with TOC and sections, score 2. Install notes: pip and CLI installation instructions provided, score 2. Naming stability: No naming conventions or stability guarantees, score 1. Examples and FAQ: Code examples and links to docs, but no FAQ, score 2. Known limitations: Disclaimer present, but no explicit list of limitations, score 1. License: LICENSE file clearly AGPLv3, score 2. Versioning and changelog: No changelog provided, score 1. Maintenance responsibility: Contribution guide and contact info, score 2.
Output usability: Multiple output methods (Python, API, CLI), score 2. Marginal value: Unique value as data integration platform, score 2. Cost benefit: No cost information, score 1.
Claim traceability: Claims in README not backed by specific evidence, score 1. Cross-source corroboration: No external verification, score 1. Fact-inference separation: No clear separation of facts and inferences, score 1.
- Publisher identity unverified, treat as unknown.
- Static review, no code execution, conclusions based on file contents only.
- Trust-related criteria all unmet, further review of permissions and data flow needed.
What does this agent do, and when should you use it?
OpenBB Open Data Platform (ODP) is an open-source toolkit that helps data engineers integrate proprietary, licensed, and public data sources into downstream applications like AI copilots and research dashboards. It serves as a 'connect once, consume everywhere' infrastructure layer, exposing data to multiple surfaces: Python environments, OpenBB Workspace, Excel, MCP servers for AI agents, and REST APIs. The core library is installable via `pip install openbb`, providing the `obb` object for data access, e.g., `obb.equity.price.historical("AAPL")`. It also includes a CLI (`openbb-cli`) and a FastAPI server (`openbb-api`) that can be integrated with the OpenBB Workspace. Licensed under AGPLv3.
The platform provides a Python package (openbb) that exposes a unified data access interface through the obb object, covering asset classes like equities, crypto, derivatives, and more. Users can call functions such as obb.equity.price.historical("AAPL") from Python and convert output to DataFrames. It also offers a CLI (openbb-cli) and a REST API server (openbb-api) that starts on 127.0.0.1:6900. The API can be connected to OpenBB Workspace as a backend for data visualization. The available data integrations are listed in the documentation.
- Quantitative researchers pull historical stock data programmatically for backtesting.
- Data engineers integrate the REST API into internal data pipelines.
- Analysts connect the local ODP backend to OpenBB Workspace for interactive dashboards.
- AI application developers use MCP server endpoints to fetch financial data for agents.
- Hobbyist investors query market data from the command line using the CLI.
- Fintech startups leverage ODP as an open-source data layer to build custom research tools.
What are this agent's strengths and limitations?
- Unified interface abstracts multiple data sources, reducing integration effort.
- Supports multiple consumption modes: Python, CLI, REST API, and MCP.
- Open-source with active community, allowing customization and extension.
- Seamless integration with OpenBB Workspace enhances analyst workflows.
- AGPLv3 license may require derivative works to be open-sourced.
- Some data providers may require API keys or paid subscriptions.
- Installing all extensions can increase dependency size and complexity.
- Data accuracy is not guaranteed; users must verify information.
How do you install or deploy this agent?
Install via pip: pip install openbb (basic) or pip install "openbb[all]" for all extensions. Alternatively, clone the repository: git clone https://github.com/OpenBB-finance/OpenBB.git. For the CLI, install separately: pip install openbb-cli.
How do you use this agent?
In a Python environment, import the obb object and call methods, e.g.:
from openbb import obb
output = obb.equity.price.historical("AAPL")
df = output.to_dataframe()To start the API server, after installing openbb[all] in a Python 3.9.21-3.12 environment, run openbb-api. This launches a FastAPI server at 127.0.0.1:6900. Then, in OpenBB Workspace, navigate to the 'Apps' tab, click 'Connect backend', and enter the name and URL http://127.0.0.1:6900. Test the connection and add it.