BigBodyCobain/Shadowbroker
Shadowbroker
Open-source intelligence for the global theater. Track everything from the corporate/private jets of the wealthy, and spy satellites, to seismic events in one unified interface. Hook an AI agent up to have it parse through data and find previously unseen correlations. The knowledge is available to all but rarely aggregated in the open, until now.
Usage guide
Shadowbroker is an open-source project around air-force-one, airforce1, asdb with 8,393 GitHub stars. This guide focuses on when to use it, how to install it, how to run the first example, and what to verify before adopting it.
Key features
- Implemented mainly in Python, useful for judging integration effort in a similar stack.
- GitHub detected the AGPL-3.0 repository license, which does not by itself confirm commercial permission. Review repository obligations and any model weights, datasets, dependencies, or external services before commercial adoption.
- GitHub is the main evaluation surface; review the README, issues, and recent commits first.
Best for
- Evaluating Shadowbroker for Python AI workflows.
- Comparing a GitHub project with 8,393 stars and current repository activity.
Pros
- Shadowbroker has visible GitHub traction with 8,393 stars. Topics: air-force-one, airforce1, asdb.
- The GitHub repository is the primary evaluation surface.
Cons
- Production fit still depends on documentation depth, issue activity, and release cadence.
- License review should confirm the AGPL-3.0 terms fit your use case.
Production readiness
Shadowbroker should be validated with its README, release history, open issues, and integration requirements before production use.
License risk
AGPL-3.0 is reported by GitHub; review the repository license before redistribution or commercial use.
Shadowbroker architecture preview
Shadowbroker's main path starts at the entry surface, runs through Agent orchestration runtime, combines LLM / model client, Runtime context, GitHub, and returns Assistant response / action result.
Entry
Repository setup
Shadowbroker starts from the repository setup path and documented examples.
git clone https://github.com/BigBodyCobain/Shadowbroker.git
Runtime
Agent orchestration runtime
The orchestration layer plans tasks, calls tools, manages context, and decides the next action.
agent workflow
Model
LLM / model client
The project connects its core runtime to local models or hosted AI APIs when model inference is required.
model signal
Context
Runtime context
Runtime state, user input, repository files, or configuration provide context for each task.
context signal
Tools
GitHub
Tool adapters let the runtime act outside the model through GitHub.
GitHub
Output
Assistant response / action result
The final result is a response, action, or task completion returned through the active channel.
assistant output
Install tutorial
Before you install
- Python runtime and an isolated virtual environment
- Node.js and the package manager used by the project
- A clean working directory for the first test run
Check the runtime environment
Shadowbroker depends on a Python-style environment. Use venv, conda, or a container to keep dependencies isolated.
Get the project files
Start from the official repository or package so the first run matches the documented behavior.
$ git clone https://github.com/BigBodyCobain/Shadowbroker.gitInstall or build dependencies
Run the next setup command detected from the project documentation.
$ python -m venv venvAdoption guidance and sources
Practical use cases
Agent workflow prototype
Use it to validate task decomposition, tool calling, memory, tool permissions, and result review loops.
Open-source intelligence for the global theater. Track everything from
This is one of the documented reasons to evaluate Shadowbroker before choosing a stack.
Focus area: air-force-one
This is one of the documented reasons to evaluate Shadowbroker before choosing a stack.
AI Agents project comparison
Compare Shadowbroker with similar projects before committing to a stack.
Before adopting
- Complete one clean-environment verification using the official Shadowbroker setup path.
- Review repository license, model weights, external services, and dependency terms for your use case.
- Check recent commits, release cadence, issue response, and documentation depth.
- Evaluate output quality, latency, resource usage, and recovery behavior with a small dataset.
Configuration notes
- Review README configuration notes before using production data.
Sources checked
These links are used to verify repository, documentation, or tutorial details. Review the source pages before adopting the project.
Troubleshooting
- If installation fails, first confirm the command is being run from the README-specified directory.
- If dependencies conflict, retry in a fresh virtual environment, container, or working directory.
- If output looks wrong, return to the smallest documented Shadowbroker example before adding complex data.
- For keys, model files, or external services, verify environment variables, local paths, and permissions one by one.
- Before production use, review recent updates, open issues, license terms, and safety boundaries.
What is Shadowbroker?
Shadowbroker is an open-source ai agents project. Open-source intelligence for the global theater. Track everything from the corporate/private jets of the wealthy, and spy satellites, to seismic events in one unified interface. Hook an AI agent up to have it parse through data and find previously unseen correlations. The knowledge is available to all but rarely aggregated in the open, until now.
How do I install Shadowbroker?
Start with the official README. The first detected setup step is: git clone https://github.com/BigBodyCobain/Shadowbroker.git.
Is Shadowbroker beginner-friendly?
If you already know the Python ecosystem, start with the smallest example. Otherwise test it in an isolated environment first.
Can Shadowbroker be used commercially?
GitHub detected the AGPL-3.0 repository license, which does not by itself confirm commercial permission. Review repository obligations and any model weights, datasets, dependencies, or external services before commercial adoption.
Does Shadowbroker need a GPU?
GPU requirements depend on the workload, model, and dataset size. Start with the smallest README example before scaling up.
How should I decide whether to adopt Shadowbroker?
Evaluate setup cost, maintenance activity, issue health, license terms, and fit with your real workflow.