Practical overview
When it fits
Use Nagato AI when a Python workflow needs role-based agent composition across more than one supported LLM provider.
Who it is for
- Python developers building AI-agent applications
- Teams designing workflows with coordinator, worker, researcher, or critic roles
- Developers comparing or combining models from different LLM providers
- Builders of self-hosted agent services
Documented uses
- Create agents using supported LLM providers
- Combine specialized agents into a coordinated system
- Break objectives into tasks and evaluate task completion
- Trace LLM calls through the documented Langfuse integration
Requirements & limits
The repository is public, accessible, and specifies an MIT license. Installation is documented through the source repository with uv or through the nagatoai_core package with pip. Provider and tool credentials are configured through environment variables.
- Supported provider coverage includes OpenAI GPT, Anthropic Claude, Google Gemini, Groq models such as Llama 3, and DeepSeek.
- The library is Python-based and can be run from a self-hosted application environment.
- The documented architecture supports both coordinator-led task decomposition and pre-defined worker-and-critic task lists.
- Langfuse tracing is documented through environment-variable configuration.
- Using a provider or tool requires configuring the corresponding documented credential.
Check current requirements in the official documentation. Source-based guidance does not mean OpenViST has run or independently tested this project.
Install Nagato AI and configure provider credentials
Set up the library for a Python application using the documented package or source-repository path.
Before you start
- Python application entry point such as main.py
- uv installed when working from the source repository
- API keys for the model providers and tools the application will use
Steps
Install dependencies from the repository root with uv sync, or install the package with pip install nagatoai_core.
Create a .env file for the credentials used by the selected models and tools.
Add the relevant provider variables, such as OPENAI_API_KEY, ANTHROPIC_API_KEY, GOOGLE_API_KEY, GROQ_API_KEY, or DEEPSEEK_API_KEY.
Run the documented entry point with uv run python main.py.
Expected outcome
The library is installed and the application can load the configured provider and tool credentials through environment variables.
Guide limitations
- The documented setup does not specify application-specific agent code beyond the installation and environment configuration stages.
- Only credentials for the providers and tools actually used need to be configured.
Compare options for Agent orchestration
These projects document a common task and share a project role. This is a source-based comparison, not an execution benchmark or a guarantee of interchangeability.
| Project | Documented purpose | Platform | Access and requirements |
|---|---|---|---|
| Nagato AI | Create agents using supported LLM providers | CLI, Linux, macOS, Self-hosted | The repository is public, accessible, and specifies an MIT license. Installation is documented through the source repository with uv or through the nagatoai_core package with pip. Provider and tool credentials are configured through environment variables. |
| Agenite | Define typed tools with JSON Schema validation and error handling. | CLI | |
| Veldra | Create an agent with a policy, tools, skills, knowledge base and reasoning method. | Self-hosted | The documented deployment is self-hosted and uses a sign-in gate after a first-run administrator account is created. |
Sources & review
Source record details
- Source availability
- Active
- Sources checked
- 30 September 2026
- Original website title
- kenshiro-o/nagato-ai · Official homepage title · retained in its source language