TEN-framework/ten-framework
ten-framework
Open-source framework for conversational voice AI agents
Usage guide
ten-framework is an open-source project around multi-modal, real-time, video with 10,799 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 did not detect a repository license, so commercial permission is unconfirmed. Review the repository terms and any model weights, datasets, dependencies, or external services before commercial adoption.
- The project has a homepage, so cross-check docs, examples, and release information beyond GitHub.
Best for
- Evaluating ten-framework for Python AI workflows.
- Comparing a GitHub project with 10,799 stars and current repository activity.
Pros
- ten-framework has visible GitHub traction with 10,799 stars. Topics: ai, multi-modal, real-time.
- The project provides an external homepage for deeper evaluation.
Cons
- Production fit still depends on documentation depth, issue activity, and release cadence.
- No license was detected, so usage risk needs manual review.
Production readiness
ten-framework should be validated with its README, release history, open issues, and integration requirements before production use.
License risk
GitHub did not report a license, which usually requires manual legal review before production use.
ten-framework architecture preview
ten-framework's main path starts at the entry surface, runs through Coding agent runtime, combines LLM / model client, Files / repository context, GitHub, and returns User-facing result.
Entry
Web / product entry
Users start from a web UI, hosted product surface, or browser-based workflow.
https://agent.theten.ai/
Runtime
Coding agent runtime
The runtime reads developer intent, inspects repository context, plans edits, and returns code-oriented actions.
coding 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
Files / repository context
Context comes from Files / repository context, which constrains what the model or runtime can use.
Files / repository context
Tools
GitHub
Tool adapters let the runtime act outside the model through GitHub.
GitHub
Output
User-facing result
The final output is returned to the user, workflow, API caller, or downstream system.
output
Install tutorial
Before you install
- Python runtime and an isolated virtual environment
- Node.js and the package manager used by the project
- Docker Engine with enough disk space for images and volumes
- A clean working directory for the first test run
Check the runtime environment
ten-framework has Docker in the setup path. Confirm Docker Engine works and reserve enough disk space for images and volumes.
Get the project files
Start from the official repository or package so the first run matches the documented behavior.
$ git clone https://github.com/TEN-framework/ten-framework.gitInstall or build dependencies
Run the next setup command detected from the project documentation.
$ docker compose up -dAdoption 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 framework for conversational voice AI agents
This is one of the documented reasons to evaluate ten-framework before choosing a stack.
Focus area: ai
This is one of the documented reasons to evaluate ten-framework before choosing a stack.
AI Agents project comparison
Compare ten-framework with similar projects before committing to a stack.
Before adopting
- Complete one clean-environment verification using the official ten-framework 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
- Check exposed ports, mounted volumes, and environment variables before running the container in a shared environment.
- Keep API keys and tokens in environment variables instead of committing them to the repository.
Sources checked
These links are used to verify repository, documentation, or tutorial details. Review the source pages before adopting the project.
Troubleshooting
- If Docker startup fails, check port conflicts, image pull permissions, and volume paths first.
- 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 ten-framework example before adding complex data.
- For keys, model files, or external services, verify environment variables, local paths, and permissions one by one.
What is ten-framework?
ten-framework is an open-source ai agents project. Open-source framework for conversational voice AI agents
How do I install ten-framework?
Start with the official README. The first detected setup step is: git clone https://github.com/TEN-framework/ten-framework.git.
Is ten-framework beginner-friendly?
If you already know the Python ecosystem, start with the smallest example. Otherwise test it in an isolated environment first.
Can ten-framework be used commercially?
GitHub did not detect a repository license, so commercial permission is unconfirmed. Review the repository terms and any model weights, datasets, dependencies, or external services before commercial adoption.
Does ten-framework 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 ten-framework?
Evaluate setup cost, maintenance activity, issue health, license terms, and fit with your real workflow.