tashfeenahmed/freellmapi
freellmapi
OpenAI-compatible proxy that stacks the free tiers of 16 LLM providers (~1.7B tokens/month) behind one /v1 endpoint — plus any custom OpenAI-compatible endpoint. Smart routing, automatic failover, encrypted keys. Personal experimentation only.
Usage guide
freellmapi is an open-source project around all with 13,814 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 TypeScript, useful for judging integration effort in a similar stack.
- GitHub detected the MIT repository license, which generally permits commercial use. This signal only covers the repository license; review its obligations 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 freellmapi for TypeScript AI workflows.
- Comparing a GitHub project with 13,814 stars and current repository activity.
Pros
- freellmapi has visible GitHub traction with 13,814 stars.
- The project provides an external homepage for deeper evaluation.
Cons
- Production fit still depends on documentation depth, issue activity, and release cadence.
- License review should confirm the MIT terms fit your use case.
Production readiness
freellmapi should be validated with its README, release history, open issues, and integration requirements before production use.
License risk
MIT is reported by GitHub; review the repository license before redistribution or commercial use.
freellmapi architecture preview
freellmapi's main path starts at the entry surface, runs through Retrieval pipeline, combines OpenAI / Ollama, Vector index, GitHub / APIs / webhooks, and returns User-facing result.
Entry
Web / product entry
Users start from a web UI, hosted product surface, or browser-based workflow.
https://freellmapi.co
Runtime
Retrieval pipeline
The pipeline retrieves relevant context before the model generates an answer.
RAG / retrieval
Model
OpenAI / Ollama
Model calls are likely routed through OpenAI, Ollama based on README and topic signals.
OpenAI, Ollama
Context
Vector index
Context comes from Vector index, which constrains what the model or runtime can use.
Vector index
Tools
GitHub / APIs / webhooks
Tool adapters let the runtime act outside the model through GitHub / APIs / webhooks.
GitHub, APIs / webhooks
Output
User-facing result
The final output is returned to the user, workflow, API caller, or downstream system.
output
Featured video
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Install tutorial
Before you install
- 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
freellmapi 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/tashfeenahmed/freellmapi.gitInstall or build dependencies
Run the next setup command detected from the project documentation.
$ curl -fsSL https://freellmapi.co/install.sh | bashAdoption guidance and sources
Practical use cases
OpenAI-compatible proxy that stacks the free tiers of 16 LLM providers
This is one of the documented reasons to evaluate freellmapi before choosing a stack.
All project comparison
Compare freellmapi with similar projects before committing to a stack.
Before adopting
- Complete one clean-environment verification using the official freellmapi 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.
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 freellmapi 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 freellmapi?
freellmapi is an open-source all project. OpenAI-compatible proxy that stacks the free tiers of 16 LLM providers (~1.7B tokens/month) behind one /v1 endpoint — plus any custom OpenAI-compatible endpoint. Smart routing, automatic failover, encrypted keys. Personal experimentation only.
How do I install freellmapi?
Start with the official README. The first detected setup step is: git clone https://github.com/tashfeenahmed/freellmapi.git.
Is freellmapi beginner-friendly?
If you already know the TypeScript ecosystem, start with the smallest example. Otherwise test it in an isolated environment first.
Can freellmapi be used commercially?
GitHub detected the MIT repository license, which generally permits commercial use. This signal only covers the repository license; review its obligations and any model weights, datasets, dependencies, or external services before commercial adoption.
Does freellmapi 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 freellmapi?
Evaluate setup cost, maintenance activity, issue health, license terms, and fit with your real workflow.