Top AI Repos — open-source AI, indexed and scored
Top AI Repos tracks AI repositories on GitHub and answers two different questions about each one: is it moving right now, and would you bet a product on it.
Top AI Repos tracks AI repositories on GitHub and answers two different questions about each one: is it moving right now, and would you bet a product on it.
A feature rich implementation of Recursive Language Models, with ACP integration, REPL tool support, structured IO, advanced visualization, logging tools.
| Date | Stars |
|---|---|
| 2026-07-31 | 460 |
| 2026-08-01 | 461 |
| 2026-08-06 | 461 |
Today
— stars today
This week
— stars this week
This month
— stars this month
Momentum
0.0
growth rate 0.00%/day
# fast-rlm
[](https://pypi.org/project/fast-rlm/)
[](https://github.com/avbiswas/fast-rlm)
[](https://avbiswas.github.io/fast-rlm/)
A minimal implementation of Recursive Language Models (RLMs) using Deno and Pyodide.
[GitHub](https://github.com/avbiswas/fast-rlm) | [Documentation](https://avbiswas.github.io/fast-rlm/) | [PyPI](https://pypi.org/project/fast-rlm/)
> **Watch the full video on YouTube**
> **[RLM Tutorial](https://youtu.be/nxaVvvrezbY)**
## What are RLMs
RLMs are an inference technique where an LLM interacts with arbitrarily long prompts through an external REPL. The LLM can write code to explore, decompose, and transform the prompt. It can recursively invoke sub-agents to complete smaller subtasks. Crucially, sub-agent responses are not automatically loaded into the parent agent's context — they are returned as symbols or variables inside the parent's REPL.
## Support
If you find this helpful, consider supporting on Patreon — it hosts all code, projects, slides, and write-ups from the YouTube channel.
[<img src="https://c5.patreon.com/external/logo/become_a_patron_button.png" alt="Become a Patron!" width="200">](https://www.patreon.com/NeuralBreakdownwithAVB)
---
## Demo
<video src="https://github.com/user-attachments/assets/fcaeab69-e384-4b26-8d6a-b71c1464e7f2" controls width="100%"></video>
---
## Install
```bash
pip install fast-rlm
```
### Requirements
- Python 3.10+
- [Deno](https://deno.land/) 2+
- macOS/Linux: `curl -fsSL https://deno.land/install.sh | sh`
- Windows (npm): `npm install -g deno`
- (Optional) [Bun](https://bun.sh/) — only needed for the TUI log viewer
### Environment Variables
Set your LLM API key before running:
```bash
export RLM_MODEL_API_KEY=sk-or-...
```
| Variable | Description | Default |
|----------|-------------|---------|
| `RLM_MODEL_API_KEY` | API key for the OpenAI-compatible backend (falls back to `OPENAI_API_KEY`, then `OPENROUTER_API_KEY`) | — |
| `RLM_MODEL_BASE_URL` | OpenAI-compatible base URL | `https://openrouter.ai/api/v1` |
That's all you need to get started. By default, fast-rlm uses [OpenRouter](https://openrouter.ai); you can point it at any OpenAI-compatible API by setting `RLM_MODEL_BASE_URL`. fast-rlm also runs on Vertex AI, the native Anthropic API, and local ACP coding agents — see **Backend setup** at the end of this README.
## Quick Start
```python
import fast_rlm
from fast_rlm import RLMConfig
# primary_agent is REQUIRED — there is no default model.
config = RLMConfig(primary_agent="z-ai/glm-5")
result = fast_rlm.run("Generate 50 fruits and count number of r", config=config)
print(result["results"])
print(result["usage"])
```
> **`primary_agent` is required.** Every `run()` needs a config that sets it (e.g. `RLMConfig(primary_agent="...")`); `sub_agent` is optional and defaults to `primary_agent`. The shorter examples below omit `config=` for brevity — pass the `config` above to run them.
### From the command line
The same engine is available as a `fast-rlm` CLI — handy for one-off runs and shell pipelines:
```bash
# A plain prompt
fast-rlm "Generate 50 fruits and count number of r" --primary-agent z-ai/glm-5
# Feed a file as the context. Parsed by extension:
# .json/.yaml/.yml -> dict/list .jsonl/.ndjson -> list[dict]
# anything else (.csv, .tsv, .xml, .toml, .txt, ...) -> raw text the model parses
# itself (its extension is noted so it knows the format).
# The prompt becomes the instruction; for a dict input with no "instruction" key,
# it's also injected into the dict.
fast-rlm "Aggregate the reviews into a verdict" --input-file reviews.json --primary-agent z-ai/glm-5
# -q prints only the result (clean for piping); other knobs mirror RLMConfig:
fast-rlm "..." --primary-agent acp:Excerpt of 33,247 characters
Read on GitHubWould you bet a product on this? Bounded 0–100 and slow moving.
matched fp:227f65ee4c1462dd, llm:Repository description: 'A feature rich implementation of Recursive Language Models, with ACP integration, REPL tool support, structured IO, advanced visualization, logging tools.' Topics: rlm, rlms
matched fp:227f65ee4c1462dd, llm:Repository description: 'A feature rich implementation of Recursive Language Models, with ACP integration, REPL tool support, structured IO, advanced visualization, logging tools.' Topics: rlm, rlms
matched fp:227f65ee4c1462dd, llm:Repository description: 'A feature rich implementation of Recursive Language Models, with ACP integration, REPL tool support, structured IO, advanced visualization, logging tools.' Topics: rlm, rlms
matched fp:227f65ee4c1462dd, llm:Repository description: 'A feature rich implementation of Recursive Language Models, with ACP integration, REPL tool support, structured IO, advanced visualization, logging tools.' Topics: rlm, rlms