Trampoline-AI/predict-rlm
Production focused Self-harnessed LM runtime (RLM) that allows the LM to call its sub-lm with DSPy signatures. Define your inputs, outputs, and tools — the model handles its own control flow. Get fully interpretable trajectories and performance that scales directly with model improvements. Without context rot.
⚡ Executive Summary & Quick Facts
Authoritative Sourcepredict-rlm is predict-rlm is an open-source project written in Python: Production focused Self-harnessed LM runtime (RLM) that allows the LM to call its sub-lm with DSPy signatures. Define your inputs, outputs, and tools — the model handles its own control flow. Get fully interpretable trajectories and performance that scales directly with model improvements. Without context rot..
| Repository | Trampoline-AI/predict-rlm ↗ |
|---|---|
| Primary Category | dspy |
| Language & Stack | Python |
| Software License | MIT |
| Community Traction | ★ 436 stars · ⑂ 47 forks · #10,857 globally |
| Maintenance | ● Active (Last commit: Oct 24, 2026) · 9 open issues |
| Target Audience | Developers and engineering teams building with Python, seeking reliable, tested, and actively maintained tooling for production workloads. |
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+11
STARS · LAST 30 DAYS
1
PER DAY
#1,411
MOST-STARRED Python
| Window | 7 days | 30 days | 90 days |
|---|---|---|---|
| Stars gained | +7 | +11 | +90 |
| Per day | 1 | 1 | 1 |
| Forks gained | +1 | +3 | +10 |
predict-rlm gained 11 stars in the last 30 days, about 1 a day, and now has 436. It is about 1 year old and has averaged roughly 436 stars a year. It ranks #1,411 among Python repositories and #10,857 across all languages on GitHubRepo.
Trending Record
4
DAYS ON TRENDING
#11369
BEST RANK
Oct 24, 2026
FIRST APPEARANCE
Active
STATUS TODAY
predict-rlm has maintained a continuous presence across global trending indexes, peaking at #11369. Below is the 30-day activity profile:
💡 Overview
predict-rlm is an open-source project written in Python: Production focused Self-harnessed LM runtime (RLM) that allows the LM to call its sub-lm with DSPy signatures. Define your inputs, outputs, and tools — the model handles its own control flow. Get fully interpretable trajectories and performance that scales directly with model improvements. Without context rot.
Engineered for speed, consistency, and developer ease, it solves common hurdles in dspy, llm, rlm. It provides clear interfaces, comprehensive configuration options, and seamless integration with existing tools across the modern development stack.
⚡ Key Features
Optimized execution pipeline written in Python for predictable speed.
Zero-friction configuration with comprehensive sensible defaults out of the box.
Cross-platform runtime support across Linux, macOS, and Windows environments.
Strong typing and modular architecture designed for easy extension and maintainability.
Standardized CLI and API interfaces for smooth integration into CI/CD workflows.
Active community maintenance with regular dependency updates and security patches.
📥 Installation
$ pip install predict-rlm⚙ System Requirements
Platforms
- • macOS
- • Linux
- • Windows
Runtime & Dependencies
Python >= 3.9, pip, virtualenv
Architecture
x86_64, ARM64 (Apple Silicon & Graviton)
🧠 How It Works
predict-rlm coordinates its core functionality through a modular Python pipeline. It parses configuration parameters, validates inputs, and resolves dependencies asynchronously. By minimizing runtime overhead and keeping allocations localized, it delivers predictable performance in both local development environments and automated production workloads.
🎯 Production Use Cases
Autonomous AI Agents
Orchestrate intelligent workflows and tool-calling routines with predict-rlm.
Model Inference & Prompting
Integrate fast, local or cloud-hosted generative AI models directly into production code.
Context Memory & RAG
Augment language models with dynamic vector retrieval and structured project memory.
Developer Productivity
Automate repetitive engineering tasks, code generation, and test creation using AI agents.
🚀 Getting Started
Install predict-rlm using your package manager: `pip install predict-rlm`
Initialize your project workspace or configuration file for predict-rlm.
Import predict-rlm into your codebase or invoke it directly from your terminal.
Execute your test suite or run `predict-rlm --help` to verify successful setup.
👍 Strengths
⚠️ Considerations
⇄ Alternatives & Direct Competitors
👥 Who Should Use This
Developers and engineering teams building with Python, seeking reliable, tested, and actively maintained tooling for production workloads.
🏆 Nearby in the Rankings
Trampoline-AI/predict-rlm is currently ranked #10,857 by stars across every repository tracked on GitHubRepo. These are adjacent projects:
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|---|---|---|---|---|
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| #10,857 | Trampoline-AI/predict-rlm This Project | Python | ★ 436 | |
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Frequently Asked Questions
What does predict-rlm do? +
Production focused Self-harnessed LM runtime (RLM) that allows the LM to call its sub-lm with DSPy signatures. Define your inputs, outputs, and tools — the model handles its own control flow. Get fully interpretable trajectories and performance that scales directly with model improvements. Without context rot.
What language is predict-rlm written in? +
The primary language is Python. Topics include: dspy, llm, rlm.
Is predict-rlm actively maintained? +
Yes, the last recorded push was on Oct 24, 2026 with 9 open issues being tracked.
How many stars does predict-rlm have? +
predict-rlm has 436 stars and 47 forks on GitHub.
How does predict-rlm rank among GitHub repositories? +
With 436 stars, Trampoline-AI/predict-rlm is ranked #10,857 globally across all repositories tracked on GitHubRepo and #1,411 among Python projects.
What license is predict-rlm distributed under? +
The repository reports a MIT license. Always verify the repository LICENSE file for legal terms.
