rllm-org/rllm

Health v2 · maintenance only

Adoption not part of the score

5803 stars · 611 forks observed · 2026-08-28

What it is AI-extracted, prompt v1, taxonomy v1, 2026-08-29, confidence not recorded

rLLM is an open-source Python framework for training language agents with reinforcement learning, supporting any agent harness (Claude Code, LangGraph, OpenAI Agents SDK, custom agents) and multiple training backends (verl, tinker, fireworks) switchable with one flag. It includes a CLI for evaluating models on 60+ integrated benchmarks and wraps agent code with @rllm.rollout to trace LLM calls for RL training.

Use cases

• train an LLM agent with reinforcement learning like GRPO or RLOO
• evaluate a model on SWE-bench or Terminal-Bench from the CLI
• fine-tune a coding agent on software engineering tasks with RL
• run RL training on math benchmarks like AIME and MATH-500
• wrap an existing LangGraph or OpenAI Agents SDK agent for RL training
• run distributed multi-GPU RL training with verl or single-machine training with tinker
• benchmark LLM agents across 60+ tasks with auto-pulled datasets

When to choose

• you want to RL-train agents without rewriting them for a specific harness
• you need to switch between distributed multi-GPU and single-machine training backends easily
• you want integrated evaluation across many agentic, math, and coding benchmarks
• you need sandboxed rollouts at training scale with Docker, Modal, or Daytona

When to avoid

• you only need simple supervised fine-tuning without agentic rollouts
• you have no GPU or API budget for large-scale rollout generation
• you need a lightweight inference-only serving framework rather than a training framework

Facets

framework · maturity active

agent-framework llm-training reinforcement-learning benchmarking cli sdk reinforcement-learning large-language-models machine-learning developer-tools python rl-training agentic-rl verl tinker rollout swe-agent grpo llm-evaluation sandboxed-execution ai-agents docker gpu linux macos

Member repositories

For agents

markdown · JSON · MCP: product_card(name="rllm-org/rllm")