Agent-RL/ReCall

Health v2 · maintenance only

Flags: no_releases

Adoption not part of the score

1431 stars · 89 forks observed · 2026-08-28

What it is AI-extracted, prompt v1, taxonomy v1, 2026-08-30, confidence not recorded

ReCall is a framework that trains LLMs to reason with arbitrary tool calls via reinforcement learning, without supervised tool-use trajectory data. It is the successor to ReSearch, built on verl and vLLM, and includes synthetic multi-step task generation for training agentic tool-based reasoning.

Use cases

• train an llm to call tools via reinforcement learning
• teach a model to reason with search and function calling without sft data
• build agentic tool-use capabilities in open-source llms
• generate synthetic multi-step tool-use training environments
• reproduce the ReSearch reasoning-with-search training pipeline

When to choose

• you want to RL-train an LLM for agentic tool calling without supervised trajectories
• you need a drop-in replacement for ReSearch with arbitrary user-defined tools
• you want to experiment with synthetic environments for multi-step tool-use training

When to avoid

• you only need inference-time tool calling without training
• you lack multi-GPU infrastructure for RL training with vLLM rollouts
• you need a production-ready agent framework rather than a research training codebase

Facets

framework · maturity active

llm-training reinforcement-learning agent-framework rag large-language-models reinforcement-learning machine-learning python tool-calling function-calling verl vllm synthetic-data research tool-use ai-agents gpu linux

Member repositories

For agents

markdown · JSON · MCP: product_card(name="Agent-RL/ReCall")