modelscope/AgentEvolver

Health v2 · maintenance only

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1547 stars · 173 forks observed · 2026-08-28

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

AgentEvolver is an end-to-end self-evolving training framework for LLM-based agents that unifies automatic task generation (self-questioning), experience-guided exploration (self-navigating), and attribution-based credit assignment (self-attributing). It uses a service-oriented dataflow architecture integrating environment sandboxes, LLMs, and experience management into modular services.

Use cases

• train LLM agents with reinforcement learning without manually building datasets
• automatically generate diverse training tasks from an environment
• improve agent exploration by reusing cross-task experience
• assign fine-grained credit to intermediate steps in long agent trajectories
• evaluate and train agents in multi-agent game arenas like Avalon and Diplomacy
• benchmark agent performance on tasks like AppWorld and BFCL-v3

When to choose

• you need to train or fine-tune LLM agents via agentic RL with automatic task generation
• you want a modular, service-oriented stack for multi-turn agent training with environment sandboxes
• you need efficient credit assignment over long agent trajectories
• you want to reduce the cost of manual dataset construction for agent training

When to avoid

• you only need to run inference with an existing agent without training
• you need a simple single-turn prompt pipeline rather than multi-turn RL training
• you lack GPU resources or an LLM backend for rollouts
• you need a production agent deployment framework rather than a research training framework

Facets

framework · maturity active

agent-framework llm-training reinforcement-learning machine-learning rag artificial-intelligence large-language-models reinforcement-learning machine-learning python self-evolving-agents agentic-rl task-generation credit-assignment llm-agents training-framework ai-agents linux docker gpu

Member repositories

For agents

markdown · JSON · MCP: product_card(name="modelscope/AgentEvolver")