lupantech/AgentFlow

Health v2 · maintenance only

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2017 stars · 235 forks observed · 2026-08-28

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

AgentFlow is a trainable, tool-integrated agentic framework that coordinates planner, executor, verifier, and generator modules through an evolving memory. It introduces Flow-GRPO, an on-policy reinforcement learning method that optimizes the planner inside multi-turn agent loops by broadcasting trajectory-level outcomes to each turn.

Use cases

• train an llm agent with reinforcement learning in multi-turn tool-use environments
• optimize a planner for long-horizon agentic tasks with sparse rewards
• build a multi-module agent with planner, executor, verifier, and generator
• improve search, math, and scientific reasoning with tool-augmented llms
• run on-policy rl training for agentic systems instead of offline training
• evaluate a 7b agent across diverse reasoning benchmarks

When to choose

• you want to train (not just prompt) a multi-module agentic system on-policy
• you need credit assignment across long multi-turn agent trajectories
• you're doing research on agentic rl and tool-augmented reasoning
• you want a modular planner/executor/verifier architecture with evolving memory

When to avoid

• you need a simple training-free agent orchestration library
• you lack GPU resources for reinforcement learning training
• you need a production agent framework with broad tool integrations rather than a research codebase
• your use case is single-turn prompting without tool use

Facets

framework · maturity active

agent-framework machine-learning llm-training reinforcement-learning rag artificial-intelligence large-language-models reinforcement-learning machine-learning python cross-platform multi-agent flow-grpo tool-use planner-executor-verifier on-policy-training research llm-reasoning ai-agents gpu

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For agents

markdown · JSON · MCP: product_card(name="lupantech/AgentFlow")