ReAct
Reasoning + Acting in Language Models
A simple prompting framework that interleaves "thought" steps with "action" steps (like calling a search engine or calculator) in a single chain. Made it possible to build "agentic" LLM workflows — give the model tools, let it decide which to use, when, and based on what reasoning. Conceptual root of every modern agent framework.
Why it matters
The conceptual jump from "LLM as text generator" to "LLM as decision-maker that uses tools" started here. Without this framing, the 2024-26 transition from chatbots to agents happens later or differently.
Core Capabilities
Long Documents
Handles entire codebases, books, and multi-doc RAG.
Agent Workflows
Built for tool use and autonomous tasks.
Research
Foundational paper or scientific contribution.
Context Window
Context window not disclosed.
Availability
API
Not available
Product / App
Not available
Open Source
Not released
Enterprise
—
Pricing Model
Research artifact
Not commercially released.
Research Capability / Performance
Where this model sits relative to the middle 60% of models in the tree. All scores are 0–10 (higher is better).
Lower 20% Upper 80% This model
Lower 20% 20th percentile — 20% of models score below this This model Where the current model lands Upper 80% 80th percentile — only 20% of models score above this
Percentile boundaries are computed across every model in the tree that reports the underlying benchmark for each capability.
What it feels like
- Language model from Google — see the linked sources below for benchmark and review coverage
- Tool-use and agent loops are the typical fit per the published model card
Best use cases
- Agent / tool-use workflows that match the model's published benchmarks
- See the model spec and sources block for benchmarked use cases
Tools to try
Not ideal for
- Tasks far outside the modalities listed in this model's spec
- Workflows where a more recent successor in the same family scores higher