langgraph-prebuilt
Library with high-level APIs for creating and executing LangGraph agents and tools.
Decision gist · record as of 2026-08-14
Yes, with a caveat: the package is production-ready, actively maintained, and has no known vulnerabilities. However, the description explicitly states it is meant to be bundled with langgraph rather than installed directly. Install it if you are building custom LangGraph workflows and need prebuilt agent components; otherwise, install langgraph itself, which should include this as a dependency.AI-flagged interpretation of the facts on this page — verify before relying
Before you install
- Requires Python 3.10 or later; requires a compatible LLM model from langchain-core and tool definitions.
- Low install friction with a pure Python wheel.
- Actively maintained with last commit 2026-08-13.
License · maintenance · safety
MIT (permissive) — MIT license permits commercial and private use with minimal restrictions; suitable for most projects.
last release 2026-05-12 (94 days) · last repo commit 2026-08-13 · 39,639 stars
0 known vulnerabilities (OSV.dev, 2026-08-14) · 58,540,136 downloads/mo, #514 on PyPI
Alternatives
Verify before relying
pip install langgraph-prebuilt
from langgraph.prebuilt import create_react_agent
from langchain_core.messages import AIMessage
app = create_react_agent(model, tools)
app.invoke({"messages": [{"role": "user", "content": "query"}]})- Whether this package is intended for direct installation or only as a transitive dependency of langgraph (description notes it should be bundled, not installed directly)
- Performance characteristics or scalability limits for agent execution with large tool sets or complex workflows
- Whether ValidationNode supports custom validation logic beyond Pydantic schema enforcement
What it is and what it does
langgraph-prebuilt is a library of ready-made components for building agentic systems on top of LangGraph. It provides three main building blocks: create_react_agent for tool-calling agents that reason and act in loops, ToolNode for executing tool calls within a graph, and ValidationNode for validating tool calls against Pydantic schemas before execution. The library also includes schemas and utilities for Agent Inbox integration, enabling human-in-the-loop workflows where agents can pause and request human feedback.
The package depends on langchain-core for message types and LLM interfaces, plus langgraph-checkpoint for state persistence. It targets Python 3.10 and later and is marked production-stable. The description emphasizes that it is meant to be bundled with langgraph rather than installed standalone, suggesting it functions as a convenience layer over lower-level LangGraph primitives.
Use it for
- Build a ReAct agent that reasons about which tools to call and executes them iteratively to answer user queries
- Create a graph node that safely executes tool calls returned by an LLM, handling errors and formatting results
- Validate LLM-generated tool arguments against a Pydantic schema before execution to catch invalid calls early
- Implement human-in-the-loop workflows where an agent pauses to request user approval or input before proceeding
- Compose prebuilt agent components into larger multi-step workflows without reimplementing core agentic patterns
Worth the install?
AI-flagged interpretation of the facts on this page. Verify before relying on it.
Yes, with a caveat: the package is production-ready, actively maintained, and has no known vulnerabilities.
However, the description explicitly states it is meant to be bundled with langgraph rather than installed directly. Install it if you are building custom LangGraph workflows and need prebuilt agent components; otherwise, install langgraph itself, which should include this as a dependency.
Install
langgraph-prebuilt on PyPI
Before you install
Low install friction with a pure Python wheel. Actively maintained with last commit 2026-08-13. Depends on langchain-core and langgraph-checkpoint, both standard ecosystem packages.
Requires Python 3.10 or later; requires a compatible LLM model from langchain-core and tool definitions.
License in practice
MIT license permits commercial and private use with minimal restrictions; suitable for most projects.
Quickstart
pip install langgraph-prebuilt
from langgraph.prebuilt import create_react_agent
from langchain_core.messages import AIMessage
app = create_react_agent(model, tools)
app.invoke({"messages": [{"role": "user", "content": "query"}]})Verify before relying
- Whether this package is intended for direct installation or only as a transitive dependency of langgraph (description notes it should be bundled, not installed directly)
- Performance characteristics or scalability limits for agent execution with large tool sets or complex workflows
- Whether ValidationNode supports custom validation logic beyond Pydantic schema enforcement
Package facts
| License | MIT permissive |
| Python support | Supports the current Python release >=3.10 |
| Install friction | Low. Pure-Python wheel |
| Runtime dependencies | 2 packageslangchain-corelanggraph-checkpoint |
| Maintenance | Actively maintained 94 days since the last release |
| Last repo commit | |
| First released | |
| Downloads | 58,540,136 / month, #514 on PyPI 30-day window, as of 2026-08-14 |
| Known vulnerabilities | None known OSV.dev, checked 2026-08-14 |
| Classifiers | Development Status :: 5 - Production/StableProgramming Language :: PythonProgramming Language :: Python :: 3Programming Language :: Python :: 3 :: OnlyProgramming Language :: Python :: 3.10Programming Language :: Python :: 3.11Programming Language :: Python :: 3.12Programming Language :: Python :: 3.13Programming Language :: Python :: Implementation :: CPythonProgramming Language :: Python :: Implementation :: PyPy |
Evidence: langgraph_prebuilt-1.1.0-py3-none-any.whl
Tags
Let your AI agent find packages like this
Example. Real query, live index.
You found this page by searching. An agent finds it by wishing: SkillFed indexes 14,416 PyPI packages by what they can do, searchable in plain language.
wish › “react agent implementation”
- langgraph-prebuiltProvides high-level APIs for building and running LangGraph agents…
- smolagentssmolagents lets you build and run AI agents that write Python code to…
- blinkerBlinker provides a lightweight event dispatching system that lets any…
Give your agent the search over MCP, or paste the wish link into any chat.
More Application Frameworks packages
FastAPI is a Python web framework for building REST APIs using type hints, with automatic request validation, serialization, and interactive API documentation.
Provides a way to document function parameters, class attributes, return types, and variables inline using Python's `Annotated` type hint syntax instead of traditional docstrings.
Textual is a Python framework for building cross-platform user interfaces that run in the terminal or web browser using a modern, component-based API.
Install it if you're developing CLI tools, dashboards, or interactive terminal applications.
Typer builds command-line applications from Python functions using type hints, automatically generating help text, argument parsing, and shell completion.
Install it if you are building CLIs in Python.
Build and connect to Model Context Protocol servers that expose tools, resources, and prompts to LLM applications over stdio, HTTP, or SSE transports.
Install it if you need to build or connect to servers.
Werkzeug is a WSGI utility library providing request/response objects, URL routing, an interactive debugger, HTTP utilities, and a development server for building web applications.
See also langgraph-supervisor · langgraph-swarm · langgraph · langmem · langchain · uipath-langchain · langgraph-api · langgraph-sdk · langchain-cli · agent-lifecycle-toolkit