---
id: langgraph-supervisor
version: "0.0.31"
license: MIT
license_treatment: permissive
maintenance: active
---

# langgraph-supervisor — An implementation of a supervisor multi-agent architecture using LangGraph

License: permissive · Maintenance: active · Downloads: 718.7K/mo

## What it is and what it does

langgraph-supervisor provides a Python library for building hierarchical multi-agent systems on top of LangGraph. It implements a supervisor pattern where a central agent orchestrates communication and task delegation to specialized worker agents. The supervisor makes routing decisions based on context and task requirements, controlling all message flow between agents.

The library handles the mechanics of agent coordination—tool-based handoffs, message history management, and multi-level hierarchies—so you can focus on defining specialized agents and their supervisor's decision logic. It integrates with LangGraph's streaming, memory, and human-in-the-loop capabilities. However, the library's own documentation now recommends using the supervisor pattern directly via tool-calling for most use cases, suggesting this library is best suited for users upgrading existing code or cases where its abstraction provides clear value.

Use it for:

- Route customer support queries to specialized agents (billing, technical, account management) based on intent
- Coordinate research and math agents to answer complex questions requiring both data lookup and calculation
- Build multi-level hierarchies where team supervisors report to a top-level supervisor for large organizations
- Manage conversation history and context across multiple specialized agents in a single workflow
- Add persistent memory and human approval steps to multi-agent workflows via LangGraph checkpointers

## Worth the install?

AI-flagged interpretation of the facts above — verify before relying.

Builds hierarchical multi-agent systems where a supervisor agent coordinates and delegates tasks to specialized worker agents using LangGraph.

Yes, if you are building a hierarchical multi-agent system and prefer a structured abstraction over manual tool-calling. The library is actively maintained, has low install friction, and integrates well with LangGraph's ecosystem. However, review the library's own recommendation to use tool-calling directly for most new projects—install this primarily to upgrade existing code or when the supervisor abstraction clearly simplifies your architecture.

## Install

pip install langgraph-supervisor
uv add langgraph-supervisor
poetry add langgraph-supervisor

## Installing langgraph-supervisor

Before you install: Low install friction with only two runtime dependencies (langgraph and langchain-core). Active maintenance with recent releases; last commit 2026-07-15. Requires Python >= 3.10.

License in practice: MIT license permits commercial and private use with minimal restrictions, making it suitable for most production deployments.

Quickstart:

pip install langgraph-supervisor

from langgraph_supervisor import create_supervisor
from langgraph.prebuilt import create_react_agent

math_agent = create_react_agent(model=model, tools=[add, multiply], name="math_expert")
research_agent = create_react_agent(model=model, tools=[web_search], name="research_expert")

workflow = create_supervisor([research_agent, math_agent], model=model)
app = workflow.compile()
result = app.invoke({"messages": [{"role": "user", "content": "query"}]})

Requires Python >= 3.10. Requires langchain-core and langgraph to be installed and configured with a working LLM model.

Verify before relying:

- Whether the library's supervisor pattern remains the recommended approach given the note recommending tool-calling directly for most use cases
- Performance characteristics when managing large numbers of agents or deeply nested hierarchies
- Compatibility guarantees with specific langgraph and langchain-core versions beyond the stated requirements

## Package facts

- License: MIT (permissive)
- Python support: supports_current
- Install friction: low
- Maintenance: active
- Downloads: 718.7K/month (top 15,000 on PyPI)
- Known vulnerabilities: none known

## Tags

multi-agent supervisor orchestration, hierarchical agent coordination, langgraph supervisor pattern, agent task delegation framework, multi-level agent systems, agent handoff mechanism, specialized agent management, multi-agent-systems, agent-orchestration, langgraph

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