From 583d8c9499ede47c8ec3f50f4d62d32e7c4f510c Mon Sep 17 00:00:00 2001 From: Vadym Barda Date: Wed, 16 Oct 2024 11:17:28 -0400 Subject: [PATCH] docs: update tutorial names/links (#2126) --- docs/docs/tutorials/index.md | 4 ++-- docs/docs/tutorials/multi_agent/agent_supervisor.ipynb | 6 +++--- .../tutorials/multi_agent/hierarchical_agent_teams.ipynb | 4 ++-- .../tutorials/multi_agent/multi-agent-collaboration.ipynb | 6 +++--- docs/mkdocs.yml | 4 ++-- 5 files changed, 12 insertions(+), 12 deletions(-) diff --git a/docs/docs/tutorials/index.md b/docs/docs/tutorials/index.md index 5d17ac234..22953d918 100644 --- a/docs/docs/tutorials/index.md +++ b/docs/docs/tutorials/index.md @@ -25,8 +25,8 @@ Learn from example implementations of graphs designed for specific scenarios and #### Multi-Agent Systems -- [Collaboration](multi_agent/multi-agent-collaboration.ipynb): Enable two agents to collaborate on a task -- [Supervision](multi_agent/agent_supervisor.ipynb): Use an LLM to orchestrate and delegate to individual agents +- [Network](multi_agent/multi-agent-collaboration.ipynb): Enable two or more agents to collaborate on a task +- [Supervisor](multi_agent/agent_supervisor.ipynb): Use an LLM to orchestrate and delegate to individual agents - [Hierarchical Teams](multi_agent/hierarchical_agent_teams.ipynb): Orchestrate nested teams of agents to solve problems #### RAG diff --git a/docs/docs/tutorials/multi_agent/agent_supervisor.ipynb b/docs/docs/tutorials/multi_agent/agent_supervisor.ipynb index b1f94190f..7a3fea35e 100644 --- a/docs/docs/tutorials/multi_agent/agent_supervisor.ipynb +++ b/docs/docs/tutorials/multi_agent/agent_supervisor.ipynb @@ -10,11 +10,11 @@ "id": "a3e3ebc4-57af-4fe4-bdd3-36aff67bf276", "metadata": {}, "source": [ - "# Agent Supervisor\n", + "# Multi-agent supervisor\n", "\n", "The [previous example](../multi-agent-collaboration) routed messages automatically based on the output of the initial researcher agent.\n", "\n", - "We can also choose to use an LLM to orchestrate the different agents.\n", + "We can also choose to use an [LLM to orchestrate](https://langchain-ai.github.io/langgraph/concepts/multi_agent/#supervisor) the different agents.\n", "\n", "Below, we will create an agent group, with an agent supervisor to help delegate tasks.\n", "\n", @@ -376,7 +376,7 @@ "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython3", - "version": "3.11.9" + "version": "3.12.3" } }, "nbformat": 4, diff --git a/docs/docs/tutorials/multi_agent/hierarchical_agent_teams.ipynb b/docs/docs/tutorials/multi_agent/hierarchical_agent_teams.ipynb index c923976f0..0cc30658e 100644 --- a/docs/docs/tutorials/multi_agent/hierarchical_agent_teams.ipynb +++ b/docs/docs/tutorials/multi_agent/hierarchical_agent_teams.ipynb @@ -12,7 +12,7 @@ "source": [ "# Hierarchical Agent Teams\n", "\n", - "In our previous example ([Agent Supervisor](../agent_supervisor)), we introduced the concept of a single supervisor node to route work between different worker nodes.\n", + "In our previous example ([Agent Supervisor](../agent_supervisor)), we introduced the concept of a single [supervisor node](https://langchain-ai.github.io/langgraph/concepts/multi_agent/#supervisor) to route work between different worker nodes.\n", "\n", "But what if the job for a single worker becomes too complex? What if the number of workers becomes too large?\n", "\n", @@ -1117,7 +1117,7 @@ "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython3", - "version": "3.11.9" + "version": "3.12.3" } }, "nbformat": 4, diff --git a/docs/docs/tutorials/multi_agent/multi-agent-collaboration.ipynb b/docs/docs/tutorials/multi_agent/multi-agent-collaboration.ipynb index 734b1097d..1be7b0e7e 100644 --- a/docs/docs/tutorials/multi_agent/multi-agent-collaboration.ipynb +++ b/docs/docs/tutorials/multi_agent/multi-agent-collaboration.ipynb @@ -10,11 +10,11 @@ "id": "39fd1948-b5c3-48c4-b10e-2ae7e8c83334", "metadata": {}, "source": [ - "# Basic Multi-agent Collaboration\n", + "# Multi-agent network\n", "\n", "A single agent can usually operate effectively using a handful of tools within a single domain, but even using powerful models like `gpt-4`, it can be less effective at using many tools. \n", "\n", - "One way to approach complicated tasks is through a \"divide-and-conquer\" approach: create an specialized agent for each task or domain and route tasks to the correct \"expert\".\n", + "One way to approach complicated tasks is through a \"divide-and-conquer\" approach: create an specialized agent for each task or domain and route tasks to the correct \"expert\". This is an example of a [multi-agent network](https://langchain-ai.github.io/langgraph/concepts/multi_agent/#network) architecture.\n", "\n", "This notebook (inspired by the paper [AutoGen: Enabling Next-Gen LLM Applications via Multi-Agent Conversation](https://arxiv.org/abs/2308.08155), by Wu, et. al.) shows one way to do this using LangGraph.\n", "\n", @@ -535,7 +535,7 @@ "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython3", - "version": "3.11.9" + "version": "3.12.3" } }, "nbformat": 4, diff --git a/docs/mkdocs.yml b/docs/mkdocs.yml index a9e168c18..f65f065be 100644 --- a/docs/mkdocs.yml +++ b/docs/mkdocs.yml @@ -97,8 +97,8 @@ nav: - SQL Agent: tutorials/sql-agent.ipynb - Agent Architectures: - Multi-Agent Systems: - - Collaboration: tutorials/multi_agent/multi-agent-collaboration.ipynb - - Supervision: tutorials/multi_agent/agent_supervisor.ipynb + - Network: tutorials/multi_agent/multi-agent-collaboration.ipynb + - Supervisor: tutorials/multi_agent/agent_supervisor.ipynb - Hierarchical Teams: tutorials/multi_agent/hierarchical_agent_teams.ipynb - Planning Agents: - Plan-and-Execute: tutorials/plan-and-execute/plan-and-execute.ipynb