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Update readme
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William Fu-Hinthorn
@@ -454,18 +454,6 @@ We also have a lot of examples highlighting how to slightly modify the base chat
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- [Force calling a tool first](https://github.com/langchain-ai/langgraph/blob/main/examples/agent_executor/force-calling-a-tool-first.ipynb): How to always call a specific tool first
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- [Managing agent steps](https://github.com/langchain-ai/langgraph/blob/main/examples/agent_executor/managing-agent-steps.ipynb): How to more explicitly manage intermediate steps that an agent takes
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### Multi-agent Examples
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- [Multi-agent collaboration](https://github.com/langchain-ai/langgraph/blob/main/examples/multi_agent/multi-agent-collaboration.ipynb): how to create two agents that work together to accomplish a task
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- [Multi-agent with supervisor](https://github.com/langchain-ai/langgraph/blob/main/examples/multi_agent/agent_supervisor.ipynb): how to orchestrate individual agents by using an LLM as a "supervisor" to distribute work
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- [Hierarchical agent teams](https://github.com/langchain-ai/langgraph/blob/main/examples/multi_agent/hierarchical_agent_teams.ipynb): how to orchestrate "teams" of agents as nested graphs that can collaborate to solve a problem
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### Chatbot Evaluation via Simulation
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It can often be tough to evaluation chat bots in multi-turn situations. One way to do this is with simulations.
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- [Chat bot evaluation as multi-agent simulation](https://github.com/langchain-ai/langgraph/blob/main/examples/chatbot-simulation-evaluation/agent-simulation-evaluation.ipynb): How to simulate a dialogue between a "virtual user" and your chat bot
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### Async
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If you are running LangGraph in async workflows, you may want to create the nodes to be async by default.
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@@ -486,6 +474,32 @@ For a walkthrough on how to do that, see [this documentation](https://github.com
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LangGraph comes with built-in support for human-in-the-loop workflows. This is useful when you want to have a human review the current state before proceeding to a particular node.
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For a walkthrough on how to do that, see [this documentation](https://github.com/langchain-ai/langgraph/blob/main/examples/human-in-the-loop.ipynb)
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### Planning Agent Examples
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The following notebooks implement agent architectures prototypical of the "plan-and-execute" style, where an LLM planner decomposes a user request into a program, an executor executes the program, and an LLM synthesizes a response (and/or dynamically replans) based on the program outputs.
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- [Plan-and-execute](https://github.com/langchain-ai/langgraph/blob/main/examples/plan-and-execute/plan-and-execute.ipynb): a simple agent with a **planner** that generates a multi-step task list, an **executor** that invokes the tools in the plan, and a **replanner** that responds or generates an updated plan
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- [Reasoning without Observation](https://github.com/langchain-ai/langgraph/blob/main/examples/rewoo/rewoo.ipynb): planner generates a task list whose observations are saved as **variables**. Variables can be used in subsequent tasks to reduce the need for further re-planning
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- [LLMCompiler](https://github.com/langchain-ai/langgraph/blob/main/examples/llm-compiler/LLMCompiler.ipynb): planner generates a **DAG** of tasks with variable responses. Tasks are **streamed** and executed eagerly to minimize tool execution runtime.
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### Multi-agent Examples
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- [Multi-agent collaboration](https://github.com/langchain-ai/langgraph/blob/main/examples/multi_agent/multi-agent-collaboration.ipynb): how to create two agents that work together to accomplish a task
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- [Multi-agent with supervisor](https://github.com/langchain-ai/langgraph/blob/main/examples/multi_agent/agent_supervisor.ipynb): how to orchestrate individual agents by using an LLM as a "supervisor" to distribute work
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- [Hierarchical agent teams](https://github.com/langchain-ai/langgraph/blob/main/examples/multi_agent/hierarchical_agent_teams.ipynb): how to orchestrate "teams" of agents as nested graphs that can collaborate to solve a problem
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### Chatbot Evaluation via Simulation
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It can often be tough to evaluation chat bots in multi-turn situations. One way to do this is with simulations.
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- [Chat bot evaluation as multi-agent simulation](https://github.com/langchain-ai/langgraph/blob/main/examples/chatbot-simulation-evaluation/agent-simulation-evaluation.ipynb): how to simulate a dialogue between a "virtual user" and your chat bot
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### Multimodal Examples
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- [WebVoyager](https://github.com/langchain-ai/langgraph/blob/main/examples/web-navigation/web_voyager.ipynb): vision-enabled web browsing agent that uses [Set-of-marks](https://som-gpt4v.github.io/) prompting to navigate a web browser and execute tasks
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## Documentation
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There are only a few new APIs to use.
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@@ -5,7 +5,7 @@
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"id": "1c161710-fc66-426f-8c96-28440b9c9626",
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"metadata": {},
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"source": [
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"# Reasoning Without Observation\n",
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"# Reasoning without Observation\n",
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"\n",
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"In [ReWOO](https://arxiv.org/abs/2305.18323), Xu, et. al, propose an agent that combines a multi-step planner and variable substitution for effective tool use. It was designed to improve on the ReACT-style agent architecture in the following ways:\n",
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"\n",
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@@ -59,15 +59,16 @@
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"import os\n",
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"import getpass\n",
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"\n",
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"\n",
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"def _set_if_undefined(var: str):\n",
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" if not os.environ.get(var):\n",
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" os.environ[var] = getpass.getpass(f\"{var}=\")\n",
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"\n",
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"\n",
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"os.environ[\"LANGCHAIN_TRACING_V2\"] = \"true\"\n",
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"os.environ[\"LANGCHAIN_PROJECT\"] = \"ReWOO\"\n",
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"_set_if_undefined(\"TAVILY_API_KEY\")\n",
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"_set_if_undefined(\"LANGCHAIN_API_KEY\")\n",
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"os.environ['LANGCHAIN_API_KEY'] = \"ls__2d5a3fe2b3af4f4db79501c906e1c072\"\n",
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"_set_if_undefined(\"OPENAI_API_KEY\")"
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]
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},
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@@ -89,6 +90,8 @@
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"outputs": [],
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"source": [
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"from typing import TypedDict, List\n",
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"\n",
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"\n",
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"class ReWOO(TypedDict):\n",
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" task: str\n",
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" plan_string: str\n",
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@@ -229,10 +232,12 @@
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"source": [
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"import re\n",
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"from langchain_core.prompts import ChatPromptTemplate\n",
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"\n",
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"# Regex to match expressions of the form E#... = ...[...]\n",
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"regex_pattern = r\"Plan:\\s*(.+)\\s*(#E\\d+)\\s*=\\s*(\\w+)\\s*\\[([^\\]]+)\\]\"\n",
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"prompt_template = ChatPromptTemplate.from_messages([(\"user\", prompt)]) \n",
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"planner = prompt_template| model\n",
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"prompt_template = ChatPromptTemplate.from_messages([(\"user\", prompt)])\n",
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"planner = prompt_template | model\n",
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"\n",
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"\n",
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"def get_plan(state: ReWOO):\n",
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" task = state[\"task\"]\n",
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@@ -262,6 +267,7 @@
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"outputs": [],
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"source": [
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"from langchain_community.tools.tavily_search import TavilySearchResults\n",
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"\n",
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"search = TavilySearchResults()"
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]
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},
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@@ -279,15 +285,16 @@
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" return None\n",
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" else:\n",
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" return len(state[\"results\"]) + 1\n",
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" \n",
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"\n",
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"\n",
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"def tool_execution(state: ReWOO):\n",
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" \"\"\"Worker node that executes the tools of a given plan.\"\"\"\n",
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" _step =_get_current_task(state)\n",
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" _step = _get_current_task(state)\n",
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" _, step_name, tool, tool_input = state[\"steps\"][_step - 1]\n",
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" _results = state[\"results\"] or {}\n",
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" for k, v in _results.items():\n",
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" tool_input = tool_input.replace(k, v)\n",
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" if tool == \"Google\":\n",
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" if tool == \"Google\":\n",
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" result = search.invoke(tool_input)\n",
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" elif tool == \"LLM\":\n",
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" result = model.invoke(tool_input)\n",
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@@ -326,6 +333,7 @@
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"Task: {task}\n",
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"Response:\"\"\"\n",
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"\n",
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"\n",
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"def solve(state: ReWOO):\n",
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" plan = \"\"\n",
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" for _plan, step_name, tool, tool_input in state[\"steps\"]:\n",
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@@ -414,9 +422,9 @@
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}
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],
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"source": [
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"for s in app.stream({\"task\":task}):\n",
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"for s in app.stream({\"task\": task}):\n",
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" print(s)\n",
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" print('---')"
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" print(\"---\")"
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]
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},
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{
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