feat: examples in monorepo (CopilotKit#590)

* add all demo apps under "examples"

* add renovate

* remove todos and  textarea example

* fix docs

* add examples to gitignore

.github/workflows/ci.yml

@@ -6,11 +6,13 @@ on:
     paths-ignore:
       - 'docs/**'
       - 'README.md'
+      - 'examples/**'
   pull_request:
     branches: [main]
     paths-ignore:
       - 'docs/**'
       - 'README.md'
+      - 'examples/**'
 jobs:
   test:
     name: 'Test'

.github/workflows/quality.yml

@@ -6,11 +6,14 @@ on:
     paths-ignore:
       - 'docs/**'
       - 'README.md'
+      - 'examples/**'
   pull_request:
     branches: [main]    
     paths-ignore:
       - 'docs/**'
       - 'README.md'
+      - 'examples/**'
+
 jobs:
   prettier:
     name: 'Prettier'

.github/workflows/release.yml

@@ -7,6 +7,7 @@ on:
       - main
     paths-ignore:
       - 'docs/**'
+      - 'examples/**'
 
 concurrency: ${{ github.workflow }}-${{ github.ref }}
 

docs/pages/coagents/coagent-demo.mdx

@@ -16,4 +16,4 @@ into a comprehensive answer, including references to the sources used.
 The agent state is streamed live to the frontend, so that the user can see the work progressing.
 
 Both the LangGraph and the CopilotKit specific code for this demo is [available
-on GitHub](https://github.com/CopilotKit/examples/tree/main/ai-researcher).
+on GitHub](https://github.com/CopilotKit/CopilotKit/tree/main/examples/coagents-ai-researcher).

docs/pages/coagents/index.mdx

@@ -229,8 +229,8 @@ In the meantime, you can achieve agent Q&A in the following way:
 We've built a simple example with everything you need to get started:
 
 ```bash
-git clone https://github.com/copilotkit/examples ./copilotkit-examples
-cd ./copilotkit-examples/bootstrap
+git clone https://github.com/CopilotKit/CopilotKit.git
+cd ./CopilotKit/examples/coagents-starter
 
 # Install Python dependencies
 pip install -r requirements.txt

examples/coagents-ai-researcher/README.md

@@ -0,0 +1,58 @@
+# AI Researcher Example
+
+**These instructions assume you are in the `coagents-ai-researcher/` directory**
+
+## Running the Agent
+
+First, install the dependencies:
+
+```sh
+cd agent
+poetry install
+```
+
+Then, create a `.env` file inside `./agent` with the following:
+```
+OPENAI_API_KEY=...
+TAVILY_API_KEY=...
+```
+
+IMPORTANT:
+Make sure the OpenAI API Key you provide, supports gpt-4o.
+
+Then, run the demo:
+
+```sh
+poetry run demo
+```
+
+## Running the UI
+
+First, install the dependencies:
+
+```sh
+cd ./ui
+pnpm i
+```
+
+Then, create a `.env` file inside `./ui` with the following:
+```
+OPENAI_API_KEY=...
+```
+
+Then, run the Next.js project:
+
+```sh
+pnpm run dev
+```
+
+## Usage
+
+Navigate to [http://localhost:3000](http://localhost:3000).
+
+
+# LangGraph Studio
+
+Run LangGraph studio, then load the `./agent` folder into it.
+
+Make sure to create teh `.env` mentioned above first!

examples/coagents-ai-researcher/agent/.gitignore

@@ -0,0 +1,5 @@
+venv/
+__pycache__/
+*.pyc
+.env
+.vercel

examples/coagents-ai-researcher/agent/.vscode/cspell.json

@@ -0,0 +1,17 @@
+{
+  "version": "0.2",
+  "language": "en",
+  "words": [
+    "langgraph",
+    "langchain",
+    "perplexity",
+    "openai",
+    "ainvoke",
+    "pydantic",
+    "tavily",
+    "copilotkit",
+    "fastapi",
+    "uvicorn",
+    "checkpointer"
+  ]
+}

examples/coagents-ai-researcher/agent/ai_researcher/agent.py

@@ -0,0 +1,58 @@
+"""
+This is the main entry point for the AI.
+It defines the workflow graph and the entry point for the agent.
+"""
+# pylint: disable=line-too-long, unused-import
+import json
+
+from langgraph.graph import StateGraph, END
+from langgraph.checkpoint.memory import MemorySaver
+
+from ai_researcher.state import AgentState
+from ai_researcher.steps import steps_node
+from ai_researcher.search import search_node
+from ai_researcher.summarize import summarize_node
+from ai_researcher.extract import extract_node
+
+def route(state):
+    """Route to research nodes."""
+    if not state.get("steps", None):
+        return END
+
+    current_step = next((step for step in state["steps"] if step["status"] == "pending"), None)
+
+    if not current_step:
+        return "summarize_node"
+
+    if current_step["type"] == "search":
+        return "search_node"
+
+    raise ValueError(f"Unknown step type: {current_step['type']}")
+
+# Define a new graph
+workflow = StateGraph(AgentState)
+workflow.add_node("steps_node", steps_node)
+workflow.add_node("search_node", search_node)
+workflow.add_node("summarize_node", summarize_node)
+workflow.add_node("extract_node", extract_node)
+# Chatbot
+workflow.set_entry_point("steps_node")
+
+workflow.add_conditional_edges(
+    "steps_node", 
+    route,
+    ["summarize_node", "search_node", END]
+)
+
+workflow.add_edge("search_node", "extract_node")
+
+workflow.add_conditional_edges(
+    "extract_node",
+    route,
+    ["summarize_node", "search_node"]
+)
+
+workflow.add_edge("summarize_node", END)
+
+memory = MemorySaver()
+graph = workflow.compile(checkpointer=memory)

examples/coagents-ai-researcher/agent/ai_researcher/demo.py

@@ -0,0 +1,27 @@
+"""Demo"""
+
+from dotenv import load_dotenv
+load_dotenv()
+
+from fastapi import FastAPI
+import uvicorn
+from copilotkit.integrations.fastapi import add_fastapi_endpoint
+from copilotkit import CopilotKitSDK, LangGraphAgent
+from ai_researcher.agent import graph
+
+app = FastAPI()
+sdk = CopilotKitSDK(
+    agents=[
+        LangGraphAgent(
+            name="search_agent",
+            description="Search agent.",
+            agent=graph,
+        )
+    ],
+)
+
+add_fastapi_endpoint(app, sdk, "/copilotkit")
+
+def main():
+    """Run the uvicorn server."""
+    uvicorn.run("ai_researcher.demo:app", host="127.0.0.1", port=8000, reload=True)

examples/coagents-ai-researcher/agent/ai_researcher/extract.py

@@ -0,0 +1,57 @@
+"""
+The extract node is responsible for extracting information from a tavily search.
+"""
+import json
+
+from langchain_openai import ChatOpenAI
+from langchain_core.messages import SystemMessage
+
+from langchain_core.runnables import RunnableConfig
+
+from ai_researcher.state import AgentState
+
+async def extract_node(state: AgentState, config: RunnableConfig):
+    """
+    The extract node is responsible for extracting information from a tavily search.
+    """
+
+    current_step = next((step for step in state["steps"] if step["status"] == "pending"), None)
+
+    if current_step is None:
+        raise ValueError("No current step")
+
+    if current_step["type"] != "search":
+        raise ValueError("Current step is not of type search")
+
+    system_message = f"""
+This step was just executed: {json.dumps(current_step)}
+
+This is the result of the search:
+
+Please summarize ONLY the result of the search and include all relevant information from the search and reference links.
+DO NOT INCLUDE ANY EXTRA INFORMATION. ALL OF THE INFORMATION YOU ARE LOOKING FOR IS IN THE SEARCH RESULTS.
+
+DO NOT answer the user's query yet. Just summarize the search results.
+
+Use markdown formatting and put the references inline and the links at the end.
+Like this:
+This is a sentence with a reference to a source [source 1][1] and another reference [source 2][2].
+[1]: http://example.com/source1 "Title of Source 1"
+[2]: http://example.com/source2 "Title of Source 2"
+"""
+
+    response = await ChatOpenAI(model="gpt-4o").ainvoke([
+        *state["messages"],
+        SystemMessage(content=system_message)
+    ], config)
+
+    current_step["result"] = response.content
+    current_step["search_result"] = None
+    current_step["status"] = "complete"
+    current_step["updates"] = [*current_step["updates"], "Done."]
+
+    next_step = next((step for step in state["steps"] if step["status"] == "pending"), None)
+    if next_step:
+        next_step["updates"] = ["Searching the web..."]
+
+    return state

examples/coagents-ai-researcher/agent/ai_researcher/search.py

@@ -0,0 +1,62 @@
+"""
+The search node is responsible for searching the internet for information.
+"""
+import json
+from datetime import datetime
+from langchain_openai import ChatOpenAI
+from langchain_core.messages import SystemMessage
+
+from langchain_core.runnables import RunnableConfig
+from langchain_community.tools import TavilySearchResults
+
+from ai_researcher.state import AgentState
+
+async def search_node(state: AgentState, config: RunnableConfig):
+    """
+    The search node is responsible for searching the internet for information.
+    """
+    tavily_tool = TavilySearchResults(
+        max_results=10,
+        search_depth="advanced",
+        include_answer=True,
+        include_raw_content=True,
+        include_images=True,
+    )
+
+    current_step = next((step for step in state["steps"] if step["status"] == "pending"), None)
+
+    if current_step is None:
+        raise ValueError("No step to search for")
+
+    if current_step["type"] != "search":
+        raise ValueError("Current step is not a search step")
+
+    system_message = f"""
+This is a step in a series of steps that are being executed to answer the user's query.
+These are all of the steps: {json.dumps(state["steps"])}
+
+You are responsible for carrying out the step: {json.dumps(current_step)}
+
+The current date is {datetime.now().strftime("%Y-%m-%d")}.
+
+This is what you need to search for, please come up with a good search query: {current_step["description"]}
+"""
+    model = ChatOpenAI(model="gpt-4o").bind_tools(
+        [tavily_tool],
+        parallel_tool_calls=False,
+        tool_choice=tavily_tool.name
+    )
+
+    response = await model.ainvoke([
+        *state["messages"],
+        SystemMessage(
+            content=system_message
+        )
+    ], config)
+
+    tool_msg = tavily_tool.invoke(response.tool_calls[0])
+
+    current_step["search_result"] = json.loads(tool_msg.content)
+    current_step["updates"] = [*current_step["updates"],"Extracting information..."]
+
+    return state

examples/coagents-ai-researcher/agent/ai_researcher/state.py

@@ -0,0 +1,28 @@
+"""
+This is the state definition for the AI.
+It defines the state of the agent and the state of the conversation.
+"""
+
+from typing import List, TypedDict, Optional
+from langgraph.graph import MessagesState
+
+class Step(TypedDict):
+    """
+    Represents a step taken in the research process.
+    """
+    id: str
+    description: str
+    status: str
+    type: str
+    description: str
+    search_result: Optional[str]
+    result: Optional[str]
+    updates: Optional[List[str]]
+
+class AgentState(MessagesState):
+    """
+    This is the state of the agent.
+    It is a subclass of the MessagesState class from langgraph.
+    """
+    steps: List[Step]
+    answer: Optional[str]