diff --git a/.github/workflows/add-markdown-examples-to-docs.yml b/.github/workflows/add-notebook-examples-to-docs.yml similarity index 61% rename from .github/workflows/add-markdown-examples-to-docs.yml rename to .github/workflows/add-notebook-examples-to-docs.yml index fc26fb8b..176f2d6c 100644 --- a/.github/workflows/add-markdown-examples-to-docs.yml +++ b/.github/workflows/add-notebook-examples-to-docs.yml @@ -7,7 +7,10 @@ on: paths: - 'examples/**' - 'docs/v1/examples/**' - + - '.github/workflows/add-notebook-examples-to-docs.yml' + + workflow_dispatch: + permissions: contents: write pull-requests: write @@ -35,7 +38,6 @@ jobs: echo "Processing file: $file" source_file=$(grep -oP '(?<=\{/\* SOURCE_FILE: ).*(?= \*/\})' "$file" || true) if [[ -z "$source_file" ]]; then - echo "Error: No source file found in $file, skipping..." >&2 continue fi echo "Source file: $source_file" @@ -43,8 +45,10 @@ jobs: echo "Converting notebook to markdown" jupyter nbconvert --to markdown "$source_file" || { echo "Error: Failed to convert $source_file" >&2; continue; } markdown_file="${source_file%.ipynb}.md" + echo "Removing existing content after {/* SOURCE_FILE: ... */}" + sed -i '\#{/\* SOURCE_FILE:#,$d' "$file" echo "Appending markdown to $file" - echo -e "\n\n" >> "$file" + echo -e "{/* SOURCE_FILE: $source_file */}\n" >> "$file" cat "$markdown_file" >> "$file" || { echo "Error: Failed to append markdown to $file" >&2; continue; } rm "$markdown_file" || { echo "Error: Failed to remove $markdown_file" >&2; continue; } else @@ -52,26 +56,14 @@ jobs: fi done - - name: Commit changes - run: | - git config --local user.email "action@github.com" - git config --local user.name "GitHub Action" - git add docs/v1/examples/*.mdx - git diff --quiet && git diff --staged --quiet || git commit -m "GitHub Action: Update examples in docs from notebooks" - - name: Create Pull Request - uses: peter-evans/create-pull-request@v5 + uses: peter-evans/create-pull-request@v7 with: - token: ${{ secrets.GITHUB_TOKEN }} - commit-message: Update examples in docs from notebooks - title: 'Update examples in docs from notebooks' - body: | - This PR updates the examples in the docs from the corresponding notebooks. - Please review the changes before merging. - branch: update-docs-examples - base: main -# - name: Push changes -# uses: ad-m/github-push-action@master -# with: -# github_token: ${{ secrets.GITHUB_TOKEN }} -# branch: main \ No newline at end of file + committer: Howard Gil + commit-message: GitHub Action - Update examples in docs from notebooks + title: GitHub Action - Update examples in docs from notebooks + body: Changes detected in examples/** or docs/v1/examples/** triggered an update of the docs/v1/examples/**.mdx files to incorporate markdown from the corresponding notebook in examples/**. + branch: update-examples-in-docs-from-notebooks + delete-branch: true + assignees: HowieG,siyangqiu,bboynton97,areibman + reviewers: HowieG,siyangqiu,bboynton97,areibman diff --git a/.github/workflows/black-formatter.yml b/.github/workflows/black-formatter.yml index 8fa24b42..c8b3e342 100644 --- a/.github/workflows/black-formatter.yml +++ b/.github/workflows/black-formatter.yml @@ -1,7 +1,11 @@ name: Black Code Formatter Check -on: [pull_request] +on: + pull_request: + paths: + - '**/*.py' + - '**/*.ipynb' jobs: black-check: @@ -19,7 +23,6 @@ jobs: - name: Install dependencies run: | python -m pip install --upgrade pip - pip install black pip install "black[jupyter]" - name: Run Black diff --git a/.github/workflows/codecov.yml b/.github/workflows/codecov.yml index f627c1ee..c60c9a54 100644 --- a/.github/workflows/codecov.yml +++ b/.github/workflows/codecov.yml @@ -5,12 +5,12 @@ on: branches: - main paths: - - 'agentops/**' + - 'agentops/**/*.py' pull_request: branches: - main paths: - - 'agentops/**' + - 'agentops/**/*.py' jobs: test: diff --git a/.github/workflows/notebook-gen.yml b/.github/workflows/notebook-gen.yml deleted file mode 100644 index 54d00c34..00000000 --- a/.github/workflows/notebook-gen.yml +++ /dev/null @@ -1,36 +0,0 @@ -name: Check Generated Files - -on: push - -jobs: - check-diff: - runs-on: ubuntu-latest - steps: - - - name: Checkout repository - uses: actions/checkout@v2 - - - name: Setup Python - uses: actions/setup-python@v2 - with: - python-version: '3.11' - - - name: Install Dependencies - if: always() - run: | - python -m pip install --upgrade pip - # Install your project dependencies here if necessary - - - name: Run generate script - if: always() - run: python docs/v1/examples/notebooks/generate.py - - - name: Check for differences - if: always() - run: | - if [[ `git status --porcelain` ]]; then - echo "Notebooks have been changed without being regenerated. Please run `python docs/v1/examples/notebooks/generate.py`." - exit 1 - else - echo "No changes were detected. Notebooks up to date πŸ’…πŸ»" - fi \ No newline at end of file diff --git a/.github/workflows/python-testing.yml b/.github/workflows/python-testing.yml index 6f79b66f..8fd5e90b 100644 --- a/.github/workflows/python-testing.yml +++ b/.github/workflows/python-testing.yml @@ -5,14 +5,18 @@ on: branches: - main paths: - - 'agentops/**' - - 'tests/**' + - 'agentops/**/*.py' + - 'agentops/**/*.ipynb' + - 'tests/**/*.py' + - 'tests/**/*.ipynb' pull_request: branches: - main paths: - - 'agentops/**' - - 'tests/**' + - 'agentops/**/*.py' + - 'agentops/**/*.ipynb' + - 'tests/**/*.py' + - 'tests/**/*.ipynb' jobs: build: diff --git a/.github/workflows/tach-check.yml b/.github/workflows/tach-check.yml index 32208e6a..1d517d63 100644 --- a/.github/workflows/tach-check.yml +++ b/.github/workflows/tach-check.yml @@ -3,9 +3,8 @@ name: Tach Check on: pull_request: paths: - - 'agentops/**' - - 'tests/**' - - 'examples/**' + - '**/*.py' + - '**/*.ipynb' jobs: tach-check: diff --git a/.github/workflows/test-notebooks.yml b/.github/workflows/test-notebooks.yml index 303437e0..38657cff 100644 --- a/.github/workflows/test-notebooks.yml +++ b/.github/workflows/test-notebooks.yml @@ -46,7 +46,7 @@ jobs: exit_code=0 exclude_notebooks=( - "./examples/crewai_examples/job_posting.ipynb", + "./examples/crewai_examples/job_posting.ipynb" "./examples/demos/agentchat_agentops.ipynb" ) diff --git a/agentops/session.py b/agentops/session.py index a82eeb35..f5722af4 100644 --- a/agentops/session.py +++ b/agentops/session.py @@ -7,7 +7,7 @@ from termcolor import colored from typing import Optional, List, Union from uuid import UUID, uuid4 -from dateutil import parser +from datetime import datetime from .exceptions import ApiServerException from .enums import EndState @@ -106,7 +106,10 @@ def end_session( self._flush_queue() def format_duration(start_time, end_time): - duration = parser.parse(end_time) - parser.parse(start_time) + start = datetime.fromisoformat(start_time.replace("Z", "+00:00")) + end = datetime.fromisoformat(end_time.replace("Z", "+00:00")) + duration = end - start + hours, remainder = divmod(duration.total_seconds(), 3600) minutes, seconds = divmod(remainder, 60) diff --git a/docs/v0/recording-events.mdx b/docs/v0/recording-events.mdx index ff5eccbe..e07fb5c4 100644 --- a/docs/v0/recording-events.mdx +++ b/docs/v0/recording-events.mdx @@ -18,7 +18,7 @@ def sample_function(...): ... ``` -The decorator will record the function's parameters, returns, and the time duration. We suggest using this on functions that take a long time and contain nested functions. For example, if you decorate a function that makes several openai calls, then each openai call will show in the replay graph as a child of the decorated function. +The decorator will record the function's parameters, returns, and the time duration. We suggest using this on functions that take a long time and contain nested functions. For example, if you decorate a function that makes several OpenAI calls, then each openai call will show in the replay graph as a child of the decorated function. record_action: diff --git a/docs/v1/examples/langchain.mdx b/docs/v1/examples/langchain.mdx index 5e6c73a4..b8065e25 100644 --- a/docs/v1/examples/langchain.mdx +++ b/docs/v1/examples/langchain.mdx @@ -5,4 +5,325 @@ mode: "wide" --- _View Notebook on Github_ -{/* SOURCE_FILE: examples/langchain_examples.ipynb */} \ No newline at end of file + +{/* SOURCE_FILE: examples/langchain_examples/langchain_examples.ipynb */} + +# AgentOps Langchain Agent Implementation + +Using AgentOps monitoring with Langchain is simple. We've created a LangchainCallbackHandler that will do all of the heavy lifting! + +First let's install the required packages + + +```python +%pip install langchain==0.2.9 +%pip install langchain_openai +%pip install -U agentops +%pip install -U python-dotenv +``` + +Then import them + + +```python +import os +from langchain_openai import ChatOpenAI +from langchain.agents import tool, AgentExecutor, create_openai_tools_agent +from dotenv import load_dotenv +from langchain_core.prompts import ChatPromptTemplate +``` + +The only difference with using AgentOps is that we'll also import this special Callback Handler + + +```python +from agentops.partners.langchain_callback_handler import ( + LangchainCallbackHandler as AgentOpsLangchainCallbackHandler, +) +``` + +Next, we'll set our API keys. There are several ways to do this, the code below is just the most foolproof way for the purposes of this notebook. It accounts for both users who use environment variables and those who just want to set the API Key here in this notebook. + +[Get an AgentOps API key](https://agentops.ai/settings/projects) + +1. Create an environment variable in a .env file or other method. By default, the AgentOps `init()` function will look for an environment variable named `AGENTOPS_API_KEY`. Or... + +2. Replace `` below and pass in the optional `api_key` parameter to the AgentOps `init(api_key=...)` function. Remember not to commit your API key to a public repo! + + +```python +load_dotenv() +AGENTOPS_API_KEY = os.environ.get("AGENTOPS_API_KEY") +OPENAI_API_KEY = os.environ.get("OPENAI_API_KEY") +``` + +This is where AgentOps comes into play. Before creating our LLM instance via Langchain, first we'll create an instance of the AO LangchainCallbackHandler. After the handler is initialized, a session will be recorded automatically. + +Pass in your API key, and optionally any tags to describe this session for easier lookup in the AO dashboard. + +agentops_handler = AgentOpsLangchainCallbackHandler( + api_key=AGENTOPS_API_KEY, default_tags=["Langchain Example"] +) + + + +```python +agentops_handler = AgentOpsLangchainCallbackHandler( + api_key=AGENTOPS_API_KEY, default_tags=["Langchain Example"] +) + +llm = ChatOpenAI( + openai_api_key=OPENAI_API_KEY, callbacks=[agentops_handler], model="gpt-3.5-turbo" +) + +# You must pass in a callback handler to record your agent +llm.callbacks = [agentops_handler] + +prompt = ChatPromptTemplate.from_messages( + [ + ("system", "You are a helpful assistant. Respond only in Spanish."), + ("human", "{input}"), + # Placeholders fill up a **list** of messages + ("placeholder", "{agent_scratchpad}"), + # ("tool_names", "find_movie") + ] +) +``` + +You can also retrieve the `session_id` of the newly created session. + + +```python +print("Agent Ops session ID: " + str(agentops_handler.current_session_ids)) +``` + +Agents generally use tools. Let's define a simple tool here. Tool usage is also recorded. + + +```python +@tool +def find_movie(genre: str) -> str: + """Find available movies""" + if genre == "drama": + return "Dune 2" + else: + return "Pineapple Express" + + +tools = [find_movie] +``` + +For each tool, you need to also add the callback handler + + +```python +for t in tools: + t.callbacks = [agentops_handler] +``` + +Add the tools to our LLM + + +```python +llm_with_tools = llm.bind_tools([find_movie]) +``` + +Finally, let's create our agent! Pass in the callback handler to the agent, and all the actions will be recorded in the AO Dashboard + + +```python +agent = create_openai_tools_agent(llm, tools, prompt) +agent_executor = AgentExecutor(agent=agent, tools=tools) +``` + + +```python +agent_executor.invoke( + {"input": "What comedies are playing?"}, config={"callback": [agentops_handler]} +) +``` + +## Check your session +Finally, check your run on [AgentOps](https://app.agentops.ai) + +Now if we look in the AgentOps dashboard, you will see a session recorded with the LLM calls and tool usage. + +## Langchain V0.1 Example +This example is out of date. You can uncomment all the following cells and the example will run but AgentOps is deprecating support. + + +```python +# %pip install langchain==0.1.6 +``` + + +```python +# import os +# from langchain_openai import ChatOpenAI +# from langchain.agents import initialize_agent, AgentType +# from langchain.agents import tool +``` + +The only difference with using AgentOps is that we'll also import this special Callback Handler + + +```python +# from agentops.partners.langchain_callback_handler import ( +# LangchainCallbackHandler as AgentOpsLangchainCallbackHandler, +# ) +``` + +Next, we'll grab our two API keys. + + +```python +# from dotenv import load_dotenv + +# load_dotenv() +``` + +This is where AgentOps comes into play. Before creating our LLM instance via Langchain, first we'll create an instance of the AO LangchainCallbackHandler. After the handler is initialized, a session will be recorded automatically. + +Pass in your API key, and optionally any tags to describe this session for easier lookup in the AO dashboard. + + +```python +# AGENTOPS_API_KEY = os.environ.get("AGENTOPS_API_KEY") +# OPENAI_API_KEY = os.environ.get("OPENAI_API_KEY") + +# agentops_handler = AgentOpsLangchainCallbackHandler( +# api_key=AGENTOPS_API_KEY, default_tags=["Langchain Example"] +# ) + +# llm = ChatOpenAI( +# openai_api_key=OPENAI_API_KEY, callbacks=[agentops_handler], model="gpt-3.5-turbo" +# ) +``` + +You can also retrieve the `session_id` of the newly created session. + + +```python +# print("Agent Ops session ID: " + str(agentops_handler.current_session_ids)) +``` + +Agents generally use tools. Let's define a simple tool here. Tool usage is also recorded. + + +```python +# @tool +# def find_movie(genre) -> str: +# """Find available movies""" +# if genre == "drama": +# return "Dune 2" +# else: +# return "Pineapple Express" + + +# tools = [find_movie] +``` + +For each tool, you need to also add the callback handler + + +```python +# for t in tools: +# t.callbacks = [agentops_handler] +``` + +Finally, let's use our agent! Pass in the callback handler to the agent, and all the actions will be recorded in the AO Dashboard + + +```python +# agent = initialize_agent( +# tools, +# llm, +# agent=AgentType.CHAT_ZERO_SHOT_REACT_DESCRIPTION, +# verbose=True, +# callbacks=[ +# agentops_handler +# ], # You must pass in a callback handler to record your agent +# handle_parsing_errors=True, +# ) +``` + + +```python +# agent.invoke("What comedies are playing?", callbacks=[agentops_handler]) +``` + +## Check your session +Finally, check your run on [AgentOps](https://app.agentops.ai) + +# Async Agents + +Several langchain agents require async callback handlers. AgentOps also supports this. + + +```python +# import os +# from langchain.chat_models import ChatOpenAI +# from langchain.agents import initialize_agent, AgentType +# from langchain.agents import tool +``` + + +```python +# from agentops.partners.langchain_callback_handler import ( +# AsyncLangchainCallbackHandler as AgentOpsAsyncLangchainCallbackHandler, +# ) +``` + + +```python +# from dotenv import load_dotenv + +# load_dotenv() + +# AGENTOPS_API_KEY = os.environ.get("AGENTOPS_API_KEY") +# OPENAI_API_KEY = os.environ.get("OPENAI_API_KEY") +``` + + +```python +# agentops_handler = AgentOpsAsyncLangchainCallbackHandler( +# api_key=AGENTOPS_API_KEY, tags=["Async Example"] +# ) + +# llm = ChatOpenAI( +# openai_api_key=OPENAI_API_KEY, callbacks=[agentops_handler], model="gpt-3.5-turbo" +# ) + +# print("Agent Ops session ID: " + str(await agentops_handler.session_id)) +``` + + +```python +# @tool +# def find_movie(genre) -> str: +# """Find available movies""" +# if genre == "drama": +# return "Dune 2" +# else: +# return "Pineapple Express" + + +# tools = [find_movie] + +# for t in tools: +# t.callbacks = [agentops_handler] +``` + + +```python +# agent = initialize_agent( +# tools, +# llm, +# agent=AgentType.CHAT_ZERO_SHOT_REACT_DESCRIPTION, +# verbose=True, +# handle_parsing_errors=True, +# callbacks=[agentops_handler], +# ) + +# await agent.arun("What comedies are playing?") +``` diff --git a/docs/v1/examples/multi_agent.mdx b/docs/v1/examples/multi_agent.mdx index 40133490..8f51035c 100644 --- a/docs/v1/examples/multi_agent.mdx +++ b/docs/v1/examples/multi_agent.mdx @@ -5,4 +5,142 @@ mode: "wide" --- _View Notebook on Github_ -{/* SOURCE_FILE: examples/multi_agent_example.ipynb */} \ No newline at end of file + +{/* SOURCE_FILE: examples/multi_agent_example.ipynb */} + +# Multi-Agent Support +This is an example implementation of tracking events from two separate agents + +First let's install the required packages + + +```python +%pip install -U openai +%pip install -U agentops +%pip install -U python-dotenv +``` + +Then import them + + +```python +import agentops +from agentops import track_agent +from openai import OpenAI +import os +from dotenv import load_dotenv +import logging +from IPython.display import display, Markdown +``` + +Next, we'll set our API keys. There are several ways to do this, the code below is just the most foolproof way for the purposes of this notebook. It accounts for both users who use environment variables and those who just want to set the API Key here in this notebook. + +[Get an AgentOps API key](https://agentops.ai/settings/projects) + +1. Create an environment variable in a .env file or other method. By default, the AgentOps `init()` function will look for an environment variable named `AGENTOPS_API_KEY`. Or... + +2. Replace `` below and pass in the optional `api_key` parameter to the AgentOps `init(api_key=...)` function. Remember not to commit your API key to a public repo! + + +```python +load_dotenv() +OPENAI_API_KEY = os.getenv("OPENAI_API_KEY") or "" +AGENTOPS_API_KEY = os.getenv("AGENTOPS_API_KEY") or "" +logging.basicConfig( + level=logging.DEBUG +) # this will let us see that calls are assigned to an agent +``` + + +```python +agentops.init(AGENTOPS_API_KEY, default_tags=["multi-agent-notebook"]) +openai_client = OpenAI(api_key=OPENAI_API_KEY) +``` + +Now lets create a few agents! + + +```python +@track_agent(name="qa") +class QaAgent: + def completion(self, prompt: str): + res = openai_client.chat.completions.create( + model="gpt-3.5-turbo", + messages=[ + { + "role": "system", + "content": "You are a qa engineer and only output python code, no markdown tags.", + }, + {"role": "user", "content": prompt}, + ], + temperature=0.5, + ) + return res.choices[0].message.content + + +@track_agent(name="engineer") +class EngineerAgent: + def completion(self, prompt: str): + res = openai_client.chat.completions.create( + model="gpt-3.5-turbo", + messages=[ + { + "role": "system", + "content": "You are a software engineer and only output python code, no markdown tags.", + }, + {"role": "user", "content": prompt}, + ], + temperature=0.5, + ) + return res.choices[0].message.content +``` + + +```python +qa = QaAgent() +engineer = EngineerAgent() +``` + +Now we have our agents and we tagged them with the `@track_agent` decorator. Any LLM calls that go through this class will now be tagged as agent calls in AgentOps. + +Lets use these agents! + + +```python +generated_func = engineer.completion("python function to test prime number") +``` + + +```python +display(Markdown("```python\n" + generated_func + "\n```")) +``` + + +```python +generated_test = qa.completion( + "Write a python unit test that test the following function: \n " + generated_func +) +``` + + +```python +display(Markdown("```python\n" + generated_test + "\n```")) +``` + +Perfect! It generated the code as expected, and in the DEBUG logs, you can see that the calls were made by agents named "engineer" and "qa"! + +Lets verify one more thing! If we make an LLM call outside of the context of a tracked agent, we want to make sure it gets assigned to the Default Agent. + + +```python +res = openai_client.chat.completions.create( + model="gpt-3.5-turbo", + messages=[ + {"role": "system", "content": "You are not a tracked agent"}, + {"role": "user", "content": "Say hello"}, + ], +) +res.choices[0].message.content +``` + +You'll notice that we didn't log an agent name, so the AgentOps backend will assign it to the Default Agent for the session! diff --git a/docs/v1/examples/multion.mdx b/docs/v1/examples/multion.mdx index 6b1cbefe..274c6ada 100644 --- a/docs/v1/examples/multion.mdx +++ b/docs/v1/examples/multion.mdx @@ -6,4 +6,142 @@ mode: "wide" _View All Notebooks on Github_ + {/* SOURCE_FILE: examples/multion_examples/Autonomous_web_browsing.ipynb */} + +# MultiOn Tracking Web Browse Actions + + +Agents using MultiOn can launch and control remote or local web browsers to perform actions and retrieve context using natural language commands. With AgentOps, MultiOn evens such as browse, retrieve, and step are automatically tracked. + + +![AgentOps MultiOn Browse](https://github.com/AgentOps-AI/agentops/blob/main/docs/images/agentops-multion-browse.gif?raw=true) + +Furthermore, events and LLM calls in your Python program will be tracked as well. + +First let's install the required packages + + +```python +%pip install -U multion +%pip install -U agentops +%pip install -U openai +%pip install -U python-dotenv +``` + +Then import them + + +```python +from multion.client import MultiOn +from multion.core.request_options import RequestOptions +import openai +import agentops +import os +from dotenv import load_dotenv +``` + +Next, we'll set our API keys. There are several ways to do this, the code below is just the most foolproof way for the purposes of this notebook. It accounts for both users who use environment variables and those who just want to set the API Key here in this notebook. + +[Get an AgentOps API key](https://agentops.ai/settings/projects) + +1. Create an environment variable in a .env file or other method. By default, the AgentOps `init()` function will look for an environment variable named `AGENTOPS_API_KEY`. Or... + +2. Replace `` below and pass in the optional `api_key` parameter to the AgentOps `init(api_key=...)` function. Remember not to commit your API key to a public repo! + + +```python +load_dotenv() +MULTION_API_KEY = os.getenv("MULTION_API_KEY") or "" +AGENTOPS_API_KEY = os.getenv("AGENTOPS_API_KEY") or "" +OPENAI_API_KEY = os.getenv("OPENAI_API_KEY") or "" +``` + +### Tracking MultiOn events with AgentOps + +When an `agentops_api_key` is provided, MultiOn will automatically start an AgentOps session and record events. + + +```python +multion = MultiOn( + api_key=MULTION_API_KEY, + agentops_api_key=AGENTOPS_API_KEY, +) +cmd = "what three things do i get with agentops" +request_options = RequestOptions( + timeout_in_seconds=60, max_retries=4, additional_headers={"test": "ing"} +) + +browse_response = multion.browse( + cmd="what three things do i get with agentops", + url="https://www.agentops.ai/", + max_steps=4, + include_screenshot=True, + request_options=request_options, +) + +print(browse_response.message) + +# End session to see your dashboard +agentops.end_session("Success") +``` + +### Linking MultiOn events to an existing AgentOps session +When running `agentops.init()`, be sure to set `auto_start_session=False`. MultiOn will automatically launch AgentOps sessions by default, but by setting auto start to false, you can configure your AgentOps client independently. + + +```python +agentops.init( + AGENTOPS_API_KEY, auto_start_session=False, default_tags=["MultiOn browse example"] +) +``` + +Now, we can launch a MultiOn browse event. This event will automatically get added to your AgentOps session. + + +```python +multion = MultiOn( + api_key=MULTION_API_KEY, + agentops_api_key=AGENTOPS_API_KEY, +) +cmd = "what three things do i get with agentops" +request_options = RequestOptions( + timeout_in_seconds=60, max_retries=4, additional_headers={"test": "ing"} +) + +browse_response = multion.browse( + cmd="what three things do i get with agentops", + url="https://www.agentops.ai/", + max_steps=4, + include_screenshot=True, + request_options=request_options, +) + +print(browse_response.message) +``` + +Let's use OpenAI to summarize our output + + +```python +messages = [ + { + "role": "user", + "content": f"Format this data as a markdown table: {browse_response.message}", + } +] +client = openai.OpenAI() +response = client.chat.completions.create(messages=messages, model="gpt-3.5-turbo") + +print(response.choices[0].message.content) +``` + + +```python +agentops.end_session("Success") +``` + +## Check your session +Check your session on [AgentOps](https://app.agentops.ai). This session should include the MultiOn browse action and the OpenAI call. + +![AgentOps MultiOn Browse](https://github.com/AgentOps-AI/agentops/blob/main/docs/images/agentops-multion-browse.gif?raw=true) diff --git a/docs/v1/examples/notebooks/README.md b/docs/v1/examples/notebooks/README.md deleted file mode 100644 index 4501045f..00000000 --- a/docs/v1/examples/notebooks/README.md +++ /dev/null @@ -1,8 +0,0 @@ -# Generate Notebooks in Docs - -1. `brew install pandoc` -2. `python generate.py` - -This generates HTML versions of the notebooks. - -Make sure there is a corresponding \ No newline at end of file diff --git a/docs/v1/examples/notebooks/autogen/AgentChat.html b/docs/v1/examples/notebooks/autogen/AgentChat.html deleted file mode 100644 index b8910668..00000000 --- a/docs/v1/examples/notebooks/autogen/AgentChat.html +++ /dev/null @@ -1,374 +0,0 @@ - - - - - - - - AgentChat - - - -
-

Adding AgentOps -to an existing Autogen service

-

-

To get started, you'll need to install the AgentOps package and set an API key.

-

AgentOps automatically configures itself when it's initialized -meaning your agent run data will be tracked and logged to your AgentOps -account right away.

-
-
-
import agentops
-
-from autogen import ConversableAgent, UserProxyAgent
-
-# When initializing AgentOps, you can pass in optional tags to help filter sessions
-agentops.init(api_key="...", tags=["simple-autogen-example"])
-
-print("AgentOps is now running. You can view your session in the link above")
-
-
πŸ–‡ AgentOps: Session Replay: https://app.agentops.ai/drilldown?session_id=24c5d9f6-fb82-41e6-a468-2dc74a5318a3
-
-
-
-
AgentOps is now running. You can view your session in the link above
-
-
-
-
-

AutoGen will now start automatically tracking

-
    -
  • LLM prompts and completions
    • -
  • Token usage and costs
    • -
  • Agent names and actions
    • -
  • Correspondence between agents
    • -
  • Tool usage
    • -
  • Errors
    • -
-
-
-
# Define an openai api key for the agent configurations
-openai_api_key = "..."
-config_list = [
-    {"model": "gpt-4-turbo", "api_key": openai_api_key, "tags": ["gpt-4", "tool"]}
-]
-
-# Create the agent that uses the LLM.
-assistant = ConversableAgent("agent", llm_config={"config_list": config_list})
-
-# Create the agent that represents the user in the conversation.
-user_proxy = UserProxyAgent("user", code_execution_config=False)
-
-# Let the assistant start the conversation.  It will end when the user types "exit".
-assistant.initiate_chat(user_proxy, message="How can I help you today?")
-
-# Close your AgentOps session to indicate that it completed.
-agentops.end_session("Success")
-print("Success! Visit your AgentOps dashboard to see the replay")
-
-
agent (to user):
-
-How can I help you today?
-
---------------------------------------------------------------------------------
-
-
-
-
Provide feedback to agent. Press enter to skip and use auto-reply, or type 'exit' to end the conversation:  Tell me a joke about AgentOps
-
-
-
-
user (to agent):
-
-Tell me a joke about AgentOps
-
---------------------------------------------------------------------------------
-
->>>>>>>> USING AUTO REPLY...
-agent (to user):
-
-Why don't AgentOps teams ever play hide and seek?
-
-Because good luck hiding when they always know where everyone is supposed to be!
-
---------------------------------------------------------------------------------
-
-
-
-
Provide feedback to agent. Press enter to skip and use auto-reply, or type 'exit' to end the conversation:  Another
-
-
-
-
user (to agent):
-
-Another
-
---------------------------------------------------------------------------------
-
->>>>>>>> USING AUTO REPLY...
-agent (to user):
-
-Why did the AgentOps team bring a ladder to work?
-
-Because they’re always reaching for high-level optimizations!
-
---------------------------------------------------------------------------------
-
-
-
-
Provide feedback to agent. Press enter to skip and use auto-reply, or type 'exit' to end the conversation:  exit
-
-
-
-
πŸ–‡ AgentOps: This run's cost $0.001080
-πŸ–‡ AgentOps: Session Replay: https://app.agentops.ai/drilldown?session_id=24c5d9f6-fb82-41e6-a468-2dc74a5318a3
-
-
-
-
Success! Visit your AgentOps dashboard to see the replay
-
-
-
-
-

You can view data on this run at app.agentops.ai.

-

The dashboard will display LLM events for each message sent by each -agent, including those made by the human user.

-
-
-
-
- - diff --git a/docs/v1/examples/notebooks/autogen/MathAgent.html b/docs/v1/examples/notebooks/autogen/MathAgent.html deleted file mode 100644 index 9dbd9a76..00000000 --- a/docs/v1/examples/notebooks/autogen/MathAgent.html +++ /dev/null @@ -1,483 +0,0 @@ - - - - - - - - MathAgent - - - -
-

Adding AgentOps -to an existing Autogen service

-

-

To get started, you'll need to install the AgentOps package and set an API key.

-

AgentOps automatically configures itself when it's initialized -meaning your agent run data will be tracked and logged to your AgentOps -account right away.

-
-
-
from typing import Annotated, Literal
-from autogen import ConversableAgent, register_function
-import agentops
-
-agentops.init(api_key="...", tags=["autogen-tool-example"])
-
-print("AgentOps is now running. You can view your session in the link above")
-
-
πŸ–‡ AgentOps: Session Replay: https://app.agentops.ai/drilldown?session_id=51556b00-428b-4cae-b815-1320ebc7e810
-
-
-
-
AgentOps is now running. You can view your session in the link above
-
-
-
-
-

AutoGen will now start automatically tracking

-
    -
  • LLM prompts and completions
    • -
  • Token usage and costs
    • -
  • Agent names and actions
    • -
  • Correspondence between agents
    • -
  • Tool usage
    • -
  • Errors
    • -
-
-
-
# Define an openai api key for the agent configurations
-openai_api_key = "..."
-config_list = [
-    {"model": "gpt-4-turbo", "api_key": openai_api_key, "tags": ["gpt-4", "tool"]}
-]
-
-
-
Operator = Literal["+", "-", "*", "/"]
-
-
-def calculator(a: int, b: int, operator: Annotated[Operator, "operator"]) -> int:
-    if operator == "+":
-        return a + b
-    elif operator == "-":
-        return a - b
-    elif operator == "*":
-        return a * b
-    elif operator == "/":
-        return int(a / b)
-    else:
-        raise ValueError("Invalid operator")
-
-
-# Create the agent that uses the LLM.
-assistant = ConversableAgent(
-    name="Assistant",
-    system_message="You are a helpful AI assistant. "
-    "You can help with simple calculations. "
-    "Return 'TERMINATE' when the task is done.",
-    llm_config={"config_list": config_list},
-)
-
-# The user proxy agent is used for interacting with the assistant agent
-# and executes tool calls.
-user_proxy = ConversableAgent(
-    name="User",
-    llm_config=False,
-    is_termination_msg=lambda msg: msg.get("content") is not None
-    and "TERMINATE" in msg["content"],
-    human_input_mode="NEVER",
-)
-
-assistant.register_for_llm(name="calculator", description="A simple calculator")(
-    calculator
-)
-user_proxy.register_for_execution(name="calculator")(calculator)
-
-# Register the calculator function to the two agents.
-register_function(
-    calculator,
-    caller=assistant,  # The assistant agent can suggest calls to the calculator.
-    executor=user_proxy,  # The user proxy agent can execute the calculator calls.
-    name="calculator",  # By default, the function name is used as the tool name.
-    description="A simple calculator",  # A description of the tool.
-)
-
-# Let the assistant start the conversation.  It will end when the user types "exit".
-user_proxy.initiate_chat(assistant, message="What is (1423 - 123) / 3 + (32 + 23) * 5?")
-
-agentops.end_session("Success")
-
-
πŸ–‡ AgentOps: Cannot start session - session already started
-
-
-
-
User (to Assistant):
-
-What is (1423 - 123) / 3 + (32 + 23) * 5?
-
---------------------------------------------------------------------------------
-
->>>>>>>> USING AUTO REPLY...
-
-
-
-
/Users/reibs/Projects/autogen/autogen/agentchat/conversable_agent.py:2489: UserWarning: Function 'calculator' is being overridden.
-  warnings.warn(f"Function '{tool_sig['function']['name']}' is being overridden.", UserWarning)
-/Users/reibs/Projects/autogen/autogen/agentchat/conversable_agent.py:2408: UserWarning: Function 'calculator' is being overridden.
-  warnings.warn(f"Function '{name}' is being overridden.", UserWarning)
-
-
-
-
Assistant (to User):
-
-***** Suggested tool call (call_pSJXJKu1qbfRV4SVNHzTaG1z): calculator *****
-Arguments: 
-{"a": 1423, "b": 123, "operator": "-"}
-***************************************************************************
-***** Suggested tool call (call_kHRXi8vq5XsZSrGDnVTA1oy7): calculator *****
-Arguments: 
-{"a": 32, "b": 23, "operator": "+"}
-***************************************************************************
-
---------------------------------------------------------------------------------
-
->>>>>>>> EXECUTING FUNCTION calculator...
-
->>>>>>>> EXECUTING FUNCTION calculator...
-User (to Assistant):
-
-User (to Assistant):
-
-***** Response from calling tool (call_pSJXJKu1qbfRV4SVNHzTaG1z) *****
-1300
-**********************************************************************
-
---------------------------------------------------------------------------------
-User (to Assistant):
-
-***** Response from calling tool (call_kHRXi8vq5XsZSrGDnVTA1oy7) *****
-55
-**********************************************************************
-
---------------------------------------------------------------------------------
-
->>>>>>>> USING AUTO REPLY...
-Assistant (to User):
-
-***** Suggested tool call (call_srDG3bYDpkdoIVn1mVVJNTJj): calculator *****
-Arguments: 
-{"a": 1300, "b": 3, "operator": "/"}
-***************************************************************************
-***** Suggested tool call (call_jXJHjxZlnsHEbLaG4hQXUX1v): calculator *****
-Arguments: 
-{"a": 55, "b": 5, "operator": "*"}
-***************************************************************************
-
---------------------------------------------------------------------------------
-
->>>>>>>> EXECUTING FUNCTION calculator...
-
->>>>>>>> EXECUTING FUNCTION calculator...
-User (to Assistant):
-
-User (to Assistant):
-
-***** Response from calling tool (call_srDG3bYDpkdoIVn1mVVJNTJj) *****
-433
-**********************************************************************
-
---------------------------------------------------------------------------------
-User (to Assistant):
-
-***** Response from calling tool (call_jXJHjxZlnsHEbLaG4hQXUX1v) *****
-275
-**********************************************************************
-
---------------------------------------------------------------------------------
-
->>>>>>>> USING AUTO REPLY...
-Assistant (to User):
-
-***** Suggested tool call (call_jKyiL6oizDZPfX16hhVi6pa3): calculator *****
-Arguments: 
-{"a":433,"b":275,"operator":"+"}
-***************************************************************************
-
---------------------------------------------------------------------------------
-
->>>>>>>> EXECUTING FUNCTION calculator...
-User (to Assistant):
-
-User (to Assistant):
-
-***** Response from calling tool (call_jKyiL6oizDZPfX16hhVi6pa3) *****
-708
-**********************************************************************
-
---------------------------------------------------------------------------------
-
->>>>>>>> USING AUTO REPLY...
-Assistant (to User):
-
-The result of the expression \((1423 - 123) / 3 + (32 + 23) * 5\) is 708. 
-
-'TERMINATE'
-
---------------------------------------------------------------------------------
-
-
-
-
πŸ–‡ AgentOps: This run's cost $0.000600
-πŸ–‡ AgentOps: Session Replay: https://app.agentops.ai/drilldown?session_id=51556b00-428b-4cae-b815-1320ebc7e810
-
-
-
-
-

You can see your run in action at app.agentops.ai. In this example, the -AgentOps dashboard will show:

-
    -
  • Agents talking to each other
    • -
  • Each use of the calculator tool
    • -
  • Each call to OpenAI for LLM use
    • -
-
-
-
-
- - diff --git a/docs/v1/examples/notebooks/crew/job_posting.html b/docs/v1/examples/notebooks/crew/job_posting.html deleted file mode 100644 index e40e5dd6..00000000 --- a/docs/v1/examples/notebooks/crew/job_posting.html +++ /dev/null @@ -1,435 +0,0 @@ - - - - - - - - job_posting - - - -
-
# %pip install -e ../..
-
-
-
%pip install -U 'crewai[tools]'
-%pip install -U 'crewai[agentops]'
-
-
-
import os
-
-os.environ["SERPER_API_KEY"] = "..."
-os.environ["OPENAI_API_KEY"] = "..."
-os.environ["AGENTOPS_API_KEY"] = "..."
-
-
-
from crewai import Agent
-from crewai_tools.tools import WebsiteSearchTool, SerperDevTool, FileReadTool
-
-web_search_tool = WebsiteSearchTool()
-serper_dev_tool = SerperDevTool()
-file_read_tool = FileReadTool(
-    file_path="job_description_example.md",
-    description="A tool to read the job description example file.",
-)
-
-
-class Agents:
-    def research_agent(self):
-        return Agent(
-            role="Research Analyst",
-            goal="Analyze the company website and provided description to extract insights on culture, values, and specific needs.",
-            tools=[web_search_tool, serper_dev_tool],
-            backstory="Expert in analyzing company cultures and identifying key values and needs from various sources, including websites and brief descriptions.",
-            verbose=True,
-        )
-
-    def writer_agent(self):
-        return Agent(
-            role="Job Description Writer",
-            goal="Use insights from the Research Analyst to create a detailed, engaging, and enticing job posting.",
-            tools=[web_search_tool, serper_dev_tool, file_read_tool],
-            backstory="Skilled in crafting compelling job descriptions that resonate with the company's values and attract the right candidates.",
-            verbose=True,
-        )
-
-    def review_agent(self):
-        return Agent(
-            role="Review and Editing Specialist",
-            goal="Review the job posting for clarity, engagement, grammatical accuracy, and alignment with company values and refine it to ensure perfection.",
-            tools=[web_search_tool, serper_dev_tool, file_read_tool],
-            backstory="A meticulous editor with an eye for detail, ensuring every piece of content is clear, engaging, and grammatically perfect.",
-            verbose=True,
-        )
-
-
-
from textwrap import dedent
-from crewai import Task
-
-
-class Tasks:
-    def research_company_culture_task(self, agent, company_description, company_domain):
-        return Task(
-            description=dedent(
-                f"""\
-								Analyze the provided company website and the hiring manager's company's domain {company_domain}, description: "{company_description}". Focus on understanding the company's culture, values, and mission. Identify unique selling points and specific projects or achievements highlighted on the site.
-								Compile a report summarizing these insights, specifically how they can be leveraged in a job posting to attract the right candidates."""
-            ),
-            expected_output=dedent(
-                """\
-								A comprehensive report detailing the company's culture, values, and mission, along with specific selling points relevant to the job role. Suggestions on incorporating these insights into the job posting should be included."""
-            ),
-            agent=agent,
-        )
-
-    def research_role_requirements_task(self, agent, hiring_needs):
-        return Task(
-            description=dedent(
-                f"""\
-								Based on the hiring manager's needs: "{hiring_needs}", identify the key skills, experiences, and qualities the ideal candidate should possess for the role. Consider the company's current projects, its competitive landscape, and industry trends. Prepare a list of recommended job requirements and qualifications that align with the company's needs and values."""
-            ),
-            expected_output=dedent(
-                """\
-								A list of recommended skills, experiences, and qualities for the ideal candidate, aligned with the company's culture, ongoing projects, and the specific role's requirements."""
-            ),
-            agent=agent,
-        )
-
-    def draft_job_posting_task(
-        self, agent, company_description, hiring_needs, specific_benefits
-    ):
-        return Task(
-            description=dedent(
-                f"""\
-								Draft a job posting for the role described by the hiring manager: "{hiring_needs}". Use the insights on "{company_description}" to start with a compelling introduction, followed by a detailed role description, responsibilities, and required skills and qualifications. Ensure the tone aligns with the company's culture and incorporate any unique benefits or opportunities offered by the company.
-								Specfic benefits: "{specific_benefits}"""
-            ),
-            expected_output=dedent(
-                """\
-								A detailed, engaging job posting that includes an introduction, role description, responsibilities, requirements, and unique company benefits. The tone should resonate with the company's culture and values, aimed at attracting the right candidates."""
-            ),
-            agent=agent,
-        )
-
-    def review_and_edit_job_posting_task(self, agent, hiring_needs):
-        return Task(
-            description=dedent(
-                f"""\
-								Review the draft job posting for the role: "{hiring_needs}". Check for clarity, engagement, grammatical accuracy, and alignment with the company's culture and values. Edit and refine the content, ensuring it speaks directly to the desired candidates and accurately reflects the role's unique benefits and opportunities. Provide feedback for any necessary revisions."""
-            ),
-            expected_output=dedent(
-                """\
-								A polished, error-free job posting that is clear, engaging, and perfectly aligned with the company's culture and values. Feedback on potential improvements and final approval for publishing. Formated in markdown."""
-            ),
-            agent=agent,
-            output_file="job_posting.md",
-        )
-
-    def industry_analysis_task(self, agent, company_domain, company_description):
-        return Task(
-            description=dedent(
-                f"""\
-								Conduct an in-depth analysis of the industry related to the company's domain: "{company_domain}". Investigate current trends, challenges, and opportunities within the industry, utilizing market reports, recent developments, and expert opinions. Assess how these factors could impact the role being hired for and the overall attractiveness of the position to potential candidates.
-								Consider how the company's position within this industry and its response to these trends could be leveraged to attract top talent. Include in your report how the role contributes to addressing industry challenges or seizing opportunities."""
-            ),
-            expected_output=dedent(
-                """\
-								A detailed analysis report that identifies major industry trends, challenges, and opportunities relevant to the company's domain and the specific job role. This report should provide strategic insights on positioning the job role and the company as an attractive choice for potential candidates."""
-            ),
-            agent=agent,
-        )
-
-
-
from crewai import Crew
-import agentops
-
-agentops.init(tags=["crew-job-posting-example"])
-
-tasks = Tasks()
-agents = Agents()
-
-company_description = input("What is the company description?\n")
-company_domain = input("What is the company domain?\n")
-hiring_needs = input("What are the hiring needs?\n")
-specific_benefits = input("What are specific_benefits you offer?\n")
-
-# Create Agents
-researcher_agent = agents.research_agent()
-writer_agent = agents.writer_agent()
-review_agent = agents.review_agent()
-
-# Define Tasks for each agent
-research_company_culture_task = tasks.research_company_culture_task(
-    researcher_agent, company_description, company_domain
-)
-industry_analysis_task = tasks.industry_analysis_task(
-    researcher_agent, company_domain, company_description
-)
-research_role_requirements_task = tasks.research_role_requirements_task(
-    researcher_agent, hiring_needs
-)
-draft_job_posting_task = tasks.draft_job_posting_task(
-    writer_agent, company_description, hiring_needs, specific_benefits
-)
-review_and_edit_job_posting_task = tasks.review_and_edit_job_posting_task(
-    review_agent, hiring_needs
-)
-
-# Instantiate the crew with a sequential process
-crew = Crew(
-    agents=[researcher_agent, writer_agent, review_agent],
-    tasks=[
-        research_company_culture_task,
-        industry_analysis_task,
-        research_role_requirements_task,
-        draft_job_posting_task,
-        review_and_edit_job_posting_task,
-    ],
-)
-
-# Kick off the process
-result = crew.kickoff()
-
-print("Job Posting Creation Process Completed.")
-print("Final Job Posting:")
-print(result)
-
-agentops.end_session("Success")
-
-
- -
- - diff --git a/docs/v1/examples/notebooks/generate.py b/docs/v1/examples/notebooks/generate.py deleted file mode 100644 index 3119591b..00000000 --- a/docs/v1/examples/notebooks/generate.py +++ /dev/null @@ -1,51 +0,0 @@ -import os -import subprocess - -# Before generating, `brew install pandoc` - - -import os -import subprocess - - -def convert_notebooks_to_html(source_dir: str, target_dir: str): - # Ensure the target directory exists - os.makedirs(target_dir, exist_ok=True) - - # Loop over all files in the source directory and subdirectories - for root, dirs, files in os.walk(source_dir): - for filename in files: - # Check if current file has a .ipynb extension - if filename.endswith(".ipynb"): - # Construct full file path - source_file = os.path.join(root, filename) - # Create a mirrored directory structure in the target directory - relative_dir = os.path.relpath(root, source_dir) - html_target_dir = os.path.join(target_dir, relative_dir) - os.makedirs(html_target_dir, exist_ok=True) - # Construct target file path - html_target_file = os.path.join( - html_target_dir, filename[:-6] + ".html" - ) # remove '.ipynb' and add '.html' - - # Convert notebook to HTML using pandoc - # -c https://app.agentops.ai/notebook_styles.css to add custom styles - subprocess.check_call( - "pandoc {source_file} -s -o {html_target_file} && " - "{{ echo ''; cat {html_target_file}; }} > temp.md && mv temp.md {html_target_file}".format( - source_file=source_file, html_target_file=html_target_file - ), - shell=True, - ) - - -# Example usage: -try: - convert_notebooks_to_html("../../../../examples", "./") -except FileNotFoundError: - try: - convert_notebooks_to_html("docs/v1/examples", "./") - except FileNotFoundError: - print( - "Could not find notebooks folder. Run this script from project root or at the script location." - ) diff --git a/docs/v1/examples/notebooks/langchain_examples.html b/docs/v1/examples/notebooks/langchain_examples.html deleted file mode 100644 index 0b401431..00000000 --- a/docs/v1/examples/notebooks/langchain_examples.html +++ /dev/null @@ -1,431 +0,0 @@ - - - - - - - - langchain_examples - - - -
-

AgentOps Langchain -Agent Implementation

-

Using AgentOps monitoring with Langchain is simple. We've created a -LangchainCallbackHandler that will do all of the heavy lifting!

-

First we'll import the typical Langchain packages:

-
-
-
import os
-from langchain.chat_models import ChatOpenAI
-from langchain.agents import initialize_agent, AgentType
-from langchain.agents import tool
-
-
-

The only difference with using AgentOps is that we'll also import -this special Callback Handler

-
-
-
from agentops.partners.langchain_callback_handler import (
-    LangchainCallbackHandler as AgentOpsLangchainCallbackHandler,
-)
-
-
-

Next, we'll grab our two API keys. You can use dotenv like below or -however else you like to load environment variables

-
-
-
from dotenv import load_dotenv
-
-load_dotenv()
-
-
-

This is where AgentOps comes into play. Before creating our LLM -instance via Langchain, first we'll create an instance of the AO -LangchainCallbackHandler. After the handler is initialized, a session -will be recorded automatically.

-

Pass in your API key, and optionally any tags to describe this -session for easier lookup in the AO dashboard.

-
-
-
AGENTOPS_API_KEY = os.environ.get("AGENTOPS_API_KEY")
-OPENAI_API_KEY = os.environ.get("OPENAI_API_KEY")
-
-agentops_handler = AgentOpsLangchainCallbackHandler(
-    api_key=AGENTOPS_API_KEY, tags=["Langchain Example"]
-)
-
-llm = ChatOpenAI(
-    openai_api_key=OPENAI_API_KEY, callbacks=[agentops_handler], model="gpt-3.5-turbo"
-)
-
-
-

You can also retrieve the session_id of the newly -created session.

-
-
-
print("Agent Ops session ID: " + str(agentops_handler.session_id))
-
-
-

Agents generally use tools. Let's define a simple tool here. Tool -usage is also recorded.

-
-
-
@tool
-def find_movie(genre) -> str:
-    """Find available movies"""
-    if genre == "drama":
-        return "Dune 2"
-    else:
-        return "Pineapple Express"
-
-
-tools = [find_movie]
-
-
-

For each tool, you need to also add the callback handler

-
-
-
for t in tools:
-    t.callbacks = [agentops_handler]
-
-
-

Finally, let's use our agent! Pass in the callback handler to the -agent, and all the actions will be recorded in the AO Dashboard

-
-
-
agent = initialize_agent(
-    tools,
-    llm,
-    agent=AgentType.CHAT_ZERO_SHOT_REACT_DESCRIPTION,
-    verbose=True,
-    callbacks=[
-        agentops_handler
-    ],  # You must pass in a callback handler to record your agent
-    handle_parsing_errors=True,
-)
-
-
-
agent.run("What comedies are playing?", callbacks=[agentops_handler])
-
-
-

Check your session

-

Finally, check your run on AgentOps

-
-
-

Async Agents

-

Several langchain agents require async callback handlers. AgentOps -also supports this.

-
-
-
import os
-from langchain.chat_models import ChatOpenAI
-from langchain.agents import initialize_agent, AgentType
-from langchain.agents import tool
-
-
-
from agentops.partners.langchain_callback_handler import (
-    AsyncLangchainCallbackHandler as AgentOpsAsyncLangchainCallbackHandler,
-)
-
-
-
from dotenv import load_dotenv
-
-load_dotenv()
-
-AGENTOPS_API_KEY = os.environ.get("AGENTOPS_API_KEY")
-OPENAI_API_KEY = os.environ.get("OPENAI_API_KEY")
-
-
-
agentops_handler = AgentOpsAsyncLangchainCallbackHandler(
-    api_key=AGENTOPS_API_KEY, tags=["Async Example"]
-)
-
-llm = ChatOpenAI(
-    openai_api_key=OPENAI_API_KEY, callbacks=[agentops_handler], model="gpt-3.5-turbo"
-)
-
-print("Agent Ops session ID: " + str(await agentops_handler.session_id))
-
-
-
@tool
-def find_movie(genre) -> str:
-    """Find available movies"""
-    if genre == "drama":
-        return "Dune 2"
-    else:
-        return "Pineapple Express"
-
-
-tools = [find_movie]
-
-for t in tools:
-    t.callbacks = [agentops_handler]
-
-
-
agent = initialize_agent(
-    tools,
-    llm,
-    agent=AgentType.CHAT_ZERO_SHOT_REACT_DESCRIPTION,
-    verbose=True,
-    handle_parsing_errors=True,
-    callbacks=[agentops_handler],
-)
-
-await agent.arun("What comedies are playing?")
-
-
-

Check your session

-

Finally, check your run on AgentOps

-

-
- - diff --git a/docs/v1/examples/notebooks/multi_agent_example.html b/docs/v1/examples/notebooks/multi_agent_example.html deleted file mode 100644 index 41d60817..00000000 --- a/docs/v1/examples/notebooks/multi_agent_example.html +++ /dev/null @@ -1,375 +0,0 @@ - - - - - - - - multi_agent_example - - - -
-

Multi-Agent Support

-

This is an example implementation of tracking events from two -separate agents

-
-
-
import agentops
-from agentops.agent import track_agent
-from dotenv import load_dotenv
-import os
-from openai import OpenAI
-import logging
-
-from IPython.display import display, Markdown
-
-
-
load_dotenv()
-OPENAI_API_KEY = os.getenv("OPENAI_API_KEY", "<your_openai_key>")
-AGENTOPS_API_KEY = os.getenv("AGENTOPS_API_KEY", "<your_agentops_key>")
-logging.basicConfig(
-    level=logging.DEBUG
-)  # this will let us see that calls are assigned to an agent
-
-
-
agentops.init(AGENTOPS_API_KEY, tags=["multi-agent-notebook"])
-openai_client = OpenAI(api_key=OPENAI_API_KEY)
-
-
-

Now lets create a few agents!

-
-
-
@track_agent(name="qa")
-class QaAgent:
-    def completion(self, prompt: str):
-        res = openai_client.chat.completions.create(
-            model="gpt-3.5-turbo",
-            messages=[
-                {
-                    "role": "system",
-                    "content": "You are a qa engineer and only output python code, no markdown tags.",
-                },
-                {"role": "user", "content": prompt},
-            ],
-            temperature=0.5,
-        )
-        return res.choices[0].message.content
-
-
-@track_agent(name="engineer")
-class EngineerAgent:
-    def completion(self, prompt: str):
-        res = openai_client.chat.completions.create(
-            model="gpt-3.5-turbo",
-            messages=[
-                {
-                    "role": "system",
-                    "content": "You are a software engineer and only output python code, no markdown tags.",
-                },
-                {"role": "user", "content": prompt},
-            ],
-            temperature=0.5,
-        )
-        return res.choices[0].message.content
-
-
-
qa = QaAgent()
-engineer = EngineerAgent()
-
-
-

Now we have our agents and we tagged them with the -@track_agent decorator. Any LLM calls that go through this -class will now be tagged as agent calls in AgentOps.

-

Lets use these agents!

-
-
-
generated_func = engineer.completion("python function to test prime number")
-
-
-
display(Markdown("```python\n" + generated_func + "\n```"))
-
-
-
generated_test = qa.completion(
-    "Write a python unit test that test the following function: \n " + generated_func
-)
-
-
-
display(Markdown("```python\n" + generated_test + "\n```"))
-
-
-

Perfect! It generated the code as expected, and in the DEBUG logs, -you can see that the calls were made by agents named "engineer" and -"qa"!

-
-
-

Lets verify one more thing! If we make an LLM call outside of the -context of a tracked agent, we want to make sure it gets assigned to the -Default Agent.

-
-
-
res = openai_client.chat.completions.create(
-    model="gpt-3.5-turbo",
-    messages=[
-        {"role": "system", "content": "You are not a tracked agent"},
-        {"role": "user", "content": "Say hello"},
-    ],
-)
-res.choices[0].message.content
-
-
-

You'll notice that we didn't log an agent name, so the AgentOps -backend will assign it to the Default Agent for the session!

-
- - diff --git a/docs/v1/examples/notebooks/multi_agent_groq_example.html b/docs/v1/examples/notebooks/multi_agent_groq_example.html deleted file mode 100644 index efb4620f..00000000 --- a/docs/v1/examples/notebooks/multi_agent_groq_example.html +++ /dev/null @@ -1,471 +0,0 @@ - - - - - - - - multi_agent_groq_example - - - -
-

Multi-Agent Support

-

This is an example implementation of tracking events from two -separate agents

-
-
-
import agentops
-from agentops.agent import track_agent
-from dotenv import load_dotenv
-import os
-from groq import Groq
-import logging
-
-from IPython.display import display, Markdown
-
-
-
load_dotenv()
-GROQ_API_KEY = os.getenv("GROQ_API_KEY", "<your_openai_key>")
-AGENTOPS_API_KEY = os.getenv("AGENTOPS_API_KEY", "<your_agentops_key>")
-logging.basicConfig(
-    level=logging.DEBUG
-)  # this will let us see that calls are assigned to an agent
-
-
-
agentops.init(AGENTOPS_API_KEY, tags=["multi-agent-groq-notebook"])
-groq_client = Groq(api_key=GROQ_API_KEY)
-
-
DEBUG:urllib3.connectionpool:Starting new HTTPS connection (1): api.agentops.ai:443
-DEBUG:urllib3.connectionpool:https://api.agentops.ai:443 "POST /v2/create_session HTTP/11" 200 204
-πŸ–‡ AgentOps: Session Replay: https://app.agentops.ai/drilldown?session_id=892edb44-774d-4f52-a9b8-4d4eada5b434
-INFO:agentops:Session Replay: https://app.agentops.ai/drilldown?session_id=892edb44-774d-4f52-a9b8-4d4eada5b434
-DEBUG:httpx:load_ssl_context verify=True cert=None trust_env=True http2=False
-DEBUG:httpx:load_verify_locations cafile='/Users/manu_suryavansh/miniforge3/envs/agentsops_dev/lib/python3.11/site-packages/certifi/cacert.pem'
-DEBUG:urllib3.connectionpool:Starting new HTTPS connection (1): api.agentops.ai:443
-DEBUG:urllib3.connectionpool:https://api.agentops.ai:443 "POST /v2/create_events HTTP/11" 200 9
-
-
-
-
-

Now lets create a few agents!

-
-
-
@track_agent(name="qa")
-class QaAgent:
-    def completion(self, prompt: str):
-        res = groq_client.chat.completions.create(
-            model="llama3-70b-8192",
-            messages=[
-                {
-                    "role": "system",
-                    "content": "You are a qa engineer and only output python code, no markdown tags.",
-                },
-                {"role": "user", "content": prompt},
-            ],
-            temperature=0.5,
-        )
-        return res.choices[0].message.content
-
-
-@track_agent(name="engineer")
-class EngineerAgent:
-    def completion(self, prompt: str):
-        res = groq_client.chat.completions.create(
-            model="llama3-70b-8192",
-            messages=[
-                {
-                    "role": "system",
-                    "content": "You are a software engineer and only output python code, no markdown tags.",
-                },
-                {"role": "user", "content": prompt},
-            ],
-            temperature=0.5,
-        )
-        return res.choices[0].message.content
-
-
-
qa = QaAgent()
-engineer = EngineerAgent()
-
-
DEBUG:urllib3.connectionpool:Starting new HTTPS connection (1): api.agentops.ai:443
-DEBUG:urllib3.connectionpool:https://api.agentops.ai:443 "POST /v2/create_agent HTTP/11" 200 9
-DEBUG:urllib3.connectionpool:Starting new HTTPS connection (1): api.agentops.ai:443
-DEBUG:urllib3.connectionpool:https://api.agentops.ai:443 "POST /v2/create_agent HTTP/11" 200 9
-
-
-
-
-

Now we have our agents and we tagged them with the -@track_agent decorator. Any LLM calls that go through this -class will now be tagged as agent calls in AgentOps.

-

Lets use these agents!

-
-
-
generated_func = engineer.completion("python function to test prime number")
-
-
DEBUG:groq._base_client:Request options: {'method': 'post', 'url': '/openai/v1/chat/completions', 'files': None, 'json_data': {'messages': [{'role': 'system', 'content': 'You are a software engineer and only output python code, no markdown tags.'}, {'role': 'user', 'content': 'python function to test prime number'}], 'model': 'llama3-70b-8192', 'temperature': 0.5}}
-DEBUG:groq._base_client:Sending HTTP Request: POST https://api.groq.com/openai/v1/chat/completions
-DEBUG:httpcore.connection:connect_tcp.started host='api.groq.com' port=443 local_address=None timeout=5.0 socket_options=None
-DEBUG:httpcore.connection:connect_tcp.complete return_value=<httpcore._backends.sync.SyncStream object at 0x108dc1010>
-DEBUG:httpcore.connection:start_tls.started ssl_context=<ssl.SSLContext object at 0x10a6e4d40> server_hostname='api.groq.com' timeout=5.0
-DEBUG:httpcore.connection:start_tls.complete return_value=<httpcore._backends.sync.SyncStream object at 0x1098df650>
-DEBUG:httpcore.http11:send_request_headers.started request=<Request [b'POST']>
-DEBUG:httpcore.http11:send_request_headers.complete
-DEBUG:httpcore.http11:send_request_body.started request=<Request [b'POST']>
-DEBUG:httpcore.http11:send_request_body.complete
-DEBUG:httpcore.http11:receive_response_headers.started request=<Request [b'POST']>
-DEBUG:httpcore.http11:receive_response_headers.complete return_value=(b'HTTP/1.1', 200, b'OK', [(b'Date', b'Sun, 21 Jul 2024 05:55:22 GMT'), (b'Content-Type', b'application/json'), (b'Transfer-Encoding', b'chunked'), (b'Connection', b'keep-alive'), (b'Cache-Control', b'private, max-age=0, no-store, no-cache, must-revalidate'), (b'vary', b'Origin'), (b'x-ratelimit-limit-requests', b'50000'), (b'x-ratelimit-limit-tokens', b'30000'), (b'x-ratelimit-remaining-requests', b'49999'), (b'x-ratelimit-remaining-tokens', b'29963'), (b'x-ratelimit-reset-requests', b'1.728s'), (b'x-ratelimit-reset-tokens', b'74ms'), (b'x-request-id', b'req_01j39xqscce4dbg5h08vrftym2'), (b'via', b'1.1 google'), (b'alt-svc', b'h3=":443"; ma=86400'), (b'CF-Cache-Status', b'DYNAMIC'), (b'Set-Cookie', b'__cf_bm=vDBNcm.4NuP7B9MJyHy7WVBS7CVF.SyvXXsf7ZXdpT8-1721541322-1.0.1.1-QRg7ZBBgC845heu3O2ZfJySw1nqhlOCwpF29NmD1H9xnMUNFOstcyHCHabYKSBZXq6iNGbkYaId01XpPYOfuWQ; path=/; expires=Sun, 21-Jul-24 06:25:22 GMT; domain=.groq.com; HttpOnly; Secure; SameSite=None'), (b'Server', b'cloudflare'), (b'CF-RAY', b'8a68f10f2ba89652-SJC'), (b'Content-Encoding', b'gzip')])
-INFO:httpx:HTTP Request: POST https://api.groq.com/openai/v1/chat/completions "HTTP/1.1 200 OK"
-DEBUG:httpcore.http11:receive_response_body.started request=<Request [b'POST']>
-DEBUG:httpcore.http11:receive_response_body.complete
-DEBUG:httpcore.http11:response_closed.started
-DEBUG:httpcore.http11:response_closed.complete
-DEBUG:groq._base_client:HTTP Response: POST https://api.groq.com/openai/v1/chat/completions "200 OK" Headers({'date': 'Sun, 21 Jul 2024 05:55:22 GMT', 'content-type': 'application/json', 'transfer-encoding': 'chunked', 'connection': 'keep-alive', 'cache-control': 'private, max-age=0, no-store, no-cache, must-revalidate', 'vary': 'Origin', 'x-ratelimit-limit-requests': '50000', 'x-ratelimit-limit-tokens': '30000', 'x-ratelimit-remaining-requests': '49999', 'x-ratelimit-remaining-tokens': '29963', 'x-ratelimit-reset-requests': '1.728s', 'x-ratelimit-reset-tokens': '74ms', 'x-request-id': 'req_01j39xqscce4dbg5h08vrftym2', 'via': '1.1 google', 'alt-svc': 'h3=":443"; ma=86400', 'cf-cache-status': 'DYNAMIC', 'set-cookie': '__cf_bm=vDBNcm.4NuP7B9MJyHy7WVBS7CVF.SyvXXsf7ZXdpT8-1721541322-1.0.1.1-QRg7ZBBgC845heu3O2ZfJySw1nqhlOCwpF29NmD1H9xnMUNFOstcyHCHabYKSBZXq6iNGbkYaId01XpPYOfuWQ; path=/; expires=Sun, 21-Jul-24 06:25:22 GMT; domain=.groq.com; HttpOnly; Secure; SameSite=None', 'server': 'cloudflare', 'cf-ray': '8a68f10f2ba89652-SJC', 'content-encoding': 'gzip'})
-
-
-
-
-
display(Markdown("```python\n" + generated_func + "\n```"))
-
-
<IPython.core.display.Markdown object>
-
-
-
-
generated_test = qa.completion(
-    "Write a python unit test that test the following function: \n " + generated_func
-)
-
-
DEBUG:groq._base_client:Request options: {'method': 'post', 'url': '/openai/v1/chat/completions', 'files': None, 'json_data': {'messages': [{'role': 'system', 'content': 'You are a qa engineer and only output python code, no markdown tags.'}, {'role': 'user', 'content': 'Write a python unit test that test the following function: \n def is_prime(n):\n    if n <= 1:\n        return False\n    if n == 2:\n        return True\n    if n % 2 == 0:\n        return False\n    max_divisor = int(n**0.5) + 1\n    for d in range(3, max_divisor, 2):\n        if n % d == 0:\n            return False\n    return True'}], 'model': 'llama3-70b-8192', 'temperature': 0.5}}
-DEBUG:groq._base_client:Sending HTTP Request: POST https://api.groq.com/openai/v1/chat/completions
-DEBUG:httpcore.http11:send_request_headers.started request=<Request [b'POST']>
-DEBUG:httpcore.http11:send_request_headers.complete
-DEBUG:httpcore.http11:send_request_body.started request=<Request [b'POST']>
-DEBUG:httpcore.http11:send_request_body.complete
-DEBUG:httpcore.http11:receive_response_headers.started request=<Request [b'POST']>
-DEBUG:httpcore.http11:receive_response_headers.complete return_value=(b'HTTP/1.1', 200, b'OK', [(b'Date', b'Sun, 21 Jul 2024 05:55:23 GMT'), (b'Content-Type', b'application/json'), (b'Transfer-Encoding', b'chunked'), (b'Connection', b'keep-alive'), (b'Cache-Control', b'private, max-age=0, no-store, no-cache, must-revalidate'), (b'vary', b'Origin'), (b'x-ratelimit-limit-requests', b'50000'), (b'x-ratelimit-limit-tokens', b'30000'), (b'x-ratelimit-remaining-requests', b'49998'), (b'x-ratelimit-remaining-tokens', b'29845'), (b'x-ratelimit-reset-requests', b'2.960999999s'), (b'x-ratelimit-reset-tokens', b'310ms'), (b'x-request-id', b'req_01j39xqsy5fxgth4w9q6r24h9w'), (b'via', b'1.1 google'), (b'alt-svc', b'h3=":443"; ma=86400'), (b'CF-Cache-Status', b'DYNAMIC'), (b'Server', b'cloudflare'), (b'CF-RAY', b'8a68f112be2c9652-SJC'), (b'Content-Encoding', b'gzip')])
-INFO:httpx:HTTP Request: POST https://api.groq.com/openai/v1/chat/completions "HTTP/1.1 200 OK"
-DEBUG:httpcore.http11:receive_response_body.started request=<Request [b'POST']>
-DEBUG:httpcore.http11:receive_response_body.complete
-DEBUG:httpcore.http11:response_closed.started
-DEBUG:httpcore.http11:response_closed.complete
-DEBUG:groq._base_client:HTTP Response: POST https://api.groq.com/openai/v1/chat/completions "200 OK" Headers({'date': 'Sun, 21 Jul 2024 05:55:23 GMT', 'content-type': 'application/json', 'transfer-encoding': 'chunked', 'connection': 'keep-alive', 'cache-control': 'private, max-age=0, no-store, no-cache, must-revalidate', 'vary': 'Origin', 'x-ratelimit-limit-requests': '50000', 'x-ratelimit-limit-tokens': '30000', 'x-ratelimit-remaining-requests': '49998', 'x-ratelimit-remaining-tokens': '29845', 'x-ratelimit-reset-requests': '2.960999999s', 'x-ratelimit-reset-tokens': '310ms', 'x-request-id': 'req_01j39xqsy5fxgth4w9q6r24h9w', 'via': '1.1 google', 'alt-svc': 'h3=":443"; ma=86400', 'cf-cache-status': 'DYNAMIC', 'server': 'cloudflare', 'cf-ray': '8a68f112be2c9652-SJC', 'content-encoding': 'gzip'})
-
-
-
-
-
display(Markdown("```python\n" + generated_test + "\n```"))
-
-
<IPython.core.display.Markdown object>
-
-
-
-

Perfect! It generated the code as expected, and in the DEBUG logs, -you can see that the calls were made by agents named "engineer" and -"qa"!

-
-
-

Lets verify one more thing! If we make an LLM call outside of the -context of a tracked agent, we want to make sure it gets assigned to the -Default Agent.

-
-
-
res = groq_client.chat.completions.create(
-    model="llama3-70b-8192",
-    messages=[
-        {"role": "system", "content": "You are not a tracked agent"},
-        {"role": "user", "content": "Say hello"},
-    ],
-)
-res.choices[0].message.content
-
-
DEBUG:groq._base_client:Request options: {'method': 'post', 'url': '/openai/v1/chat/completions', 'files': None, 'json_data': {'messages': [{'role': 'system', 'content': 'You are not a tracked agent'}, {'role': 'user', 'content': 'Say hello'}], 'model': 'llama3-70b-8192'}}
-DEBUG:groq._base_client:Sending HTTP Request: POST https://api.groq.com/openai/v1/chat/completions
-DEBUG:httpcore.http11:send_request_headers.started request=<Request [b'POST']>
-DEBUG:httpcore.http11:send_request_headers.complete
-DEBUG:httpcore.http11:send_request_body.started request=<Request [b'POST']>
-DEBUG:httpcore.http11:send_request_body.complete
-DEBUG:httpcore.http11:receive_response_headers.started request=<Request [b'POST']>
-DEBUG:httpcore.http11:receive_response_headers.complete return_value=(b'HTTP/1.1', 200, b'OK', [(b'Date', b'Sun, 21 Jul 2024 05:55:24 GMT'), (b'Content-Type', b'application/json'), (b'Transfer-Encoding', b'chunked'), (b'Connection', b'keep-alive'), (b'Cache-Control', b'private, max-age=0, no-store, no-cache, must-revalidate'), (b'vary', b'Origin'), (b'x-ratelimit-limit-requests', b'50000'), (b'x-ratelimit-limit-tokens', b'30000'), (b'x-ratelimit-remaining-requests', b'49998'), (b'x-ratelimit-remaining-tokens', b'29982'), (b'x-ratelimit-reset-requests', b'3.318s'), (b'x-ratelimit-reset-tokens', b'36ms'), (b'x-request-id', b'req_01j39xqvrgem4bfd3gqybths6c'), (b'via', b'1.1 google'), (b'alt-svc', b'h3=":443"; ma=86400'), (b'CF-Cache-Status', b'DYNAMIC'), (b'Server', b'cloudflare'), (b'CF-RAY', b'8a68f11e6dd59652-SJC'), (b'Content-Encoding', b'gzip')])
-INFO:httpx:HTTP Request: POST https://api.groq.com/openai/v1/chat/completions "HTTP/1.1 200 OK"
-DEBUG:httpcore.http11:receive_response_body.started request=<Request [b'POST']>
-DEBUG:httpcore.http11:receive_response_body.complete
-DEBUG:httpcore.http11:response_closed.started
-DEBUG:httpcore.http11:response_closed.complete
-DEBUG:groq._base_client:HTTP Response: POST https://api.groq.com/openai/v1/chat/completions "200 OK" Headers({'date': 'Sun, 21 Jul 2024 05:55:24 GMT', 'content-type': 'application/json', 'transfer-encoding': 'chunked', 'connection': 'keep-alive', 'cache-control': 'private, max-age=0, no-store, no-cache, must-revalidate', 'vary': 'Origin', 'x-ratelimit-limit-requests': '50000', 'x-ratelimit-limit-tokens': '30000', 'x-ratelimit-remaining-requests': '49998', 'x-ratelimit-remaining-tokens': '29982', 'x-ratelimit-reset-requests': '3.318s', 'x-ratelimit-reset-tokens': '36ms', 'x-request-id': 'req_01j39xqvrgem4bfd3gqybths6c', 'via': '1.1 google', 'alt-svc': 'h3=":443"; ma=86400', 'cf-cache-status': 'DYNAMIC', 'server': 'cloudflare', 'cf-ray': '8a68f11e6dd59652-SJC', 'content-encoding': 'gzip'})
-
-
-
-
'Hello!'
-
-
-
-

You'll notice that we didn't log an agent name, so the AgentOps -backend will assign it to the Default Agent for the session!

-
-
-
-
- - diff --git a/docs/v1/examples/notebooks/multi_session_llm.html b/docs/v1/examples/notebooks/multi_session_llm.html deleted file mode 100644 index e87df74a..00000000 --- a/docs/v1/examples/notebooks/multi_session_llm.html +++ /dev/null @@ -1,356 +0,0 @@ - - - - - - - - multi_session_llm - - - -
-

Multiple Concurrent Sessions

-

This example will show you how to run multiple sessions concurrently, -assigning LLM cals to a specific session.

-
-
-
import agentops
-from openai import OpenAI
-from dotenv import load_dotenv
-from agentops import ActionEvent
-
-load_dotenv()
-
-
-

First, of course, lets init AgentOps. We're going to bypass creating -a session automatically for the sake of showing it below.

-
-
-
agentops.init(auto_start_session=False)
-openai = OpenAI()
-
-
-

Now lets create two sessions, each with an identifiable tag.

-
-
-
session_1 = agentops.start_session(tags=["multi-session-test-1"])
-session_2 = agentops.start_session(tags=["multi-session-test-2"])
-
-print("session_id_1: {}".format(session_1.session_id))
-print("session_id_2: {}".format(session_2.session_id))
-
-
-

LLM Calls

-

Now lets go ahead and make our first OpenAI LLM call. The challenge -with having multiple sessions at the same time is that there is no way -for AgentOps to know what LLM call is intended to pertain to what active -session. This means we need to do a little extra work in one of two -ways.

-
-
-
messages = [{"role": "user", "content": "Hello"}]
-
-
-

Patching Function

-

This method involves wrapping the LLM call withing a function on -session. It can look a little counter-intuitive, but it easily tells us -what session the call belongs to.

-
-
-
# option 1: use session.patch
-response = session_1.patch(openai.chat.completions.create)(
-    model="gpt-3.5-turbo",
-    messages=messages,
-    temperature=0.5,
-)
-
-
-

Create patched function

-

If you're using the create function multiple times, you can create a -new function with the same method

-
-
-
observed_create = session_1.patch(openai.chat.completions.create)
-obs_response = observed_create(
-    model="gpt-3.5-turbo",
-    messages=messages,
-    temperature=0.5,
-)
-
-
-

Keyword Argument

-

Alternatively, you can also pass the session into the LLM function -call as a keyword argument. While this method works and is a bit more -readable, it is not a "pythonic" pattern and can lead to linting errors -in the code, as the base function is not expecting a -session keyword.

-
-
-
# option 2: add session as a keyword argument
-response2 = openai.chat.completions.create(
-    model="gpt-3.5-turbo", messages=messages, temperature=0.5, session=session_2
-)
-
-
-

Recording Events

-

Outside of LLM calls, there are plenty of other events that we want -to track. You can learn more about these events here.

-

Recording these events on a session is as simple as -session.record(...)

-
-
-
session_1.record(ActionEvent(action_type="test event"))
-
-
-

Now let's go ahead and end the sessions

-
-
-
session_1.end_session(end_state="Success")
-session_2.end_session(end_state="Success")
-
-
-

If you look in the AgentOps dashboard for these sessions, you will -see two unique sessions, both with one LLM Event each, one with an -Action Event as well.

-
-
- -
- - diff --git a/docs/v1/examples/notebooks/multion/Autonomous_web_browsing.html b/docs/v1/examples/notebooks/multion/Autonomous_web_browsing.html deleted file mode 100644 index 8e581e8c..00000000 --- a/docs/v1/examples/notebooks/multion/Autonomous_web_browsing.html +++ /dev/null @@ -1,366 +0,0 @@ - - - - - - - - Autonomous_web_browsing - - - -
-

MultiOn Tracking Web Browse Actions

-

-

Agents using MultiOn can launch and control remote or local web -browsers to perform actions and retrieve context using natural language -commands. With AgentOps, MultiOn evens such as browse, retrieve, and -step are automatically tracked.

-

-

Furthermore, events and LLM calls in your Python program will be -tracked as well.

-
-
-
# Install dependencies
-%pip install -U multion
-%pip install -U agentops
-%pip install -U openai
-
-
-

Tracking MultiOn events with AgentOps

-

When an agentops_api_key is provided, MultiOn will -automatically start an AgentOps session and record events.

-
-
-
# Set your API keys
-import os
-
-os.environ["MULTION_API_KEY"] = "multion_key"
-os.environ["AGENTOPS_API_KEY"] = "agentops_key"
-
-
-
from multion.client import MultiOn
-from multion.core.request_options import RequestOptions
-import openai
-import agentops
-
-multion = MultiOn(
-    api_key=os.environ.get("MULTION_API_KEY"),
-    agentops_api_key=os.environ.get("AGENTOPS_API_KEY"),
-)
-cmd = "what three things do i get with agentops"
-request_options = RequestOptions(
-    timeout_in_seconds=60, max_retries=4, additional_headers={"test": "ing"}
-)
-
-browse_response = multion.browse(
-    cmd="what three things do i get with agentops",
-    url="https://www.agentops.ai/",
-    max_steps=4,
-    include_screenshot=True,
-    request_options=request_options,
-)
-
-print(browse_response.message)
-
-
-

Linking MultiOn events to an existing AgentOps session

-

When running agentops.init(), be sure to set -auto_start_session=False. MultiOn will automatically launch -AgentOps sessions by default, but by setting auto start to false, you -can configure your AgentOps client independently.

-
-
-
agentops.init(auto_start_session=False, tags=["MultiOn browse example"])
-
-
-

Now, we can launch a MultiOn browse event. This event will -automatically get added to your AgentOps session.

-
-
-
multion = MultiOn(
-    api_key=os.environ.get("MULTION_API_KEY"),
-    agentops_api_key=os.environ.get("AGENTOPS_API_KEY"),
-)
-cmd = "what three things do i get with agentops"
-request_options = RequestOptions(
-    timeout_in_seconds=60, max_retries=4, additional_headers={"test": "ing"}
-)
-
-browse_response = multion.browse(
-    cmd="what three things do i get with agentops",
-    url="https://www.agentops.ai/",
-    max_steps=4,
-    include_screenshot=True,
-    request_options=request_options,
-)
-
-print(browse_response.message)
-
-
-

Let's use OpenAI to summarize our output

-
-
-
messages = [
-    {
-        "role": "user",
-        "content": f"Format this data as a markdown table: {browse_response.message}",
-    }
-]
-client = openai.OpenAI()
-response = client.chat.completions.create(messages=messages, model="gpt-3.5-turbo")
-
-print(response.choices[0].message.content)
-
-
-
# End session to see your dashboard
-agentops.end_session("Success")
-
-
-

Check your session

-

Check your session on AgentOps. -This session should include the MultiOn browse action and the OpenAI -call.

-

image.png

-
-
- -
- - diff --git a/docs/v1/examples/notebooks/multion/Sample_browsing_agent.html b/docs/v1/examples/notebooks/multion/Sample_browsing_agent.html deleted file mode 100644 index 44d16c78..00000000 --- a/docs/v1/examples/notebooks/multion/Sample_browsing_agent.html +++ /dev/null @@ -1,337 +0,0 @@ - - - - - - - - Sample_browsing_agent - - - -
-

MultiOn Tracking Web Browse Actions

-

-

Agents using MultiOn can launch and control remote or local web -browsers to perform actions and retrieve context using natural language -commands. With AgentOps, MultiOn evens such as browse, retrieve, and -step are automatically tracked.

-

-

Furthermore, events and LLM calls in your Python program will be -tracked as well.

-
-
-
# Install dependencies
-%pip install -U multion
-%pip install -U agentops
-%pip install -U openai
-
-
-
import os
-from multion.client import MultiOn
-from multion.core.request_options import RequestOptions
-import openai
-import agentops
-
-
-
# Set your API keys
-import os
-
-os.environ["MULTION_API_KEY"] = "multion_key"
-os.environ["AGENTOPS_API_KEY"] = "agentops_key"
-
-
-

Create an AgentOps session

-

When running agentops.init(), be sure to set -auto_start_session=False. MultiOn will automatically launch -AgentOps sessions by default, but by setting auto start to false, you -can configure your AgentOps client independently.

-
-
-
agentops.init(auto_start_session=False, tags=["MultiOn browse example"])
-
-
-

Now, we can launch a MultiOn browse event. This event will -automatically get added to your AgentOps session.

-
-
-
multion = MultiOn(
-    api_key=os.environ.get("MULTION_API_KEY"),
-    agentops_api_key=os.environ.get("AGENTOPS_API_KEY"),
-)
-cmd = "what three things do i get with agentops"
-request_options = RequestOptions(
-    timeout_in_seconds=60, max_retries=4, additional_headers={"test": "ing"}
-)
-
-browse_response = multion.browse(
-    cmd="what three things do i get with agentops",
-    url="https://www.agentops.ai/",
-    max_steps=4,
-    include_screenshot=True,
-    request_options=request_options,
-)
-
-print(browse_response.message)
-
-
-

Let's use OpenAI to summarize our output

-
-
-
messages = [
-    {
-        "role": "user",
-        "content": f"Format this data as a markdown table: {browse_response.message}",
-    }
-]
-client = openai.OpenAI()
-response = client.chat.completions.create(messages=messages, model="gpt-3.5-turbo")
-
-print(response.choices[0].message.content)
-
-
-
# End session to see your dashboard
-agentops.end_session("Success")
-
-
-

Check your session

-

Check your session on AgentOps. -This session should include the MultiOn browse action and the OpenAI -call.

-
- - diff --git a/docs/v1/examples/notebooks/multion/Step_by_step_web_browsing.html b/docs/v1/examples/notebooks/multion/Step_by_step_web_browsing.html deleted file mode 100644 index c7c5143d..00000000 --- a/docs/v1/examples/notebooks/multion/Step_by_step_web_browsing.html +++ /dev/null @@ -1,404 +0,0 @@ - - - - - - - - Step_by_step_web_browsing - - - -
-

MultiOn Step Tracing

-

-

With AgentOps, agent actions as well as MultiOn browse events will -get tracked. MultiOn browse events automatically trace screenshots as -well.

-

This example shows how to use MultiOn's session creator to launch a -self-directed browser agent that accomplishes a specified objective -using Step Mode. MultiOn agents can either accomplish tasks fully -autonomously or managed one step at a time. In this example, we will -launch a MutliOn agent and manage each individual step.

-
-
-
%pip install -U multion
-%pip install -U agentops
-
-
-

Session streaming

-

In this example, we'll use MultiOn to stream individual steps to -accomplish a task. To track your runs in the AgentOps dashboard, specify -an agentops_api_key when initializing -MultiOn()

-

You can run MultiOn without running agentops.init(). -However, you will only see events from MultiOn, and not any from your -own agent.

-
-
-
# Set your API keys
-import os
-
-os.environ["MULTION_API_KEY"] = "multion_key"
-os.environ["AGENTOPS_API_KEY"] = "agentops_key"
-
-
-
import multion
-from multion.client import MultiOn
-from multion.sessions.types.sessions_step_request_browser_params import (
-    SessionsStepRequestBrowserParams,
-)
-from multion.core.request_options import RequestOptions
-
-multion = MultiOn(
-    api_key=os.environ.get("MULTION_API_KEY"),
-    agentops_api_key=os.environ.get("AGENTOPS_API_KEY"),
-)
-
-url = "https://www.agentops.ai/"
-cmd = "what three things do i get with agentops"
-create_session_response = multion.sessions.create(url=url)
-session_id = create_session_response.session_id
-print(create_session_response.message)
-browser_params = SessionsStepRequestBrowserParams(height=1.1, width=1.1)
-request_options = RequestOptions(
-    timeout_in_seconds=60, max_retries=2, additional_headers={"test": "ing"}
-)
-step_session_response = multion.sessions.step(
-    session_id=session_id,
-    cmd=cmd,
-    url=url,
-    browser_params=browser_params,
-    include_screenshot=True,
-    request_options=request_options,
-)
-print(step_session_response.message)
-
-while step_session_response.status == "CONTINUE":
-    step_session_response = multion.sessions.step(
-        session_id=session_id,
-        cmd=cmd,
-        url=url,
-        include_screenshot=True,
-    )
-    print(step_session_response.message)
-
-if step_session_response.status == "DONE":
-    print("task completed")
-
-get_screenshot = multion.sessions.screenshot(session_id=session_id)
-close_session_response = multion.sessions.close(session_id=session_id)
-
-
-

Step Stream

-

Step stream is just like step, but it streams responses in the same -way a streamed LLM response is received. Instead of waiting for the -entire step to complete, MultiOn will return work in progress. To track -your runs in the AgentOps dashboard, specify an -agentops_api_key when initializing -MultiOn()

-
-
-
import os
-
-os.environ["MULTION_API_KEY"] =
-os.environ["AGENTOPS_API_KEY"] =
-
-
-
import multion
-from multion.client import MultiOn
-from multion.sessions.types.sessions_step_stream_request_browser_params import (
-    SessionsStepStreamRequestBrowserParams,
-)
-from multion.core.request_options import RequestOptions
-import os
-
-multion = MultiOn(
-    api_key=os.environ.get("MULTION_API_KEY"),
-    agentops_api_key=os.environ.get("AGENTOPS_API_KEY"),
-)
-
-url = "https://www.agentops.ai/"
-cmd = "what three things do i get with agentops"
-create_session_response = multion.sessions.create(url=url)
-session_id = create_session_response.session_id
-print(create_session_response.message)
-browser_params = SessionsStepStreamRequestBrowserParams(height=1.1, width=1.1)
-request_options = RequestOptions(
-    timeout_in_seconds=60, max_retries=2, additional_headers={"test": "ing"}
-)
-step_session_response = multion.sessions.step_stream(
-    session_id=session_id,
-    cmd=cmd,
-    url=url,
-    browser_params=browser_params,
-    include_screenshot=True,
-    request_options=request_options,
-)
-
-for chunk in step_session_response:
-    if chunk.type == "final_event":
-        break
-    print(chunk.delta["content"])
-
-while chunk.delta["status"] == "CONTINUE":
-    step_session_response = multion.sessions.step_stream(
-        session_id=session_id,
-        cmd=cmd,
-        url=url,
-        include_screenshot=True,
-    )
-    for chunk in step_session_response:
-        if chunk.type == "final_event":
-            break
-        print(chunk.delta["content"])
-
-if chunk.delta["status"] == "DONE":
-    print("task completed")
-
-get_screenshot = multion.sessions.screenshot(session_id=session_id)
-close_session_response = multion.sessions.close(session_id=session_id)
-
-
-

Check your session

-

Check your session on AgentOps

-

-

-
- - diff --git a/docs/v1/examples/notebooks/multion/Webpage_data_retrieval.html b/docs/v1/examples/notebooks/multion/Webpage_data_retrieval.html deleted file mode 100644 index ea3a87d7..00000000 --- a/docs/v1/examples/notebooks/multion/Webpage_data_retrieval.html +++ /dev/null @@ -1,302 +0,0 @@ - - - - - - - - Webpage_data_retrieval - - - -
-

MultiOn Autonomous Session Tracing

-

-

With AgentOps, agent actions as well as MultiOn retrieve events will -get tracked. MultiOn browse events automatically trace screenshots as -well.

-

This example shows how to use MultiOn's session creator to launch a -self-directed browser agent that accomplishes a specified objective.

-
-
-
%pip install -U multion
-%pip install -U agentops
-
-
-
# Set your API keys
-import os
-
-os.environ["MULTION_API_KEY"] = "multion_key"
-os.environ["AGENTOPS_API_KEY"] = "agentops_key"
-
-
-
import multion
-from multion.client import MultiOn
-
-multion = MultiOn(
-    api_key=os.environ.get("MULTION_API_KEY"),
-    agentops_api_key=os.environ.get("AGENTOPS_API_KEY"),
-)
-
-cmd = "what three things do i get with agentops"
-url = "https://www.agentops.ai/"
-
-retrieve_response = multion.retrieve(
-    cmd=cmd, url=url, fields=["price"], include_screenshot=True
-)
-print(retrieve_response.message)
-
-while retrieve_response.status == "CONTINUE":
-    retrieve_response = multion.retrieve(
-        cmd=cmd,
-        url=url,
-        fields=["price"],
-        include_screenshot=True,
-    )
-    print(retrieve_response.message)
-
-if retrieve_response.status == "DONE":
-    print("task completed")
-
-
-

Check your session

-

Check your session on AgentOps

-

-
-
-
-
- - diff --git a/docs/v1/examples/notebooks/openai-gpt.html b/docs/v1/examples/notebooks/openai-gpt.html deleted file mode 100644 index dc112599..00000000 --- a/docs/v1/examples/notebooks/openai-gpt.html +++ /dev/null @@ -1,346 +0,0 @@ - - - - - - - - openai-gpt - - - -
-

AgentOps Basic Monitoring

-

This is an example of how to use the AgentOps library for basic Agent -monitoring with OpenAI's GPT

-
-
-
from openai import OpenAI
-import agentops
-import os
-from dotenv import load_dotenv
-
-
-

Next, we'll grab our two API keys. You can use dotenv like below or -however else you like to load environment variables

-
-
-
load_dotenv()
-OPENAI_API_KEY = os.getenv("OPENAI_API_KEY") or "<your_openai_key>"
-AGENTOPS_API_KEY = os.getenv("AGENTOPS_API_KEY") or "<your_agentops_key>"
-
-
-

The AgentOps library is designed to be a plug-and-play replacement -for the OpenAI Client, maximizing use with minimal install effort.

-
-
-
openai = OpenAI(api_key=OPENAI_API_KEY)
-agentops.init(AGENTOPS_API_KEY, tags=["openai-gpt-notebook"])
-
-
-

Now just use OpenAI as you would normally!

-
-
-

Single Session with -ChatCompletion

-
-
-
message = ({"role": "user", "content": "Write a 12 word poem about secret agents."},)
-res = openai.chat.completions.create(
-    model="gpt-3.5-turbo", messages=message, temperature=0.5, stream=True
-)
-
-
-

Make sure to end your session with a Result -(Success|Fail|Indeterminate) for better tracking

-
-
-
agentops.end_session("Success")
-
-
-

Now if you check the AgentOps dashboard, you should see information -related to this run!

-
-
-

Events

-

Additionally, you can track custom events via AgentOps. Let's start a -new session and record some events

-
-
-
# Create new session
-agentops.start_session(tags=["openai-gpt-notebook-events"])
-
-
-

The easiest way to record actions is through the use of AgentOp's -decorators

-
-
-
from agentops import record_action
-
-
-@record_action("add numbers")
-def add(x, y):
-    return x + y
-
-
-add(2, 4)
-
-
-

We can also manually craft an event exactly the way we want

-
-
-
from agentops import ActionEvent
-
-message = ({"role": "user", "content": "Hello"},)
-response = openai.chat.completions.create(
-    model="gpt-3.5-turbo", messages=message, temperature=0.5
-)
-
-if "hello" in str(response.choices[0].message.content).lower():
-    agentops.record(
-        ActionEvent(
-            action_type="Agent says hello",
-            params=str(message),
-            returns=str(response.choices[0].message.content),
-        )
-    )
-
-
-
agentops.end_session("Success")
-
- - diff --git a/docs/v1/examples/notebooks/recording-events.html b/docs/v1/examples/notebooks/recording-events.html deleted file mode 100644 index aa7b5b7d..00000000 --- a/docs/v1/examples/notebooks/recording-events.html +++ /dev/null @@ -1,340 +0,0 @@ - - - - - - - - recording-events - - - -
-

Recording Events

-

AgentOps has a number of different Event Types

-

AgentOps automatically instruments your LLM Calls from OpenAI, -LiteLLM, and Cohere. Just make sure their SDKs are imported before -initializing AgentOps like we see below

-
-
-
import agentops
-import openai
-
-# Create new session
-agentops.init()
-
-# Optionally, we can add tags to the session
-# agentops.init(tags=['Hello Tracker'])
-
-message = ({"role": "user", "content": "Hello"},)
-response = openai.chat.completions.create(
-    model="gpt-3.5-turbo", messages=message, temperature=0.5
-)
-
-
-

Click the AgentOps link above to see your session!

-
-
-

Action Event

-

AgentOps allows you to record other actions. The easiest way to -record actions is through the use of AgentOp's decorators

-
-
-
from agentops import record_function
-
-
-@record_function("add numbers")
-def add(x, y):
-    return x + y
-
-
-add(2, 4)
-
-
-

We can also manually craft an event exactly the way we want by -creating and recording an ActionEvent

-
-
-
from agentops import ActionEvent
-
-agentops.record(
-    ActionEvent(
-        action_type="Agent says hello", params={"message": "Hi"}, returns="Hi Back!"
-    )
-)
-
-
-

Tool Event

-

Agents use tools. These tools are useful to track with information -such as name, end status, runtime, etc. To record tool usage, you can -create and record a ToolEvent similar to above.

-
-
-
from agentops import ToolEvent, record
-
-
-def scrape_website(url: str):
-    tool_event = ToolEvent(
-        name="scrape_website", params={"url": url}
-    )  # the start timestamp is set when the obj is created
-    result = integration.scrape_website(data)  # perform tool logic
-    tool_event.returns = result
-    record(tool_event)
-
-
-

Error Events

-

Error events can be used alone or in reference to another event. Lets -add a catch block to the code above

-
-
-
from agentops import ToolEvent, record, ErrorEvent
-
-
-def scrape_website(url: str):
-    tool_event = ToolEvent(
-        name="scrape_website", params={"url": url}
-    )  # the start timestamp is set when the obj is created
-
-    try:
-        1 / 0  # Ooops! Something went wrong
-    except Exception as e:
-        record(ErrorEvent(exception=e, trigger_event=tool_event))
-
-
-scrape_website("https://app.agentops.ai")
-
-agentops.end_session("Success")
-
- - diff --git a/docs/v1/examples/recording_events.mdx b/docs/v1/examples/recording_events.mdx index 7d08f7f7..3bdee3ca 100644 --- a/docs/v1/examples/recording_events.mdx +++ b/docs/v1/examples/recording_events.mdx @@ -5,4 +5,134 @@ mode: "wide" --- _View Notebook on Github_ -{/* SOURCE_FILE: examples/recording-events.ipynb */} \ No newline at end of file + +{/* SOURCE_FILE: examples/recording-events.ipynb */} + +# Recording Events +AgentOps has a number of different [Event Types](https://docs.agentops.ai/v1/details/events) + +We automatically instrument your LLM Calls from OpenAI, LiteLLM, Cohere, and more. Just make sure their SDKs are imported before initializing AgentOps like we see below + +First let's install the required packages + + +```python +%pip install -U openai +%pip install -U agentops +%pip install -U python-dotenv +``` + +Then import them + + +```python +from openai import OpenAI +import agentops +import os +from dotenv import load_dotenv +``` + +Next, we'll set our API keys. There are several ways to do this, the code below is just the most foolproof way for the purposes of this notebook. It accounts for both users who use environment variables and those who just want to set the API Key here in this notebook. + +[Get an AgentOps API key](https://agentops.ai/settings/projects) + +1. Create an environment variable in a .env file or other method. By default, the AgentOps `init()` function will look for an environment variable named `AGENTOPS_API_KEY`. Or... + +2. Replace `` below and pass in the optional `api_key` parameter to the AgentOps `init(api_key=...)` function. Remember not to commit your API key to a public repo! + + +```python +load_dotenv() +OPENAI_API_KEY = os.getenv("OPENAI_API_KEY") or "" +AGENTOPS_API_KEY = os.getenv("AGENTOPS_API_KEY") or "" +``` + + +```python +# Initialize the client, which will automatically start a session +agentops.init() + +# Optionally, we can add default tags to all sessions +# agentops.init(default_tags=['Hello Tracker']) + +openai = OpenAI() + +messages = [{"role": "user", "content": "Hello"}] +response = openai.chat.completions.create( + model="gpt-3.5-turbo", messages=messages, temperature=0.5 +) +print(response.choices[0].message.content) +``` + +Click the AgentOps link above to see your session! + +## Action Event + +AgentOps allows you to record other actions. The easiest way to record actions is through the use of AgentOp's decorators + + +```python +from agentops import record_action + + +@record_action("add numbers") +def add(x, y): + return x + y + + +add(2, 4) +``` + +We can also manually craft an event exactly the way we want by creating and recording an `ActionEvent` + + +```python +from agentops import ActionEvent + +agentops.record( + ActionEvent( + action_type="Agent says hello", params={"message": "Hi"}, returns="Hi Back!" + ) +) +``` + +## Tool Event +Agents use tools. These tools are useful to track with information such as name, end status, runtime, etc. To record tool usage, you can create and record a `ToolEvent` similar to above. + + +```python +from agentops import ToolEvent, record + + +def scrape_website(url: str): + tool_event = ToolEvent( + name="scrape_website", params={"url": url} + ) # the start timestamp is set when the obj is created + result = "scraped data" # perform tool logic + tool_event.returns = result + record(tool_event) +``` + +## Error Events +Error events can be used alone or in reference to another event. Lets add a catch block to the code above + + +```python +from agentops import ToolEvent, record, ErrorEvent + + +def scrape_website(url: str): + tool_event = ToolEvent( + name="scrape_website", params={"url": url} + ) # the start timestamp is set when the obj is created + + try: + 1 / 0 # Ooops! Something went wrong + except Exception as e: + record(ErrorEvent(exception=e, trigger_event=tool_event)) + + +scrape_website("https://app.agentops.ai") + +agentops.end_session("Success") +``` diff --git a/docs/v1/examples/simple_agent.mdx b/docs/v1/examples/simple_agent.mdx index 07a41318..48dde402 100644 --- a/docs/v1/examples/simple_agent.mdx +++ b/docs/v1/examples/simple_agent.mdx @@ -5,4 +5,123 @@ mode: "wide" --- _View Notebook on Github_ + {/* SOURCE_FILE: examples/openai-gpt.ipynb */} + +# AgentOps Basic Monitoring +This is an example of how to use the AgentOps library for basic Agent monitoring with OpenAI's GPT + +First let's install the required packages + + +```python +%pip install -U openai +%pip install -U agentops +%pip install -U python-dotenv +``` + +Then import them + + +```python +from openai import OpenAI +import agentops +import os +from dotenv import load_dotenv +``` + +Next, we'll set our API keys. There are several ways to do this, the code below is just the most foolproof way for the purposes of this notebook. It accounts for both users who use environment variables and those who just want to set the API Key here in this notebook. + +[Get an AgentOps API key](https://agentops.ai/settings/projects) + +1. Create an environment variable in a .env file or other method. By default, the AgentOps `init()` function will look for an environment variable named `AGENTOPS_API_KEY`. Or... + +2. Replace `` below and pass in the optional `api_key` parameter to the AgentOps `init(api_key=...)` function. Remember not to commit your API key to a public repo! + + +```python +load_dotenv() +OPENAI_API_KEY = os.getenv("OPENAI_API_KEY") or "" +AGENTOPS_API_KEY = os.getenv("AGENTOPS_API_KEY") or "" +``` + +The AgentOps library is designed to be a plug-and-play replacement for the OpenAI Client, maximizing use with minimal install effort. + + +```python +openai = OpenAI(api_key=OPENAI_API_KEY) +agentops.init(AGENTOPS_API_KEY, default_tags=["openai-gpt-notebook"]) +``` + +Now just use OpenAI as you would normally! + +## Single Session with ChatCompletion + + +```python +message = [{"role": "user", "content": "Write a 12 word poem about secret agents."}] +response = openai.chat.completions.create( + model="gpt-3.5-turbo", messages=message, temperature=0.5, stream=False +) +print(response.choices[0].message.content) +``` + +Make sure to end your session with a `Result` (Success|Fail|Indeterminate) for better tracking + + +```python +agentops.end_session("Success") +``` + +Now if you check the AgentOps dashboard, you should see information related to this run! + +# Events +Additionally, you can track custom events via AgentOps. +Let's start a new session and record some events + + +```python +# Create new session +agentops.start_session(tags=["openai-gpt-notebook-events"]) +``` + +The easiest way to record actions is through the use of AgentOp's decorators + + +```python +from agentops import record_action + + +@record_action("add numbers") +def add(x, y): + return x + y + + +add(2, 4) +``` + +We can also manually craft an event exactly the way we want + + +```python +from agentops import ActionEvent + +message = ({"role": "user", "content": "Hello"},) +response = openai.chat.completions.create( + model="gpt-3.5-turbo", messages=message, temperature=0.5 +) + +if "hello" in str(response.choices[0].message.content).lower(): + agentops.record( + ActionEvent( + action_type="Agent says hello", + params=str(message), + returns=str(response.choices[0].message.content), + ) + ) +``` + + +```python +agentops.end_session("Success") +``` diff --git a/examples/multion_examples/Autonomous_web_browsing.ipynb b/examples/multion_examples/Autonomous_web_browsing.ipynb index d44329b7..c79a3313 100644 --- a/examples/multion_examples/Autonomous_web_browsing.ipynb +++ b/examples/multion_examples/Autonomous_web_browsing.ipynb @@ -205,18 +205,13 @@ ] }, { - "attachments": { - "image.png": { - "image/png": "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" - } - }, "cell_type": "markdown", "metadata": {}, "source": [ "## Check your session\n", "Check your session on [AgentOps](https://app.agentops.ai). This session should include the MultiOn browse action and the OpenAI call.\n", "\n", - "![image.png](attachment:image.png)" + "![AgentOps MultiOn Browse](https://github.com/AgentOps-AI/agentops/blob/main/docs/images/agentops-multion-browse.gif?raw=true)" ] } ],