diff --git a/.github/workflows/publish.yml b/.github/workflows/publish.yml index 1b3000647..5097231b9 100644 --- a/.github/workflows/publish.yml +++ b/.github/workflows/publish.yml @@ -59,6 +59,8 @@ jobs: contents: read steps: - uses: actions/checkout@v4 + with: + ref: ${{ inputs.release_tag || github.ref }} - name: Install uv uses: astral-sh/setup-uv@v6 @@ -93,3 +95,72 @@ jobs: echo "Some packages failed to publish" exit 1 fi + + - name: Build Slack payload + if: success() + id: slack + env: + GH_TOKEN: ${{ secrets.GITHUB_TOKEN }} + RELEASE_TAG: ${{ inputs.release_tag }} + run: | + payload=$(uv run python -c " + import json, re, subprocess, sys + + with open('lib/crewai/src/crewai/__init__.py') as f: + m = re.search(r\"__version__\s*=\s*[\\\"']([^\\\"']+)\", f.read()) + version = m.group(1) if m else 'unknown' + + import os + tag = os.environ.get('RELEASE_TAG') or version + + try: + r = subprocess.run(['gh','release','view',tag,'--json','body','-q','.body'], + capture_output=True, text=True, check=True) + body = r.stdout.strip() + except Exception: + body = '' + + blocks = [ + {'type':'section','text':{'type':'mrkdwn', + 'text':f':rocket: \`crewai v{version}\` published to PyPI'}}, + {'type':'section','text':{'type':'mrkdwn', + 'text':f' ยท '}}, + {'type':'divider'}, + ] + + if body: + heading, items = '', [] + for line in body.split('\n'): + line = line.strip() + if not line: continue + hm = re.match(r'^#{2,3}\s+(.*)', line) + if hm: + if heading and items: + skip = heading in ('What\\'s Changed','') or 'Contributors' in heading + if not skip: + txt = f'*{heading}*\n' + '\n'.join(f'โ€ข {i}' for i in items) + blocks.append({'type':'section','text':{'type':'mrkdwn','text':txt}}) + heading, items = hm.group(1), [] + elif line.startswith('- ') or line.startswith('* '): + items.append(re.sub(r'\*\*([^*]*)\*\*', r'*\1*', line[2:])) + if heading and items: + skip = heading in ('What\\'s Changed','') or 'Contributors' in heading + if not skip: + txt = f'*{heading}*\n' + '\n'.join(f'โ€ข {i}' for i in items) + blocks.append({'type':'section','text':{'type':'mrkdwn','text':txt}}) + + blocks.append({'type':'divider'}) + blocks.append({'type':'section','text':{'type':'mrkdwn', + 'text':f'\`\`\`uv add \"crewai[tools]=={version}\"\`\`\`'}}) + + print(json.dumps({'blocks':blocks})) + ") + echo "payload=$payload" >> $GITHUB_OUTPUT + + - name: Notify Slack + if: success() + uses: slackapi/slack-github-action@v2.1.0 + with: + webhook: ${{ secrets.SLACK_WEBHOOK_URL }} + webhook-type: incoming-webhook + payload: ${{ steps.slack.outputs.payload }} diff --git a/lib/crewai/tests/cassettes/test_before_kickoff_callback.yaml b/lib/crewai/tests/cassettes/test_before_kickoff_callback.yaml index 05251afc2..5bd25552c 100644 --- a/lib/crewai/tests/cassettes/test_before_kickoff_callback.yaml +++ b/lib/crewai/tests/cassettes/test_before_kickoff_callback.yaml @@ -44,8 +44,8 @@ interactions: uri: https://api.openai.com/v1/chat/completions response: body: - string: "{\n \"id\": \"chatcmpl-DIjv3LqL0QS4iw3OM5b28B4VOMZPA\",\n \"object\": - \"chat.completion\",\n \"created\": 1773358789,\n \"model\": \"gpt-4.1-mini-2025-04-14\",\n + string: "{\n \"id\": \"chatcmpl-DIqrxbdWncBetSyqX8P36UUXoil9d\",\n \"object\": + \"chat.completion\",\n \"created\": 1773385505,\n \"model\": \"gpt-4.1-mini-2025-04-14\",\n \ \"choices\": [\n {\n \"index\": 0,\n \"message\": {\n \"role\": \"assistant\",\n \"content\": \"Test expected output\",\n \"refusal\": null,\n \"annotations\": []\n },\n \"logprobs\": null,\n @@ -59,13 +59,13 @@ interactions: CF-Cache-Status: - DYNAMIC CF-Ray: - - 9db6a3f31e087b0e-EWR + - 9db9302f7f411efc-EWR Connection: - keep-alive Content-Type: - application/json Date: - - Thu, 12 Mar 2026 23:39:50 GMT + - Fri, 13 Mar 2026 07:05:06 GMT Server: - cloudflare Strict-Transport-Security: @@ -81,7 +81,7 @@ interactions: openai-organization: - OPENAI-ORG-XXX openai-processing-ms: - - '360' + - '376' openai-project: - OPENAI-PROJECT-XXX openai-version: diff --git a/lib/crewai/tests/cassettes/test_crew_kickoff_usage_metrics.yaml b/lib/crewai/tests/cassettes/test_crew_kickoff_usage_metrics.yaml index c316caf7c..337de4f6b 100644 --- a/lib/crewai/tests/cassettes/test_crew_kickoff_usage_metrics.yaml +++ b/lib/crewai/tests/cassettes/test_crew_kickoff_usage_metrics.yaml @@ -1,616 +1,331 @@ interactions: - request: - body: null + body: '{"messages":[{"role":"system","content":"You are dog Researcher. You have + a lot of experience with dog.\nYour personal goal is: Express hot takes on dog."},{"role":"user","content":"\nCurrent + Task: Give me an analysis around dog.\n\nThis is the expected criteria for your + final answer: 1 bullet point about dog that''s under 15 words.\nyou MUST return + the actual complete content as the final answer, not a summary.\n\nProvide your + complete response:"}],"model":"gpt-4o"}' headers: - Accept: - - '*/*' - Accept-Encoding: - - gzip, deflate, zstd - Connection: - - keep-alive User-Agent: - - python-requests/2.32.2 - method: GET - uri: https://pypi.org/pypi/agentops/json - response: - body: - string: '{"info":{"author":null,"author_email":"Alex Reibman , Shawn Qiu , Braelyn Boynton , Howard Gil , Constantin Teodorescu , Pratyush Shukla ","bugtrack_url":null,"classifiers":["License :: OSI Approved :: MIT License","Operating System :: OS Independent","Programming Language :: Python :: 3","Programming Language :: Python :: 3.10","Programming Language :: Python :: 3.11","Programming Language :: Python :: 3.12","Programming Language :: Python :: 3.13","Programming Language :: Python :: 3.9"],"description":"
\n \n \"Logo\"\n \n
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\n Observability and DevTool platform for AI Agents\n
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\n \n \"Downloads\"\n \n \n \"git\n \n \"PyPI\n \n \"License:\n \n
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\n \n \"Twitter\"\n \n \n \"Discord\"\n \n \n \"Dashboard\"\n \n \n \"Documentation\"\n \n \n \"Chat\n \n

\n\n\n\n
\n \"Dashboard\n
\n\n
\n\n\nAgentOps helps developers build, evaluate, and monitor AI agents. From prototype to production.\n\n| | |\n| ------------------------------------- | ------------------------------------------------------------- |\n| ๐Ÿ“Š **Replay Analytics and Debugging** | Step-by-step - agent execution graphs |\n| ๐Ÿ’ธ **LLM Cost Management** | Track spend with LLM foundation model providers |\n| ๐Ÿงช **Agent Benchmarking** | Test your agents against 1,000+ evals |\n| ๐Ÿ” **Compliance and Security** | Detect common prompt injection and data exfiltration exploits |\n| ๐Ÿค **Framework Integrations** | Native Integrations with CrewAI, AG2(AutoGen), Camel AI, & LangChain |\n\n## Quick Start โŒจ๏ธ\n\n```bash\npip install agentops\n```\n\n\n#### Session replays in 2 lines of code\n\nInitialize the AgentOps client and automatically get analytics on all your LLM calls.\n\n[Get an API key](https://app.agentops.ai/settings/projects)\n\n```python\nimport agentops\n\n# Beginning of your program (i.e. main.py, __init__.py)\nagentops.init( < INSERT YOUR API KEY HERE >)\n\n...\n\n# End of program\nagentops.end_session(''Success'')\n```\n\nAll your sessions can be viewed on the [AgentOps - dashboard](https://app.agentops.ai?ref=gh)\n
\n\n
\n Agent Debugging\n \n \"Agent\n \n \n \"Chat\n \n \n \"Event\n \n
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\n Session Replays\n \n \"Session\n \n
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\n Summary Analytics\n \n \"Summary\n \n \n \"Summary\n \n
\n\n\n### First class Developer Experience\nAdd powerful observability to your agents, tools, and functions with as little code as possible: one line at a time.\n
\nRefer to our [documentation](http://docs.agentops.ai)\n\n```python\n# Automatically associate all Events with the agent that originated them\nfrom agentops import track_agent\n\n@track_agent(name=''SomeCustomName'')\nclass MyAgent:\n ...\n```\n\n```python\n# Automatically create ToolEvents for tools that agents will use\nfrom agentops import record_tool\n\n@record_tool(''SampleToolName'')\ndef sample_tool(...):\n ...\n```\n\n```python\n# Automatically create ActionEvents for other functions.\nfrom agentops - import record_action\n\n@agentops.record_action(''sample function being record'')\ndef sample_function(...):\n ...\n```\n\n```python\n# Manually record any other Events\nfrom agentops import record, ActionEvent\n\nrecord(ActionEvent(\"received_user_input\"))\n```\n\n## Integrations ๐Ÿฆพ\n\n### CrewAI ๐Ÿ›ถ\n\nBuild Crew agents with observability with only 2 lines of code. Simply set an `AGENTOPS_API_KEY` in your environment, and your crews will get automatic monitoring on the AgentOps dashboard.\n\n```bash\npip install ''crewai[agentops]''\n```\n\n- [AgentOps integration example](https://docs.agentops.ai/v1/integrations/crewai)\n- [Official CrewAI documentation](https://docs.crewai.com/how-to/AgentOps-Observability)\n\n### AG2 ๐Ÿค–\nWith only two lines of code, add full observability and monitoring to AG2 (formerly AutoGen) agents. Set an `AGENTOPS_API_KEY` in your environment and call `agentops.init()`\n\n- [AG2 Observability Example](https://docs.ag2.ai/notebooks/agentchat_agentops)\n- - [AG2 - AgentOps Documentation](https://docs.ag2.ai/docs/ecosystem/agentops)\n\n### Camel AI ๐Ÿช\n\nTrack and analyze CAMEL agents with full observability. Set an `AGENTOPS_API_KEY` in your environment and initialize AgentOps to get started.\n\n- [Camel AI](https://www.camel-ai.org/) - Advanced agent communication framework\n- [AgentOps integration example](https://docs.agentops.ai/v1/integrations/camel)\n- [Official Camel AI documentation](https://docs.camel-ai.org/cookbooks/agents_tracking.html)\n\n
\n Installation\n\n```bash\npip install \"camel-ai[all]==0.2.11\"\npip install agentops\n```\n\n```python\nimport os\nimport agentops\nfrom camel.agents import ChatAgent\nfrom camel.messages import BaseMessage\nfrom camel.models import ModelFactory\nfrom camel.types import ModelPlatformType, ModelType\n\n# Initialize AgentOps\nagentops.init(os.getenv(\"AGENTOPS_API_KEY\"), default_tags=[\"CAMEL Example\"])\n\n# Import toolkits after AgentOps init for tracking\nfrom - camel.toolkits import SearchToolkit\n\n# Set up the agent with search tools\nsys_msg = BaseMessage.make_assistant_message(\n role_name=''Tools calling operator'',\n content=''You are a helpful assistant''\n)\n\n# Configure tools and model\ntools = [*SearchToolkit().get_tools()]\nmodel = ModelFactory.create(\n model_platform=ModelPlatformType.OPENAI,\n model_type=ModelType.GPT_4O_MINI,\n)\n\n# Create and run the agent\ncamel_agent = ChatAgent(\n system_message=sys_msg,\n model=model,\n tools=tools,\n)\n\nresponse = camel_agent.step(\"What is AgentOps?\")\nprint(response)\n\nagentops.end_session(\"Success\")\n```\n\nCheck out our [Camel integration guide](https://docs.agentops.ai/v1/integrations/camel) for more examples including multi-agent scenarios.\n
\n\n### Langchain ๐Ÿฆœ๐Ÿ”—\n\nAgentOps works seamlessly with applications built using Langchain. To use the handler, install Langchain as an optional dependency:\n\n
\n Installation\n \n```shell\npip - install agentops[langchain]\n```\n\nTo use the handler, import and set\n\n```python\nimport os\nfrom langchain.chat_models import ChatOpenAI\nfrom langchain.agents import initialize_agent, AgentType\nfrom agentops.partners.langchain_callback_handler import LangchainCallbackHandler\n\nAGENTOPS_API_KEY = os.environ[''AGENTOPS_API_KEY'']\nhandler = LangchainCallbackHandler(api_key=AGENTOPS_API_KEY, tags=[''Langchain Example''])\n\nllm = ChatOpenAI(openai_api_key=OPENAI_API_KEY,\n callbacks=[handler],\n model=''gpt-3.5-turbo'')\n\nagent = initialize_agent(tools,\n llm,\n agent=AgentType.CHAT_ZERO_SHOT_REACT_DESCRIPTION,\n verbose=True,\n callbacks=[handler], # You must pass in a callback handler to record your agent\n handle_parsing_errors=True)\n```\n\nCheck out the [Langchain Examples Notebook](./examples/langchain_examples.ipynb) for - more details including Async handlers.\n\n
\n\n### Cohere โŒจ๏ธ\n\nFirst class support for Cohere(>=5.4.0). This is a living integration, should you need any added functionality please message us on Discord!\n\n- [AgentOps integration example](https://docs.agentops.ai/v1/integrations/cohere)\n- [Official Cohere documentation](https://docs.cohere.com/reference/about)\n\n
\n Installation\n \n```bash\npip install cohere\n```\n\n```python python\nimport cohere\nimport agentops\n\n# Beginning of program''s code (i.e. main.py, __init__.py)\nagentops.init()\nco = cohere.Client()\n\nchat = co.chat(\n message=\"Is it pronounced ceaux-hear or co-hehray?\"\n)\n\nprint(chat)\n\nagentops.end_session(''Success'')\n```\n\n```python python\nimport cohere\nimport agentops\n\n# Beginning of program''s code (i.e. main.py, __init__.py)\nagentops.init()\n\nco = cohere.Client()\n\nstream = co.chat_stream(\n message=\"Write - me a haiku about the synergies between Cohere and AgentOps\"\n)\n\nfor event in stream:\n if event.event_type == \"text-generation\":\n print(event.text, end='''')\n\nagentops.end_session(''Success'')\n```\n
\n\n\n### Anthropic ๏นจ\n\nTrack agents built with the Anthropic Python SDK (>=0.32.0).\n\n- [AgentOps integration guide](https://docs.agentops.ai/v1/integrations/anthropic)\n- [Official Anthropic documentation](https://docs.anthropic.com/en/docs/welcome)\n\n
\n Installation\n \n```bash\npip install anthropic\n```\n\n```python python\nimport anthropic\nimport agentops\n\n# Beginning of program''s code (i.e. main.py, __init__.py)\nagentops.init()\n\nclient = anthropic.Anthropic(\n # This is the default and can be omitted\n api_key=os.environ.get(\"ANTHROPIC_API_KEY\"),\n)\n\nmessage = client.messages.create(\n max_tokens=1024,\n messages=[\n {\n \"role\": \"user\",\n \"content\": - \"Tell me a cool fact about AgentOps\",\n }\n ],\n model=\"claude-3-opus-20240229\",\n )\nprint(message.content)\n\nagentops.end_session(''Success'')\n```\n\nStreaming\n```python python\nimport anthropic\nimport agentops\n\n# Beginning of program''s code (i.e. main.py, __init__.py)\nagentops.init()\n\nclient = anthropic.Anthropic(\n # This is the default and can be omitted\n api_key=os.environ.get(\"ANTHROPIC_API_KEY\"),\n)\n\nstream = client.messages.create(\n max_tokens=1024,\n model=\"claude-3-opus-20240229\",\n messages=[\n {\n \"role\": \"user\",\n \"content\": \"Tell me something cool about streaming agents\",\n }\n ],\n stream=True,\n)\n\nresponse = \"\"\nfor event in stream:\n if event.type == \"content_block_delta\":\n response += event.delta.text\n elif event.type == \"message_stop\":\n print(\"\\n\")\n print(response)\n print(\"\\n\")\n```\n\nAsync\n\n```python - python\nimport asyncio\nfrom anthropic import AsyncAnthropic\n\nclient = AsyncAnthropic(\n # This is the default and can be omitted\n api_key=os.environ.get(\"ANTHROPIC_API_KEY\"),\n)\n\n\nasync def main() -> None:\n message = await client.messages.create(\n max_tokens=1024,\n messages=[\n {\n \"role\": \"user\",\n \"content\": \"Tell me something interesting about async agents\",\n }\n ],\n model=\"claude-3-opus-20240229\",\n )\n print(message.content)\n\n\nawait main()\n```\n
\n\n### Mistral ใ€ฝ๏ธ\n\nTrack agents built with the Anthropic Python SDK (>=0.32.0).\n\n- [AgentOps integration example](./examples/mistral//mistral_example.ipynb)\n- [Official Mistral documentation](https://docs.mistral.ai)\n\n
\n Installation\n \n```bash\npip install mistralai\n```\n\nSync\n\n```python python\nfrom mistralai import Mistral\nimport agentops\n\n# Beginning of program''s - code (i.e. main.py, __init__.py)\nagentops.init()\n\nclient = Mistral(\n # This is the default and can be omitted\n api_key=os.environ.get(\"MISTRAL_API_KEY\"),\n)\n\nmessage = client.chat.complete(\n messages=[\n {\n \"role\": \"user\",\n \"content\": \"Tell me a cool fact about AgentOps\",\n }\n ],\n model=\"open-mistral-nemo\",\n )\nprint(message.choices[0].message.content)\n\nagentops.end_session(''Success'')\n```\n\nStreaming\n\n```python python\nfrom mistralai import Mistral\nimport agentops\n\n# Beginning of program''s code (i.e. main.py, __init__.py)\nagentops.init()\n\nclient = Mistral(\n # This is the default and can be omitted\n api_key=os.environ.get(\"MISTRAL_API_KEY\"),\n)\n\nmessage = client.chat.stream(\n messages=[\n {\n \"role\": \"user\",\n \"content\": \"Tell me something cool - about streaming agents\",\n }\n ],\n model=\"open-mistral-nemo\",\n )\n\nresponse = \"\"\nfor event in message:\n if event.data.choices[0].finish_reason == \"stop\":\n print(\"\\n\")\n print(response)\n print(\"\\n\")\n else:\n response += event.text\n\nagentops.end_session(''Success'')\n```\n\nAsync\n\n```python python\nimport asyncio\nfrom mistralai import Mistral\n\nclient = Mistral(\n # This is the default and can be omitted\n api_key=os.environ.get(\"MISTRAL_API_KEY\"),\n)\n\n\nasync def main() -> None:\n message = await client.chat.complete_async(\n messages=[\n {\n \"role\": \"user\",\n \"content\": \"Tell me something interesting about async agents\",\n }\n ],\n model=\"open-mistral-nemo\",\n )\n print(message.choices[0].message.content)\n\n\nawait main()\n```\n\nAsync Streaming\n\n```python python\nimport asyncio\nfrom mistralai - import Mistral\n\nclient = Mistral(\n # This is the default and can be omitted\n api_key=os.environ.get(\"MISTRAL_API_KEY\"),\n)\n\n\nasync def main() -> None:\n message = await client.chat.stream_async(\n messages=[\n {\n \"role\": \"user\",\n \"content\": \"Tell me something interesting about async streaming agents\",\n }\n ],\n model=\"open-mistral-nemo\",\n )\n\n response = \"\"\n async for event in message:\n if event.data.choices[0].finish_reason == \"stop\":\n print(\"\\n\")\n print(response)\n print(\"\\n\")\n else:\n response += event.text\n\n\nawait main()\n```\n
\n\n\n\n### CamelAI ๏นจ\n\nTrack agents built with the CamelAI Python SDK (>=0.32.0).\n\n- [CamelAI integration guide](https://docs.camel-ai.org/cookbooks/agents_tracking.html#)\n- [Official CamelAI documentation](https://docs.camel-ai.org/index.html)\n\n
\n Installation\n \n```bash\npip - install camel-ai[all]\npip install agentops\n```\n\n```python python\n#Import Dependencies\nimport agentops\nimport os\nfrom getpass import getpass\nfrom dotenv import load_dotenv\n\n#Set Keys\nload_dotenv()\nopenai_api_key = os.getenv(\"OPENAI_API_KEY\") or \"\"\nagentops_api_key = os.getenv(\"AGENTOPS_API_KEY\") or \"\"\n\n\n\n```\n
\n\n[You can find usage examples here!](examples/camelai_examples/README.md).\n\n\n\n### LiteLLM ๐Ÿš…\n\nAgentOps provides support for LiteLLM(>=1.3.1), allowing you to call 100+ LLMs using the same Input/Output Format. \n\n- [AgentOps integration example](https://docs.agentops.ai/v1/integrations/litellm)\n- [Official LiteLLM documentation](https://docs.litellm.ai/docs/providers)\n\n
\n Installation\n \n```bash\npip install litellm\n```\n\n```python python\n# Do not use LiteLLM like this\n# from litellm import completion\n# ...\n# response = completion(model=\"claude-3\", - messages=messages)\n\n# Use LiteLLM like this\nimport litellm\n...\nresponse = litellm.completion(model=\"claude-3\", messages=messages)\n# or\nresponse = await litellm.acompletion(model=\"claude-3\", messages=messages)\n```\n
\n\n### LlamaIndex ๐Ÿฆ™\n\n\nAgentOps works seamlessly with applications built using LlamaIndex, a framework for building context-augmented generative AI applications with LLMs.\n\n
\n Installation\n \n```shell\npip install llama-index-instrumentation-agentops\n```\n\nTo use the handler, import and set\n\n```python\nfrom llama_index.core import set_global_handler\n\n# NOTE: Feel free to set your AgentOps environment variables (e.g., ''AGENTOPS_API_KEY'')\n# as outlined in the AgentOps documentation, or pass the equivalent keyword arguments\n# anticipated by AgentOps'' AOClient as **eval_params in set_global_handler.\n\nset_global_handler(\"agentops\")\n```\n\nCheck out the [LlamaIndex docs](https://docs.llamaindex.ai/en/stable/module_guides/observability/?h=agentops#agentops) - for more details.\n\n
\n\n### Llama Stack ๐Ÿฆ™๐Ÿฅž\n\nAgentOps provides support for Llama Stack Python Client(>=0.0.53), allowing you to monitor your Agentic applications. \n\n- [AgentOps integration example 1](https://github.com/AgentOps-AI/agentops/pull/530/files/65a5ab4fdcf310326f191d4b870d4f553591e3ea#diff-fdddf65549f3714f8f007ce7dfd1cde720329fe54155d54389dd50fbd81813cb)\n- [AgentOps integration example 2](https://github.com/AgentOps-AI/agentops/pull/530/files/65a5ab4fdcf310326f191d4b870d4f553591e3ea#diff-6688ff4fb7ab1ce7b1cc9b8362ca27264a3060c16737fb1d850305787a6e3699)\n- [Official Llama Stack Python Client](https://github.com/meta-llama/llama-stack-client-python)\n\n### SwarmZero AI ๐Ÿ\n\nTrack and analyze SwarmZero agents with full observability. Set an `AGENTOPS_API_KEY` in your environment and initialize AgentOps to get started.\n\n- [SwarmZero](https://swarmzero.ai) - Advanced multi-agent framework\n- [AgentOps integration example](https://docs.agentops.ai/v1/integrations/swarmzero)\n- - [SwarmZero AI integration example](https://docs.swarmzero.ai/examples/ai-agents/build-and-monitor-a-web-search-agent)\n- [SwarmZero AI - AgentOps documentation](https://docs.swarmzero.ai/sdk/observability/agentops)\n- [Official SwarmZero Python SDK](https://github.com/swarmzero/swarmzero)\n\n
\n Installation\n\n```bash\npip install swarmzero\npip install agentops\n```\n\n```python\nfrom dotenv import load_dotenv\nload_dotenv()\n\nimport agentops\nagentops.init()\n\nfrom swarmzero import Agent, Swarm\n# ...\n```\n
\n\n## Time travel debugging ๐Ÿ”ฎ\n\n
\n \"Time\n
\n\n
\n\n[Try it out!](https://app.agentops.ai/timetravel)\n\n## Agent Arena ๐ŸฅŠ\n\n(coming soon!)\n\n## Evaluations Roadmap ๐Ÿงญ\n\n| Platform | Dashboard | - Evals |\n| ---------------------------------------------------------------------------- | ------------------------------------------ | -------------------------------------- |\n| โœ… Python SDK | โœ… Multi-session and Cross-session metrics | โœ… Custom eval metrics |\n| ๐Ÿšง Evaluation builder API | โœ… Custom event tag trackingย  | ๐Ÿ”œ Agent scorecards |\n| โœ… [Javascript/Typescript SDK](https://github.com/AgentOps-AI/agentops-node) | โœ… Session replays | ๐Ÿ”œ Evaluation playground + leaderboard |\n\n## Debugging Roadmap ๐Ÿงญ\n\n| Performance testing | Environments | LLM Testing | Reasoning and execution testing |\n| ----------------------------------------- - | ----------------------------------------------------------------------------------- | ------------------------------------------- | ------------------------------------------------- |\n| โœ… Event latency analysis | ๐Ÿ”œ Non-stationary environment testing | ๐Ÿ”œ LLM non-deterministic function detection | ๐Ÿšง Infinite loops and recursive thought detection |\n| โœ… Agent workflow execution pricing | ๐Ÿ”œ Multi-modal environments | ๐Ÿšง Token limit overflow flags | ๐Ÿ”œ Faulty reasoning detection |\n| ๐Ÿšง Success validators (external) | ๐Ÿ”œ Execution containers | ๐Ÿ”œ Context limit overflow flags | ๐Ÿ”œ Generative code validators |\n| ๐Ÿ”œ Agent controllers/skill tests | โœ… Honeypot and prompt injection detection ([PromptArmor](https://promptarmor.com)) - | ๐Ÿ”œ API bill tracking | ๐Ÿ”œ Error breakpoint analysis |\n| ๐Ÿ”œ Information context constraint testing | ๐Ÿ”œ Anti-agent roadblocks (i.e. Captchas) | ๐Ÿ”œ CI/CD integration checks | |\n| ๐Ÿ”œ Regression testing | ๐Ÿ”œ Multi-agent framework visualization | | |\n\n### Why AgentOps? ๐Ÿค”\n\nWithout the right tools, AI agents are slow, expensive, and unreliable. Our mission is to bring your agent from prototype to production. Here''s why AgentOps stands out:\n\n- **Comprehensive Observability**: Track your AI agents'' performance, user interactions, and API usage.\n- **Real-Time Monitoring**: Get instant insights with session replays, metrics, and live monitoring tools.\n- **Cost Control**: Monitor - and manage your spend on LLM and API calls.\n- **Failure Detection**: Quickly identify and respond to agent failures and multi-agent interaction issues.\n- **Tool Usage Statistics**: Understand how your agents utilize external tools with detailed analytics.\n- **Session-Wide Metrics**: Gain a holistic view of your agents'' sessions with comprehensive statistics.\n\nAgentOps is designed to make agent observability, testing, and monitoring easy.\n\n\n## Star History\n\nCheck out our growth in the community:\n\n\"Logo\"\n\n## Popular projects using AgentOps\n\n\n| Repository | Stars |\n| :-------- | -----: |\n|\"\"   [geekan](https://github.com/geekan) / [MetaGPT](https://github.com/geekan/MetaGPT) | 42787 |\n|\"\"   [run-llama](https://github.com/run-llama) / [llama_index](https://github.com/run-llama/llama_index) | 34446 |\n|\"\"   [crewAIInc](https://github.com/crewAIInc) / [crewAI](https://github.com/crewAIInc/crewAI) | 18287 |\n|\"\"   [camel-ai](https://github.com/camel-ai) / [camel](https://github.com/camel-ai/camel) | 5166 |\n|\"\"   [superagent-ai](https://github.com/superagent-ai) / [superagent](https://github.com/superagent-ai/superagent) | 5050 |\n|\"\"   [iyaja](https://github.com/iyaja) / [llama-fs](https://github.com/iyaja/llama-fs) | 4713 |\n|\"\"   [BasedHardware](https://github.com/BasedHardware) / [Omi](https://github.com/BasedHardware/Omi) | 2723 |\n|\"\"   [MervinPraison](https://github.com/MervinPraison) / [PraisonAI](https://github.com/MervinPraison/PraisonAI) | 2007 |\n|\"\"   [AgentOps-AI](https://github.com/AgentOps-AI) / [Jaiqu](https://github.com/AgentOps-AI/Jaiqu) | 272 |\n|\"\"   [swarmzero](https://github.com/swarmzero) / [swarmzero](https://github.com/swarmzero/swarmzero) | 195 |\n|\"\"   [strnad](https://github.com/strnad) / [CrewAI-Studio](https://github.com/strnad/CrewAI-Studio) | 134 |\n|\"\"   [alejandro-ao](https://github.com/alejandro-ao) / [exa-crewai](https://github.com/alejandro-ao/exa-crewai) | 55 |\n|\"\"   [tonykipkemboi](https://github.com/tonykipkemboi) / [youtube_yapper_trapper](https://github.com/tonykipkemboi/youtube_yapper_trapper) | 47 |\n|\"\"   [sethcoast](https://github.com/sethcoast) / [cover-letter-builder](https://github.com/sethcoast/cover-letter-builder) | 27 |\n|\"\"   [bhancockio](https://github.com/bhancockio) / [chatgpt4o-analysis](https://github.com/bhancockio/chatgpt4o-analysis) | 19 |\n|\"\"   [breakstring](https://github.com/breakstring) / [Agentic_Story_Book_Workflow](https://github.com/breakstring/Agentic_Story_Book_Workflow) | 14 |\n|\"\"   [MULTI-ON](https://github.com/MULTI-ON) / [multion-python](https://github.com/MULTI-ON/multion-python) | 13 |\n\n\n_Generated 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You have + a lot of experience with cat.\nYour personal goal is: Express hot takes on cat."},{"role":"user","content":"\nCurrent + Task: Give me an analysis around cat.\n\nThis is the expected criteria for your + final answer: 1 bullet point about cat that''s under 15 words.\nyou MUST return + the actual complete content as the final answer, not a summary.\n\nProvide your + complete response:"}],"model":"gpt-4o"}' headers: - Accept: - - '*/*' - Accept-Encoding: - - gzip, deflate, zstd - Connection: - - keep-alive User-Agent: - - python-requests/2.32.2 - method: GET - uri: https://pypi.org/pypi/agentops/json - response: - body: - string: '{"info":{"author":null,"author_email":"Alex Reibman , Shawn Qiu , Braelyn Boynton , Howard Gil , Constantin Teodorescu , Pratyush Shukla ","bugtrack_url":null,"classifiers":["License :: OSI Approved :: MIT License","Operating System :: OS Independent","Programming Language :: Python :: 3","Programming Language :: Python :: 3.10","Programming Language :: Python :: 3.11","Programming Language :: Python :: 3.12","Programming Language :: Python :: 3.13","Programming Language :: Python :: 3.9"],"description":"
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\n Observability and DevTool platform for AI Agents\n
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\n \n \"Twitter\"\n \n \n \"Discord\"\n \n \n \"Dashboard\"\n \n \n \"Documentation\"\n \n \n \"Chat\n \n

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\n\n\nAgentOps helps developers build, evaluate, and monitor AI agents. From prototype to production.\n\n| | |\n| ------------------------------------- | ------------------------------------------------------------- |\n| ๐Ÿ“Š **Replay Analytics and Debugging** | Step-by-step - agent execution graphs |\n| ๐Ÿ’ธ **LLM Cost Management** | Track spend with LLM foundation model providers |\n| ๐Ÿงช **Agent Benchmarking** | Test your agents against 1,000+ evals |\n| ๐Ÿ” **Compliance and Security** | Detect common prompt injection and data exfiltration exploits |\n| ๐Ÿค **Framework Integrations** | Native Integrations with CrewAI, AG2(AutoGen), Camel AI, & LangChain |\n\n## Quick Start โŒจ๏ธ\n\n```bash\npip install agentops\n```\n\n\n#### Session replays in 2 lines of code\n\nInitialize the AgentOps client and automatically get analytics on all your LLM calls.\n\n[Get an API key](https://app.agentops.ai/settings/projects)\n\n```python\nimport agentops\n\n# Beginning of your program (i.e. main.py, __init__.py)\nagentops.init( < INSERT YOUR API KEY HERE >)\n\n...\n\n# End of program\nagentops.end_session(''Success'')\n```\n\nAll your sessions can be viewed on the [AgentOps - dashboard](https://app.agentops.ai?ref=gh)\n
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\n Agent Debugging\n \n \"Agent\n \n \n \"Chat\n \n \n \"Event\n \n
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\n Session Replays\n \n \"Session\n \n
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\n Summary Analytics\n \n \"Summary\n \n \n \"Summary\n \n
\n\n\n### First class Developer Experience\nAdd powerful observability to your agents, tools, and functions with as little code as possible: one line at a time.\n
\nRefer to our [documentation](http://docs.agentops.ai)\n\n```python\n# Automatically associate all Events with the agent that originated them\nfrom agentops import track_agent\n\n@track_agent(name=''SomeCustomName'')\nclass MyAgent:\n ...\n```\n\n```python\n# Automatically create ToolEvents for tools that agents will use\nfrom agentops import record_tool\n\n@record_tool(''SampleToolName'')\ndef sample_tool(...):\n ...\n```\n\n```python\n# Automatically create ActionEvents for other functions.\nfrom agentops - import record_action\n\n@agentops.record_action(''sample function being record'')\ndef sample_function(...):\n ...\n```\n\n```python\n# Manually record any other Events\nfrom agentops import record, ActionEvent\n\nrecord(ActionEvent(\"received_user_input\"))\n```\n\n## Integrations ๐Ÿฆพ\n\n### CrewAI ๐Ÿ›ถ\n\nBuild Crew agents with observability with only 2 lines of code. Simply set an `AGENTOPS_API_KEY` in your environment, and your crews will get automatic monitoring on the AgentOps dashboard.\n\n```bash\npip install ''crewai[agentops]''\n```\n\n- [AgentOps integration example](https://docs.agentops.ai/v1/integrations/crewai)\n- [Official CrewAI documentation](https://docs.crewai.com/how-to/AgentOps-Observability)\n\n### AG2 ๐Ÿค–\nWith only two lines of code, add full observability and monitoring to AG2 (formerly AutoGen) agents. Set an `AGENTOPS_API_KEY` in your environment and call `agentops.init()`\n\n- [AG2 Observability Example](https://docs.ag2.ai/notebooks/agentchat_agentops)\n- - [AG2 - AgentOps Documentation](https://docs.ag2.ai/docs/ecosystem/agentops)\n\n### Camel AI ๐Ÿช\n\nTrack and analyze CAMEL agents with full observability. Set an `AGENTOPS_API_KEY` in your environment and initialize AgentOps to get started.\n\n- [Camel AI](https://www.camel-ai.org/) - Advanced agent communication framework\n- [AgentOps integration example](https://docs.agentops.ai/v1/integrations/camel)\n- [Official Camel AI documentation](https://docs.camel-ai.org/cookbooks/agents_tracking.html)\n\n
\n Installation\n\n```bash\npip install \"camel-ai[all]==0.2.11\"\npip install agentops\n```\n\n```python\nimport os\nimport agentops\nfrom camel.agents import ChatAgent\nfrom camel.messages import BaseMessage\nfrom camel.models import ModelFactory\nfrom camel.types import ModelPlatformType, ModelType\n\n# Initialize AgentOps\nagentops.init(os.getenv(\"AGENTOPS_API_KEY\"), default_tags=[\"CAMEL Example\"])\n\n# Import toolkits after AgentOps init for tracking\nfrom - camel.toolkits import SearchToolkit\n\n# Set up the agent with search tools\nsys_msg = BaseMessage.make_assistant_message(\n role_name=''Tools calling operator'',\n content=''You are a helpful assistant''\n)\n\n# Configure tools and model\ntools = [*SearchToolkit().get_tools()]\nmodel = ModelFactory.create(\n model_platform=ModelPlatformType.OPENAI,\n model_type=ModelType.GPT_4O_MINI,\n)\n\n# Create and run the agent\ncamel_agent = ChatAgent(\n system_message=sys_msg,\n model=model,\n tools=tools,\n)\n\nresponse = camel_agent.step(\"What is AgentOps?\")\nprint(response)\n\nagentops.end_session(\"Success\")\n```\n\nCheck out our [Camel integration guide](https://docs.agentops.ai/v1/integrations/camel) for more examples including multi-agent scenarios.\n
\n\n### Langchain ๐Ÿฆœ๐Ÿ”—\n\nAgentOps works seamlessly with applications built using Langchain. To use the handler, install Langchain as an optional dependency:\n\n
\n Installation\n \n```shell\npip - install agentops[langchain]\n```\n\nTo use the handler, import and set\n\n```python\nimport os\nfrom langchain.chat_models import ChatOpenAI\nfrom langchain.agents import initialize_agent, AgentType\nfrom agentops.partners.langchain_callback_handler import LangchainCallbackHandler\n\nAGENTOPS_API_KEY = os.environ[''AGENTOPS_API_KEY'']\nhandler = LangchainCallbackHandler(api_key=AGENTOPS_API_KEY, tags=[''Langchain Example''])\n\nllm = ChatOpenAI(openai_api_key=OPENAI_API_KEY,\n callbacks=[handler],\n model=''gpt-3.5-turbo'')\n\nagent = initialize_agent(tools,\n llm,\n agent=AgentType.CHAT_ZERO_SHOT_REACT_DESCRIPTION,\n verbose=True,\n callbacks=[handler], # You must pass in a callback handler to record your agent\n handle_parsing_errors=True)\n```\n\nCheck out the [Langchain Examples Notebook](./examples/langchain_examples.ipynb) for - more details including Async handlers.\n\n
\n\n### Cohere โŒจ๏ธ\n\nFirst class support for Cohere(>=5.4.0). This is a living integration, should you need any added functionality please message us on Discord!\n\n- [AgentOps integration example](https://docs.agentops.ai/v1/integrations/cohere)\n- [Official Cohere documentation](https://docs.cohere.com/reference/about)\n\n
\n Installation\n \n```bash\npip install cohere\n```\n\n```python python\nimport cohere\nimport agentops\n\n# Beginning of program''s code (i.e. main.py, __init__.py)\nagentops.init()\nco = cohere.Client()\n\nchat = co.chat(\n message=\"Is it pronounced ceaux-hear or co-hehray?\"\n)\n\nprint(chat)\n\nagentops.end_session(''Success'')\n```\n\n```python python\nimport cohere\nimport agentops\n\n# Beginning of program''s code (i.e. main.py, __init__.py)\nagentops.init()\n\nco = cohere.Client()\n\nstream = co.chat_stream(\n message=\"Write - me a haiku about the synergies between Cohere and AgentOps\"\n)\n\nfor event in stream:\n if event.event_type == \"text-generation\":\n print(event.text, end='''')\n\nagentops.end_session(''Success'')\n```\n
\n\n\n### Anthropic ๏นจ\n\nTrack agents built with the Anthropic Python SDK (>=0.32.0).\n\n- [AgentOps integration guide](https://docs.agentops.ai/v1/integrations/anthropic)\n- [Official Anthropic documentation](https://docs.anthropic.com/en/docs/welcome)\n\n
\n Installation\n \n```bash\npip install anthropic\n```\n\n```python python\nimport anthropic\nimport agentops\n\n# Beginning of program''s code (i.e. main.py, __init__.py)\nagentops.init()\n\nclient = anthropic.Anthropic(\n # This is the default and can be omitted\n api_key=os.environ.get(\"ANTHROPIC_API_KEY\"),\n)\n\nmessage = client.messages.create(\n max_tokens=1024,\n messages=[\n {\n \"role\": \"user\",\n \"content\": - \"Tell me a cool fact about AgentOps\",\n }\n ],\n model=\"claude-3-opus-20240229\",\n )\nprint(message.content)\n\nagentops.end_session(''Success'')\n```\n\nStreaming\n```python python\nimport anthropic\nimport agentops\n\n# Beginning of program''s code (i.e. main.py, __init__.py)\nagentops.init()\n\nclient = anthropic.Anthropic(\n # This is the default and can be omitted\n api_key=os.environ.get(\"ANTHROPIC_API_KEY\"),\n)\n\nstream = client.messages.create(\n max_tokens=1024,\n model=\"claude-3-opus-20240229\",\n messages=[\n {\n \"role\": \"user\",\n \"content\": \"Tell me something cool about streaming agents\",\n }\n ],\n stream=True,\n)\n\nresponse = \"\"\nfor event in stream:\n if event.type == \"content_block_delta\":\n response += event.delta.text\n elif event.type == \"message_stop\":\n print(\"\\n\")\n print(response)\n print(\"\\n\")\n```\n\nAsync\n\n```python - python\nimport asyncio\nfrom anthropic import AsyncAnthropic\n\nclient = AsyncAnthropic(\n # This is the default and can be omitted\n api_key=os.environ.get(\"ANTHROPIC_API_KEY\"),\n)\n\n\nasync def main() -> None:\n message = await client.messages.create(\n max_tokens=1024,\n messages=[\n {\n \"role\": \"user\",\n \"content\": \"Tell me something interesting about async agents\",\n }\n ],\n model=\"claude-3-opus-20240229\",\n )\n print(message.content)\n\n\nawait main()\n```\n
\n\n### Mistral ใ€ฝ๏ธ\n\nTrack agents built with the Anthropic Python SDK (>=0.32.0).\n\n- [AgentOps integration example](./examples/mistral//mistral_example.ipynb)\n- [Official Mistral documentation](https://docs.mistral.ai)\n\n
\n Installation\n \n```bash\npip install mistralai\n```\n\nSync\n\n```python python\nfrom mistralai import Mistral\nimport agentops\n\n# Beginning of program''s - code (i.e. main.py, __init__.py)\nagentops.init()\n\nclient = Mistral(\n # This is the default and can be omitted\n api_key=os.environ.get(\"MISTRAL_API_KEY\"),\n)\n\nmessage = client.chat.complete(\n messages=[\n {\n \"role\": \"user\",\n \"content\": \"Tell me a cool fact about AgentOps\",\n }\n ],\n model=\"open-mistral-nemo\",\n )\nprint(message.choices[0].message.content)\n\nagentops.end_session(''Success'')\n```\n\nStreaming\n\n```python python\nfrom mistralai import Mistral\nimport agentops\n\n# Beginning of program''s code (i.e. main.py, __init__.py)\nagentops.init()\n\nclient = Mistral(\n # This is the default and can be omitted\n api_key=os.environ.get(\"MISTRAL_API_KEY\"),\n)\n\nmessage = client.chat.stream(\n messages=[\n {\n \"role\": \"user\",\n \"content\": \"Tell me something cool - about streaming agents\",\n }\n ],\n model=\"open-mistral-nemo\",\n )\n\nresponse = \"\"\nfor event in message:\n if event.data.choices[0].finish_reason == \"stop\":\n print(\"\\n\")\n print(response)\n print(\"\\n\")\n else:\n response += event.text\n\nagentops.end_session(''Success'')\n```\n\nAsync\n\n```python python\nimport asyncio\nfrom mistralai import Mistral\n\nclient = Mistral(\n # This is the default and can be omitted\n api_key=os.environ.get(\"MISTRAL_API_KEY\"),\n)\n\n\nasync def main() -> None:\n message = await client.chat.complete_async(\n messages=[\n {\n \"role\": \"user\",\n \"content\": \"Tell me something interesting about async agents\",\n }\n ],\n model=\"open-mistral-nemo\",\n )\n print(message.choices[0].message.content)\n\n\nawait main()\n```\n\nAsync Streaming\n\n```python python\nimport asyncio\nfrom mistralai - import Mistral\n\nclient = Mistral(\n # This is the default and can be omitted\n api_key=os.environ.get(\"MISTRAL_API_KEY\"),\n)\n\n\nasync def main() -> None:\n message = await client.chat.stream_async(\n messages=[\n {\n \"role\": \"user\",\n \"content\": \"Tell me something interesting about async streaming agents\",\n }\n ],\n model=\"open-mistral-nemo\",\n )\n\n response = \"\"\n async for event in message:\n if event.data.choices[0].finish_reason == \"stop\":\n print(\"\\n\")\n print(response)\n print(\"\\n\")\n else:\n response += event.text\n\n\nawait main()\n```\n
\n\n\n\n### CamelAI ๏นจ\n\nTrack agents built with the CamelAI Python SDK (>=0.32.0).\n\n- [CamelAI integration guide](https://docs.camel-ai.org/cookbooks/agents_tracking.html#)\n- [Official CamelAI documentation](https://docs.camel-ai.org/index.html)\n\n
\n Installation\n \n```bash\npip - install camel-ai[all]\npip install agentops\n```\n\n```python python\n#Import Dependencies\nimport agentops\nimport os\nfrom getpass import getpass\nfrom dotenv import load_dotenv\n\n#Set Keys\nload_dotenv()\nopenai_api_key = os.getenv(\"OPENAI_API_KEY\") or \"\"\nagentops_api_key = os.getenv(\"AGENTOPS_API_KEY\") or \"\"\n\n\n\n```\n
\n\n[You can find usage examples here!](examples/camelai_examples/README.md).\n\n\n\n### LiteLLM ๐Ÿš…\n\nAgentOps provides support for LiteLLM(>=1.3.1), allowing you to call 100+ LLMs using the same Input/Output Format. \n\n- [AgentOps integration example](https://docs.agentops.ai/v1/integrations/litellm)\n- [Official LiteLLM documentation](https://docs.litellm.ai/docs/providers)\n\n
\n Installation\n \n```bash\npip install litellm\n```\n\n```python python\n# Do not use LiteLLM like this\n# from litellm import completion\n# ...\n# response = completion(model=\"claude-3\", - messages=messages)\n\n# Use LiteLLM like this\nimport litellm\n...\nresponse = litellm.completion(model=\"claude-3\", messages=messages)\n# or\nresponse = await litellm.acompletion(model=\"claude-3\", messages=messages)\n```\n
\n\n### LlamaIndex ๐Ÿฆ™\n\n\nAgentOps works seamlessly with applications built using LlamaIndex, a framework for building context-augmented generative AI applications with LLMs.\n\n
\n Installation\n \n```shell\npip install llama-index-instrumentation-agentops\n```\n\nTo use the handler, import and set\n\n```python\nfrom llama_index.core import set_global_handler\n\n# NOTE: Feel free to set your AgentOps environment variables (e.g., ''AGENTOPS_API_KEY'')\n# as outlined in the AgentOps documentation, or pass the equivalent keyword arguments\n# anticipated by AgentOps'' AOClient as **eval_params in set_global_handler.\n\nset_global_handler(\"agentops\")\n```\n\nCheck out the [LlamaIndex docs](https://docs.llamaindex.ai/en/stable/module_guides/observability/?h=agentops#agentops) - for more details.\n\n
\n\n### Llama Stack ๐Ÿฆ™๐Ÿฅž\n\nAgentOps provides support for Llama Stack Python Client(>=0.0.53), allowing you to monitor your Agentic applications. \n\n- [AgentOps integration example 1](https://github.com/AgentOps-AI/agentops/pull/530/files/65a5ab4fdcf310326f191d4b870d4f553591e3ea#diff-fdddf65549f3714f8f007ce7dfd1cde720329fe54155d54389dd50fbd81813cb)\n- [AgentOps integration example 2](https://github.com/AgentOps-AI/agentops/pull/530/files/65a5ab4fdcf310326f191d4b870d4f553591e3ea#diff-6688ff4fb7ab1ce7b1cc9b8362ca27264a3060c16737fb1d850305787a6e3699)\n- [Official Llama Stack Python Client](https://github.com/meta-llama/llama-stack-client-python)\n\n### SwarmZero AI ๐Ÿ\n\nTrack and analyze SwarmZero agents with full observability. Set an `AGENTOPS_API_KEY` in your environment and initialize AgentOps to get started.\n\n- [SwarmZero](https://swarmzero.ai) - Advanced multi-agent framework\n- [AgentOps integration example](https://docs.agentops.ai/v1/integrations/swarmzero)\n- - [SwarmZero AI integration example](https://docs.swarmzero.ai/examples/ai-agents/build-and-monitor-a-web-search-agent)\n- [SwarmZero AI - AgentOps documentation](https://docs.swarmzero.ai/sdk/observability/agentops)\n- [Official SwarmZero Python SDK](https://github.com/swarmzero/swarmzero)\n\n
\n Installation\n\n```bash\npip install swarmzero\npip install agentops\n```\n\n```python\nfrom dotenv import load_dotenv\nload_dotenv()\n\nimport agentops\nagentops.init()\n\nfrom swarmzero import Agent, Swarm\n# ...\n```\n
\n\n## Time travel debugging ๐Ÿ”ฎ\n\n
\n \"Time\n
\n\n
\n\n[Try it out!](https://app.agentops.ai/timetravel)\n\n## Agent Arena ๐ŸฅŠ\n\n(coming soon!)\n\n## Evaluations Roadmap ๐Ÿงญ\n\n| Platform | Dashboard | - Evals |\n| ---------------------------------------------------------------------------- | ------------------------------------------ | -------------------------------------- |\n| โœ… Python SDK | โœ… Multi-session and Cross-session metrics | โœ… Custom eval metrics |\n| ๐Ÿšง Evaluation builder API | โœ… Custom event tag trackingย  | ๐Ÿ”œ Agent scorecards |\n| โœ… [Javascript/Typescript SDK](https://github.com/AgentOps-AI/agentops-node) | โœ… Session replays | ๐Ÿ”œ Evaluation playground + leaderboard |\n\n## Debugging Roadmap ๐Ÿงญ\n\n| Performance testing | Environments | LLM Testing | Reasoning and execution testing |\n| ----------------------------------------- - | ----------------------------------------------------------------------------------- | ------------------------------------------- | ------------------------------------------------- |\n| โœ… Event latency analysis | ๐Ÿ”œ Non-stationary environment testing | ๐Ÿ”œ LLM non-deterministic function detection | ๐Ÿšง Infinite loops and recursive thought detection |\n| โœ… Agent workflow execution pricing | ๐Ÿ”œ Multi-modal environments | ๐Ÿšง Token limit overflow flags | ๐Ÿ”œ Faulty reasoning detection |\n| ๐Ÿšง Success validators (external) | ๐Ÿ”œ Execution containers | ๐Ÿ”œ Context limit overflow flags | ๐Ÿ”œ Generative code validators |\n| ๐Ÿ”œ Agent controllers/skill tests | โœ… Honeypot and prompt injection detection ([PromptArmor](https://promptarmor.com)) - | ๐Ÿ”œ API bill tracking | ๐Ÿ”œ Error breakpoint analysis |\n| ๐Ÿ”œ Information context constraint testing | ๐Ÿ”œ Anti-agent roadblocks (i.e. Captchas) | ๐Ÿ”œ CI/CD integration checks | |\n| ๐Ÿ”œ Regression testing | ๐Ÿ”œ Multi-agent framework visualization | | |\n\n### Why AgentOps? ๐Ÿค”\n\nWithout the right tools, AI agents are slow, expensive, and unreliable. Our mission is to bring your agent from prototype to production. Here''s why AgentOps stands out:\n\n- **Comprehensive Observability**: Track your AI agents'' performance, user interactions, and API usage.\n- **Real-Time Monitoring**: Get instant insights with session replays, metrics, and live monitoring tools.\n- **Cost Control**: Monitor - and manage your spend on LLM and API calls.\n- **Failure Detection**: Quickly identify and respond to agent failures and multi-agent interaction issues.\n- **Tool Usage Statistics**: Understand how your agents utilize external tools with detailed analytics.\n- **Session-Wide Metrics**: Gain a holistic view of your agents'' sessions with comprehensive statistics.\n\nAgentOps is designed to make agent observability, testing, and monitoring easy.\n\n\n## Star History\n\nCheck out our growth in the community:\n\n\"Logo\"\n\n## Popular projects using AgentOps\n\n\n| Repository | Stars |\n| :-------- | -----: |\n|\"\"   [geekan](https://github.com/geekan) / [MetaGPT](https://github.com/geekan/MetaGPT) | 42787 |\n|\"\"   [run-llama](https://github.com/run-llama) / [llama_index](https://github.com/run-llama/llama_index) | 34446 |\n|\"\"   [crewAIInc](https://github.com/crewAIInc) / [crewAI](https://github.com/crewAIInc/crewAI) | 18287 |\n|\"\"   [camel-ai](https://github.com/camel-ai) / [camel](https://github.com/camel-ai/camel) | 5166 |\n|\"\"   [superagent-ai](https://github.com/superagent-ai) / [superagent](https://github.com/superagent-ai/superagent) | 5050 |\n|\"\"   [iyaja](https://github.com/iyaja) / [llama-fs](https://github.com/iyaja/llama-fs) | 4713 |\n|\"\"   [BasedHardware](https://github.com/BasedHardware) / [Omi](https://github.com/BasedHardware/Omi) | 2723 |\n|\"\"   [MervinPraison](https://github.com/MervinPraison) / [PraisonAI](https://github.com/MervinPraison/PraisonAI) | 2007 |\n|\"\"   [AgentOps-AI](https://github.com/AgentOps-AI) / [Jaiqu](https://github.com/AgentOps-AI/Jaiqu) | 272 |\n|\"\"   [swarmzero](https://github.com/swarmzero) / [swarmzero](https://github.com/swarmzero/swarmzero) | 195 |\n|\"\"   [strnad](https://github.com/strnad) / [CrewAI-Studio](https://github.com/strnad/CrewAI-Studio) | 134 |\n|\"\"   [alejandro-ao](https://github.com/alejandro-ao) / [exa-crewai](https://github.com/alejandro-ao/exa-crewai) | 55 |\n|\"\"   [tonykipkemboi](https://github.com/tonykipkemboi) / [youtube_yapper_trapper](https://github.com/tonykipkemboi/youtube_yapper_trapper) | 47 |\n|\"\"   [sethcoast](https://github.com/sethcoast) / [cover-letter-builder](https://github.com/sethcoast/cover-letter-builder) | 27 |\n|\"\"   [bhancockio](https://github.com/bhancockio) / [chatgpt4o-analysis](https://github.com/bhancockio/chatgpt4o-analysis) | 19 |\n|\"\"   [breakstring](https://github.com/breakstring) / [Agentic_Story_Book_Workflow](https://github.com/breakstring/Agentic_Story_Book_Workflow) | 14 |\n|\"\"   [MULTI-ON](https://github.com/MULTI-ON) / [multion-python](https://github.com/MULTI-ON/multion-python) | 13 |\n\n\n_Generated using [github-dependents-info](https://github.com/nvuillam/github-dependents-info), - by [Nicolas Vuillamy](https://github.com/nvuillam)_\n","description_content_type":"text/markdown","docs_url":null,"download_url":null,"downloads":{"last_day":-1,"last_month":-1,"last_week":-1},"dynamic":null,"home_page":null,"keywords":null,"license":null,"license_expression":null,"license_files":["LICENSE"],"maintainer":null,"maintainer_email":null,"name":"agentops","package_url":"https://pypi.org/project/agentops/","platform":null,"project_url":"https://pypi.org/project/agentops/","project_urls":{"Homepage":"https://github.com/AgentOps-AI/agentops","Issues":"https://github.com/AgentOps-AI/agentops/issues"},"provides_extra":null,"release_url":"https://pypi.org/project/agentops/0.3.26/","requires_dist":["opentelemetry-api==1.22.0; python_version < \"3.10\"","opentelemetry-api>=1.27.0; python_version >= \"3.10\"","opentelemetry-exporter-otlp-proto-http==1.22.0; python_version < 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\n Observability and DevTool platform for AI Agents\n
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\n\n\nAgentOps helps developers build, evaluate, and monitor AI agents. From prototype to production.\n\n| | |\n| ------------------------------------- | ------------------------------------------------------------- |\n| ๐Ÿ“Š **Replay Analytics and Debugging** | Step-by-step - agent execution graphs |\n| ๐Ÿ’ธ **LLM Cost Management** | Track spend with LLM foundation model providers |\n| ๐Ÿงช **Agent Benchmarking** | Test your agents against 1,000+ evals |\n| ๐Ÿ” **Compliance and Security** | Detect common prompt injection and data exfiltration exploits |\n| ๐Ÿค **Framework Integrations** | Native Integrations with CrewAI, AG2(AutoGen), Camel AI, & LangChain |\n\n## Quick Start โŒจ๏ธ\n\n```bash\npip install agentops\n```\n\n\n#### Session replays in 2 lines of code\n\nInitialize the AgentOps client and automatically get analytics on all your LLM calls.\n\n[Get an API key](https://app.agentops.ai/settings/projects)\n\n```python\nimport agentops\n\n# Beginning of your program (i.e. main.py, __init__.py)\nagentops.init( < INSERT YOUR API KEY HERE >)\n\n...\n\n# End of program\nagentops.end_session(''Success'')\n```\n\nAll your sessions can be viewed on the [AgentOps - dashboard](https://app.agentops.ai?ref=gh)\n
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\n Agent Debugging\n \n \"Agent\n \n \n \"Chat\n \n \n \"Event\n \n
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\n\n\n### First class Developer Experience\nAdd powerful observability to your agents, tools, and functions with as little code as possible: one line at a time.\n
\nRefer to our [documentation](http://docs.agentops.ai)\n\n```python\n# Automatically associate all Events with the agent that originated them\nfrom agentops import track_agent\n\n@track_agent(name=''SomeCustomName'')\nclass MyAgent:\n ...\n```\n\n```python\n# Automatically create ToolEvents for tools that agents will use\nfrom agentops import record_tool\n\n@record_tool(''SampleToolName'')\ndef sample_tool(...):\n ...\n```\n\n```python\n# Automatically create ActionEvents for other functions.\nfrom agentops - import record_action\n\n@agentops.record_action(''sample function being record'')\ndef sample_function(...):\n ...\n```\n\n```python\n# Manually record any other Events\nfrom agentops import record, ActionEvent\n\nrecord(ActionEvent(\"received_user_input\"))\n```\n\n## Integrations ๐Ÿฆพ\n\n### CrewAI ๐Ÿ›ถ\n\nBuild Crew agents with observability with only 2 lines of code. Simply set an `AGENTOPS_API_KEY` in your environment, and your crews will get automatic monitoring on the AgentOps dashboard.\n\n```bash\npip install ''crewai[agentops]''\n```\n\n- [AgentOps integration example](https://docs.agentops.ai/v1/integrations/crewai)\n- [Official CrewAI documentation](https://docs.crewai.com/how-to/AgentOps-Observability)\n\n### AG2 ๐Ÿค–\nWith only two lines of code, add full observability and monitoring to AG2 (formerly AutoGen) agents. Set an `AGENTOPS_API_KEY` in your environment and call `agentops.init()`\n\n- [AG2 Observability Example](https://docs.ag2.ai/notebooks/agentchat_agentops)\n- - [AG2 - AgentOps Documentation](https://docs.ag2.ai/docs/ecosystem/agentops)\n\n### Camel AI ๐Ÿช\n\nTrack and analyze CAMEL agents with full observability. Set an `AGENTOPS_API_KEY` in your environment and initialize AgentOps to get started.\n\n- [Camel AI](https://www.camel-ai.org/) - Advanced agent communication framework\n- [AgentOps integration example](https://docs.agentops.ai/v1/integrations/camel)\n- [Official Camel AI documentation](https://docs.camel-ai.org/cookbooks/agents_tracking.html)\n\n
\n Installation\n\n```bash\npip install \"camel-ai[all]==0.2.11\"\npip install agentops\n```\n\n```python\nimport os\nimport agentops\nfrom camel.agents import ChatAgent\nfrom camel.messages import BaseMessage\nfrom camel.models import ModelFactory\nfrom camel.types import ModelPlatformType, ModelType\n\n# Initialize AgentOps\nagentops.init(os.getenv(\"AGENTOPS_API_KEY\"), default_tags=[\"CAMEL Example\"])\n\n# Import toolkits after AgentOps init for tracking\nfrom - camel.toolkits import SearchToolkit\n\n# Set up the agent with search tools\nsys_msg = BaseMessage.make_assistant_message(\n role_name=''Tools calling operator'',\n content=''You are a helpful assistant''\n)\n\n# Configure tools and model\ntools = [*SearchToolkit().get_tools()]\nmodel = ModelFactory.create(\n model_platform=ModelPlatformType.OPENAI,\n model_type=ModelType.GPT_4O_MINI,\n)\n\n# Create and run the agent\ncamel_agent = ChatAgent(\n system_message=sys_msg,\n model=model,\n tools=tools,\n)\n\nresponse = camel_agent.step(\"What is AgentOps?\")\nprint(response)\n\nagentops.end_session(\"Success\")\n```\n\nCheck out our [Camel integration guide](https://docs.agentops.ai/v1/integrations/camel) for more examples including multi-agent scenarios.\n
\n\n### Langchain ๐Ÿฆœ๐Ÿ”—\n\nAgentOps works seamlessly with applications built using Langchain. To use the handler, install Langchain as an optional dependency:\n\n
\n Installation\n \n```shell\npip - install agentops[langchain]\n```\n\nTo use the handler, import and set\n\n```python\nimport os\nfrom langchain.chat_models import ChatOpenAI\nfrom langchain.agents import initialize_agent, AgentType\nfrom agentops.partners.langchain_callback_handler import LangchainCallbackHandler\n\nAGENTOPS_API_KEY = os.environ[''AGENTOPS_API_KEY'']\nhandler = LangchainCallbackHandler(api_key=AGENTOPS_API_KEY, tags=[''Langchain Example''])\n\nllm = ChatOpenAI(openai_api_key=OPENAI_API_KEY,\n callbacks=[handler],\n model=''gpt-3.5-turbo'')\n\nagent = initialize_agent(tools,\n llm,\n agent=AgentType.CHAT_ZERO_SHOT_REACT_DESCRIPTION,\n verbose=True,\n callbacks=[handler], # You must pass in a callback handler to record your agent\n handle_parsing_errors=True)\n```\n\nCheck out the [Langchain Examples Notebook](./examples/langchain_examples.ipynb) for - more details including Async handlers.\n\n
\n\n### Cohere โŒจ๏ธ\n\nFirst class support for Cohere(>=5.4.0). This is a living integration, should you need any added functionality please message us on Discord!\n\n- [AgentOps integration example](https://docs.agentops.ai/v1/integrations/cohere)\n- [Official Cohere documentation](https://docs.cohere.com/reference/about)\n\n
\n Installation\n \n```bash\npip install cohere\n```\n\n```python python\nimport cohere\nimport agentops\n\n# Beginning of program''s code (i.e. main.py, __init__.py)\nagentops.init()\nco = cohere.Client()\n\nchat = co.chat(\n message=\"Is it pronounced ceaux-hear or co-hehray?\"\n)\n\nprint(chat)\n\nagentops.end_session(''Success'')\n```\n\n```python python\nimport cohere\nimport agentops\n\n# Beginning of program''s code (i.e. main.py, __init__.py)\nagentops.init()\n\nco = cohere.Client()\n\nstream = co.chat_stream(\n message=\"Write - me a haiku about the synergies between Cohere and AgentOps\"\n)\n\nfor event in stream:\n if event.event_type == \"text-generation\":\n print(event.text, end='''')\n\nagentops.end_session(''Success'')\n```\n
\n\n\n### Anthropic ๏นจ\n\nTrack agents built with the Anthropic Python SDK (>=0.32.0).\n\n- [AgentOps integration guide](https://docs.agentops.ai/v1/integrations/anthropic)\n- [Official Anthropic documentation](https://docs.anthropic.com/en/docs/welcome)\n\n
\n Installation\n \n```bash\npip install anthropic\n```\n\n```python python\nimport anthropic\nimport agentops\n\n# Beginning of program''s code (i.e. main.py, __init__.py)\nagentops.init()\n\nclient = anthropic.Anthropic(\n # This is the default and can be omitted\n api_key=os.environ.get(\"ANTHROPIC_API_KEY\"),\n)\n\nmessage = client.messages.create(\n max_tokens=1024,\n messages=[\n {\n \"role\": \"user\",\n \"content\": - \"Tell me a cool fact about AgentOps\",\n }\n ],\n model=\"claude-3-opus-20240229\",\n )\nprint(message.content)\n\nagentops.end_session(''Success'')\n```\n\nStreaming\n```python python\nimport anthropic\nimport agentops\n\n# Beginning of program''s code (i.e. main.py, __init__.py)\nagentops.init()\n\nclient = anthropic.Anthropic(\n # This is the default and can be omitted\n api_key=os.environ.get(\"ANTHROPIC_API_KEY\"),\n)\n\nstream = client.messages.create(\n max_tokens=1024,\n model=\"claude-3-opus-20240229\",\n messages=[\n {\n \"role\": \"user\",\n \"content\": \"Tell me something cool about streaming agents\",\n }\n ],\n stream=True,\n)\n\nresponse = \"\"\nfor event in stream:\n if event.type == \"content_block_delta\":\n response += event.delta.text\n elif event.type == \"message_stop\":\n print(\"\\n\")\n print(response)\n print(\"\\n\")\n```\n\nAsync\n\n```python - python\nimport asyncio\nfrom anthropic import AsyncAnthropic\n\nclient = AsyncAnthropic(\n # This is the default and can be omitted\n api_key=os.environ.get(\"ANTHROPIC_API_KEY\"),\n)\n\n\nasync def main() -> None:\n message = await client.messages.create(\n max_tokens=1024,\n messages=[\n {\n \"role\": \"user\",\n \"content\": \"Tell me something interesting about async agents\",\n }\n ],\n model=\"claude-3-opus-20240229\",\n )\n print(message.content)\n\n\nawait main()\n```\n
\n\n### Mistral ใ€ฝ๏ธ\n\nTrack agents built with the Anthropic Python SDK (>=0.32.0).\n\n- [AgentOps integration example](./examples/mistral//mistral_example.ipynb)\n- [Official Mistral documentation](https://docs.mistral.ai)\n\n
\n Installation\n \n```bash\npip install mistralai\n```\n\nSync\n\n```python python\nfrom mistralai import Mistral\nimport agentops\n\n# Beginning of program''s - code (i.e. main.py, __init__.py)\nagentops.init()\n\nclient = Mistral(\n # This is the default and can be omitted\n api_key=os.environ.get(\"MISTRAL_API_KEY\"),\n)\n\nmessage = client.chat.complete(\n messages=[\n {\n \"role\": \"user\",\n \"content\": \"Tell me a cool fact about AgentOps\",\n }\n ],\n model=\"open-mistral-nemo\",\n )\nprint(message.choices[0].message.content)\n\nagentops.end_session(''Success'')\n```\n\nStreaming\n\n```python python\nfrom mistralai import Mistral\nimport agentops\n\n# Beginning of program''s code (i.e. main.py, __init__.py)\nagentops.init()\n\nclient = Mistral(\n # This is the default and can be omitted\n api_key=os.environ.get(\"MISTRAL_API_KEY\"),\n)\n\nmessage = client.chat.stream(\n messages=[\n {\n \"role\": \"user\",\n \"content\": \"Tell me something cool - about streaming agents\",\n }\n ],\n model=\"open-mistral-nemo\",\n )\n\nresponse = \"\"\nfor event in message:\n if event.data.choices[0].finish_reason == \"stop\":\n print(\"\\n\")\n print(response)\n print(\"\\n\")\n else:\n response += event.text\n\nagentops.end_session(''Success'')\n```\n\nAsync\n\n```python python\nimport asyncio\nfrom mistralai import Mistral\n\nclient = Mistral(\n # This is the default and can be omitted\n api_key=os.environ.get(\"MISTRAL_API_KEY\"),\n)\n\n\nasync def main() -> None:\n message = await client.chat.complete_async(\n messages=[\n {\n \"role\": \"user\",\n \"content\": \"Tell me something interesting about async agents\",\n }\n ],\n model=\"open-mistral-nemo\",\n )\n print(message.choices[0].message.content)\n\n\nawait main()\n```\n\nAsync Streaming\n\n```python python\nimport asyncio\nfrom mistralai - import Mistral\n\nclient = Mistral(\n # This is the default and can be omitted\n api_key=os.environ.get(\"MISTRAL_API_KEY\"),\n)\n\n\nasync def main() -> None:\n message = await client.chat.stream_async(\n messages=[\n {\n \"role\": \"user\",\n \"content\": \"Tell me something interesting about async streaming agents\",\n }\n ],\n model=\"open-mistral-nemo\",\n )\n\n response = \"\"\n async for event in message:\n if event.data.choices[0].finish_reason == \"stop\":\n print(\"\\n\")\n print(response)\n print(\"\\n\")\n else:\n response += event.text\n\n\nawait main()\n```\n
\n\n\n\n### CamelAI ๏นจ\n\nTrack agents built with the CamelAI Python SDK (>=0.32.0).\n\n- [CamelAI integration guide](https://docs.camel-ai.org/cookbooks/agents_tracking.html#)\n- [Official CamelAI documentation](https://docs.camel-ai.org/index.html)\n\n
\n Installation\n \n```bash\npip - install camel-ai[all]\npip install agentops\n```\n\n```python python\n#Import Dependencies\nimport agentops\nimport os\nfrom getpass import getpass\nfrom dotenv import load_dotenv\n\n#Set Keys\nload_dotenv()\nopenai_api_key = os.getenv(\"OPENAI_API_KEY\") or \"\"\nagentops_api_key = os.getenv(\"AGENTOPS_API_KEY\") or \"\"\n\n\n\n```\n
\n\n[You can find usage examples here!](examples/camelai_examples/README.md).\n\n\n\n### LiteLLM ๐Ÿš…\n\nAgentOps provides support for LiteLLM(>=1.3.1), allowing you to call 100+ LLMs using the same Input/Output Format. \n\n- [AgentOps integration example](https://docs.agentops.ai/v1/integrations/litellm)\n- [Official LiteLLM documentation](https://docs.litellm.ai/docs/providers)\n\n
\n Installation\n \n```bash\npip install litellm\n```\n\n```python python\n# Do not use LiteLLM like this\n# from litellm import completion\n# ...\n# response = completion(model=\"claude-3\", - messages=messages)\n\n# Use LiteLLM like this\nimport litellm\n...\nresponse = litellm.completion(model=\"claude-3\", messages=messages)\n# or\nresponse = await litellm.acompletion(model=\"claude-3\", messages=messages)\n```\n
\n\n### LlamaIndex ๐Ÿฆ™\n\n\nAgentOps works seamlessly with applications built using LlamaIndex, a framework for building context-augmented generative AI applications with LLMs.\n\n
\n Installation\n \n```shell\npip install llama-index-instrumentation-agentops\n```\n\nTo use the handler, import and set\n\n```python\nfrom llama_index.core import set_global_handler\n\n# NOTE: Feel free to set your AgentOps environment variables (e.g., ''AGENTOPS_API_KEY'')\n# as outlined in the AgentOps documentation, or pass the equivalent keyword arguments\n# anticipated by AgentOps'' AOClient as **eval_params in set_global_handler.\n\nset_global_handler(\"agentops\")\n```\n\nCheck out the [LlamaIndex docs](https://docs.llamaindex.ai/en/stable/module_guides/observability/?h=agentops#agentops) - for more details.\n\n
\n\n### Llama Stack ๐Ÿฆ™๐Ÿฅž\n\nAgentOps provides support for Llama Stack Python Client(>=0.0.53), allowing you to monitor your Agentic applications. \n\n- [AgentOps integration example 1](https://github.com/AgentOps-AI/agentops/pull/530/files/65a5ab4fdcf310326f191d4b870d4f553591e3ea#diff-fdddf65549f3714f8f007ce7dfd1cde720329fe54155d54389dd50fbd81813cb)\n- [AgentOps integration example 2](https://github.com/AgentOps-AI/agentops/pull/530/files/65a5ab4fdcf310326f191d4b870d4f553591e3ea#diff-6688ff4fb7ab1ce7b1cc9b8362ca27264a3060c16737fb1d850305787a6e3699)\n- [Official Llama Stack Python Client](https://github.com/meta-llama/llama-stack-client-python)\n\n### SwarmZero AI ๐Ÿ\n\nTrack and analyze SwarmZero agents with full observability. Set an `AGENTOPS_API_KEY` in your environment and initialize AgentOps to get started.\n\n- [SwarmZero](https://swarmzero.ai) - Advanced multi-agent framework\n- [AgentOps integration example](https://docs.agentops.ai/v1/integrations/swarmzero)\n- - [SwarmZero AI integration example](https://docs.swarmzero.ai/examples/ai-agents/build-and-monitor-a-web-search-agent)\n- [SwarmZero AI - AgentOps documentation](https://docs.swarmzero.ai/sdk/observability/agentops)\n- [Official SwarmZero Python SDK](https://github.com/swarmzero/swarmzero)\n\n
\n Installation\n\n```bash\npip install swarmzero\npip install agentops\n```\n\n```python\nfrom dotenv import load_dotenv\nload_dotenv()\n\nimport agentops\nagentops.init()\n\nfrom swarmzero import Agent, Swarm\n# ...\n```\n
\n\n## Time travel debugging ๐Ÿ”ฎ\n\n
\n \"Time\n
\n\n
\n\n[Try it out!](https://app.agentops.ai/timetravel)\n\n## Agent Arena ๐ŸฅŠ\n\n(coming soon!)\n\n## Evaluations Roadmap ๐Ÿงญ\n\n| Platform | Dashboard | - Evals |\n| ---------------------------------------------------------------------------- | ------------------------------------------ | -------------------------------------- |\n| โœ… Python SDK | โœ… Multi-session and Cross-session metrics | โœ… Custom eval metrics |\n| ๐Ÿšง Evaluation builder API | โœ… Custom event tag trackingย  | ๐Ÿ”œ Agent scorecards |\n| โœ… [Javascript/Typescript SDK](https://github.com/AgentOps-AI/agentops-node) | โœ… Session replays | ๐Ÿ”œ Evaluation playground + leaderboard |\n\n## Debugging Roadmap ๐Ÿงญ\n\n| Performance testing | Environments | LLM Testing | Reasoning and execution testing |\n| ----------------------------------------- - | ----------------------------------------------------------------------------------- | ------------------------------------------- | ------------------------------------------------- |\n| โœ… Event latency analysis | ๐Ÿ”œ Non-stationary environment testing | ๐Ÿ”œ LLM non-deterministic function detection | ๐Ÿšง Infinite loops and recursive thought detection |\n| โœ… Agent workflow execution pricing | ๐Ÿ”œ Multi-modal environments | ๐Ÿšง Token limit overflow flags | ๐Ÿ”œ Faulty reasoning detection |\n| ๐Ÿšง Success validators (external) | ๐Ÿ”œ Execution containers | ๐Ÿ”œ Context limit overflow flags | ๐Ÿ”œ Generative code validators |\n| ๐Ÿ”œ Agent controllers/skill tests | โœ… Honeypot and prompt injection detection ([PromptArmor](https://promptarmor.com)) - | ๐Ÿ”œ API bill tracking | ๐Ÿ”œ Error breakpoint analysis |\n| ๐Ÿ”œ Information context constraint testing | ๐Ÿ”œ Anti-agent roadblocks (i.e. Captchas) | ๐Ÿ”œ CI/CD integration checks | |\n| ๐Ÿ”œ Regression testing | ๐Ÿ”œ Multi-agent framework visualization | | |\n\n### Why AgentOps? ๐Ÿค”\n\nWithout the right tools, AI agents are slow, expensive, and unreliable. Our mission is to bring your agent from prototype to production. Here''s why AgentOps stands out:\n\n- **Comprehensive Observability**: Track your AI agents'' performance, user interactions, and API usage.\n- **Real-Time Monitoring**: Get instant insights with session replays, metrics, and live monitoring tools.\n- **Cost Control**: Monitor - and manage your spend on LLM and API calls.\n- **Failure Detection**: Quickly identify and respond to agent failures and multi-agent interaction issues.\n- **Tool Usage Statistics**: Understand how your agents utilize external tools with detailed analytics.\n- **Session-Wide Metrics**: Gain a holistic view of your agents'' sessions with comprehensive statistics.\n\nAgentOps is designed to make agent observability, testing, and monitoring easy.\n\n\n## Star History\n\nCheck out our growth in the community:\n\n\"Logo\"\n\n## Popular projects using AgentOps\n\n\n| Repository | Stars |\n| :-------- | -----: |\n|\"\"   [geekan](https://github.com/geekan) / [MetaGPT](https://github.com/geekan/MetaGPT) | 42787 |\n|\"\"   [run-llama](https://github.com/run-llama) / [llama_index](https://github.com/run-llama/llama_index) | 34446 |\n|\"\"   [crewAIInc](https://github.com/crewAIInc) / [crewAI](https://github.com/crewAIInc/crewAI) | 18287 |\n|\"\"   [camel-ai](https://github.com/camel-ai) / [camel](https://github.com/camel-ai/camel) | 5166 |\n|\"\"   [superagent-ai](https://github.com/superagent-ai) / [superagent](https://github.com/superagent-ai/superagent) | 5050 |\n|\"\"   [iyaja](https://github.com/iyaja) / [llama-fs](https://github.com/iyaja/llama-fs) | 4713 |\n|\"\"   [BasedHardware](https://github.com/BasedHardware) / [Omi](https://github.com/BasedHardware/Omi) | 2723 |\n|\"\"   [MervinPraison](https://github.com/MervinPraison) / [PraisonAI](https://github.com/MervinPraison/PraisonAI) | 2007 |\n|\"\"   [AgentOps-AI](https://github.com/AgentOps-AI) / [Jaiqu](https://github.com/AgentOps-AI/Jaiqu) | 272 |\n|\"\"   [swarmzero](https://github.com/swarmzero) / [swarmzero](https://github.com/swarmzero/swarmzero) | 195 |\n|\"\"   [strnad](https://github.com/strnad) / [CrewAI-Studio](https://github.com/strnad/CrewAI-Studio) | 134 |\n|\"\"   [alejandro-ao](https://github.com/alejandro-ao) / [exa-crewai](https://github.com/alejandro-ao/exa-crewai) | 55 |\n|\"\"   [tonykipkemboi](https://github.com/tonykipkemboi) / [youtube_yapper_trapper](https://github.com/tonykipkemboi/youtube_yapper_trapper) | 47 |\n|\"\"   [sethcoast](https://github.com/sethcoast) / [cover-letter-builder](https://github.com/sethcoast/cover-letter-builder) | 27 |\n|\"\"   [bhancockio](https://github.com/bhancockio) / [chatgpt4o-analysis](https://github.com/bhancockio/chatgpt4o-analysis) | 19 |\n|\"\"   [breakstring](https://github.com/breakstring) / [Agentic_Story_Book_Workflow](https://github.com/breakstring/Agentic_Story_Book_Workflow) | 14 |\n|\"\"   [MULTI-ON](https://github.com/MULTI-ON) / [multion-python](https://github.com/MULTI-ON/multion-python) | 13 |\n\n\n_Generated 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You - are a seasoned manager with a knack for getting the best out of your team.\nYou - are also known for your ability to delegate work to the right people, and to - ask the right questions to get the best out of your team.\nEven though you don''t - perform tasks by yourself, you have a lot of experience in the field, which - allows you to properly evaluate the work of your team members.\nYour personal - goal is: Manage the team to complete the task in the best way possible.\nYou - ONLY have access to the following tools, and should NEVER make up tools that - are not listed here:\n\nTool Name: Delegate work to coworker(task: str, context: - str, coworker: Optional[str] = None, **kwargs)\nTool Description: Delegate a - specific task to one of the following coworkers: Researcher, Senior Writer\nThe - input to this tool should be the coworker, the task you want them to do, and - ALL necessary context to execute the task, they know nothing about the task, - so share absolute everything you know, don''t reference things but instead explain - them.\nTool Arguments: {''task'': {''title'': ''Task'', ''type'': ''string''}, - ''context'': {''title'': ''Context'', ''type'': ''string''}, ''coworker'': {''title'': - ''Coworker'', ''type'': ''string''}, ''kwargs'': {''title'': ''Kwargs'', ''type'': - ''object''}}\nTool Name: Ask question to coworker(question: str, context: str, - coworker: Optional[str] = None, **kwargs)\nTool Description: Ask a specific - question to one of the following coworkers: Researcher, Senior Writer\nThe input - to this tool should be the coworker, the question you have for them, and ALL - necessary context to ask the question properly, they know nothing about the - question, so share absolute everything you know, don''t reference things but - instead explain them.\nTool Arguments: {''question'': {''title'': ''Question'', - ''type'': ''string''}, ''context'': {''title'': ''Context'', ''type'': ''string''}, - ''coworker'': {''title'': ''Coworker'', ''type'': ''string''}, ''kwargs'': {''title'': - ''Kwargs'', ''type'': ''object''}}\n\nUse the following format:\n\nThought: - you should always think about what to do\nAction: the action to take, only one - name of [Delegate work to coworker, Ask question to coworker], just the name, - exactly as it''s written.\nAction Input: the input to the action, just a simple - python dictionary, enclosed in curly braces, using \" to wrap keys and values.\nObservation: - the result of the action\n\nOnce all necessary information is gathered:\n\nThought: - I now know the final answer\nFinal Answer: the final answer to the original - input question\n"}, {"role": "user", "content": "\nCurrent Task: Come up with - a list of 5 interesting ideas to explore for an article, then write one amazing - paragraph highlight for each idea that showcases how good an article about this - topic could be. 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I will delegate this task to the Senior Writer,\ - \ ensuring they have all the context they need.\\n\\nAction: Delegate work\ - \ to coworker\\nAction Input: {\\\"task\\\": \\\"Write one amazing paragraph\ - \ highlight for each of the following ideas:\\\\n\\\\n1. The Rise of AI in\ - \ Healthcare: Transforming Patient Care and Diagnostics\\\\n2. AI Agents in\ - \ the Workplace: Boosting Productivity and Shaping the Future of Jobs\\\\\ - n3. Ethical AI: Ensuring Fairness, Accountability, and Transparency\\\\n4.\ - \ AI-Powered Startups: Innovators Changing the Tech Landscape\\\\n5. The Future\ - \ of AI in Entertainment: From Movies to Video Games\\\\n\\\", \\\"context\\\ - \": \\\"We need an engaging paragraph for each idea that showcases how interesting\ - \ an article on that topic could be. 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The ethical and privacy concerns, however, pose new challenges that\ - \ the industry must navigate to ensure responsible advancement.\\\"\\n\\n2.\ - \ **AI Agents in the Workplace: Boosting Productivity and Shaping the Future\ - \ of Jobs** - \\\"The modern workplace is being redefined by the integration\ - \ of AI agents, intelligent systems designed to handle repetitive tasks, analyze\ - \ vast amounts of data, and assist in complex decision-making processes. These\ - \ innovations are boosting productivity, allowing employees to focus on creativity\ - \ and strategic thinking. The rise of AI in the workspace heralds a future\ - \ where human and machine collaboration leads to unparalleled efficiencies\ - \ and novel job opportunities. 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Investors are taking notice, pouring capital\ - \ into these ventures, and fueling a new wave of growth and competition in\ - \ the AI ecosystem.\\\"\\n\\n5. **The Future of AI in Entertainment: From\ - \ Movies to Video Games** - \\\"The entertainment industry is undergoing a\ - \ seismic shift driven by AI technology. In movies, AI is revolutionizing\ - \ special effects, enabling filmmakers to create stunning, hyper-realistic\ - \ worlds. 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AI in healthcare is rapidly transforming this vision\ - \ into reality, enhancing patient care through early and accurate disease\ - \ detection, personalized treatment plans, and continuous health monitoring.\ - \ This revolution is not just a futuristic concept; it's already happening\ - \ with AI-driven technologies enabling doctors to make more informed decisions\ - \ and offering patients a more proactive approach to their health. The ethical\ - \ and privacy concerns, however, pose new challenges that the industry must\ - \ navigate to ensure responsible advancement.\\\"\\n\\n2. **AI Agents in the\ - \ Workplace: Boosting Productivity and Shaping the Future of Jobs**\\n \\\ - \"The modern workplace is being redefined by the integration of AI agents,\ - \ intelligent systems designed to handle repetitive tasks, analyze vast amounts\ - \ of data, and assist in complex decision-making processes. 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You - are a seasoned manager with a knack for getting the best out of your team.\nYou - are also known for your ability to delegate work to the right people, and to - ask the right questions to get the best out of your team.\nEven though you don''t - perform tasks by yourself, you have a lot of experience in the field, which - allows you to properly evaluate the work of your team members.\nYour personal - goal is: Manage the team to complete the task in the best way possible.\nYou - ONLY have access to the following tools, and should NEVER make up tools that - are not listed here:\n\nTool Name: Delegate work to coworker(task: str, context: - str, coworker: Optional[str] = None, **kwargs)\nTool Description: Delegate a - specific task to one of the following coworkers: Researcher, Senior Writer\nThe - input to this tool should be the coworker, the task you want them to do, and - ALL necessary context to execute the task, they know nothing about the task, - so share absolute everything you know, don''t reference things but instead explain - them.\nTool Arguments: {''task'': {''title'': ''Task'', ''type'': ''string''}, - ''context'': {''title'': ''Context'', ''type'': ''string''}, ''coworker'': {''title'': - ''Coworker'', ''type'': ''string''}, ''kwargs'': {''title'': ''Kwargs'', ''type'': - ''object''}}\nTool Name: Ask question to coworker(question: str, context: str, - coworker: Optional[str] = None, **kwargs)\nTool Description: Ask a specific - question to one of the following coworkers: Researcher, Senior Writer\nThe input - to this tool should be the coworker, the question you have for them, and ALL - necessary context to ask the question properly, they know nothing about the - question, so share absolute everything you know, don''t reference things but - instead explain them.\nTool Arguments: {''question'': {''title'': ''Question'', - ''type'': ''string''}, ''context'': {''title'': ''Context'', ''type'': ''string''}, - ''coworker'': {''title'': ''Coworker'', ''type'': ''string''}, ''kwargs'': {''title'': - ''Kwargs'', ''type'': ''object''}}\n\nUse the following format:\n\nThought: - you should always think about what to do\nAction: the action to take, only one - name of [Delegate work to coworker, Ask question to coworker], just the name, - exactly as it''s written.\nAction Input: the input to the action, just a simple - python dictionary, enclosed in curly braces, using \" to wrap keys and values.\nObservation: - the result of the action\n\nOnce all necessary information is gathered:\n\nThought: - I now know the final answer\nFinal Answer: the final answer to the original - input question\n"}, {"role": "user", "content": "\nCurrent Task: Come up with - a list of 5 interesting ideas to explore for an article, then write one amazing - paragraph highlight for each idea that showcases how good an article about this - topic could be. 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Offer - predictions about future trends and possible impacts on content creation and - consumption.\n\nThese topics are designed to be timely, relevant, and engaging, - ensuring a broad audience will find them compelling."}, {"role": "user", "content": - "I did it wrong. 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The Rise\ - \ of AI in Healthcare: Transforming Patient Care and Diagnostics - Explore\ - \ how AI is revolutionizing healthcare through enhanced diagnostic tools,\ - \ personalized treatment plans, and post-operative care. Discuss current achievements,\ - \ potential future applications, and ethical considerations.\\\\n2. AI Agents\ - \ in the Workplace: Boosting Productivity and Shaping the Future of Jobs -\ - \ Examine the integration of AI agents in various industries. Highlight how\ - \ these intelligent agents are improving efficiency, automating mundane tasks,\ - \ and assisting in decision-making processes. Address potential job displacement\ - \ and the need for upskilling the workforce.\\\\n3. Ethical AI: Ensuring Fairness,\ - \ Accountability, and Transparency - Delve into the ethical implications of\ - \ AI deployment. Analyze the frameworks and guidelines being proposed to ensure\ - \ AI systems are fair, transparent, and accountable. 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You''re - a senior writer, specialized in technology, software engineering, AI and startups. - You work as a freelancer and are now working on writing content for a new customer.\nYour - personal goal is: Write the best content about AI and AI agents.\nTo give my - best complete final answer to the task use the exact following format:\n\nThought: - I now can give a great answer\nFinal Answer: Your final answer must be the great - and the most complete as possible, it must be outcome described.\n\nI MUST use - these formats, my job depends on it!"}, {"role": "user", "content": "\nCurrent - Task: Write one amazing paragraph highlight for each of these 5 ideas that showcases - how good an article about these topics could be.\n\nThis is the expect criteria - for your final answer: Your best answer to your coworker asking you this, accounting - for the context shared.\nyou MUST return the actual complete content as the - final answer, not a summary.\n\nThis is the context you''re working with:\nHere - are the 5 ideas:\n1. 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With the advent of advanced diagnostic tools powered by\ - \ AI, healthcare professionals can now detect and monitor diseases with unprecedented\ - \ accuracy and speed. Personalized treatment plans, which were once a distant\ - \ dream, are becoming a reality as AI algorithms analyze vast amounts of patient\ - \ data, tailoring treatments that are specific to individual needs. Post-operative\ - \ care has also seen significant improvements, with AI-driven monitoring systems\ - \ ensuring that patients receive timely and effective care. 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For instance, AI chatbots\ - \ in customer service can handle routine inquiries, freeing human agents to\ - \ tackle more complex issues. In manufacturing, AI systems predict equipment\ - \ failures, reducing downtime and maintaining peak productivity. By enhancing\ - \ operational efficiency, AI agents not only boost productivity but also reshape\ - \ the future of jobs. While the risk of job displacement is a valid concern,\ - \ it concurrently heralds opportunities for upskilling and creating new roles\ - \ that harness human creativity and strategic thinking. Preparing the workforce\ - \ for these changes is crucial, fostering a symbiotic relationship between\ - \ AI and human labor to drive innovation and economic growth.\\n\\n3. **Ethical\ - \ AI: Ensuring Fairness, Accountability, and Transparency**\\n As AI technologies\ - \ become increasingly pervasive, ensuring their ethical deployment is paramount.\ - \ Ethical AI demands the formulation and implementation of robust frameworks\ - \ that enshrine fairness, accountability, and transparency. Such frameworks\ - \ are essential in mitigating biases embedded within AI systems, which can\ - \ perpetuate and even exacerbate inequality. Case studies, such as the controversy\ - \ surrounding facial recognition systems and their racial biases, highlight\ - \ the pressing need for ethical oversight. Transparency in how AI models make\ - \ decisions, coupled with accountability measures for their outcomes, establishes\ - \ trust and integrity in AI applications. Legislative frameworks from the\ - \ EUโ€™s GDPR to the IEEEโ€™s global initiatives for ethical AI provide the scaffolding\ - \ for these principles. Companies are now faced with the challenge of integrating\ - \ these ethical considerations into their development processes, ensuring\ - \ that AI advancements benefit all of society equitably.\\n\\n4. **AI-Powered\ - \ Startups: Innovators Changing the Tech Landscape**\\n AI-powered startups\ - \ are at the forefront of a technological renaissance, disrupting traditional\ - \ business models and reshaping industries. These startups leverage AI to\ - \ offer innovative solutions, whether it's automating financial trading with\ - \ precision algorithms or enhancing cybersecurity through predictive analytics.\ - \ High-profile examples like OpenAI and UiPath illustrate the transformative\ - \ potential of AI-driven innovation. 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You - are a seasoned manager with a knack for getting the best out of your team.\nYou - are also known for your ability to delegate work to the right people, and to - ask the right questions to get the best out of your team.\nEven though you don''t - perform tasks by yourself, you have a lot of experience in the field, which - allows you to properly evaluate the work of your team members.\nYour personal - goal is: Manage the team to complete the task in the best way possible.\nYou - ONLY have access to the following tools, and should NEVER make up tools that - are not listed here:\n\nTool Name: Delegate work to coworker(task: str, context: - str, coworker: Optional[str] = None, **kwargs)\nTool Description: Delegate a - specific task to one of the following coworkers: Researcher, Senior Writer\nThe - input to this tool should be the coworker, the task you want them to do, and - ALL necessary context to execute the task, they know nothing about the task, - so share absolute everything you know, don''t reference things but instead explain - them.\nTool Arguments: {''task'': {''title'': ''Task'', ''type'': ''string''}, - ''context'': {''title'': ''Context'', ''type'': ''string''}, ''coworker'': {''title'': - ''Coworker'', ''type'': ''string''}, ''kwargs'': {''title'': ''Kwargs'', ''type'': - ''object''}}\nTool Name: Ask question to coworker(question: str, context: str, - coworker: Optional[str] = None, **kwargs)\nTool Description: Ask a specific - question to one of the following coworkers: Researcher, Senior Writer\nThe input - to this tool should be the coworker, the question you have for them, and ALL - necessary context to ask the question properly, they know nothing about the - question, so share absolute everything you know, don''t reference things but - instead explain them.\nTool Arguments: {''question'': {''title'': ''Question'', - ''type'': ''string''}, ''context'': {''title'': ''Context'', ''type'': ''string''}, - ''coworker'': {''title'': ''Coworker'', ''type'': ''string''}, ''kwargs'': {''title'': - ''Kwargs'', ''type'': ''object''}}\n\nUse the following format:\n\nThought: - you should always think about what to do\nAction: the action to take, only one - name of [Delegate work to coworker, Ask question to coworker], just the name, - exactly as it''s written.\nAction Input: the input to the action, just a simple - python dictionary, enclosed in curly braces, using \" to wrap keys and values.\nObservation: - the result of the action\n\nOnce all necessary information is gathered:\n\nThought: - I now know the final answer\nFinal Answer: the final answer to the original - input question\n"}, {"role": "user", "content": "\nCurrent Task: Come up with - a list of 5 interesting ideas to explore for an article, then write one amazing - paragraph highlight for each idea that showcases how good an article about this - topic could be. Return the list of ideas with their paragraph and your notes.\n\nThis - is the expect criteria for your final answer: 5 bullet points with a paragraph - for each idea.\nyou MUST return the actual complete content as the final answer, - not a summary.\n\nBegin! This is VERY important to you, use the tools available - and give your best Final Answer, your job depends on it!\n\nThought:"}, {"role": - "assistant", "content": "Thought: The first step is to brainstorm and research - interesting ideas for the article. Since the task involves both research and - writing, I will need to delegate the brainstorming of ideas to the Researcher - and the writing of the paragraphs to the Senior Writer. \n\nAction: Delegate - work to coworker\nAction Input: {\"task\": \"Come up with a list of 5 interesting - ideas to explore for an article.\", \"context\": \"We need a list of 5 interesting - ideas that would make compelling articles. The topics should be relevant, timely, - and engaging for a broad audience.\", \"coworker\": \"Researcher\"}\nObservation: - 1. **The Rise of AI in Healthcare: Transforming Patient Care and Diagnostics** - - Explore how AI is revolutionizing healthcare through enhanced diagnostic tools, - personalized treatment plans, and post-operative care. Discuss current achievements, - potential future applications, and ethical considerations.\n\n2. **AI Agents - in the Workplace: Boosting Productivity and Shaping the Future of Jobs** - Examine - the integration of AI agents in various industries. Highlight how these intelligent - agents are improving efficiency, automating mundane tasks, and assisting in - decision-making processes. Address potential job displacement and the need for - upskilling the workforce.\n\n3. **Ethical AI: Ensuring Fairness, Accountability, - and Transparency** - Delve into the ethical implications of AI deployment. Analyze - the frameworks and guidelines being proposed to ensure AI systems are fair, - transparent, and accountable. Include case studies of ethical dilemmas faced - by companies using AI.\n\n4. **AI-Powered Startups: Innovators Changing the - Tech Landscape** - Provide an overview of how AI-driven startups are disrupting - traditional business models and industries. Profile some of the most innovative - startups, their business models, and the unique challenges they face. Discuss - the role of venture capital in fostering AI innovation.\n\n5. **The Future of - AI in Entertainment: From Movies to Video Games** - Investigate how AI is transforming - the entertainment industry, from scriptwriting and special effects in movies - to creating more immersive and intelligent experiences in video games. Offer - predictions about future trends and possible impacts on content creation and - consumption.\n\nThese topics are designed to be timely, relevant, and engaging, - ensuring a broad audience will find them compelling."}, {"role": "user", "content": - "I did it wrong. Tried to both perform Action and give a Final Answer at the - same time, I must do one or the other"}, {"role": "assistant", "content": "Thought: - I''ll proceed to delegate the writing of one amazing paragraph highlight for - each of the identified ideas to the Senior Writer, ensuring each topic is adequately - detailed to showcase its potential.\n\nAction: Delegate work to coworker\nAction - Input: {\"task\": \"Write one amazing paragraph highlight for each of these - 5 ideas that showcases how good an article about these topics could be.\", \"context\": - \"Here are the 5 ideas:\\n1. The Rise of AI in Healthcare: Transforming Patient - Care and Diagnostics - Explore how AI is revolutionizing healthcare through - enhanced diagnostic tools, personalized treatment plans, and post-operative - care. Discuss current achievements, potential future applications, and ethical - considerations.\\n2. AI Agents in the Workplace: Boosting Productivity and Shaping - the Future of Jobs - Examine the integration of AI agents in various industries. - Highlight how these intelligent agents are improving efficiency, automating - mundane tasks, and assisting in decision-making processes. Address potential - job displacement and the need for upskilling the workforce.\\n3. Ethical AI: - Ensuring Fairness, Accountability, and Transparency - Delve into the ethical - implications of AI deployment. Analyze the frameworks and guidelines being proposed - to ensure AI systems are fair, transparent, and accountable. Include case studies - of ethical dilemmas faced by companies using AI.\\n4. AI-Powered Startups: Innovators - Changing the Tech Landscape - Provide an overview of how AI-driven startups - are disrupting traditional business models and industries. Profile some of the - most innovative startups, their business models, and the unique challenges they - face. Discuss the role of venture capital in fostering AI innovation.\\n5. The - Future of AI in Entertainment: From Movies to Video Games - Investigate how - AI is transforming the entertainment industry, from scriptwriting and special - effects in movies to creating more immersive and intelligent experiences in - video games. Offer predictions about future trends and possible impacts on content - creation and consumption.\", \"coworker\": \"Senior Writer\"}\nObservation:\nObservation: - 1. **The Rise of AI in Healthcare: Transforming Patient Care and Diagnostics**\n AI - is undeniably revolutionizing the healthcare sector, introducing transformative - changes in patient care and diagnostics. With the advent of advanced diagnostic - tools powered by AI, healthcare professionals can now detect and monitor diseases - with unprecedented accuracy and speed. Personalized treatment plans, which were - once a distant dream, are becoming a reality as AI algorithms analyze vast amounts - of patient data, tailoring treatments that are specific to individual needs. - Post-operative care has also seen significant improvements, with AI-driven monitoring - systems ensuring that patients receive timely and effective care. Current achievements - like IBM Watson''s oncology platform or Google''s DeepMind in eye care are just - the beginning, with future applications promising to further enhance surgical - robotics, predictive analytics, and remote patient monitoring. However, alongside - these advancements, ethical considerations such as patient privacy, data security, - and decision-making transparency must be rigorously addressed to fully harness - AI\u2019s potential in healthcare.\n\n2. **AI Agents in the Workplace: Boosting - Productivity and Shaping the Future of Jobs**\n The integration of AI agents - in the workplace marks a pivotal shift in how businesses operate and compete. - These intelligent agents are revolutionizing various industries by automating - mundane tasks, optimizing workflows, and providing actionable insights from - data analysis. For instance, AI chatbots in customer service can handle routine - inquiries, freeing human agents to tackle more complex issues. In manufacturing, - AI systems predict equipment failures, reducing downtime and maintaining peak - productivity. By enhancing operational efficiency, AI agents not only boost - productivity but also reshape the future of jobs. While the risk of job displacement - is a valid concern, it concurrently heralds opportunities for upskilling and - creating new roles that harness human creativity and strategic thinking. Preparing - the workforce for these changes is crucial, fostering a symbiotic relationship - between AI and human labor to drive innovation and economic growth.\n\n3. **Ethical - AI: Ensuring Fairness, Accountability, and Transparency**\n As AI technologies - become increasingly pervasive, ensuring their ethical deployment is paramount. - Ethical AI demands the formulation and implementation of robust frameworks that - enshrine fairness, accountability, and transparency. Such frameworks are essential - in mitigating biases embedded within AI systems, which can perpetuate and even - exacerbate inequality. Case studies, such as the controversy surrounding facial - recognition systems and their racial biases, highlight the pressing need for - ethical oversight. Transparency in how AI models make decisions, coupled with - accountability measures for their outcomes, establishes trust and integrity - in AI applications. Legislative frameworks from the EU\u2019s GDPR to the IEEE\u2019s - global initiatives for ethical AI provide the scaffolding for these principles. - Companies are now faced with the challenge of integrating these ethical considerations - into their development processes, ensuring that AI advancements benefit all - of society equitably.\n\n4. **AI-Powered Startups: Innovators Changing the Tech - Landscape**\n AI-powered startups are at the forefront of a technological - renaissance, disrupting traditional business models and reshaping industries. - These startups leverage AI to offer innovative solutions, whether it''s automating - financial trading with precision algorithms or enhancing cybersecurity through - predictive analytics. High-profile examples like OpenAI and UiPath illustrate - the transformative potential of AI-driven innovation. These companies face unique - challenges, from securing sufficient funding to navigating the complexities - of large-scale implementation. Venture capital plays a critical role in this - ecosystem, providing the necessary resources and support to turn bold ideas - into impactful realities. The synergy between AI innovation and entrepreneurial - spirit is fostering a dynamic landscape where the boundaries of technology and - commerce are continually expanding.\n\n5. **The Future of AI in Entertainment: - From Movies to Video Games**\n The entertainment industry is undergoing a - metamorphic change with the integration of AI technologies, paving the way for - more immersive and engaging experiences. In movies, AI streamlines the scriptwriting - process, enhances special effects, and even predicts box office success. Machine - learning models analyze vast datasets to create compelling narratives and optimize - marketing strategies. Video games have also been revolutionized by AI, with - non-player characters (NPCs) displaying more lifelike behaviors and adaptive - learning algorithms personalizing gaming experiences. Future trends indicate - a seamless blend of AI with augmented reality (AR) and virtual reality (VR), - creating interactive environments where the line between virtual and real worlds - blur. These advancements are not only transforming content creation but also - revolutionizing how audiences consume and interact with entertainment, predicting - a future where AI is an indispensable tool in the creative arsenal."}], "model": - "gpt-4o"}' - headers: - accept: - - application/json - accept-encoding: - - gzip, deflate - connection: - - keep-alive - content-length: - - '13340' - content-type: - - application/json - cookie: - - __cf_bm=9.8sBYBkvBR8R1K_bVF7xgU..80XKlEIg3N2OBbTSCU-1727214102-1.0.1.1-.qiTLXbPamYUMSuyNsOEB9jhGu.jOifujOrx9E2JZvStbIZ9RTIiE44xKKNfLPxQkOi6qAT3h6htK8lPDGV_5g; - _cfuvid=lbRdAddVWV6W3f5Dm9SaOPWDUOxqtZBSPr_fTW26nEA-1727213194587-0.0.1.1-604800000 - host: - - api.openai.com - user-agent: - - OpenAI/Python 1.47.0 - x-stainless-arch: - - arm64 - x-stainless-async: - - 'false' - x-stainless-lang: - - python - x-stainless-os: - - MacOS - x-stainless-package-version: - - 1.47.0 - x-stainless-raw-response: - - 'true' - x-stainless-runtime: - - CPython - x-stainless-runtime-version: - - 3.11.7 - method: POST - uri: https://api.openai.com/v1/chat/completions - response: - body: - string: "{\n \"id\": \"chatcmpl-AB7fbCD4yK6bj5QJntUtu2G9jbXzH\",\n \"object\"\ - : \"chat.completion\",\n \"created\": 1727214455,\n \"model\": \"gpt-4o-2024-05-13\"\ - ,\n \"choices\": [\n {\n \"index\": 0,\n \"message\": {\n \ - \ \"role\": \"assistant\",\n \"content\": \"Thought: I now have\ - \ the final deliverables, consisting of the five article ideas along with\ - \ their detailed paragraphs. This completes the task as requested.\\n\\nFinal\ - \ Answer: \\n\\n1. **The Rise of AI in Healthcare: Transforming Patient Care\ - \ and Diagnostics**\\n AI is undeniably revolutionizing the healthcare sector,\ - \ introducing transformative changes in patient care and diagnostics. With\ - \ the advent of advanced diagnostic tools powered by AI, healthcare professionals\ - \ can now detect and monitor diseases with unprecedented accuracy and speed.\ - \ Personalized treatment plans, which were once a distant dream, are becoming\ - \ a reality as AI algorithms analyze vast amounts of patient data, tailoring\ - \ treatments that are specific to individual needs. Post-operative care has\ - \ also seen significant improvements, with AI-driven monitoring systems ensuring\ - \ that patients receive timely and effective care. Current achievements like\ - \ IBM Watson's oncology platform or Google's DeepMind in eye care are just\ - \ the beginning, with future applications promising to further enhance surgical\ - \ robotics, predictive analytics, and remote patient monitoring. However,\ - \ alongside these advancements, ethical considerations such as patient privacy,\ - \ data security, and decision-making transparency must be rigorously addressed\ - \ to fully harness AIโ€™s potential in healthcare.\\n\\n2. **AI Agents in the\ - \ Workplace: Boosting Productivity and Shaping the Future of Jobs**\\n The\ - \ integration of AI agents in the workplace marks a pivotal shift in how businesses\ - \ operate and compete. These intelligent agents are revolutionizing various\ - \ industries by automating mundane tasks, optimizing workflows, and providing\ - \ actionable insights from data analysis. For instance, AI chatbots in customer\ - \ service can handle routine inquiries, freeing human agents to tackle more\ - \ complex issues. In manufacturing, AI systems predict equipment failures,\ - \ reducing downtime and maintaining peak productivity. By enhancing operational\ - \ efficiency, AI agents not only boost productivity but also reshape the future\ - \ of jobs. While the risk of job displacement is a valid concern, it concurrently\ - \ heralds opportunities for upskilling and creating new roles that harness\ - \ human creativity and strategic thinking. Preparing the workforce for these\ - \ changes is crucial, fostering a symbiotic relationship between AI and human\ - \ labor to drive innovation and economic growth.\\n\\n3. **Ethical AI: Ensuring\ - \ Fairness, Accountability, and Transparency**\\n As AI technologies become\ - \ increasingly pervasive, ensuring their ethical deployment is paramount.\ - \ Ethical AI demands the formulation and implementation of robust frameworks\ - \ that enshrine fairness, accountability, and transparency. Such frameworks\ - \ are essential in mitigating biases embedded within AI systems, which can\ - \ perpetuate and even exacerbate inequality. Case studies, such as the controversy\ - \ surrounding facial recognition systems and their racial biases, highlight\ - \ the pressing need for ethical oversight. Transparency in how AI models make\ - \ decisions, coupled with accountability measures for their outcomes, establishes\ - \ trust and integrity in AI applications. Legislative frameworks from the\ - \ EUโ€™s GDPR to the IEEEโ€™s global initiatives for ethical AI provide the scaffolding\ - \ for these principles. Companies are now faced with the challenge of integrating\ - \ these ethical considerations into their development processes, ensuring\ - \ that AI advancements benefit all of society equitably.\\n\\n4. **AI-Powered\ - \ Startups: Innovators Changing the Tech Landscape**\\n AI-powered startups\ - \ are at the forefront of a technological renaissance, disrupting traditional\ - \ business models and reshaping industries. These startups leverage AI to\ - \ offer innovative solutions, whether it's automating financial trading with\ - \ precision algorithms or enhancing cybersecurity through predictive analytics.\ - \ High-profile examples like OpenAI and UiPath illustrate the transformative\ - \ potential of AI-driven innovation. These companies face unique challenges,\ - \ from securing sufficient funding to navigating the complexities of large-scale\ - \ implementation. Venture capital plays a critical role in this ecosystem,\ - \ providing the necessary resources and support to turn bold ideas into impactful\ - \ realities. The synergy between AI innovation and entrepreneurial spirit\ - \ is fostering a dynamic landscape where the boundaries of technology and\ - \ commerce are continually expanding.\\n\\n5. **The Future of AI in Entertainment:\ - \ From Movies to Video Games**\\n The entertainment industry is undergoing\ - \ a metamorphic change with the integration of AI technologies, paving the\ - \ way for more immersive and engaging experiences. In movies, AI streamlines\ - \ the scriptwriting process, enhances special effects, and even predicts box\ - \ office success. Machine learning models analyze vast datasets to create\ - \ compelling narratives and optimize marketing strategies. Video games have\ - \ also been revolutionized by AI, with non-player characters (NPCs) displaying\ - \ more lifelike behaviors and adaptive learning algorithms personalizing gaming\ - \ experiences. Future trends indicate a seamless blend of AI with augmented\ - \ reality (AR) and virtual reality (VR), creating interactive environments\ - \ where the line between virtual and real worlds blur. These advancements\ - \ are not only transforming content creation but also revolutionizing how\ - \ audiences consume and interact with entertainment, predicting a future where\ - \ AI is an indispensable tool in the creative arsenal.\",\n \"refusal\"\ - : null\n },\n \"logprobs\": null,\n \"finish_reason\": \"stop\"\ - \n }\n ],\n \"usage\": {\n \"prompt_tokens\": 2453,\n \"completion_tokens\"\ - : 907,\n \"total_tokens\": 3360,\n \"completion_tokens_details\": {\n\ - \ \"reasoning_tokens\": 0\n }\n },\n \"system_fingerprint\": \"\ - fp_e375328146\"\n}\n" - headers: - CF-Cache-Status: - - DYNAMIC - CF-RAY: - - 8c85f94a0de91cf3-GRU - Connection: - - keep-alive - Content-Type: - - application/json - Date: - - Tue, 24 Sep 2024 21:47:48 GMT - Server: - - cloudflare - Transfer-Encoding: - - chunked - X-Content-Type-Options: - - nosniff - access-control-expose-headers: - - X-Request-ID - openai-organization: - - crewai-iuxna1 - openai-processing-ms: - - '13152' - openai-version: - - '2020-10-01' - strict-transport-security: - - max-age=31536000; includeSubDomains; preload - x-ratelimit-limit-requests: - - '10000' - x-ratelimit-limit-tokens: - - '30000000' - x-ratelimit-remaining-requests: - - '9999' - x-ratelimit-remaining-tokens: - - '29996722' - x-ratelimit-reset-requests: - - 6ms - x-ratelimit-reset-tokens: - - 6ms - x-request-id: - - req_7fdc097dc5895a4d2d953e8b1a2479d1 - status: - code: 200 - message: OK -- request: - body: '{"messages": [{"role": "system", "content": "You are Crew Manager. You - are a seasoned manager with a knack for getting the best out of your team.\nYou - are also known for your ability to delegate work to the right people, and to - ask the right questions to get the best out of your team.\nEven though you don''t - perform tasks by yourself, you have a lot of experience in the field, which - allows you to properly evaluate the work of your team members.\nYour personal - goal is: Manage the team to complete the task in the best way possible.\nYou - ONLY have access to the following tools, and should NEVER make up tools that - are not listed here:\n\nTool Name: Delegate work to coworker\nTool Arguments: - {''task'': {''description'': ''The task to delegate'', ''type'': ''str''}, ''context'': - {''description'': ''The context for the task'', ''type'': ''str''}, ''coworker'': - {''description'': ''The role/name of the coworker to delegate to'', ''type'': - ''str''}}\nTool Description: Delegate a specific task to one of the following - coworkers: Researcher, Senior Writer\nThe input to this tool should be the coworker, - the task you want them to do, and ALL necessary context to execute the task, - they know nothing about the task, so share absolutely everything you know, don''t - reference things but instead explain them.\nTool Name: Ask question to coworker\nTool - Arguments: {''question'': {''description'': ''The question to ask'', ''type'': - ''str''}, ''context'': {''description'': ''The context for the question'', ''type'': - ''str''}, ''coworker'': {''description'': ''The role/name of the coworker to - ask'', ''type'': ''str''}}\nTool Description: Ask a specific question to one - of the following coworkers: Researcher, Senior Writer\nThe input to this tool - should be the coworker, the question you have for them, and ALL necessary context - to ask the question properly, they know nothing about the question, so share - absolutely everything you know, don''t reference things but instead explain - them.\n\nIMPORTANT: Use the following format in your response:\n\n```\nThought: - you should always think about what to do\nAction: the action to take, only one - name of [Delegate work to coworker, Ask question to coworker], just the name, - exactly as it''s written.\nAction Input: the input to the action, just a simple - JSON object, enclosed in curly braces, using \" to wrap keys and values.\nObservation: - the result of the action\n```\n\nOnce all necessary information is gathered, - return the following format:\n\n```\nThought: I now know the final answer\nFinal - Answer: the final answer to the original input question\n```"}, {"role": "user", - "content": "\nCurrent Task: Come up with a list of 5 interesting ideas to explore - for an article, then write one amazing paragraph highlight for each idea that - showcases how good an article about this topic could be. Return the list of - ideas with their paragraph and your notes.\n\nThis is the expected criteria - for your final answer: 5 bullet points with a paragraph for each idea.\nyou - MUST return the actual complete content as the final answer, not a summary.\n\nBegin! - This is VERY important to you, use the tools available and give your best Final - Answer, your job depends on it!\n\nThought:"}, {"role": "assistant", "content": - "Thought: The first step is to brainstorm and research interesting ideas for - the article. Since the task involves both research and writing, I will need - to delegate the brainstorming of ideas to the Researcher and the writing of - the paragraphs to the Senior Writer. \n\nAction: Delegate work to coworker\nAction - Input: {\"task\": \"Come up with a list of 5 interesting ideas to explore for - an article.\", \"context\": \"We need a list of 5 interesting ideas that would - make compelling articles. The topics should be relevant, timely, and engaging - for a broad audience.\", \"coworker\": \"Researcher\"}\nObservation: 1. **The - Rise of AI in Healthcare: Transforming Patient Care and Diagnostics**\n \"Imagine - a world where routine check-ups and complex diagnostics are seamlessly conducted - by intelligent systems. AI in healthcare is rapidly transforming this vision - into reality, enhancing patient care through early and accurate disease detection, - personalized treatment plans, and continuous health monitoring. This revolution - is not just a futuristic concept; it''s already happening with AI-driven technologies - enabling doctors to make more informed decisions and offering patients a more - proactive approach to their health. The ethical and privacy concerns, however, - pose new challenges that the industry must navigate to ensure responsible advancement.\"\n\n2. - **AI Agents in the Workplace: Boosting Productivity and Shaping the Future of - Jobs**\n \"The modern workplace is being redefined by the integration of AI - agents, intelligent systems designed to handle repetitive tasks, analyze vast - amounts of data, and assist in complex decision-making processes. These innovations - are boosting productivity, allowing employees to focus on creativity and strategic - thinking. The rise of AI in the workspace heralds a future where human and machine - collaboration leads to unparalleled efficiencies and novel job opportunities. - However, this shift also brings with it challenges related to job displacement - and the urgent need for reskilling the workforce to thrive in an AI-augmented - environment.\"\n\n3. **Ethical AI: Ensuring Fairness, Accountability, and Transparency**\n \"As - AI technology becomes more pervasive, the quest for ethical AI has become critical. - Ensuring fairness, accountability, and transparency in AI systems is essential - to prevent biases and promote trust. This involves creating robust frameworks - and guidelines that govern AI development and deployment. Real-world case studies - highlight both the triumphs and the tribulations of tech companies as they navigate - the complex moral landscape that AI presents. As society grapples with these - challenges, the push for ethical AI continues to drive innovation and policy-making - in the tech industry.\"\n\n4. **AI-Powered Startups: Innovators Changing the - Tech Landscape**\n \"In the bustling world of tech startups, AI-powered companies - are emerging as the vanguards of innovation, disrupting traditional business - models and reshaping entire industries. From revolutionizing financial services - to pioneering advances in healthcare and beyond, these startups are at the forefront - of technological progress. By leveraging machine learning and AI, they are solving - complex problems and bringing novel solutions to market at an unprecedented - pace. Investors are taking notice, pouring capital into these ventures, and - fueling a new wave of growth and competition in the AI ecosystem.\"\n\n5. **The - Future of AI in Entertainment: From Movies to Video Games**\n \"The entertainment - industry is undergoing a seismic shift driven by AI technology. In movies, AI - is revolutionizing special effects, enabling filmmakers to create stunning, - hyper-realistic worlds. In video games, AI is enhancing player experiences by - creating adaptive, intelligent characters and dynamic storylines that respond - to player choices. This transformative power of AI promises to push the boundaries - of creativity, offering audiences more immersive and interactive entertainment - experiences. As the technology continues to evolve, the future of entertainment - looks set to be more exciting and unpredictable than ever before.\""}, {"role": - "assistant", "content": "Thought: I''ll proceed to delegate the writing of one - amazing paragraph highlight for each of the identified ideas to the Senior Writer, - ensuring each topic is adequately detailed to showcase its potential.\n\nAction: - Delegate work to coworker\nAction Input: {\"task\": \"Write one amazing paragraph - highlight for each of these 5 ideas that showcases how good an article about - these topics could be.\", \"context\": \"Here are the 5 ideas:\\n1. The Rise - of AI in Healthcare: Transforming Patient Care and Diagnostics - Explore how - AI is revolutionizing healthcare through enhanced diagnostic tools, personalized - treatment plans, and post-operative care. Discuss current achievements, potential - future applications, and ethical considerations.\\n2. AI Agents in the Workplace: - Boosting Productivity and Shaping the Future of Jobs - Examine the integration - of AI agents in various industries. Highlight how these intelligent agents are - improving efficiency, automating mundane tasks, and assisting in decision-making - processes. Address potential job displacement and the need for upskilling the - workforce.\\n3. Ethical AI: Ensuring Fairness, Accountability, and Transparency - - Delve into the ethical implications of AI deployment. Analyze the frameworks - and guidelines being proposed to ensure AI systems are fair, transparent, and - accountable. Include case studies of ethical dilemmas faced by companies using - AI.\\n4. AI-Powered Startups: Innovators Changing the Tech Landscape - Provide - an overview of how AI-driven startups are disrupting traditional business models - and industries. Profile some of the most innovative startups, their business - models, and the unique challenges they face. Discuss the role of venture capital - in fostering AI innovation.\\n5. The Future of AI in Entertainment: From Movies - to Video Games - Investigate how AI is transforming the entertainment industry, - from scriptwriting and special effects in movies to creating more immersive - and intelligent experiences in video games. Offer predictions about future trends - and possible impacts on content creation and consumption.\", \"coworker\": \"Senior - Writer\"}\nObservation:\nObservation: 1. **The Rise of AI in Healthcare: Transforming - Patient Care and Diagnostics**\n AI is undeniably revolutionizing the healthcare - sector, introducing transformative changes in patient care and diagnostics. - With the advent of advanced diagnostic tools powered by AI, healthcare professionals - can now detect and monitor diseases with unprecedented accuracy and speed. Personalized - treatment plans, which were once a distant dream, are becoming a reality as - AI algorithms analyze vast amounts of patient data, tailoring treatments that - are specific to individual needs. Post-operative care has also seen significant - improvements, with AI-driven monitoring systems ensuring that patients receive - timely and effective care. Current achievements like IBM Watson''s oncology - platform or Google''s DeepMind in eye care are just the beginning, with future - applications promising to further enhance surgical robotics, predictive analytics, - and remote patient monitoring. However, alongside these advancements, ethical - considerations such as patient privacy, data security, and decision-making transparency - must be rigorously addressed to fully harness AI\u2019s potential in healthcare.\n\n2. - **AI Agents in the Workplace: Boosting Productivity and Shaping the Future of - Jobs**\n The integration of AI agents in the workplace marks a pivotal shift - in how businesses operate and compete. These intelligent agents are revolutionizing - various industries by automating mundane tasks, optimizing workflows, and providing - actionable insights from data analysis. For instance, AI chatbots in customer - service can handle routine inquiries, freeing human agents to tackle more complex - issues. In manufacturing, AI systems predict equipment failures, reducing downtime - and maintaining peak productivity. By enhancing operational efficiency, AI agents - not only boost productivity but also reshape the future of jobs. While the risk - of job displacement is a valid concern, it concurrently heralds opportunities - for upskilling and creating new roles that harness human creativity and strategic - thinking. Preparing the workforce for these changes is crucial, fostering a - symbiotic relationship between AI and human labor to drive innovation and economic - growth.\n\n3. **Ethical AI: Ensuring Fairness, Accountability, and Transparency**\n As - AI technologies become increasingly pervasive, ensuring their ethical deployment - is paramount. Ethical AI demands the formulation and implementation of robust - frameworks that enshrine fairness, accountability, and transparency. Such frameworks - are essential in mitigating biases embedded within AI systems, which can perpetuate - and even exacerbate inequality. Case studies, such as the controversy surrounding - facial recognition systems and their racial biases, highlight the pressing need - for ethical oversight. 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Machine - learning models analyze vast datasets to create compelling narratives and optimize - marketing strategies. Video games have also been revolutionized by AI, with - non-player characters (NPCs) displaying more lifelike behaviors and adaptive - learning algorithms personalizing gaming experiences. Future trends indicate - a seamless blend of AI with augmented reality (AR) and virtual reality (VR), - creating interactive environments where the line between virtual and real worlds - blur. 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I have gathered the required ideas and crafted corresponding\ - \ paragraphs to meet the expectations of the task.\\n\\nFinal Answer:\\n1.\ - \ **The Rise of AI in Healthcare: Transforming Patient Care and Diagnostics**\\\ - n AI is undeniably revolutionizing the healthcare sector, introducing transformative\ - \ changes in patient care and diagnostics. With the advent of advanced diagnostic\ - \ tools powered by AI, healthcare professionals can now detect and monitor\ - \ diseases with unprecedented accuracy and speed. Personalized treatment plans,\ - \ which were once a distant dream, are becoming a reality as AI algorithms\ - \ analyze vast amounts of patient data, tailoring treatments that are specific\ - \ to individual needs. Post-operative care has also seen significant improvements,\ - \ with AI-driven monitoring systems ensuring that patients receive timely\ - \ and effective care. 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Ethical AI demands the formulation\ - \ and implementation of robust frameworks that enshrine fairness, accountability,\ - \ and transparency. Such frameworks are essential in mitigating biases embedded\ - \ within AI systems, which can perpetuate and even exacerbate inequality.\ - \ Case studies, such as the controversy surrounding facial recognition systems\ - \ and their racial biases, highlight the pressing need for ethical oversight.\ - \ Transparency in how AI models make decisions, coupled with accountability\ - \ measures for their outcomes, establishes trust and integrity in AI applications.\ - \ Legislative frameworks from the EUโ€™s GDPR to the IEEEโ€™s global initiatives\ - \ for ethical AI provide the scaffolding for these principles. Companies are\ - \ now faced with the challenge of integrating these ethical considerations\ - \ into their development processes, ensuring that AI advancements benefit\ - \ all of society equitably.\\n\\n4. **AI-Powered Startups: Innovators Changing\ - \ the Tech Landscape**\\n AI-powered startups are at the forefront of a\ - \ technological renaissance, disrupting traditional business models and reshaping\ - \ industries. These startups leverage AI to offer innovative solutions, whether\ - \ it's automating financial trading with precision algorithms or enhancing\ - \ cybersecurity through predictive analytics. High-profile examples like OpenAI\ - \ and UiPath illustrate the transformative potential of AI-driven innovation.\ - \ These companies face unique challenges, from securing sufficient funding\ - \ to navigating the complexities of large-scale implementation. Venture capital\ - \ plays a critical role in this ecosystem, providing the necessary resources\ - \ and support to turn bold ideas into impactful realities. The synergy between\ - \ AI innovation and entrepreneurial spirit is fostering a dynamic landscape\ - \ where the boundaries of technology and commerce are continually expanding.\\\ - n\\n5. **The Future of AI in Entertainment: From Movies to Video Games**\\\ - n The entertainment industry is undergoing a metamorphic change with the\ - \ integration of AI technologies, paving the way for more immersive and engaging\ - \ experiences. In movies, AI streamlines the scriptwriting process, enhances\ - \ special effects, and even predicts box office success. Machine learning\ - \ models analyze vast datasets to create compelling narratives and optimize\ - \ marketing strategies. Video games have also been revolutionized by AI, with\ - \ non-player characters (NPCs) displaying more lifelike behaviors and adaptive\ - \ learning algorithms personalizing gaming experiences. Future trends indicate\ - \ a seamless blend of AI with augmented reality (AR) and virtual reality (VR),\ - \ creating interactive environments where the line between virtual and real\ - \ worlds blur. 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Discuss how these advances are aiding + policymakers in making informed decisions to address climate risks and sustainability + goals.\\n\\n4. **The Ethical Frontiers of AI in Healthcare: Balancing Innovation + with Patient Privacy** \\nInvestigate ethical challenges posed by AI applications + in healthcare, including diagnosis, personalized treatment, and patient data + management. Focus on balancing rapid technological innovation with privacy, + bias mitigation, and regulatory frameworks to ensure equitable access and trust.\\n\\n5. + **Quantum Computing Meets AI: Exploring the Next Leap in Computational Power** + \ \\nCover the intersection of quantum computing and artificial intelligence, + exploring how quantum algorithms could accelerate AI training processes and + solve problems beyond the reach of classical computers. Outline current research, + potential breakthroughs, and the timeline for real-world applications.\\n\\nEach + of these topics is timely, relevant, and has the potential to engage readers + interested in cutting-edge technology, societal impact, and future trends. Let + me know if you want me to help develop an outline or deeper research into any + of these areas!\"},{\"role\":\"tool\",\"tool_call_id\":\"call_j4KH2SGZvNeioql0HcRQ9NTp\",\"name\":\"ask_question_to_coworker\",\"content\":\"Absolutely! + To create compelling and engaging AI articles that stand out, we need to go + beyond surface-level discussions and deliver fresh perspectives that challenge + assumptions and spark curiosity. Here are five unique angles with their highlight + paragraphs that could really captivate our readers:\\n\\n1. **The Hidden Psychology + of AI Agents: How They Learn Human Biases and What That Means for Our Future** + \ \\n*Highlight:* AI agents don\u2019t just process data\u2014they absorb the + subtle nuances and biases embedded in human language, behavior, and culture. + This article dives deep into the psychological parallels between AI learning + mechanisms and human cognitive biases, revealing surprising ways AI can both + mirror and amplify our prejudices. Understanding these dynamics is crucial for + building trustworthy AI systems and reshaping the future relationship between + humans and machines.\\n\\n2. **From Assistants to Autonomous Creators: The Rise + of AI Agents as Artists, Writers, and Innovators** \\n*Highlight:* What do + we lose and gain when AI agents start producing original art, literature, and + innovations? This piece explores groundbreaking examples where AI isn\u2019t + just a tool but a creative partner that challenges our definition of authorship + and genius. We\u2019ll examine ethical dilemmas, collaborative workflows, and + the exciting frontier where human intuition meets algorithmic originality.\\n\\n3. + **AI Agents in the Wild: How Decentralized Autonomous Organizations Could Redefine + Economy and Governance** \\n*Highlight:* Imagine AI agents operating autonomously + in decentralized networks, making real-time decisions that affect finances, + resource management, and governance without human intervention. This article + uncovers how DAOs powered by AI agents might spontaneously evolve new forms + of organization\u2014transparent, efficient, and resistant to traditional corruption. + We\u2019ll investigate early case studies and speculate on how this might disrupt + centuries-old societal structures.\\n\\n4. **Beyond Chatbots: The Next Generation + of AI Agents as Empathetic Digital Companions** \\n*Highlight:* Moving past + scripted conversations, emerging AI agents simulate empathy and emotional intelligence + in ways that can transform mental health care, education, and companionship. + This article provides an insider look at the complex algorithms and biofeedback + mechanisms enabling AI to recognize, respond to, and foster human emotions\u2014potentially + filling gaps in underserved populations while raising profound questions about + authenticity and connection.\\n\\n5. **The Environmental Toll of AI Agents: + Unmasking the Ecological Cost of Intelligent Automation** \\n*Highlight:* While + AI promises efficiency and innovation, the environmental footprint of training + and deploying millions of AI agents is rarely discussed. This eye-opening article + quantifies the energy demands of current models, challenges the narrative of + AI as an unequivocal green solution, and explores emerging approaches pathing + toward sustainable intelligent automation\u2014an urgent conversation for an + increasingly eco-conscious tech landscape.\\n\\nEach of these angles opens a + door to rich storytelling that blends technical depth, ethical inquiry, and + visionary implications\u2014perfect for readers hungry for insight that\u2019s + both sophisticated and accessible. Let me know which ones resonate most, or + if you want me to refine any into full article outlines!\"},{\"role\":\"user\",\"content\":\"Analyze + the tool result. If requirements are met, provide the Final Answer. Otherwise, + call the next tool. 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Discuss implications + for job automation, human-AI collaboration, and ethical considerations surrounding + decision-making autonomy.\\n\\n2. **Generative AI Beyond Text: Innovations + in Audio, Video, and 3D Content Creation** \\nDelve into advancements in + generative AI models that create not only text but also realistic audio, video + content, virtual environments, and 3D models. Highlight applications in gaming, + entertainment, education, and digital marketing, as well as challenges like + misinformation and deepfake detection.\\n\\n3. **AI-Driven Climate Modeling: + Enhancing Predictive Accuracy to Combat Climate Change** \\nExamine how AI + and machine learning are improving climate models by analyzing vast datasets, + uncovering patterns, and simulating environmental scenarios. Discuss how these + advances are aiding policymakers in making informed decisions to address climate + risks and sustainability goals.\\n\\n4. **The Ethical Frontiers of AI in Healthcare: + Balancing Innovation with Patient Privacy** \\nInvestigate ethical challenges + posed by AI applications in healthcare, including diagnosis, personalized + treatment, and patient data management. Focus on balancing rapid technological + innovation with privacy, bias mitigation, and regulatory frameworks to ensure + equitable access and trust.\\n\\n5. **Quantum Computing Meets AI: Exploring + the Next Leap in Computational Power** \\nCover the intersection of quantum + computing and artificial intelligence, exploring how quantum algorithms could + accelerate AI training processes and solve problems beyond the reach of classical + computers. 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\\n \\n - \ \\\"Open\\n \\n

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\\n \\n \\\"crewAIInc%2FcrewAI\\n \\n

\\n\\n

\\n - \ Homepage\\n \xB7\\n Docs\\n - \ \xB7\\n Start Cloud Trial\\n - \ \xB7\\n Blog\\n \xB7\\n Forum\\n

\\n\\n

\\n - \ \\n \\\"GitHub\\n \\n \\n - \ \\\"GitHub\\n \\n \\n - \ \\\"GitHub\\n \\n \\n - \ \\\"GitHub\\n \\n \\n - \ \\\"License:\\n \\n

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\\n \\n - \ \\\"PyPI\\n - \ \\n \\n \\\"PyPI\\n \\n \\n - \ \\\"Twitter\\n \\n

\\n\\n### Fast and Flexible Multi-Agent - Automation Framework\\n\\n> CrewAI is a lean, lightning-fast Python framework - built entirely from scratch\u2014completely **independent of LangChain or - other agent frameworks**.\\n> It empowers developers with both high-level - simplicity and precise low-level control, ideal for creating autonomous AI - agents tailored to any scenario.\\n\\n- **CrewAI Crews**: Optimize for autonomy - and collaborative intelligence.\\n- **CrewAI Flows**: Enable granular, event-driven - control, single LLM calls for precise task orchestration and supports Crews - natively\\n\\nWith over 100,000 developers certified through our community - courses at [learn.crewai.com](https://learn.crewai.com), CrewAI is rapidly - becoming the\\nstandard for enterprise-ready AI automation.\\n\\n# CrewAI - AMP Suite\\n\\nCrewAI AMP Suite is a comprehensive bundle tailored for organizations - that require secure, scalable, and easy-to-manage agent-driven automation.\\n\\nYou - can try one part of the suite the [Crew Control Plane for free](https://app.crewai.com)\\n\\n## - Crew Control Plane Key Features:\\n\\n- **Tracing & Observability**: Monitor - and track your AI agents and workflows in real-time, including metrics, logs, - and traces.\\n- **Unified Control Plane**: A centralized platform for managing, - monitoring, and scaling your AI agents and workflows.\\n- **Seamless Integrations**: - Easily connect with existing enterprise systems, data sources, and cloud infrastructure.\\n- - **Advanced Security**: Built-in robust security and compliance measures ensuring - safe deployment and management.\\n- **Actionable Insights**: Real-time analytics - and reporting to optimize performance and decision-making.\\n- **24/7 Support**: - Dedicated enterprise support to ensure uninterrupted operation and quick resolution - of issues.\\n- **On-premise and Cloud Deployment Options**: Deploy CrewAI - AMP on-premise or in the cloud, depending on your security and compliance - requirements.\\n\\nCrewAI AMP is designed for enterprises seeking a powerful, - reliable solution to transform complex business processes into efficient,\\nintelligent - automations.\\n\\n## Table of contents\\n\\n- [Why CrewAI?](#why-crewai)\\n- - [Getting Started](#getting-started)\\n- [Key Features](#key-features)\\n- - [Understanding Flows and Crews](#understanding-flows-and-crews)\\n- [CrewAI - vs LangGraph](#how-crewai-compares)\\n- [Examples](#examples)\\n - [Quick - Tutorial](#quick-tutorial)\\n - [Write Job Descriptions](#write-job-descriptions)\\n - \ - [Trip Planner](#trip-planner)\\n - [Stock Analysis](#stock-analysis)\\n - \ - [Using Crews and Flows Together](#using-crews-and-flows-together)\\n- - [Connecting Your Crew to a Model](#connecting-your-crew-to-a-model)\\n- [How - CrewAI Compares](#how-crewai-compares)\\n- [Frequently Asked Questions (FAQ)](#frequently-asked-questions-faq)\\n- - [Contribution](#contribution)\\n- [Telemetry](#telemetry)\\n- [License](#license)\\n\\n## - Why CrewAI?\\n\\n
\\n - \ \\\"CrewAI\\n
\\n\\nCrewAI - unlocks the true potential of multi-agent automation, delivering the best-in-class - combination of speed, flexibility, and control with either Crews of AI Agents - or Flows of Events:\\n\\n- **Standalone Framework**: Built from scratch, independent - of LangChain or any other agent framework.\\n- **High Performance**: Optimized - for speed and minimal resource usage, enabling faster execution.\\n- **Flexible - Low Level Customization**: Complete freedom to customize at both high and - low levels - from overall workflows and system architecture to granular agent - behaviors, internal prompts, and execution logic.\\n- **Ideal for Every Use - Case**: Proven effective for both simple tasks and highly complex, real-world, - enterprise-grade scenarios.\\n- **Robust Community**: Backed by a rapidly - growing community of over **100,000 certified** developers offering comprehensive - support and resources.\\n\\nCrewAI empowers developers and enterprises to - confidently build intelligent automations, bridging the gap between simplicity, - flexibility, and performance.\\n\\n## Getting Started\\n\\nSetup and run your - first CrewAI agents by following this tutorial.\\n\\n[![CrewAI Getting Started - Tutorial](https://img.youtube.com/vi/-kSOTtYzgEw/hqdefault.jpg)](https://www.youtube.com/watch?v=-kSOTtYzgEw - \\\"CrewAI Getting Started Tutorial\\\")\\n\\n###\\n\\nLearning Resources\\n\\nLearn - CrewAI through our comprehensive courses:\\n\\n- [Multi AI Agent Systems with - CrewAI](https://www.deeplearning.ai/short-courses/multi-ai-agent-systems-with-crewai/) - - Master the fundamentals of multi-agent systems\\n- [Practical Multi AI Agents - and Advanced Use Cases](https://www.deeplearning.ai/short-courses/practical-multi-ai-agents-and-advanced-use-cases-with-crewai/) - - Deep dive into advanced implementations\\n\\n### Understanding Flows and - Crews\\n\\nCrewAI offers two powerful, complementary approaches that work - seamlessly together to build sophisticated AI applications:\\n\\n1. **Crews**: - Teams of AI agents with true autonomy and agency, working together to accomplish - complex tasks through role-based collaboration. Crews enable:\\n\\n - Natural, - autonomous decision-making between agents\\n - Dynamic task delegation and - collaboration\\n - Specialized roles with defined goals and expertise\\n - \ - Flexible problem-solving approaches\\n\\n2. **Flows**: Production-ready, - event-driven workflows that deliver precise control over complex automations. - Flows provide:\\n\\n - Fine-grained control over execution paths for real-world - scenarios\\n - Secure, consistent state management between tasks\\n - - Clean integration of AI agents with production Python code\\n - Conditional - branching for complex business logic\\n\\nThe true power of CrewAI emerges - when combining Crews and Flows. This synergy allows you to:\\n\\n- Build complex, - production-grade applications\\n- Balance autonomy with precise control\\n- - Handle sophisticated real-world scenarios\\n- Maintain clean, maintainable - code structure\\n\\n### Getting Started with Installation\\n\\nTo get started - with CrewAI, follow these simple steps:\\n\\n### 1. Installation\\n\\nEnsure - you have Python >=3.10 <3.14 installed on your system. CrewAI uses [UV](https://docs.astral.sh/uv/) - for dependency management and package handling, offering a seamless setup - and execution experience.\\n\\nFirst, install CrewAI:\\n\\n```shell\\npip - install crewai\\n```\\n\\nIf you want to install the 'crewai' package along - with its optional features that include additional tools for agents, you can - do so by using the following command:\\n\\n```shell\\npip install 'crewai[tools]'\\n```\\n\\nThe - command above installs the basic package and also adds extra components which - require more dependencies to function.\\n\\n### Troubleshooting Dependencies\\n\\nIf - you encounter issues during installation or usage, here are some common solutions:\\n\\n#### - Common Issues\\n\\n1. **ModuleNotFoundError: No module named 'tiktoken'**\\n\\n - \ - Install tiktoken explicitly: `pip install 'crewai[embeddings]'`\\n - - If using embedchain or other tools: `pip install 'crewai[tools]'`\\n\\n2. - **Failed building wheel for tiktoken**\\n\\n - Ensure Rust compiler is installed - (see installation steps above)\\n - For Windows: Verify Visual C++ Build - Tools are installed\\n - Try upgrading pip: `pip install --upgrade pip`\\n - \ - If issues persist, use a pre-built wheel: `pip install tiktoken --prefer-binary`\\n\\n### - 2. Setting Up Your Crew with the YAML Configuration\\n\\nTo create a new CrewAI - project, run the following CLI (Command Line Interface) command:\\n\\n```shell\\ncrewai - create crew \\n```\\n\\nThis command creates a new project folder - with the following structure:\\n\\n```\\nmy_project/\\n\u251C\u2500\u2500 - .gitignore\\n\u251C\u2500\u2500 pyproject.toml\\n\u251C\u2500\u2500 README.md\\n\u251C\u2500\u2500 - .env\\n\u2514\u2500\u2500 src/\\n \u2514\u2500\u2500 my_project/\\n \u251C\u2500\u2500 - __init__.py\\n \u251C\u2500\u2500 main.py\\n \u251C\u2500\u2500 - crew.py\\n \u251C\u2500\u2500 tools/\\n \u2502 \u251C\u2500\u2500 - custom_tool.py\\n \u2502 \u2514\u2500\u2500 __init__.py\\n \u2514\u2500\u2500 - config/\\n \u251C\u2500\u2500 agents.yaml\\n \u2514\u2500\u2500 - tasks.yaml\\n```\\n\\nYou can now start developing your crew by editing the - files in the `src/my_project` folder. The `main.py` file is the entry point - of the project, the `crew.py` file is where you define your crew, the `agents.yaml` - file is where you define your agents, and the `tasks.yaml` file is where you - define your tasks.\\n\\n#### To customize your project, you can:\\n\\n- Modify - `src/my_project/config/agents.yaml` to define your agents.\\n- Modify `src/my_project/config/tasks.yaml` - to define your tasks.\\n- Modify `src/my_project/crew.py` to add your own - logic, tools, and specific arguments.\\n- Modify `src/my_project/main.py` - to add custom inputs for your agents and tasks.\\n- Add your environment variables - into the `.env` file.\\n\\n#### Example of a simple crew with a sequential - process:\\n\\nInstantiate your crew:\\n\\n```shell\\ncrewai create crew latest-ai-development\\n```\\n\\nModify - the files as needed to fit your use case:\\n\\n**agents.yaml**\\n\\n```yaml\\n# - src/my_project/config/agents.yaml\\nresearcher:\\n role: >\\n {topic} - Senior Data Researcher\\n goal: >\\n Uncover cutting-edge developments - in {topic}\\n backstory: >\\n You're a seasoned researcher with a knack - for uncovering the latest\\n developments in {topic}. Known for your ability - to find the most relevant\\n information and present it in a clear and - concise manner.\\n\\nreporting_analyst:\\n role: >\\n {topic} Reporting - Analyst\\n goal: >\\n Create detailed reports based on {topic} data analysis - and research findings\\n backstory: >\\n You're a meticulous analyst with - a keen eye for detail. You're known for\\n your ability to turn complex - data into clear and concise reports, making\\n it easy for others to understand - and act on the information you provide.\\n```\\n\\n**tasks.yaml**\\n\\n````yaml\\n# - src/my_project/config/tasks.yaml\\nresearch_task:\\n description: >\\n Conduct - a thorough research about {topic}\\n Make sure you find any interesting - and relevant information given\\n the current year is 2025.\\n expected_output: - >\\n A list with 10 bullet points of the most relevant information about - {topic}\\n agent: researcher\\n\\nreporting_task:\\n description: >\\n Review - the context you got and expand each topic into a full section for a report.\\n - \ Make sure the report is detailed and contains any and all relevant information.\\n - \ expected_output: >\\n A fully fledge reports with the mains topics, each - with a full section of information.\\n Formatted as markdown without '```'\\n - \ agent: reporting_analyst\\n output_file: report.md\\n````\\n\\n**crew.py**\\n\\n```python\\n# - src/my_project/crew.py\\nfrom crewai import Agent, Crew, Process, Task\\nfrom - crewai.project import CrewBase, agent, crew, task\\nfrom crewai_tools import - SerperDevTool\\nfrom crewai.agents.agent_builder.base_agent import BaseAgent\\nfrom - typing import List\\n\\n@CrewBase\\nclass LatestAiDevelopmentCrew():\\n\\t\\\"\\\"\\\"LatestAiDevelopment - crew\\\"\\\"\\\"\\n\\tagents: List[BaseAgent]\\n\\ttasks: List[Task]\\n\\n\\t@agent\\n\\tdef - researcher(self) -> Agent:\\n\\t\\treturn Agent(\\n\\t\\t\\tconfig=self.agents_config['researcher'],\\n\\t\\t\\tverbose=True,\\n\\t\\t\\ttools=[SerperDevTool()]\\n\\t\\t)\\n\\n\\t@agent\\n\\tdef - reporting_analyst(self) -> Agent:\\n\\t\\treturn Agent(\\n\\t\\t\\tconfig=self.agents_config['reporting_analyst'],\\n\\t\\t\\tverbose=True\\n\\t\\t)\\n\\n\\t@task\\n\\tdef - research_task(self) -> Task:\\n\\t\\treturn Task(\\n\\t\\t\\tconfig=self.tasks_config['research_task'],\\n\\t\\t)\\n\\n\\t@task\\n\\tdef - reporting_task(self) -> Task:\\n\\t\\treturn Task(\\n\\t\\t\\tconfig=self.tasks_config['reporting_task'],\\n\\t\\t\\toutput_file='report.md'\\n\\t\\t)\\n\\n\\t@crew\\n\\tdef - crew(self) -> Crew:\\n\\t\\t\\\"\\\"\\\"Creates the LatestAiDevelopment crew\\\"\\\"\\\"\\n\\t\\treturn - Crew(\\n\\t\\t\\tagents=self.agents, # Automatically created by the @agent - decorator\\n\\t\\t\\ttasks=self.tasks, # Automatically created by the @task - decorator\\n\\t\\t\\tprocess=Process.sequential,\\n\\t\\t\\tverbose=True,\\n\\t\\t)\\n```\\n\\n**main.py**\\n\\n```python\\n#!/usr/bin/env - python\\n# src/my_project/main.py\\nimport sys\\nfrom latest_ai_development.crew - import LatestAiDevelopmentCrew\\n\\ndef run():\\n \\\"\\\"\\\"\\n Run - the crew.\\n \\\"\\\"\\\"\\n inputs = {\\n 'topic': 'AI Agents'\\n - \ }\\n LatestAiDevelopmentCrew().crew().kickoff(inputs=inputs)\\n```\\n\\n### - 3. Running Your Crew\\n\\nBefore running your crew, make sure you have the - following keys set as environment variables in your `.env` file:\\n\\n- An - [OpenAI API key](https://platform.openai.com/account/api-keys) (or other LLM - API key): `OPENAI_API_KEY=sk-...`\\n- A [Serper.dev](https://serper.dev/) - API key: `SERPER_API_KEY=YOUR_KEY_HERE`\\n\\nLock the dependencies and install - them by using the CLI command but first, navigate to your project directory:\\n\\n```shell\\ncd - my_project\\ncrewai install (Optional)\\n```\\n\\nTo run your crew, execute - the following command in the root of your project:\\n\\n```bash\\ncrewai run\\n```\\n\\nor\\n\\n```bash\\npython - src/my_project/main.py\\n```\\n\\nIf an error happens due to the usage of - poetry, please run the following command to update your crewai package:\\n\\n```bash\\ncrewai - update\\n```\\n\\nYou should see the output in the console and the `report.md` - file should be created in the root of your project with the full final report.\\n\\nIn - addition to the sequential process, you can use the hierarchical process, - which automatically assigns a manager to the defined crew to properly coordinate - the planning and execution of tasks through delegation and validation of results. - [See more about the processes here](https://docs.crewai.com/core-concepts/Processes/).\\n\\n## - Key Features\\n\\nCrewAI stands apart as a lean, standalone, high-performance - multi-AI Agent framework delivering simplicity, flexibility, and precise control\u2014free - from the complexity and limitations found in other agent frameworks.\\n\\n- - **Standalone & Lean**: Completely independent from other frameworks like LangChain, - offering faster execution and lighter resource demands.\\n- **Flexible & Precise**: - Easily orchestrate autonomous agents through intuitive [Crews](https://docs.crewai.com/concepts/crews) - or precise [Flows](https://docs.crewai.com/concepts/flows), achieving perfect - balance for your needs.\\n- **Seamless Integration**: Effortlessly combine - Crews (autonomy) and Flows (precision) to create complex, real-world automations.\\n- - **Deep Customization**: Tailor every aspect\u2014from high-level workflows - down to low-level internal prompts and agent behaviors.\\n- **Reliable Performance**: - Consistent results across simple tasks and complex, enterprise-level automations.\\n- - **Thriving Community**: Backed by robust documentation and over 100,000 certified - developers, providing exceptional support and guidance.\\n\\nChoose CrewAI - to easily build powerful, adaptable, and production-ready AI automations.\\n\\n## - Examples\\n\\nYou can test different real life examples of AI crews in the - [CrewAI-examples repo](https://github.com/crewAIInc/crewAI-examples?tab=readme-ov-file):\\n\\n- - [Landing Page Generator](https://github.com/crewAIInc/crewAI-examples/tree/main/crews/landing_page_generator)\\n- - [Having Human input on the execution](https://docs.crewai.com/how-to/Human-Input-on-Execution)\\n- - [Trip Planner](https://github.com/crewAIInc/crewAI-examples/tree/main/crews/trip_planner)\\n- - [Stock Analysis](https://github.com/crewAIInc/crewAI-examples/tree/main/crews/stock_analysis)\\n\\n### - Quick Tutorial\\n\\n[![CrewAI Tutorial](https://img.youtube.com/vi/tnejrr-0a94/maxresdefault.jpg)](https://www.youtube.com/watch?v=tnejrr-0a94 - \\\"CrewAI Tutorial\\\")\\n\\n### Write Job Descriptions\\n\\n[Check out code - for this example](https://github.com/crewAIInc/crewAI-examples/tree/main/crews/job-posting) - or watch a video below:\\n\\n[![Jobs postings](https://img.youtube.com/vi/u98wEMz-9to/maxresdefault.jpg)](https://www.youtube.com/watch?v=u98wEMz-9to - \\\"Jobs postings\\\")\\n\\n### Trip Planner\\n\\n[Check out code for this - example](https://github.com/crewAIInc/crewAI-examples/tree/main/crews/trip_planner) - or watch a video below:\\n\\n[![Trip Planner](https://img.youtube.com/vi/xis7rWp-hjs/maxresdefault.jpg)](https://www.youtube.com/watch?v=xis7rWp-hjs - \\\"Trip Planner\\\")\\n\\n### Stock Analysis\\n\\n[Check out code for this - example](https://github.com/crewAIInc/crewAI-examples/tree/main/crews/stock_analysis) - or watch a video below:\\n\\n[![Stock Analysis](https://img.youtube.com/vi/e0Uj4yWdaAg/maxresdefault.jpg)](https://www.youtube.com/watch?v=e0Uj4yWdaAg - \\\"Stock Analysis\\\")\\n\\n### Using Crews and Flows Together\\n\\nCrewAI's - power truly shines when combining Crews with Flows to create sophisticated - automation pipelines.\\nCrewAI flows support logical operators like `or_` - and `and_` to combine multiple conditions. This can be used with `@start`, - `@listen`, or `@router` decorators to create complex triggering conditions.\\n\\n- - `or_`: Triggers when any of the specified conditions are met.\\n- `and_`Triggers - when all of the specified conditions are met.\\n\\nHere's how you can orchestrate - multiple Crews within a Flow:\\n\\n```python\\nfrom crewai.flow.flow import - Flow, listen, start, router, or_\\nfrom crewai import Crew, Agent, Task, Process\\nfrom - pydantic import BaseModel\\n\\n# Define structured state for precise control\\nclass - MarketState(BaseModel):\\n sentiment: str = \\\"neutral\\\"\\n confidence: - float = 0.0\\n recommendations: list = []\\n\\nclass AdvancedAnalysisFlow(Flow[MarketState]):\\n - \ @start()\\n def fetch_market_data(self):\\n # Demonstrate low-level - control with structured state\\n self.state.sentiment = \\\"analyzing\\\"\\n - \ return {\\\"sector\\\": \\\"tech\\\", \\\"timeframe\\\": \\\"1W\\\"} - \ # These parameters match the task description template\\n\\n @listen(fetch_market_data)\\n - \ def analyze_with_crew(self, market_data):\\n # Show crew agency - through specialized roles\\n analyst = Agent(\\n role=\\\"Senior - Market Analyst\\\",\\n goal=\\\"Conduct deep market analysis with - expert insight\\\",\\n backstory=\\\"You're a veteran analyst known - for identifying subtle market patterns\\\"\\n )\\n researcher - = Agent(\\n role=\\\"Data Researcher\\\",\\n goal=\\\"Gather - and validate supporting market data\\\",\\n backstory=\\\"You excel - at finding and correlating multiple data sources\\\"\\n )\\n\\n analysis_task - = Task(\\n description=\\\"Analyze {sector} sector data for the - past {timeframe}\\\",\\n expected_output=\\\"Detailed market analysis - with confidence score\\\",\\n agent=analyst\\n )\\n research_task - = Task(\\n description=\\\"Find supporting data to validate the - analysis\\\",\\n expected_output=\\\"Corroborating evidence and - potential contradictions\\\",\\n agent=researcher\\n )\\n\\n - \ # Demonstrate crew autonomy\\n analysis_crew = Crew(\\n agents=[analyst, - researcher],\\n tasks=[analysis_task, research_task],\\n process=Process.sequential,\\n - \ verbose=True\\n )\\n return analysis_crew.kickoff(inputs=market_data) - \ # Pass market_data as named inputs\\n\\n @router(analyze_with_crew)\\n - \ def determine_next_steps(self):\\n # Show flow control with conditional - routing\\n if self.state.confidence > 0.8:\\n return \\\"high_confidence\\\"\\n - \ elif self.state.confidence > 0.5:\\n return \\\"medium_confidence\\\"\\n - \ return \\\"low_confidence\\\"\\n\\n @listen(\\\"high_confidence\\\")\\n - \ def execute_strategy(self):\\n # Demonstrate complex decision making\\n - \ strategy_crew = Crew(\\n agents=[\\n Agent(role=\\\"Strategy - Expert\\\",\\n goal=\\\"Develop optimal market strategy\\\")\\n - \ ],\\n tasks=[\\n Task(description=\\\"Create - detailed strategy based on analysis\\\",\\n expected_output=\\\"Step-by-step - action plan\\\")\\n ]\\n )\\n return strategy_crew.kickoff()\\n\\n - \ @listen(or_(\\\"medium_confidence\\\", \\\"low_confidence\\\"))\\n def - request_additional_analysis(self):\\n self.state.recommendations.append(\\\"Gather - more data\\\")\\n return \\\"Additional analysis required\\\"\\n```\\n\\nThis - example demonstrates how to:\\n\\n1. Use Python code for basic data operations\\n2. - Create and execute Crews as steps in your workflow\\n3. Use Flow decorators - to manage the sequence of operations\\n4. Implement conditional branching - based on Crew results\\n\\n## Connecting Your Crew to a Model\\n\\nCrewAI - supports using various LLMs through a variety of connection options. By default - your agents will use the OpenAI API when querying the model. However, there - are several other ways to allow your agents to connect to models. For example, - you can configure your agents to use a local model via the Ollama tool.\\n\\nPlease - refer to the [Connect CrewAI to LLMs](https://docs.crewai.com/how-to/LLM-Connections/) - page for details on configuring your agents' connections to models.\\n\\n## - How CrewAI Compares\\n\\n**CrewAI's Advantage**: CrewAI combines autonomous - agent intelligence with precise workflow control through its unique Crews - and Flows architecture. The framework excels at both high-level orchestration - and low-level customization, enabling complex, production-grade systems with - granular control.\\n\\n- **LangGraph**: While LangGraph provides a foundation - for building agent workflows, its approach requires significant boilerplate - code and complex state management patterns. The framework's tight coupling - with LangChain can limit flexibility when implementing custom agent behaviors - or integrating with external systems.\\n\\n_P.S. CrewAI demonstrates significant - performance advantages over LangGraph, executing 5.76x faster in certain cases - like this QA task example ([see comparison](https://github.com/crewAIInc/crewAI-examples/tree/main/Notebooks/CrewAI%20Flows%20%26%20Langgraph/QA%20Agent)) - while achieving higher evaluation scores with faster completion times in certain - coding tasks, like in this example ([detailed analysis](https://github.com/crewAIInc/crewAI-examples/blob/main/Notebooks/CrewAI%20Flows%20%26%20Langgraph/Coding%20Assistant/coding_assistant_eval.ipynb))._\\n\\n- - **Autogen**: While Autogen excels at creating conversational agents capable - of working together, it lacks an inherent concept of process. In Autogen, - orchestrating agents' interactions requires additional programming, which - can become complex and cumbersome as the scale of tasks grows.\\n- **ChatDev**: - ChatDev introduced the idea of processes into the realm of AI agents, but - its implementation is quite rigid. Customizations in ChatDev are limited and - not geared towards production environments, which can hinder scalability and - flexibility in real-world applications.\\n\\n## Contribution\\n\\nCrewAI is - open-source and we welcome contributions. If you're looking to contribute, - please:\\n\\n- Fork the repository.\\n- Create a new branch for your feature.\\n- - Add your feature or improvement.\\n- Send a pull request.\\n- We appreciate - your input!\\n\\n### Installing Dependencies\\n\\n```bash\\nuv lock\\nuv sync\\n```\\n\\n### - Virtual Env\\n\\n```bash\\nuv venv\\n```\\n\\n### Pre-commit hooks\\n\\n```bash\\npre-commit - install\\n```\\n\\n### Running Tests\\n\\n```bash\\nuv run pytest .\\n```\\n\\n### - Running static type checks\\n\\n```bash\\nuvx mypy src\\n```\\n\\n### Packaging\\n\\n```bash\\nuv - build\\n```\\n\\n### Installing Locally\\n\\n```bash\\npip install dist/*.tar.gz\\n```\\n\\n## - Telemetry\\n\\nCrewAI uses anonymous telemetry to collect usage data with - the main purpose of helping us improve the library by focusing our efforts - on the most used features, integrations and tools.\\n\\nIt's pivotal to understand - that **NO data is collected** concerning prompts, task descriptions, agents' - backstories or goals, usage of tools, API calls, responses, any data processed - by the agents, or secrets and environment variables, with the exception of - the conditions mentioned. When the `share_crew` feature is enabled, detailed - data including task descriptions, agents' backstories or goals, and other - specific attributes are collected to provide deeper insights while respecting - user privacy. Users can disable telemetry by setting the environment variable - OTEL_SDK_DISABLED to true.\\n\\nData collected includes:\\n\\n- Version of - CrewAI\\n - So we can understand how many users are using the latest version\\n- - Version of Python\\n - So we can decide on what versions to better support\\n- - General OS (e.g. number of CPUs, macOS/Windows/Linux)\\n - So we know what - OS we should focus on and if we could build specific OS related features\\n- - Number of agents and tasks in a crew\\n - So we make sure we are testing - internally with similar use cases and educate people on the best practices\\n- - Crew Process being used\\n - Understand where we should focus our efforts\\n- - If Agents are using memory or allowing delegation\\n - Understand if we improved - the features or maybe even drop them\\n- If Tasks are being executed in parallel - or sequentially\\n - Understand if we should focus more on parallel execution\\n- - Language model being used\\n - Improved support on most used languages\\n- - Roles of agents in a crew\\n - Understand high level use cases so we can - build better tools, integrations and examples about it\\n- Tools names available\\n - \ - Understand out of the publicly available tools, which ones are being used - the most so we can improve them\\n\\nUsers can opt-in to Further Telemetry, - sharing the complete telemetry data by setting the `share_crew` attribute - to `True` on their Crews. Enabling `share_crew` results in the collection - of detailed crew and task execution data, including `goal`, `backstory`, `context`, - and `output` of tasks. This enables a deeper insight into usage patterns while - respecting the user's choice to share.\\n\\n## License\\n\\nCrewAI is released - under the [MIT License](https://github.com/crewAIInc/crewAI/blob/main/LICENSE).\\n\\n## - Frequently Asked Questions (FAQ)\\n\\n### General\\n\\n- [What exactly is - CrewAI?](#q-what-exactly-is-crewai)\\n- [How do I install CrewAI?](#q-how-do-i-install-crewai)\\n- - [Does CrewAI depend on LangChain?](#q-does-crewai-depend-on-langchain)\\n- - [Is CrewAI open-source?](#q-is-crewai-open-source)\\n- [Does CrewAI collect - data from users?](#q-does-crewai-collect-data-from-users)\\n\\n### Features - and Capabilities\\n\\n- [Can CrewAI handle complex use cases?](#q-can-crewai-handle-complex-use-cases)\\n- - [Can I use CrewAI with local AI models?](#q-can-i-use-crewai-with-local-ai-models)\\n- - [What makes Crews different from Flows?](#q-what-makes-crews-different-from-flows)\\n- - [How is CrewAI better than LangChain?](#q-how-is-crewai-better-than-langchain)\\n- - [Does CrewAI support fine-tuning or training custom models?](#q-does-crewai-support-fine-tuning-or-training-custom-models)\\n\\n### - Resources and Community\\n\\n- [Where can I find real-world CrewAI examples?](#q-where-can-i-find-real-world-crewai-examples)\\n- - [How can I contribute to CrewAI?](#q-how-can-i-contribute-to-crewai)\\n\\n### - Enterprise Features\\n\\n- [What additional features does CrewAI AMP offer?](#q-what-additional-features-does-crewai-enterprise-offer)\\n- - [Is CrewAI AMP available for cloud and on-premise deployments?](#q-is-crewai-enterprise-available-for-cloud-and-on-premise-deployments)\\n- - [Can I try CrewAI AMP for free?](#q-can-i-try-crewai-enterprise-for-free)\\n\\n### - Q: What exactly is CrewAI?\\n\\nA: CrewAI is a standalone, lean, and fast - Python framework built specifically for orchestrating autonomous AI agents. - Unlike frameworks like LangChain, CrewAI does not rely on external dependencies, - making it leaner, faster, and simpler.\\n\\n### Q: How do I install CrewAI?\\n\\nA: - Install CrewAI using pip:\\n\\n```shell\\npip install crewai\\n```\\n\\nFor - additional tools, use:\\n\\n```shell\\npip install 'crewai[tools]'\\n```\\n\\n### - Q: Does CrewAI depend on LangChain?\\n\\nA: No. CrewAI is built entirely from - the ground up, with no dependencies on LangChain or other agent frameworks. - This ensures a lean, fast, and flexible experience.\\n\\n### Q: Can CrewAI - handle complex use cases?\\n\\nA: Yes. CrewAI excels at both simple and highly - complex real-world scenarios, offering deep customization options at both - high and low levels, from internal prompts to sophisticated workflow orchestration.\\n\\n### - Q: Can I use CrewAI with local AI models?\\n\\nA: Absolutely! CrewAI supports - various language models, including local ones. Tools like Ollama and LM Studio - allow seamless integration. Check the [LLM Connections documentation](https://docs.crewai.com/how-to/LLM-Connections/) - for more details.\\n\\n### Q: What makes Crews different from Flows?\\n\\nA: - Crews provide autonomous agent collaboration, ideal for tasks requiring flexible - decision-making and dynamic interaction. Flows offer precise, event-driven - control, ideal for managing detailed execution paths and secure state management. - You can seamlessly combine both for maximum effectiveness.\\n\\n### Q: How - is CrewAI better than LangChain?\\n\\nA: CrewAI provides simpler, more intuitive - APIs, faster execution speeds, more reliable and consistent results, robust - documentation, and an active community\u2014addressing common criticisms and - limitations associated with LangChain.\\n\\n### Q: Is CrewAI open-source?\\n\\nA: - Yes, CrewAI is open-source and actively encourages community contributions - and collaboration.\\n\\n### Q: Does CrewAI collect data from users?\\n\\nA: - CrewAI collects anonymous telemetry data strictly for improvement purposes. - Sensitive data such as prompts, tasks, or API responses are never collected - unless explicitly enabled by the user.\\n\\n### Q: Where can I find real-world - CrewAI examples?\\n\\nA: Check out practical examples in the [CrewAI-examples - repository](https://github.com/crewAIInc/crewAI-examples), covering use cases - like trip planners, stock analysis, and job postings.\\n\\n### Q: How can - I contribute to CrewAI?\\n\\nA: Contributions are warmly welcomed! Fork the - repository, create your branch, implement your changes, and submit a pull - request. See the Contribution section of the README for detailed guidelines.\\n\\n### - Q: What additional features does CrewAI AMP offer?\\n\\nA: CrewAI AMP provides - advanced features such as a unified control plane, real-time observability, - secure integrations, advanced security, actionable insights, and dedicated - 24/7 enterprise support.\\n\\n### Q: Is CrewAI AMP available for cloud and - on-premise deployments?\\n\\nA: Yes, CrewAI AMP supports both cloud-based - and on-premise deployment options, allowing enterprises to meet their specific - security and compliance requirements.\\n\\n### Q: Can I try CrewAI AMP for - free?\\n\\nA: Yes, you can explore part of the CrewAI AMP Suite by accessing - the [Crew Control Plane](https://app.crewai.com) for free.\\n\\n### Q: Does - CrewAI support fine-tuning or training custom models?\\n\\nA: Yes, CrewAI - can integrate with custom-trained or fine-tuned models, allowing you to enhance - your agents with domain-specific knowledge and accuracy.\\n\\n### Q: Can CrewAI - agents interact with external tools and APIs?\\n\\nA: Absolutely! CrewAI agents - can easily integrate with external tools, APIs, and databases, empowering - them to leverage real-world data and resources.\\n\\n### Q: Is CrewAI suitable - for production environments?\\n\\nA: Yes, CrewAI is explicitly designed with - production-grade standards, ensuring reliability, stability, and scalability - for enterprise deployments.\\n\\n### Q: How scalable is CrewAI?\\n\\nA: CrewAI - is highly scalable, supporting simple automations and large-scale enterprise - workflows involving numerous agents and complex tasks simultaneously.\\n\\n### - Q: Does CrewAI offer debugging and monitoring tools?\\n\\nA: Yes, CrewAI AMP - includes advanced debugging, tracing, and real-time observability features, - simplifying the management and troubleshooting of your automations.\\n\\n### - Q: What programming languages does CrewAI support?\\n\\nA: CrewAI is primarily - Python-based but easily integrates with services and APIs written in any programming - language through its flexible API integration capabilities.\\n\\n### Q: Does - CrewAI offer educational resources for beginners?\\n\\nA: Yes, CrewAI provides - extensive beginner-friendly tutorials, courses, and documentation through - learn.crewai.com, supporting developers at all skill levels.\\n\\n### Q: Can - CrewAI automate human-in-the-loop workflows?\\n\\nA: Yes, CrewAI fully supports - human-in-the-loop workflows, allowing seamless collaboration between human - experts and AI agents for enhanced decision-making.\\n\",\"description_content_type\":\"text/markdown\",\"docs_url\":null,\"download_url\":null,\"downloads\":{\"last_day\":-1,\"last_month\":-1,\"last_week\":-1},\"dynamic\":null,\"home_page\":null,\"keywords\":null,\"license\":null,\"license_expression\":null,\"license_files\":null,\"maintainer\":null,\"maintainer_email\":null,\"name\":\"crewai\",\"package_url\":\"https://pypi.org/project/crewai/\",\"platform\":null,\"project_url\":\"https://pypi.org/project/crewai/\",\"project_urls\":{\"Documentation\":\"https://docs.crewai.com\",\"Homepage\":\"https://crewai.com\",\"Repository\":\"https://github.com/crewAIInc/crewAI\"},\"provides_extra\":[\"a2a\",\"anthropic\",\"aws\",\"azure-ai-inference\",\"bedrock\",\"docling\",\"embeddings\",\"file-processing\",\"google-genai\",\"litellm\",\"mem0\",\"openpyxl\",\"pandas\",\"qdrant\",\"tools\",\"voyageai\",\"watson\"],\"release_url\":\"https://pypi.org/project/crewai/1.10.1/\",\"requires_dist\":[\"aiosqlite~=0.21.0\",\"appdirs~=1.4.4\",\"chromadb~=1.1.0\",\"click~=8.1.7\",\"httpx~=0.28.1\",\"instructor>=1.3.3\",\"json-repair~=0.25.2\",\"json5~=0.10.0\",\"jsonref~=1.1.0\",\"lancedb>=0.29.2\",\"mcp~=1.26.0\",\"openai<3,>=1.83.0\",\"openpyxl~=3.1.5\",\"opentelemetry-api~=1.34.0\",\"opentelemetry-exporter-otlp-proto-http~=1.34.0\",\"opentelemetry-sdk~=1.34.0\",\"pdfplumber~=0.11.4\",\"portalocker~=2.7.0\",\"pydantic-settings~=2.10.1\",\"pydantic~=2.11.9\",\"pyjwt<3,>=2.9.0\",\"python-dotenv~=1.1.1\",\"regex~=2026.1.15\",\"textual>=7.5.0\",\"tokenizers<1,>=0.21\",\"tomli-w~=1.1.0\",\"tomli~=2.0.2\",\"uv~=0.9.13\",\"a2a-sdk~=0.3.10; - extra == \\\"a2a\\\"\",\"aiocache[memcached,redis]~=0.12.3; extra == \\\"a2a\\\"\",\"httpx-auth~=0.23.1; - extra == \\\"a2a\\\"\",\"httpx-sse~=0.4.0; extra == \\\"a2a\\\"\",\"anthropic~=0.73.0; - extra == \\\"anthropic\\\"\",\"aiobotocore~=2.25.2; extra == \\\"aws\\\"\",\"boto3~=1.40.38; - extra == \\\"aws\\\"\",\"azure-ai-inference~=1.0.0b9; extra == \\\"azure-ai-inference\\\"\",\"boto3~=1.40.45; - extra == \\\"bedrock\\\"\",\"docling~=2.75.0; extra == \\\"docling\\\"\",\"tiktoken~=0.8.0; - extra == \\\"embeddings\\\"\",\"crewai-files; extra == \\\"file-processing\\\"\",\"google-genai~=1.65.0; - extra == \\\"google-genai\\\"\",\"litellm<3,>=1.74.9; extra == \\\"litellm\\\"\",\"mem0ai~=0.1.94; - extra == \\\"mem0\\\"\",\"openpyxl~=3.1.5; extra == \\\"openpyxl\\\"\",\"pandas~=2.2.3; - extra == \\\"pandas\\\"\",\"qdrant-client[fastembed]~=1.14.3; extra == \\\"qdrant\\\"\",\"crewai-tools==1.10.1; - extra == \\\"tools\\\"\",\"voyageai~=0.3.5; extra == \\\"voyageai\\\"\",\"ibm-watsonx-ai~=1.3.39; - extra == \\\"watson\\\"\"],\"requires_python\":\"<3.14,>=3.10\",\"summary\":\"Cutting-edge - framework for orchestrating role-playing, autonomous AI agents. 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The paragraph should serve as a teaser that showcases the potential - of each article topic.\n\nProvide your complete response:"}],"model":"gpt-4.1-mini"}' - headers: - User-Agent: - - X-USER-AGENT-XXX - accept: - - application/json - accept-encoding: - - ACCEPT-ENCODING-XXX - authorization: - - AUTHORIZATION-XXX - connection: - - keep-alive - content-length: - - '988' - content-type: - - application/json - host: - - api.openai.com - x-stainless-arch: - - X-STAINLESS-ARCH-XXX - x-stainless-async: - - 'false' - x-stainless-lang: - - python - x-stainless-os: - - X-STAINLESS-OS-XXX - x-stainless-package-version: - - 1.83.0 - x-stainless-read-timeout: - - X-STAINLESS-READ-TIMEOUT-XXX - x-stainless-retry-count: - - '0' - x-stainless-runtime: - - CPython - x-stainless-runtime-version: - - 3.13.3 - method: POST - uri: https://api.openai.com/v1/chat/completions - response: - body: - string: "{\n \"id\": \"chatcmpl-DIkUeS7UZne0uF581fpySTW76WSTy\",\n \"object\": - \"chat.completion\",\n \"created\": 1773360996,\n \"model\": \"gpt-4.1-mini-2025-04-14\",\n - \ \"choices\": [\n {\n \"index\": 0,\n \"message\": {\n \"role\": - \"assistant\",\n \"content\": \"Certainly! Here\u2019s a compelling - paragraph that teases the potential of each article idea, crafted to captivate - readers right from the opening line:\\n\\nImagine a world where artificial - intelligence doesn\u2019t just assist but actively thinks, learns, and decides - autonomously\u2014this is the thrilling frontier of AI agents, reshaping industries - and daily life alike. From the ground-breaking ways AI-driven agents revolutionize - software engineering by optimizing code and accelerating innovation, to their - pivotal role in startups where smart automation fuels unprecedented growth - and competitive edges, each article delves into a facet of AI that promises - to redefine our future. Readers will be drawn into stories of how these intelligent - systems mimic human cognition, transform mundane workflows, and unlock new - realms of possibility, all while navigating ethical considerations and the - evolving landscape of AI research. This series invites you to explore not - just the technology itself, but the profound impact AI agents are set to have - on how we create, collaborate, and innovate in an increasingly connected world.\",\n - \ \"refusal\": null,\n \"annotations\": []\n },\n \"logprobs\": - null,\n \"finish_reason\": \"stop\"\n }\n ],\n \"usage\": {\n \"prompt_tokens\": - 179,\n \"completion_tokens\": 193,\n \"total_tokens\": 372,\n \"prompt_tokens_details\": + \ \"arguments\": \"{\\\"task\\\": \\\"Generate 5 interesting ideas + for an article that have current relevance or intrigue.\\\", \\\"context\\\": + \\\"I need a list of 5 intriguing article topics that capture the imagination + and relevance for today's readers. Each idea should be based on current trends, + global issues, scientific advancements, or human interest stories that are + likely to engage a broad audience.\\\", \\\"coworker\\\": \\\"Researcher\\\"}\"\n + \ }\n },\n {\n \"id\": \"call_7m0XmBFCIDpYkvS9HnBZcdWc\",\n + \ \"type\": \"function\",\n \"function\": {\n \"name\": + \"ask_question_to_coworker\",\n \"arguments\": \"{\\\"question\\\": + \\\"How can we write compelling paragraph highlights for the selected article + ideas to showcase their potential?\\\", \\\"context\\\": \\\"Once we have + a list of 5 intriguing article ideas, we need to craft a paragraph for each + that captures the essence and potential of the article. Each paragraph should + highlight why the topic is interesting and worth delving into. These paragraphs + are key to drawing the audience's interest.\\\", \\\"coworker\\\": \\\"Senior + Writer\\\"}\"\n }\n }\n ],\n \"refusal\": + null,\n \"annotations\": []\n },\n \"logprobs\": null,\n + \ \"finish_reason\": \"tool_calls\"\n }\n ],\n \"usage\": {\n \"prompt_tokens\": + 476,\n \"completion_tokens\": 205,\n \"total_tokens\": 681,\n \"prompt_tokens_details\": {\n \"cached_tokens\": 0,\n \"audio_tokens\": 0\n },\n \"completion_tokens_details\": {\n \"reasoning_tokens\": 0,\n \"audio_tokens\": 0,\n \"accepted_prediction_tokens\": 0,\n \"rejected_prediction_tokens\": 0\n }\n },\n \"service_tier\": - \"default\",\n \"system_fingerprint\": \"fp_ffab8c3ad6\"\n}\n" + \"default\",\n \"system_fingerprint\": \"fp_b7c8e3f100\"\n}\n" headers: CF-Cache-Status: - DYNAMIC CF-Ray: - - 9db6d9d3cb3e27f6-EWR + - 9db91c3dfb08fccc-EWR Connection: - keep-alive Content-Type: - application/json Date: - - Fri, 13 Mar 2026 00:16:40 GMT + - Fri, 13 Mar 2026 06:51:32 GMT Server: - cloudflare Strict-Transport-Security: @@ -765,7 +123,7 @@ interactions: openai-organization: - OPENAI-ORG-XXX openai-processing-ms: - - '3686' + - '3462' openai-project: - OPENAI-PROJECT-XXX openai-version: @@ -797,13 +155,15 @@ interactions: You work as a freelancer and is now working on doing research and analysis for a new customer.\nYour personal goal is: Make the best research and analysis on content about AI and AI agents"},{"role":"user","content":"\nCurrent Task: - Generate a list of 5 interesting ideas to explore for an article.\n\nThis is - the expected criteria for your final answer: Your best answer to your coworker + Generate 5 interesting ideas for an article that have current relevance or intrigue.\n\nThis + is the expected criteria for your final answer: Your best answer to your coworker asking you this, accounting for the context shared.\nyou MUST return the actual complete content as the final answer, not a summary.\n\nThis is the context - you''re working with:\nWe need creative and engaging topics that are current - and have a broad appeal. Consider trending topics or evergreen themes that can - capture a wide audience.\n\nProvide your complete response:"}],"model":"gpt-4.1-mini"}' + you''re working with:\nI need a list of 5 intriguing article topics that capture + the imagination and relevance for today''s readers. Each idea should be based + on current trends, global issues, scientific advancements, or human interest + stories that are likely to engage a broad audience.\n\nProvide your complete + response:"}],"model":"gpt-4.1-mini"}' headers: User-Agent: - X-USER-AGENT-XXX @@ -816,7 +176,7 @@ interactions: connection: - keep-alive content-length: - - '942' + - '1067' content-type: - application/json host: @@ -843,42 +203,39 @@ interactions: uri: https://api.openai.com/v1/chat/completions response: body: - string: "{\n \"id\": \"chatcmpl-DIkUeKwuiS9KOXP8tK1q724zQHhrg\",\n \"object\": - \"chat.completion\",\n \"created\": 1773360996,\n \"model\": \"gpt-4.1-mini-2025-04-14\",\n + string: "{\n \"id\": \"chatcmpl-DIqercRDOhy5hCO778GaNoDRfjrmG\",\n \"object\": + \"chat.completion\",\n \"created\": 1773384693,\n \"model\": \"gpt-4.1-mini-2025-04-14\",\n \ \"choices\": [\n {\n \"index\": 0,\n \"message\": {\n \"role\": - \"assistant\",\n \"content\": \"Here are five interesting and timely - ideas to explore for an article that can engage a broad audience, especially - given the current landscape of AI and technology:\\n\\n1. **The Rise of Autonomous - AI Agents: How Intelligent Bots Are Changing Workflows Across Industries** - \ \\n Explore how AI agents\u2014self-directed software entities\u2014are - being integrated into business processes, from customer service chatbots to - autonomous data analysts. Discuss the benefits, challenges, and future implications - of these AI-driven workflows.\\n\\n2. **Beyond ChatGPT: The Next Generation - of AI Models and What They Mean for Everyday Users** \\n An in-depth look - at emerging AI models beyond the popular ones, focusing on advancements in - multi-modal AI, real-time learning agents, and personalized AI assistants. - Highlight how these innovations could transform everyday tasks and user experiences.\\n\\n3. - **Ethics in AI: Balancing Innovation and Responsibility in the Age of Intelligent - Machines** \\n A timely examination of ethical considerations surrounding - AI deployment\u2014privacy, bias, transparency, and accountability. Provide - examples of companies successfully implementing ethical AI practices and discuss - frameworks that can help guide responsible AI development.\\n\\n4. **AI for - Creative Minds: How Artificial Intelligence is Revolutionizing Art, Music, - and Writing** \\n Investigate how AI tools are collaborating with human - creativity in producing art, music, and literature. Discuss current AI creativity - platforms, the debate over authorship, and the opportunities AI presents for - creative professionals and amateurs alike.\\n\\n5. **The Future of Work with - AI: Preparing for Collaboration Between Humans and Intelligent Agents** \\n - \ Discuss how AI augmentation is reshaping job roles and skill requirements. - Focus on strategies for upskilling the workforce, fostering human-AI collaboration, - and what it means for employers, employees, and education systems moving forward.\\n\\nThese - topics are designed to be engaging, accessible, and relevant to both tech-savvy - audiences and general readers curious about AI\u2019s impact on society. Let - me know if you want me to develop outlines or gather recent data and case - studies for any of these ideas!\",\n \"refusal\": null,\n \"annotations\": + \"assistant\",\n \"content\": \"1. **The Rise of Autonomous AI Agents: + How Smart Software Is Redefining Work and Creativity** \\n Explore how + advanced AI agents are transforming industries by autonomously performing + complex tasks\u2014from coding and content creation to customer service and + scientific research. Discuss the implications for the future of employment, + creativity, and human-AI collaboration, highlighting cutting-edge examples + and ethical considerations.\\n\\n2. **AI for Climate Action: Innovative Technologies + Tackling the Global Environmental Crisis** \\n Delve into the latest breakthroughs + in AI-driven climate solutions, such as predictive models for natural disasters, + AI-enhanced renewable energy optimization, and smart agriculture. Illustrate + how these technologies are being deployed worldwide to address urgent environmental + challenges and the roadblocks they still face.\\n\\n3. **The Future of Mental + Health: AI-Powered Therapies and Emotional Support Systems** \\n Investigate + the burgeoning field of AI in mental health care, including virtual therapists, + emotional AI companions, and early detection algorithms. Examine real-world + applications, efficacy studies, privacy concerns, and how these tools could + democratize access to mental health resources globally.\\n\\n4. **Quantum + Computing Meets AI: Unleashing a New Era of Problem Solving** \\n Analyze + recent advances at the intersection of quantum computing and artificial intelligence, + focusing on how this synergy could exponentially accelerate data processing, + optimize complex systems, and enable breakthroughs in materials science, cryptography, + and drug discovery.\\n\\n5. **AI and the Ethics of Deepfake Technology: Navigating + Truth and Misinformation in the Digital Age** \\n Discuss the challenges + posed by AI-generated deepfakes in politics, entertainment, and social media. + Examine current detection techniques, regulatory responses, and the societal + impact of manipulated media, emphasizing the importance of digital literacy + and responsible AI development.\",\n \"refusal\": null,\n \"annotations\": []\n },\n \"logprobs\": null,\n \"finish_reason\": \"stop\"\n - \ }\n ],\n \"usage\": {\n \"prompt_tokens\": 171,\n \"completion_tokens\": - 403,\n \"total_tokens\": 574,\n \"prompt_tokens_details\": {\n \"cached_tokens\": + \ }\n ],\n \"usage\": {\n \"prompt_tokens\": 192,\n \"completion_tokens\": + 352,\n \"total_tokens\": 544,\n \"prompt_tokens_details\": {\n \"cached_tokens\": 0,\n \"audio_tokens\": 0\n },\n \"completion_tokens_details\": {\n \"reasoning_tokens\": 0,\n \"audio_tokens\": 0,\n \"accepted_prediction_tokens\": 0,\n \"rejected_prediction_tokens\": 0\n }\n },\n \"service_tier\": @@ -887,13 +244,13 @@ interactions: CF-Cache-Status: - DYNAMIC CF-Ray: - - 9db6d9d3bcc3f21e-EWR + - 9db91c5a7cbcb9c6-EWR Connection: - keep-alive Content-Type: - application/json Date: - - Fri, 13 Mar 2026 00:16:44 GMT + - Fri, 13 Mar 2026 06:51:38 GMT Server: - cloudflare Strict-Transport-Security: @@ -909,7 +266,176 @@ interactions: openai-organization: - OPENAI-ORG-XXX openai-processing-ms: - - '7618' + - '4722' + openai-project: + - OPENAI-PROJECT-XXX + openai-version: + - '2020-10-01' + set-cookie: + - SET-COOKIE-XXX + x-openai-proxy-wasm: + - v0.1 + x-ratelimit-limit-requests: + - X-RATELIMIT-LIMIT-REQUESTS-XXX + x-ratelimit-limit-tokens: + - X-RATELIMIT-LIMIT-TOKENS-XXX + x-ratelimit-remaining-requests: + - X-RATELIMIT-REMAINING-REQUESTS-XXX + x-ratelimit-remaining-tokens: + - X-RATELIMIT-REMAINING-TOKENS-XXX + x-ratelimit-reset-requests: + - X-RATELIMIT-RESET-REQUESTS-XXX + x-ratelimit-reset-tokens: + - X-RATELIMIT-RESET-TOKENS-XXX + x-request-id: + - X-REQUEST-ID-XXX + status: + code: 200 + message: OK +- request: + body: '{"messages":[{"role":"system","content":"You are Senior Writer. You''re + a senior writer, specialized in technology, software engineering, AI and startups. + You work as a freelancer and are now working on writing content for a new customer.\nYour + personal goal is: Write the best content about AI and AI agents."},{"role":"user","content":"\nCurrent + Task: How can we write compelling paragraph highlights for the selected article + ideas to showcase their potential?\n\nThis is the expected criteria for your + final answer: Your best answer to your coworker asking you this, accounting + for the context shared.\nyou MUST return the actual complete content as the + final answer, not a summary.\n\nThis is the context you''re working with:\nOnce + we have a list of 5 intriguing article ideas, we need to craft a paragraph for + each that captures the essence and potential of the article. Each paragraph + should highlight why the topic is interesting and worth delving into. These + paragraphs are key to drawing the audience''s interest.\n\nProvide your complete + response:"}],"model":"gpt-4.1-mini"}' + headers: + User-Agent: + - X-USER-AGENT-XXX + accept: + - application/json + accept-encoding: + - ACCEPT-ENCODING-XXX + authorization: + - AUTHORIZATION-XXX + connection: + - keep-alive + content-length: + - '1082' + content-type: + - application/json + host: + - api.openai.com + x-stainless-arch: + - X-STAINLESS-ARCH-XXX + x-stainless-async: + - 'false' + x-stainless-lang: + - python + x-stainless-os: + - X-STAINLESS-OS-XXX + x-stainless-package-version: + - 1.83.0 + x-stainless-read-timeout: + - X-STAINLESS-READ-TIMEOUT-XXX + x-stainless-retry-count: + - '0' + x-stainless-runtime: + - CPython + x-stainless-runtime-version: + - 3.13.3 + method: POST + uri: https://api.openai.com/v1/chat/completions + response: + body: + string: "{\n \"id\": \"chatcmpl-DIqer4D90JWJ0CX6kzRgC64blREos\",\n \"object\": + \"chat.completion\",\n \"created\": 1773384693,\n \"model\": \"gpt-4.1-mini-2025-04-14\",\n + \ \"choices\": [\n {\n \"index\": 0,\n \"message\": {\n \"role\": + \"assistant\",\n \"content\": \"Absolutely, crafting compelling paragraph + highlights is all about distilling the core intrigue and relevance of each + article idea into a concise, engaging pitch. Here\u2019s how I approach it, + and a few best practices to keep in mind as we develop these:\\n\\n1. **Lead + with the Hook:** Start the paragraph with a sentence that immediately grabs + attention\u2014this could be a provocative question, a surprising statistic, + or a bold statement. This sets the tone and sparks curiosity.\\n\\n2. **Showcase + the Relevance:** Quickly connect the topic to current trends, challenges, + or emerging opportunities in AI and tech. Readers want to know why this matters + now.\\n\\n3. **Highlight the Unique Angle:** Emphasize what makes this article + stand out. Maybe it\u2019s exploring a newly emerging AI agent technology, + debunking common misconceptions, or offering exclusive insights from industry + pioneers.\\n\\n4. **Promise Value:** Convey what readers will gain\u2014be + it actionable insights, deep understanding, or foresight into the future of + AI.\\n\\n5. **Maintain an Engaging Tone:** Use clear, compelling, and accessible + language that appeals to both tech-savvy professionals and interested newcomers.\\n\\n---\\n\\n**Example + Paragraph Highlights for Article Ideas on AI Agents:**\\n\\n- *\u201CThe Rise + of Autonomous AI Agents: Changing the Landscape of Software Development\u201D* + \ \\nImagine software that doesn't just follow instructions but thinks and + acts independently to solve complex problems. This article dives into how + autonomous AI agents are revolutionizing software engineering, automating + workflows, and elevating productivity. We\u2019ll explore cutting-edge use + cases, the technological breakthroughs behind these agents, and the profound + impact they\u2019re poised to have on startups and enterprises alike.\\n\\n- + *\u201CDemystifying AI Agent Architectures: Navigating Complexity with Clarity\u201D* + \ \\nAI agents come in many forms, but understanding their underlying architectures + is key to harnessing their power. This piece breaks down complex concepts + into digestible insights, comparing various AI agent designs and evaluating + their strengths and limitations. It\u2019s essential reading for engineers + and decision-makers who want to build or invest in intelligent systems that + can learn, adapt, and excel.\\n\\n- *\u201CEthics and AI Agents: Navigating + the Moral Frontier in Autonomous Systems\u201D* \\nAs AI agents grow more + capable, the ethical challenges they pose become more urgent. From biases + to accountability, this article tackles the moral questions we must address + to develop responsible AI. We\u2019ll unpack real-world dilemmas and highlight + frameworks that can guide ethical deployment\u2014critical knowledge for anyone + shaping the future of AI technology.\\n\\n- *\u201CStartups Leveraging AI + Agents for Disruptive Innovation\u201D* \\nStartups are at the forefront + of AI agent innovation, using these intelligent systems to disrupt traditional + industries. This article showcases inspiring case studies where AI agents + have transformed sectors like healthcare, finance, and customer service. Readers + will discover how these nimble companies are overcoming obstacles, capturing + value, and setting new standards in AI-driven innovation.\\n\\n- *\u201CThe + Future of Human-AI Collaboration: Partnering with Intelligent Agents\u201D* + \ \\nAI agents are not just tools\u2014they\u2019re collaborators redefining + how humans work with technology. This article envisions the future of human-AI + partnerships, exploring how intelligent agents can complement human creativity + and decision-making. It offers a compelling glimpse into a future workplace + energized by synergy between humans and smart machines.\\n\\n---\\n\\nBy focusing + each highlight on a strong hook, contextual relevance, unique insights, and + reader value, we ensure these paragraphs not only inform but also excite the + audience about the articles to come. This approach will make our content stand + out and drive deeper engagement from the start.\",\n \"refusal\": null,\n + \ \"annotations\": []\n },\n \"logprobs\": null,\n \"finish_reason\": + \"stop\"\n }\n ],\n \"usage\": {\n \"prompt_tokens\": 197,\n \"completion_tokens\": + 716,\n \"total_tokens\": 913,\n \"prompt_tokens_details\": {\n \"cached_tokens\": + 0,\n \"audio_tokens\": 0\n },\n \"completion_tokens_details\": + {\n \"reasoning_tokens\": 0,\n \"audio_tokens\": 0,\n \"accepted_prediction_tokens\": + 0,\n \"rejected_prediction_tokens\": 0\n }\n },\n \"service_tier\": + \"default\",\n \"system_fingerprint\": \"fp_a1611518a7\"\n}\n" + headers: + CF-Cache-Status: + - DYNAMIC + CF-Ray: + - 9db91c5a7c0e8c51-EWR + Connection: + - keep-alive + Content-Type: + - application/json + Date: + - Fri, 13 Mar 2026 06:51:43 GMT + Server: + - cloudflare + Strict-Transport-Security: + - STS-XXX + Transfer-Encoding: + - chunked + X-Content-Type-Options: + - X-CONTENT-TYPE-XXX + access-control-expose-headers: + - ACCESS-CONTROL-XXX + alt-svc: + - h3=":443"; ma=86400 + openai-organization: + - OPENAI-ORG-XXX + openai-processing-ms: + - '10541' openai-project: - OPENAI-PROJECT-XXX openai-version: @@ -948,62 +474,98 @@ interactions: good an article about this topic could be. Return the list of ideas with their paragraph and your notes.\\n\\nThis is the expected criteria for your final answer: 5 bullet points with a paragraph for each idea.\\nyou MUST return the - actual complete content as the final answer, not a summary.\"},{\"role\":\"assistant\",\"content\":null,\"tool_calls\":[{\"id\":\"call_WpDMT9puoRVrcOvAlMSfLutE\",\"type\":\"function\",\"function\":{\"name\":\"delegate_work_to_coworker\",\"arguments\":\"{\\\"task\\\": - \\\"Generate a list of 5 interesting ideas to explore for an article.\\\", \\\"context\\\": - \\\"We need creative and engaging topics that are current and have a broad appeal. - Consider trending topics or evergreen themes that can capture a wide audience.\\\", - \\\"coworker\\\": \\\"Researcher\\\"}\"}},{\"id\":\"call_gp08bFk7SulDl7vHkCoNx4cG\",\"type\":\"function\",\"function\":{\"name\":\"delegate_work_to_coworker\",\"arguments\":\"{\\\"task\\\": - \\\"Compile an amazing paragraph that highlights how an article could be engaging - for each idea provided by the Researcher.\\\", \\\"context\\\": \\\"The ideas - need to be compelling enough to capture the reader's attention right from the - start. The paragraph should serve as a teaser that showcases the potential of - each article topic.\\\", \\\"coworker\\\": \\\"Senior Writer\\\"}\"}}]},{\"role\":\"tool\",\"tool_call_id\":\"call_WpDMT9puoRVrcOvAlMSfLutE\",\"name\":\"delegate_work_to_coworker\",\"content\":\"Here - are five interesting and timely ideas to explore for an article that can engage - a broad audience, especially given the current landscape of AI and technology:\\n\\n1. - **The Rise of Autonomous AI Agents: How Intelligent Bots Are Changing Workflows - Across Industries** \\n Explore how AI agents\u2014self-directed software - entities\u2014are being integrated into business processes, from customer service - chatbots to autonomous data analysts. Discuss the benefits, challenges, and - future implications of these AI-driven workflows.\\n\\n2. **Beyond ChatGPT: - The Next Generation of AI Models and What They Mean for Everyday Users** \\n - \ An in-depth look at emerging AI models beyond the popular ones, focusing - on advancements in multi-modal AI, real-time learning agents, and personalized - AI assistants. Highlight how these innovations could transform everyday tasks - and user experiences.\\n\\n3. **Ethics in AI: Balancing Innovation and Responsibility - in the Age of Intelligent Machines** \\n A timely examination of ethical - considerations surrounding AI deployment\u2014privacy, bias, transparency, and - accountability. Provide examples of companies successfully implementing ethical - AI practices and discuss frameworks that can help guide responsible AI development.\\n\\n4. - **AI for Creative Minds: How Artificial Intelligence is Revolutionizing Art, - Music, and Writing** \\n Investigate how AI tools are collaborating with - human creativity in producing art, music, and literature. Discuss current AI - creativity platforms, the debate over authorship, and the opportunities AI presents - for creative professionals and amateurs alike.\\n\\n5. **The Future of Work - with AI: Preparing for Collaboration Between Humans and Intelligent Agents** - \ \\n Discuss how AI augmentation is reshaping job roles and skill requirements. - Focus on strategies for upskilling the workforce, fostering human-AI collaboration, - and what it means for employers, employees, and education systems moving forward.\\n\\nThese - topics are designed to be engaging, accessible, and relevant to both tech-savvy - audiences and general readers curious about AI\u2019s impact on society. Let - me know if you want me to develop outlines or gather recent data and case studies - for any of these ideas!\"},{\"role\":\"tool\",\"tool_call_id\":\"call_gp08bFk7SulDl7vHkCoNx4cG\",\"name\":\"delegate_work_to_coworker\",\"content\":\"Certainly! - Here\u2019s a compelling paragraph that teases the potential of each article - idea, crafted to captivate readers right from the opening line:\\n\\nImagine - a world where artificial intelligence doesn\u2019t just assist but actively - thinks, learns, and decides autonomously\u2014this is the thrilling frontier - of AI agents, reshaping industries and daily life alike. From the ground-breaking - ways AI-driven agents revolutionize software engineering by optimizing code - and accelerating innovation, to their pivotal role in startups where smart automation - fuels unprecedented growth and competitive edges, each article delves into a - facet of AI that promises to redefine our future. Readers will be drawn into - stories of how these intelligent systems mimic human cognition, transform mundane - workflows, and unlock new realms of possibility, all while navigating ethical - considerations and the evolving landscape of AI research. This series invites - you to explore not just the technology itself, but the profound impact AI agents - are set to have on how we create, collaborate, and innovate in an increasingly - connected world.\"},{\"role\":\"user\",\"content\":\"Analyze the tool result. - If requirements are met, provide the Final Answer. Otherwise, call the next - tool. Deliver only the answer without meta-commentary.\"}],\"model\":\"gpt-4o\",\"tool_choice\":\"auto\",\"tools\":[{\"type\":\"function\",\"function\":{\"name\":\"delegate_work_to_coworker\",\"description\":\"Delegate + actual complete content as the final answer, not a summary.\"},{\"role\":\"assistant\",\"content\":null,\"tool_calls\":[{\"id\":\"call_5omViKE1yqLKGtws4THp877m\",\"type\":\"function\",\"function\":{\"name\":\"delegate_work_to_coworker\",\"arguments\":\"{\\\"task\\\": + \\\"Generate 5 interesting ideas for an article that have current relevance + or intrigue.\\\", \\\"context\\\": \\\"I need a list of 5 intriguing article + topics that capture the imagination and relevance for today's readers. Each + idea should be based on current trends, global issues, scientific advancements, + or human interest stories that are likely to engage a broad audience.\\\", \\\"coworker\\\": + \\\"Researcher\\\"}\"}},{\"id\":\"call_7m0XmBFCIDpYkvS9HnBZcdWc\",\"type\":\"function\",\"function\":{\"name\":\"ask_question_to_coworker\",\"arguments\":\"{\\\"question\\\": + \\\"How can we write compelling paragraph highlights for the selected article + ideas to showcase their potential?\\\", \\\"context\\\": \\\"Once we have a + list of 5 intriguing article ideas, we need to craft a paragraph for each that + captures the essence and potential of the article. Each paragraph should highlight + why the topic is interesting and worth delving into. These paragraphs are key + to drawing the audience's interest.\\\", \\\"coworker\\\": \\\"Senior Writer\\\"}\"}}]},{\"role\":\"tool\",\"tool_call_id\":\"call_5omViKE1yqLKGtws4THp877m\",\"name\":\"delegate_work_to_coworker\",\"content\":\"1. + **The Rise of Autonomous AI Agents: How Smart Software Is Redefining Work and + Creativity** \\n Explore how advanced AI agents are transforming industries + by autonomously performing complex tasks\u2014from coding and content creation + to customer service and scientific research. Discuss the implications for the + future of employment, creativity, and human-AI collaboration, highlighting cutting-edge + examples and ethical considerations.\\n\\n2. **AI for Climate Action: Innovative + Technologies Tackling the Global Environmental Crisis** \\n Delve into the + latest breakthroughs in AI-driven climate solutions, such as predictive models + for natural disasters, AI-enhanced renewable energy optimization, and smart + agriculture. Illustrate how these technologies are being deployed worldwide + to address urgent environmental challenges and the roadblocks they still face.\\n\\n3. + **The Future of Mental Health: AI-Powered Therapies and Emotional Support Systems** + \ \\n Investigate the burgeoning field of AI in mental health care, including + virtual therapists, emotional AI companions, and early detection algorithms. + Examine real-world applications, efficacy studies, privacy concerns, and how + these tools could democratize access to mental health resources globally.\\n\\n4. + **Quantum Computing Meets AI: Unleashing a New Era of Problem Solving** \\n + \ Analyze recent advances at the intersection of quantum computing and artificial + intelligence, focusing on how this synergy could exponentially accelerate data + processing, optimize complex systems, and enable breakthroughs in materials + science, cryptography, and drug discovery.\\n\\n5. **AI and the Ethics of Deepfake + Technology: Navigating Truth and Misinformation in the Digital Age** \\n Discuss + the challenges posed by AI-generated deepfakes in politics, entertainment, and + social media. Examine current detection techniques, regulatory responses, and + the societal impact of manipulated media, emphasizing the importance of digital + literacy and responsible AI development.\"},{\"role\":\"tool\",\"tool_call_id\":\"call_7m0XmBFCIDpYkvS9HnBZcdWc\",\"name\":\"ask_question_to_coworker\",\"content\":\"Absolutely, + crafting compelling paragraph highlights is all about distilling the core intrigue + and relevance of each article idea into a concise, engaging pitch. Here\u2019s + how I approach it, and a few best practices to keep in mind as we develop these:\\n\\n1. + **Lead with the Hook:** Start the paragraph with a sentence that immediately + grabs attention\u2014this could be a provocative question, a surprising statistic, + or a bold statement. This sets the tone and sparks curiosity.\\n\\n2. **Showcase + the Relevance:** Quickly connect the topic to current trends, challenges, or + emerging opportunities in AI and tech. Readers want to know why this matters + now.\\n\\n3. **Highlight the Unique Angle:** Emphasize what makes this article + stand out. Maybe it\u2019s exploring a newly emerging AI agent technology, debunking + common misconceptions, or offering exclusive insights from industry pioneers.\\n\\n4. + **Promise Value:** Convey what readers will gain\u2014be it actionable insights, + deep understanding, or foresight into the future of AI.\\n\\n5. **Maintain an + Engaging Tone:** Use clear, compelling, and accessible language that appeals + to both tech-savvy professionals and interested newcomers.\\n\\n---\\n\\n**Example + Paragraph Highlights for Article Ideas on AI Agents:**\\n\\n- *\u201CThe Rise + of Autonomous AI Agents: Changing the Landscape of Software Development\u201D* + \ \\nImagine software that doesn't just follow instructions but thinks and acts + independently to solve complex problems. This article dives into how autonomous + AI agents are revolutionizing software engineering, automating workflows, and + elevating productivity. We\u2019ll explore cutting-edge use cases, the technological + breakthroughs behind these agents, and the profound impact they\u2019re poised + to have on startups and enterprises alike.\\n\\n- *\u201CDemystifying AI Agent + Architectures: Navigating Complexity with Clarity\u201D* \\nAI agents come + in many forms, but understanding their underlying architectures is key to harnessing + their power. This piece breaks down complex concepts into digestible insights, + comparing various AI agent designs and evaluating their strengths and limitations. + It\u2019s essential reading for engineers and decision-makers who want to build + or invest in intelligent systems that can learn, adapt, and excel.\\n\\n- *\u201CEthics + and AI Agents: Navigating the Moral Frontier in Autonomous Systems\u201D* \\nAs + AI agents grow more capable, the ethical challenges they pose become more urgent. + From biases to accountability, this article tackles the moral questions we must + address to develop responsible AI. We\u2019ll unpack real-world dilemmas and + highlight frameworks that can guide ethical deployment\u2014critical knowledge + for anyone shaping the future of AI technology.\\n\\n- *\u201CStartups Leveraging + AI Agents for Disruptive Innovation\u201D* \\nStartups are at the forefront + of AI agent innovation, using these intelligent systems to disrupt traditional + industries. This article showcases inspiring case studies where AI agents have + transformed sectors like healthcare, finance, and customer service. Readers + will discover how these nimble companies are overcoming obstacles, capturing + value, and setting new standards in AI-driven innovation.\\n\\n- *\u201CThe + Future of Human-AI Collaboration: Partnering with Intelligent Agents\u201D* + \ \\nAI agents are not just tools\u2014they\u2019re collaborators redefining + how humans work with technology. This article envisions the future of human-AI + partnerships, exploring how intelligent agents can complement human creativity + and decision-making. It offers a compelling glimpse into a future workplace + energized by synergy between humans and smart machines.\\n\\n---\\n\\nBy focusing + each highlight on a strong hook, contextual relevance, unique insights, and + reader value, we ensure these paragraphs not only inform but also excite the + audience about the articles to come. This approach will make our content stand + out and drive deeper engagement from the start.\"},{\"role\":\"user\",\"content\":\"Analyze + the tool result. If requirements are met, provide the Final Answer. Otherwise, + call the next tool. Deliver only the answer without meta-commentary.\"}],\"model\":\"gpt-4o\",\"tool_choice\":\"auto\",\"tools\":[{\"type\":\"function\",\"function\":{\"name\":\"delegate_work_to_coworker\",\"description\":\"Delegate a specific task to one of the following coworkers: Researcher, Senior Writer\\nThe input to this tool should be the coworker, the task you want them to do, and ALL necessary context to execute the task, they know nothing about the task, @@ -1032,7 +594,7 @@ interactions: connection: - keep-alive content-length: - - '7446' + - '10277' content-type: - application/json cookie: @@ -1061,47 +623,48 @@ interactions: uri: https://api.openai.com/v1/chat/completions response: body: - string: "{\n \"id\": \"chatcmpl-DIkUm0bjt0yGuvOoncvm10PDjwYdu\",\n \"object\": - \"chat.completion\",\n \"created\": 1773361004,\n \"model\": \"gpt-4o-2024-08-06\",\n + string: "{\n \"id\": \"chatcmpl-DIqf2xX10Hgre0gAt0AY57U4IJfn0\",\n \"object\": + \"chat.completion\",\n \"created\": 1773384704,\n \"model\": \"gpt-4o-2024-08-06\",\n \ \"choices\": [\n {\n \"index\": 0,\n \"message\": {\n \"role\": \"assistant\",\n \"content\": \"1. **The Rise of Autonomous AI Agents: - How Intelligent Bots Are Changing Workflows Across Industries** \\n Imagine - a world where artificial intelligence doesn\u2019t just assist but actively - thinks, learns, and decides autonomously\u2014this is the thrilling frontier - of AI agents, reshaping industries and daily life alike. From the groundbreaking - ways AI-driven agents revolutionize software engineering by optimizing code - and accelerating innovation, to their pivotal role in startups where smart - automation fuels unprecedented growth and competitive edges, each article - delves into a facet of AI that promises to redefine our future. Readers will - be drawn into stories of how these intelligent systems mimic human cognition, - transform mundane workflows, and unlock new realms of possibility, all while - navigating ethical considerations and the evolving landscape of AI research. - This series invites you to explore not just the technology itself, but the - profound impact AI agents are set to have on how we create, collaborate, and - innovate in an increasingly connected world.\\n\\n2. **Beyond ChatGPT: The - Next Generation of AI Models and What They Mean for Everyday Users** \\n - \ An in-depth look at emerging AI models beyond the popular ones, focusing - on advancements in multi-modal AI, real-time learning agents, and personalized - AI assistants. Highlight how these innovations could transform everyday tasks - and user experiences.\\n\\n3. **Ethics in AI: Balancing Innovation and Responsibility - in the Age of Intelligent Machines** \\n A timely examination of ethical - considerations surrounding AI deployment\u2014privacy, bias, transparency, - and accountability. Provide examples of companies successfully implementing - ethical AI practices and discuss frameworks that can help guide responsible - AI development.\\n\\n4. **AI for Creative Minds: How Artificial Intelligence - is Revolutionizing Art, Music, and Writing** \\n Investigate how AI tools - are collaborating with human creativity in producing art, music, and literature. - Discuss current AI creativity platforms, the debate over authorship, and the - opportunities AI presents for creative professionals and amateurs alike.\\n\\n5. - **The Future of Work with AI: Preparing for Collaboration Between Humans and - Intelligent Agents** \\n Discuss how AI augmentation is reshaping job roles - and skill requirements. Focus on strategies for upskilling the workforce, - fostering human-AI collaboration, and what it means for employers, employees, - and education systems moving forward.\",\n \"refusal\": null,\n \"annotations\": - []\n },\n \"logprobs\": null,\n \"finish_reason\": \"stop\"\n - \ }\n ],\n \"usage\": {\n \"prompt_tokens\": 1309,\n \"completion_tokens\": - 445,\n \"total_tokens\": 1754,\n \"prompt_tokens_details\": {\n \"cached_tokens\": - 0,\n \"audio_tokens\": 0\n },\n \"completion_tokens_details\": + How Smart Software Is Redefining Work and Creativity** \\n Imagine software + that doesn't just follow instructions but thinks and acts independently to + solve complex problems. This article dives into how autonomous AI agents are + revolutionizing industries by automating workflows, elevating productivity, + and transforming creative processes. Explore cutting-edge examples and the + implications for future employment, creativity, and human-AI collaboration, + all while considering the ethical dimensions of this technological leap.\\n\\n2. + **AI for Climate Action: Innovative Technologies Tackling the Global Environmental + Crisis** \\n The fight against climate change is getting a high-tech upgrade + with AI-driven innovations. Delve into how predictive models for natural disasters, + AI-enhanced energy optimization, and smart agriculture are revolutionizing + global efforts to address environmental challenges. Unveil the roadblocks + still faced and explore the tangible impact these technologies are having + across the globe in the urgent quest for sustainability.\\n\\n3. **The Future + of Mental Health: AI-Powered Therapies and Emotional Support Systems** \\n + \ Discover the frontier of mental health care where AI is providing new hope + through virtual therapists, emotional AI companions, and early detection algorithms. + This article investigates real-world applications, examines efficacy studies, + and explores how these tools could democratize access to mental health resources + globally, while scrutinizing privacy concerns and ethical implications in + this delicate field.\\n\\n4. **Quantum Computing Meets AI: Unleashing a New + Era of Problem Solving** \\n At the intersection of quantum computing and + artificial intelligence lies a potential revolution in data processing speed. + Analyze how this synergy promises to optimize complex systems and enable breakthroughs + in fields like materials science and cryptography. Dive into the cutting-edge + research that may soon transform industries and redefine the boundaries of + what\u2019s computationally possible.\\n\\n5. **AI and the Ethics of Deepfake + Technology: Navigating Truth and Misinformation in the Digital Age** \\n + \ Deepfake technology poses significant challenges across politics, entertainment, + and social media. This article discusses the methods being developed to detect + these manipulations, examines current regulatory responses, and explores the + societal impact of manipulated media. Emphasizing the importance of digital + literacy and responsible AI development, this piece illuminates the path forward + in navigating the truths and falsehoods of the digital world.\",\n \"refusal\": + null,\n \"annotations\": []\n },\n \"logprobs\": null,\n + \ \"finish_reason\": \"stop\"\n }\n ],\n \"usage\": {\n \"prompt_tokens\": + 1855,\n \"completion_tokens\": 456,\n \"total_tokens\": 2311,\n \"prompt_tokens_details\": + {\n \"cached_tokens\": 0,\n \"audio_tokens\": 0\n },\n \"completion_tokens_details\": {\n \"reasoning_tokens\": 0,\n \"audio_tokens\": 0,\n \"accepted_prediction_tokens\": 0,\n \"rejected_prediction_tokens\": 0\n }\n },\n \"service_tier\": \"default\",\n \"system_fingerprint\": \"fp_b7c8e3f100\"\n}\n" @@ -1109,13 +672,13 @@ interactions: CF-Cache-Status: - DYNAMIC CF-Ray: - - 9db6da052e188095-EWR + - 9db91c9e39b690c2-EWR Connection: - keep-alive Content-Type: - application/json Date: - - Fri, 13 Mar 2026 00:16:47 GMT + - Fri, 13 Mar 2026 06:51:48 GMT Server: - cloudflare Strict-Transport-Security: @@ -1131,7 +694,7 @@ interactions: openai-organization: - OPENAI-ORG-XXX openai-processing-ms: - - '2809' + - '4236' openai-project: - OPENAI-PROJECT-XXX openai-version: