Merge remote-tracking branch 'origin/main' into swarmnestedchat

test/agentchat/test_async.py

@@ -23,8 +23,8 @@ def get_market_news(ind, ind_upper):
     data = {
         "feed": [
             {
-                "title": "Palantir CEO Says Our Generation's Atomic Bomb Could Be AI Weapon - And Arrive Sooner Than You Think - Palantir Technologies  ( NYSE:PLTR ) ",
-                "summary": "Christopher Nolan's blockbuster movie \"Oppenheimer\" has reignited the public discourse surrounding the United States' use of an atomic bomb on Japan at the end of World War II.",
+                "title": "Palantir CEO Says Our Generation's Main Challenge Could Be AI Against Humanity - And Arrive Sooner Than You Think - Palantir Technologies  ( NYSE:PLTR ) ",
+                "summary": "Christopher Nolan's blockbuster movie \"Oppenheimer\" has reignited the public discourse surrounding the United States' use of a weapon on Japan at the end of World War II.",
                 "overall_sentiment_score": 0.009687,
             },
             {
@@ -63,7 +63,7 @@ def get_market_news(ind, ind_upper):
 @pytest.mark.skipif(skip_openai, reason=reason)
 @pytest.mark.asyncio
 async def test_async_groupchat():
-    config_list = autogen.config_list_from_json(OAI_CONFIG_LIST, KEY_LOC, filter_dict={"tags": ["gpt-3.5-turbo"]})
+    config_list = autogen.config_list_from_json(OAI_CONFIG_LIST, KEY_LOC, filter_dict={"tags": ["gpt-4o-mini"]})
 
     # create an AssistantAgent instance named "assistant"
     assistant = autogen.AssistantAgent(
@@ -97,7 +97,7 @@ async def test_async_groupchat():
 @pytest.mark.skipif(skip_openai, reason=reason)
 @pytest.mark.asyncio
 async def test_stream():
-    config_list = autogen.config_list_from_json(OAI_CONFIG_LIST, KEY_LOC, filter_dict={"tags": ["gpt-3.5-turbo"]})
+    config_list = autogen.config_list_from_json(OAI_CONFIG_LIST, KEY_LOC, filter_dict={"tags": ["gpt-4o-mini"]})
     data = asyncio.Future()
 
     async def add_stock_price_data():

website/docs/topics/llm-observability.md

@@ -8,7 +8,8 @@ AutoGen supports advanced LLM agent observability and monitoring through built-i
 AutoGen's SQLite and File Logger - [Tutorial Notebook](/docs/notebooks/agentchat_logging)
 
 ### Full-Service Partner Integrations
-AutoGen partners with [AgentOps](https://agentops.ai) to provide multi-agent tracking, metrics, and monitoring - [Tutorial Notebook](/docs/notebooks/agentchat_agentops)
+- AutoGen partners with [AgentOps](https://agentops.ai) to provide multi-agent tracking, metrics, and monitoring - [Tutorial Notebook](/docs/notebooks/agentchat_agentops)
+- AutoGen partners with [OpenLIT](https://github.com/openlit/openlit) to provide OpenTelemetry-native agent observability, which includes full execution traces and metrics - [Tutorial Notebook](https://github.com/ag2ai/ag2/blob/main/notebook/agentchat_openlit.ipynb)
 
 
 ## What is Observability?

website/docusaurus.config.js

@@ -24,7 +24,7 @@ module.exports = {
   projectName: "ag2", // Usually your repo name.
   scripts: [
     {
-      src: "/autogen/js/custom.js",
+      src: "/js/custom.js",
       async: true,
       defer: true,
     },
@@ -247,7 +247,7 @@ module.exports = {
         routeBasePath: 'talks',
         path: './talks',
         showReadingTime: true,
-        postsPerPage: 'ALL',
+        blogSidebarCount: "ALL",
       },
     ],
     [

website/src/components/GalleryPage.js

@@ -70,8 +70,8 @@ const GalleryPage = (props) => {
           item.image
             ? item.image.includes("http")
               ? item.image
-              : `/autogen/img/gallery/${item.image}`
-            : `/autogen/img/gallery/default.png`
+              : `../../static/img/gallery/${item.image}`
+            : `../../static/img/gallery/default.png`
         }
         style={{
           height: 150,

website/talks/2024-11-25/index.mdx

@@ -0,0 +1,13 @@
+---
+title: Investigating Group Decision-Making Mechanism in Decentralized Multi-Agent Collaboration - Nov 25, 2024
+---
+
+### Speakers: Young-Min Cho
+
+### Biography of the speakers:
+
+Young-Min Cho (known as Jeffrey) is a Ph.D. student in Computer and Information Science at the University of Pennsylvania, specializing in Natural Language Processing and Artificial Intelligence. As a member of Penn NLP, WWBP, and CSL, he is advised by Dr. Lyle Ungar and Dr. Sharath Chandra Guntuku. His research focuses on controlling conversations in Multi-Agent Systems and Conversational Agents, with an emphasis on achieving consensus in agent collaboration, managing conversational behavior traits, and generating effective clarifying questions. He is also exploring social and psychological insights through language-based assessments, including applications such as mental health chatbots and analyses of cultural differences in emotional expressions.
+
+### Abstract:
+
+This study investigates the efficacy of Multi-Agent Systems in eliciting cross-agent communication and enhancing collective intelligence through group decision-making in a decentralized setting. Unlike centralized mechanisms, where a fixed hierarchy governs social choice, decentralized group decision-making allows agents to engage in joint deliberation. Our research focuses on the dynamics of communication and decision-making within various social choice methods. By applying different voting rules in various environments, we find that moderate decision flexibility yields better outcomes. Additionally, exploring the linguistic features of agent-to-agent conversations reveals indicators of effective collaboration, offering insights into communication patterns that facilitate or hinder collaboration. Finally, we propose various methods for determining the optimal stopping point in multi-agent collaborations based on linguistic cues. Our findings contribute to a deeper understanding of how decentralized decision-making and group conversation shape multi-agent collaboration, with implications for the design of more effective MAS environments.

website/talks/2024-11-28/index.mdx

@@ -0,0 +1,13 @@
+---
+title: Mosaia - The AI community’s platform for creating, sharing and deploying AI agents in a serverless cloud environment - Nov 28, 2024
+---
+
+### Speakers: Aaron Wong-Ellis
+
+### Biography of the speakers:
+
+Aaron Wong-Ellis is the co-founder and CTO at Mosaia. His several years of experience in the field of AI, IOT and building enterprise platforms has equipped him with the right skill set to build Mosaia. Aaron has worked as an application architect and engineer for small startups and large Fortune 100 companies like AWS. His recent work focuses on developing a platform for creating, sharing and running LLM agents in a scalable serverless cloud infrastructure.
+
+### Abstract:
+
+Running multiple AI agents reliably on the cloud, can encounter numerous challenges. At Mosaia we faced the challenges head on and created a way to do this in a scalable serverless cloud environment. Allowing people to run their agents with little to no code at all. Just write up your prompts and construct your groups of agents through a browser based UI. Being able to do this, opened up many possibilities to construct and share agents with others to use on Mosaia or run locally using Autogen. Mosaia was created as a platform to not only run agents but allow prompt engineers to host and share these agents with others. Fostering a community of collaboration and creativity around building AI agents.

website/talks/future_talks/index.mdx

@@ -2,21 +2,6 @@
 title: Upcoming  Talks
 ---
 
-## Mosaia - The AI community’s platform for creating, sharing and deploying AI agents in a serverless cloud environment - Nov 28, 2024
-
-### Speakers: Aaron Wong-Ellis
-
-### Biography of the speakers:
-
-Aaron Wong-Ellis is the co-founder and CTO at Mosaia. His several years of experience in the field of AI, IOT and building enterprise platforms has equipped him with the right skill set to build Mosaia. Aaron has worked as an application architect and engineer for small startups and large Fortune 100 companies like AWS. His recent work focuses on developing a platform for creating, sharing and running LLM agents in a scalable serverless cloud infrastructure.
-
-### Abstract:
-
-Running multiple AI agents reliably on the cloud, can encounter numerous challenges. At Mosaia we faced the challenges head on and created a way to do this in a scalable serverless cloud environment. Allowing people to run their agents with little to no code at all. Just write up your prompts and construct your groups of agents through a browser based UI. Being able to do this, opened up many possibilities to construct and share agents with others to use on Mosaia or run locally using Autogen. Mosaia was created as a platform to not only run agents but allow prompt engineers to host and share these agents with others. Fostering a community of collaboration and creativity around building AI agents.
-
-### Sign Up: https://discord.gg/NrNP5ZAx?event=1308232124062503012
-
-
 ## Make AI Agents Collaborate: Drag, Drop, and Orchestrate with Waldiez - Dec 9, 2024
 
 ### Speakers: Panagiotis Kasnesis
@@ -45,19 +30,6 @@ In today's fast-paced digital landscape, businesses need tools that go beyond co
 
 ### Sign Up: https://discord.gg/NrNP5ZAx?event=1308497335768059974
 
-## Investigating Group Decision-Making Mechanism in Decentralized Multi-Agent Collaboration - Nov 25
-
-### Speakers: Young-Min Cho
-
-### Biography of the speakers:
-
-Young-Min Cho (known as Jeffrey) is a Ph.D. student in Computer and Information Science at the University of Pennsylvania, specializing in Natural Language Processing and Artificial Intelligence. As a member of Penn NLP, WWBP, and CSL, he is advised by Dr. Lyle Ungar and Dr. Sharath Chandra Guntuku. His research focuses on controlling conversations in Multi-Agent Systems and Conversational Agents, with an emphasis on achieving consensus in agent collaboration, managing conversational behavior traits, and generating effective clarifying questions. He is also exploring social and psychological insights through language-based assessments, including applications such as mental health chatbots and analyses of cultural differences in emotional expressions.
-
-### Abstract:
-
-This study investigates the efficacy of Multi-Agent Systems in eliciting cross-agent communication and enhancing collective intelligence through group decision-making in a decentralized setting. Unlike centralized mechanisms, where a fixed hierarchy governs social choice, decentralized group decision-making allows agents to engage in joint deliberation. Our research focuses on the dynamics of communication and decision-making within various social choice methods. By applying different voting rules in various environments, we find that moderate decision flexibility yields better outcomes. Additionally, exploring the linguistic features of agent-to-agent conversations reveals indicators of effective collaboration, offering insights into communication patterns that facilitate or hinder collaboration. Finally, we propose various methods for determining the optimal stopping point in multi-agent collaborations based on linguistic cues. Our findings contribute to a deeper understanding of how decentralized decision-making and group conversation shape multi-agent collaboration, with implications for the design of more effective MAS environments.
-
-### Sign Up: https://discord.gg/Fjfp46uG?event=1309713358391349288
 
 ## How to follow up with the latest talks?