That’s very easy to do, this has that already for support agents in loop
AGENTS
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LangChain Named to InfraRed 100 by Redpoint Venture
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We’re honored to be named to the InfraRed 100 by @Redpoint
, recognizing the top 100 companies transforming cloud infrastructure! At LangChain, we help developers build mission-critical AI applications across the entire agent development lifecycle. In great company: -
LangSmith: Unified Platform for AI App Debugging and Monitoring
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Learn the basics of LangSmith LangSmith is our unified observability & evals platform where teams can debug, test, and monitor AI app performance — whether building with LangChain or not. Find failures fast with agent observability Evaluate your agent's performance
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Anthropic Search Results Integration with LangChain Documentation
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Learn more from @AnthropicAI docs:
https://
docs.anthropic.com/en/docs/build-
with-claude/search-results
… LangChain Python docs:
https://
python.langchain.com/docs/integrati
ons/chat/anthropic/#in-tool-results-agentic-rag
… LangChain JS docs: -
Character AI’s New CEO Focuses on Entertainment and Creator Tools
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"We're doubling down on entertainment, doubling down on trust and safety." Our new CEO, Karandeep Anand, sat down with @CNN to tease the next chapter for @character_ai
. In his first 60 days, he's focused on: Smarter tools for creators A richer social feed experience -
Task B: Summarize Text Using Keywords and Agents
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Define Task B: Summarize based on Task A
summary = http://
pypilot.run( f"Summarize the text using only these keywords: {keywords}", agents=[agent_b], result_type=str, context={'text': long_text}
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Task Orchestration Pipelines for Real-World AI Applications
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This ensures Task B only runs after Task A completes—creating clean, interpretable, and robust pipelines. Perfect for real-world use-cases: Keyword-based summarization Sentiment-guided user feedback Topic-powered content generation Context-aware chatbots
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Setting Up Multi-Agent AI Systems for Task Automation
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Setup agents for each task:
agent_a = pypilot.Agent(model="gpt-4o-mini", instructions="Extract keywords")
agent_b = pypilot.Agent(model="gpt-4o-mini", instructions="Generate summary using keywords") -
Automated Keyword Extraction Using AI Agents
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Define Task A: Extract keywords
keywords = http://
pypilot.run( "Extract key topics from this text", agents=[agent_a], result_type=list, context={'text': long_text}
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Pypilot’s Task Capabilities and Problem-Solving with LLMs
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Learn more about pypilot's tasks capabilities and problems it can solve using llms.