#agents

Dynamic visualization of Microsoft AutoGen agent chats. Use a messaging interface style where each bubble indicates which agent (Assistant, Critic, User) spoke. Include a 'Context Window' sidebar that shows the tokens used and cost per message. Highlight when the 'TERMINATE' command is triggered.

Build AI agents with LangChain. Components: 1. LLM wrapper (OpenAI, Anthropic, local). 2. Prompt templates with variables. 3. Chains for sequential operations. 4. Agents with tool selection. 5. Memory for conversation context. 6. Vector stores for embeddings. 7. Document loaders and splitters. 8. Output parsers for structured data. Use LCEL (LangChain Expression Language) for complex flows and implement human-in-the-loop patterns.

Inspect what an AI agent 'remembers'. Sections: 1. Short-term Memory (Chat History). 2. Long-term Memory (Vector retrieval). 3. Entity Memory (Facts about the user). 4. Importance/Weighting adjustment knobs. 5. Visual graph of related memories. Useful for building multi-turn complex agents.

Visualize a complex LangChain agent flow. Flow components: 1. User Input -> Embedding Model. 2. Vector DB (Pinecone) retrieval. 3. LLM (GPT-4) reasoning step. 4. Tool execution (Google Search, Python Repl). 5. Final Output. Use a node-based diagram style with directed arrows and color-coded component boxes.