> Source: https://builder-docs.ema.ai/agent-reference/core/conversation-summarizer > Title: Conversation Summarizer Agent # Conversation Summarizer Agent The Conversation Summarizer agent converts a multi-turn chat conversation into a concise search query that captures the user's current intent. It distills the conversation history so downstream search agents receive a focused query rather than the entire conversation. > **Important:** By default, this agent does _not_ use an LLM. It concatenates recent messages into a formatted string. Enable LLM mode in the agent's configuration if you need intelligent summarization that interprets context and resolves references across turns. ## Use Cases - Your workflow handles multi-turn conversations and needs to search a knowledge base based on the user's latest intent. - Passing the full conversation to a search agent degrades search quality, and you need a distilled query instead. - The user's most recent message alone lacks enough context for an accurate search (e.g., "What about the second one?"). ## Inputs Input Type Description `chat_conversation` Conversation The multi-turn chat conversation to summarize. ## Outputs Output Type Description `query` Text A concise search query derived from the conversation. `tags` List of Text Extracted tags, if tag extraction is configured. ## Configurations Parameter Description Default **LLM mode** When enabled, uses an LLM to intelligently summarize the conversation into a search query. When disabled, simply concatenates recent messages. Off **Context window** Number of recent messages to include. Older messages are excluded. 10 **Instructions** Custom guidance for the LLM (e.g., "Focus on the most recent question" or "Ignore greetings"). Only applies when LLM mode is enabled. None **Glossary** Company-specific terms and definitions to help the LLM interpret domain language correctly. None **Tag extraction** Rules for extracting tags from the conversation alongside the search query. None ## How the Two Modes Work ### Default Mode (LLM off) The agent takes the most recent messages (up to the context window) and concatenates them with speaker labels: ``` User: I need help with my subscription Bot: Sure, I can help. What's the issue? User: I was charged twice this month ``` This is fast and cost-effective, but the output is a formatted transcript -- not an optimized search query. ### LLM Mode The agent uses an LLM to interpret the conversation and produce a focused search query. For the same conversation above, it might output: ``` duplicate subscription charge billing issue ``` Use LLM mode when the conversation involves context-dependent references, topic shifts, or when downstream search quality matters. ## How to Use This Agent In a multi-turn chat workflow, place the Conversation Summarizer before a search agent: ``` chat_trigger -> conversation_summarizer -> knowledge_search -> respond_using_search_results -> workflow_output ``` ## Tips - Start with the default mode. Switch to LLM mode only if you notice search quality degrading on multi-turn conversations. - If your conversations are typically short (1-2 turns), you may not need this agent at all -- wire the conversation directly to your search agent. - Use the instructions field to steer LLM summarization for your domain: "Focus on the product name and issue type" or "Treat the last user message as the primary query." - The context window of 10 messages works well for most support conversations. Increase it if your users have long-running threads where early context matters. ## Related Agents - [Knowledge Search](/legacy-docs/agent-reference/core/knowledge-search) -- the typical downstream consumer of the summarized query. - [Thread Summarizer](/legacy-docs/agent-reference/core/thread-summarizer) -- summarizes support ticket threads rather than chat conversations.