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 monthThis 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 issueUse 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_outputTips
- 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 -- the typical downstream consumer of the summarized query.
- Thread Summarizer -- summarizes support ticket threads rather than chat conversations.