Post-Processors¶

Configure post-processors to analyze, summarize, and process conversations after user turns or when conversations expire.

Overview¶

Post-processors execute logic after the agent has responded — either after each user turn or when a conversation ends. They can analyze messages, generate summaries, ingest conversation transcripts into knowledge bases, and more.

Post-processors are categorized into two groups based on their execution trigger:

Category	Trigger	Use Case
Turn Post-Processor	After every user turn	Real-time message analysis, tagging, sentiment tracking
Conversation Post-Processor	On conversation expiry or completion	Summaries, transcript ingestion, final actions

Post-processors are configured at the workspace level under Settings > Processors > Post-Processors, then assigned to individual agents.

Post-Processors List

Available Types¶

Add Post-Processor Modal

Category	Type
Conversation Post-Processor	Analysis (After The Conversation), Knowledge Ingestion, Summary
Turn Post Processor	Analysis (After Each Turn)

Turn Post-Processors¶

Turn post-processors execute after every user interaction (turn) within a conversation. They provide real-time analysis as the conversation progresses.

Analysis (After Each Turn)¶

Analysis after each turn configuration

Analysis (After Each Turn) (previously called Message Analyzer) evaluates each message exchange and applies tags or analysis in real time.

Field	Description
Post-Processor Name	Descriptive name for this analyzer
Model for Batch Processing	The model used for analysis. Required for the Re-Run capability
Prompt to Analyze Messages	Instructions for the analysis

Batch Model Requirement

To enable the Re-Run capability for Analysis (After Each Turn) configurations, a Batch Model must be defined in its settings.

Conversation Post-Processors¶

Conversation post-processors execute when a conversation expires or completes. The expiry is controlled by the Conversation Inactivity Duration setting (default: 60 minutes) under the agent's Behavior Flags module.

Analysis (After The Conversation)¶

Analysis after the conversation configuration

Select Analysis (After The Conversation) from Create Post-Processor. Name the processor, select the batch model, review the prompt, then scroll down to define the extraction fields before saving.

Analyzes completed conversations based on a prompt and extracts structured data as JSON. Useful for extracting key fields (names, dates, decisions) from conversation transcripts.

Field	Required	Description
Post-Processor Name	Yes	Descriptive name for this analyzer
Model for Batch Processing	Yes	The batch model used to analyze the conversation
Prompt	Yes	The extraction prompt. Use `{{fields}}` placeholder — it is replaced with the fields defined in the Fields table below

Default Prompt: The system provides a default prompt that instructs the model to extract fields as valid JSON, using null for missing values, YYYY-MM-DD for dates, exact values only (no inference), final corrected values, and true/false for booleans.

Fields Table:

Define the properties to extract from conversations:

Column	Required	Description
Name	Yes	The field name (becomes the JSON key)
Type	Yes	Data type (string, number, boolean, date, etc.)
Description	Yes	Description of what to extract for this field

Click "Add" to create additional fields.

Filter & Inspect Analyzer Outputs

Analysis (After The Conversation) outputs are usable at the individual-conversation level, not just in aggregate reports:

Filter conversations by analyzer field/value via the Analysis option under Filters on the Conversations page (free text, contains-match, case-insensitive).
Inspect every post-processor run on a conversation via the Analysis button on its detail view — status, extracted values, errors, and last-updated timestamps in one place.

See Conversations → Conversation Analysis.

Knowledge Ingestion¶

Knowledge Ingestion Configuration

Automatically pushes the full transcript of expired or completed conversations to a defined Knowledge Base, enabling continuous learning and data enrichment.

Configuration:

Field	Required	Description
Post-Processor Name	Yes	Descriptive name
Prefix Text	No	Text automatically prepended to transcripts before saving. Supports dynamic placeholders (see below)
API Key	Yes	API key for authenticating with the repository. Use `{{SECRETS.KEY}}` for secrets configured for the workspace
Knowledge Base (UUID)	Yes	The UUID of the destination knowledge base for conversation transcripts

Supported Placeholders for Prefix Text:

Placeholder	Description
`{{user.email}}`	The authenticated user's email
`{{user.name}}`	The authenticated user's name
`{{date}}`	Timestamp of the conversation
`{{conversation.id}}`	Unique conversation identifier
`{{conversation.duration}}`	Total conversation duration
`{{agent.name}}`	Name of the agent
`{{workspace.name}}`	Name of the workspace

Conversation Memory

Using prefix placeholders ensures stored conversations are fully searchable by user identity and date, transforming standard transcripts into actionable "user conversation memory."

Summary¶

Summary Configuration

Analyzes expired or completed conversation transcripts and automatically generates a summary. Summaries can be viewed as a report on the agent reports dashboard.

Field	Required	Description
Post-Processor Name	Yes	Descriptive name
Model for Batch Processing	Yes	The batch model used for summary generation
Prompt to analyze messages	Yes	The prompt used to analyze conversations. A Default link resets to the built-in prompt. Example: "Analyze this conversation between Human, AI and Support Assistant. Summarize the conversation in less than 50 words."

Conversation Inactivity Duration¶

This setting controls when conversation post-processors trigger. Located under the agent's Behavior Flags module, it defines the duration of inactivity (in minutes) after which a conversation reaches a soft expiry.

Default: 60 minutes
When the inactivity duration is reached, all configured Conversation Post-Processors execute (soft expiry on the backend)
If the user resumes the conversation later, the new message is appended to the existing context. The next time the conversation reaches the inactivity duration, post-processors re-run and update / overwrite the previously generated outcomes (summary, metadata, CRM updates) based on the newly accumulated context
The chat input itself remains active — there is no hard, user-visible expiry. See Continuous Conversations
Changing this value affects when summaries are generated and transcripts are ingested

Conversation Inactivity Message¶

Open Brain > Additional prompts, enable Conversation Inactivity Message, and enter the text. The current field help says the message is sent exactly as written when the conversation becomes inactive.

Use Behavior Flags > Conversation Inactivity Duration to review the inactivity interval. Do not treat inactivity as closure of the user conversation; see Continuous Conversations.

Manual Re-Run¶

Post-processors can be manually re-executed for all or selected conversations directly from the Conversation Monitoring screen.

Re-run Post Processor

Conversations page — clicking Re-run opens the Re-run Post Processor modal: pick which post-processor to execute, then choose Re-run scope (All Conversations or Only Failed)

Navigate to the agent's Conversations page
Click the Re-run action in the toolbar (alongside Select and Show filters)
In the Re-run Post Processor modal, pick the post-processor from the dropdown
Choose the Re-run scope — All Conversations or Only Failed
Click Re-run

Permission

The Re-Run Post Processor capability is controlled by a dedicated permission, enabled by default for Admin, Workspace Admin, and Supervisor roles.

Batch Monitoring¶

A centralized Batch Monitoring screen provides visibility into the status of all running batch processes within a workspace — including post-processor jobs.

Batch Monitoring

Access: Open Account menu > Batch Monitoring. Switch between Conversation Processor, Message Processor, and Documents, then use the search and status filters to inspect jobs.