In Review

Planned

In Progress

Completed

Integration

Integrate with ZeroEntropy API for Vectorization, Ingestion and Query

Engineering

Integration to ZeroEntropy

Ghita

Problem: No clear way to terminate workflows early or break out of loops based on conditions, forcing users to create workarounds with maximum iteration counts.Use Case: When API calls fail or specific conditions are met, users want to cleanly exit workflows or break out of polling loops without continuing unnecessary iterations.Proposed Solution: Implement dedicated break and end nodes that allow conditional workflow termination and loop breaking.

Break Node and End Node

Aman Sharma

Support Inbuilt vector search of vectorDB as tool call for AI Node

Vector Search as Tool

Customer want to build multiple agentic flows with different payload and stages within the same flowThere are various options we can provide this -<ol><li>Suspend - Resume logic </li><li>Dedicated Followup Node which sends back the response and takes the input then resumes the flow where it left</li></ol>

Support Subflows / Stages within a flow

Add search bar for the flow<img data-width="100%" data-align="left" data-featurebase-content-key="6672da561446f585be56f6d1/static/0196015b-0d37-764c-81b9-020c82d29b57/ss-0196015b-0d37-764c-81b9-020c82d29b57.png" src="https://6672da561446f585be56f6d1.featurebase-attachments.com/c/static/0196015b-0d37-764c-81b9-020c82d29b57/ss-0196015b-0d37-764c-81b9-020c82d29b57.png?X-Amz-Expires=3600&amp;X-Amz-Date=20260223T010000Z&amp;X-Amz-Algorithm=AWS4-HMAC-SHA256&amp;X-Amz-Credential=DO801TYC4FCVNNEKURKM%2F20260223%2Ffra1%2Fs3%2Faws4_request&amp;X-Amz-SignedHeaders=host&amp;X-Amz-Signature=9f5c1a79455bd9927d46aedcd26d0ddb882098d3d48ddfd7f02eb6de0c2af98b" data-featurebase-content-filename="ss-0196015b-0d37-764c-81b9-020c82d29b57.png">

Flow Searchbar Feature

Vrijraj Singh

Extend git integration to include flow metadata like tags and owners, ensuring complete version control for all flow components.

Include Flow Metadata in version controls

Improve the branch merge interface with better file naming (showing actual flow names instead of random IDs) and debugger like node structure showing changes between versions.

UI/UX

Enhanced Merge Interface with Visual Diffs

Add visual indicators (similar to VS Code's file change indicators) to show which nodes have been modified but not yet saved, helping users track changes before deployment.

Modified Node Visual Indicators

Improve visual feedback in the debugger to show only executed nodes as successful, with skipped nodes having a different visual state (grayed out or marked as "skipped").

Visual Node State Indicators for classifier

Add built-in documentation and suggested prompt structures for AI nodes to help users create more effective classification logic without external AI assistance.Examples - <ol><li>Classifier Example </li><li>Structure generate</li><li>Summarize</li><li>Extract</li><li>Image generate</li><li>Score generate etc</li></ol>

Enhanced AI Node Documentation

Users currently struggle and have to perform multiple steps when indexing a new schema in the vectorDB. Current Steps -<ol><li>Update the metadata schema</li><li>Change the mode the full refresh</li><li>deploy the flow</li><li>Schedule Runs</li><li>Data indexed</li></ol>Proposed Steps - <ol><li>Option in Jobs &gt; Force Sync &gt; Full Refresh</li><li>Automatically updates flows, Deploys it, Trigger a Job</li><li>Return back the flow to Incremental</li></ol>

Full Refresh option in Force Sync

Feature : An input box like feature, which post some characters entered provides suggestions (Gen-AI based or RAG based) and allow users to complete the input box. This could basically take a collection of objects to show the outputWhy : Can allow for quick widget suggestions based on user documents and/or basic gen-ai suggestionsExample(s) :<img data-width="100%" data-align="left" data-featurebase-content-key="6672da561446f585be56f6d1/post/68932f17fab70344c512ddcd/01987eee-983e-71bd-9770-315758965fb6/b64u-U2NyZWVuc2hvdCAyMDI1LTA4LTA2IGF0IDQuMDAuMTjigK9QTS5wbmc.png" src="https://6672da561446f585be56f6d1.featurebase-attachments.com/c/post/68932f17fab70344c512ddcd/01987eee-983e-71bd-9770-315758965fb6/b64u-U2NyZWVuc2hvdCAyMDI1LTA4LTA2IGF0IDQuMDAuMTjigK9QTS5wbmc.png?X-Amz-Expires=3600&amp;X-Amz-Date=20260223T010000Z&amp;X-Amz-Algorithm=AWS4-HMAC-SHA256&amp;X-Amz-Credential=DO801TYC4FCVNNEKURKM%2F20260223%2Ffra1%2Fs3%2Faws4_request&amp;X-Amz-SignedHeaders=host&amp;X-Amz-Signature=512e06fbd8acb1232e06f9604fedc8d8769c8b5979cd8b179b36c05ee332acc3" data-featurebase-content-filename="Screenshot 2025-08-06 at 4.00.18 PM.png"><img data-width="100%" data-align="left" data-featurebase-content-key="6672da561446f585be56f6d1/post/68932f17fab70344c512ddcd/01987eee-e6b1-7b7f-9df2-2f2e1ef6870a/b64u-U2NyZWVuc2hvdCAyMDI1LTA4LTA2IGF0IDQuMDAuMznigK9QTS5wbmc.png" src="https://6672da561446f585be56f6d1.featurebase-attachments.com/c/post/68932f17fab70344c512ddcd/01987eee-e6b1-7b7f-9df2-2f2e1ef6870a/b64u-U2NyZWVuc2hvdCAyMDI1LTA4LTA2IGF0IDQuMDAuMznigK9QTS5wbmc.png?X-Amz-Expires=3600&amp;X-Amz-Date=20260223T010000Z&amp;X-Amz-Algorithm=AWS4-HMAC-SHA256&amp;X-Amz-Credential=DO801TYC4FCVNNEKURKM%2F20260223%2Ffra1%2Fs3%2Faws4_request&amp;X-Amz-SignedHeaders=host&amp;X-Amz-Signature=351bbc382f004c40a91d767316919443ad2afec8e4d7ad169b3dee3d3fba7c13" data-featurebase-content-filename="Screenshot 2025-08-06 at 4.00.39 PM.png">

Autocomplete Widget

Dhruv Pamneja

<ul><li>Problem: A/B experiments lack clear labeling and result visualization.</li><li>Use case: Make data-driven decisions about prompts, flows, and model choices.</li><li>Functionality: Experiment labels on runs, statistical analysis with confidence intervals, dashboards for variant comparison, and filtering/grouping by experiment tags.</li></ul>

A/B Testing Analytics and Reporting in Logs

<ul><li>Problem: Manual grading of responses does not scale and is inconsistent.</li><li>Use case: Systematically assess response quality across large test suites.</li><li>Functionality: Grader agents with configurable rubrics, batch “grade all” execution, and integration with logs for historical evaluation.</li></ul>

Automated Response Evaluation System (Graders)

<ul><li>Problem: Basic TTS/STT is insufficient for production-quality voice applications.</li><li>Use case: Deliver high-quality, natural-sounding voice interactions for customer-facing apps.</li><li>Functionality: A dedicated 11Labs node with full API coverage, streaming voice generation, configurable voices, and secure credential management.</li></ul>

11Labs

<ul><li>Problem: No native voice model support, requiring external services for voice features.</li><li>Use case: Build voice companions, real-time call analysis, and other voice-first applications.</li><li>Functionality: Dedicated STT and TTS nodes with model selection, streaming audio support, real-time transcription/synthesis, and voice-based triggers and responses.</li></ul>

Integrated Voice Processing Capabilities

<ul><li>Problem: Async behavior is currently dictated by the trigger node only, limiting flexibility.</li><li>Use case: Run deep analysis or enrichment sub-flows in the background independent of the main trigger.</li><li>Functionality: Async mode selection on the execute-flow node, support for fire-and-forget sub-workflows, and decoupling from parent workflow async settings.</li></ul>

Granular Async Control for Sub-workflows

<ul><li>Problem: Classifier lacks a Chat history field, forcing context to be injected via prompt hacks.</li><li>Use case: Enable context-aware classification in multi-turn conversations.</li><li>Functionality: Add a standard memory field, automatically reference chat history, and align the classifier API with other context-aware nodes.</li></ul>

Classifier Node - Chat History Support

<ul><li>Problem: Developers must manually manage JSON and memory fields across nodes, with inconsistent support.</li><li>Use case: Simplify building and testing conversational flows with a toggle-based experience.</li><li>Functionality: Checkbox-style chat history enablement, consistent memory field support on all nodes, and automatic user-based history storage and retrieval.</li></ul>

First-Party Chat History Management

<ul><li>Problem: Users perceive latency because initial tokens are delayed visually even when backend completion is fast.</li><li>Use case: Improve UX for long-running tasks in web apps by showing progress immediately.</li><li>Functionality: Dual token streams (output and process), a "processing response" JSON key for intermediate node updates, and an async streaming API that can pipe data directly into React components.</li></ul>

Token Streaming Support in SDK

When creating Test Cases, capture expected results (in addition to capturing the name and test conditions as Lamatic does currently). Consider also,<ul><li>Using code checks and AI to find deviation and grade result quality.</li><li>Use this quality score as an input for deciding whether to deploy an update.</li><li>Assist in recommending the best option when A|B testing prompts, agents, workflows, models and other settings such as temperature.</li><li>Using these results to create benchmarks.</li><li>Measuring and alerting about “model drift” over time.</li><li>Integrating “human-in-the-loop” strategies to expand Test Cases and quality checking.</li></ul>Added Later (based on feedback from another user):<ul><li>Make it easier to see the history of Test Case executions (without leaving Experiments). Marc Greenberg didn’t realize he could find the results in Logs, but also didn’t like having to dig through logs to find the execution details for a given test.</li></ul>

Test Case Expected Results

cwhiteman

Ability to upload files and use them in flows (to, among other things, eliminate the need to find a place to host files either for testing or production purposes). For example, PDF upload for training or CSV for instructions.

Spaces for static file uploads and usage

<ul><li>Problem: Developers must manually compute speed and token metrics from logs.</li><li>Use case: Quickly benchmark and optimize workflows against performance targets.</li><li>Functionality: Real-time display of execution time, token usage, accuracy indicators, TTFT, throughput, and benchmark comparisons within the debugger.</li></ul>

Dispay Test Run Stats in Debugger

<ul><li>Problem: Debug runs are not persisted, hindering regression and performance tracking.</li><li>Use case: Enable long-term testing workflows with historical comparisons.</li><li>Functionality: Store all runs with timestamps and versions, searchable and filterable history, and side-by-side comparison of different runs and versions.</li></ul>

Persistent Test Run History and Analytics

Requirement from Pat at co-dev to connect to mySQL (on PlanetScale).

High Priority

Native integration to mySQL

So that we can connect and export it to other log systems

Make Logs OTEL Compatible

Build B2B SaaS application with Tenant isolation and scalability

Multi Tenancy

A dedicated UI to choose model preferences on project level. <ol><li>There will be different categories of models ( Tabs ) for Text | Embedding| Chat | Image | Audio | Transcript </li></ol><ol start="2"><li>For Each category customer can create 5 Settings and reorder them. </li><li>In each settings can choose 4 tabs ( Basic | Fallback | Best | A/B Test ) </li><li>When using model selection in a workflow node, choose a preferred setting options which will populate the UI with credentials and model selection. ( By Default the first preference will be chosen or create a new preferences </li></ol>

Model Preference & Fallback

Use web API to give real time TTS and STT on search and chat widget

Support Speech via widgets

Add Support for connecting custom MCP Server in the Tools section of each AI node and integration Section Desired Flow <ol><li>Adding Directly<ol><li>Go to Integration</li><li>Choose Add Integration</li><li>Choose MCP</li><li>Add MCP Server Credentials</li></ol></li><li>Via AI Node<ol><li>Choose Any AI Node </li><li>Go to tools &gt; Add Tools</li><li>Choose MCP</li><li>Select MCP Server / Setup New</li></ol></li><li>Via Node<ol><li>Create a new MCP Action Node </li><li>Choose MCP Node</li><li>Choose Credentials</li><li>Define Action to perform</li></ol></li></ol>Find details here -<a target="_blank" rel="noopener noreferrer nofollow" class="link" href="https://www.perplexity.ai/search/how-can-lamatic-ai-build-mcp-i-9.Gp.fxuQ_WWHzBBqlqFUA">https://www.perplexity.ai/search/how-can-lamatic-ai-build-mcp-i-9.Gp.fxuQ_WWHzBBqlqFUA</a>

Add MCP Support

Add a global level property that sets how many times a given node can retry. Use a default value set on project setting level then it could be over-ridden for every node seperately.

Add tries for nodes executions

Arun Addagatla

Add Webflow as Data Loader to Sync content into Lamatic

Webflow Integration

<ul><li>Problem: Classifier and condition nodes can only route to one branch at a time.</li><li>Use case: Execute multiple analyses in parallel (e.g., email, image, social checks) based on classification.</li><li>Functionality: Multi-branch execution support, per-branch on/off toggles, and optimization for concurrent rather than sequential processing.</li></ul>

Multi-Branch Execution for Parallel Processing

<ul><li>Problem: Current web scraping requires manual API integration and maintenance.</li><li>Use case: Support business verification and structured data extraction directly within workflows.</li><li>Functionality: Native Firecrawl agent tool with search, crawl, and extraction, plus automatic handling of authentication, rate limiting, and retries.</li></ul>

Firecrawl > Agent Support

<ul><li>Establish distinct URLs for different environments (such as development, staging, and production) to clearly separate functionality and reduce risk of confusion.</li><li>Add the ability to preview changes in a clear, visual manner before deploying, ensuring users know exactly what will be affected.</li><li>Implement rollback functionality with detailed version history, allowing for safe reversions to earlier states should issues arise.</li><li>Offer dedicated build and monitor modes, supporting different user access levels and safeguarding production deployments.</li></ul>

Environment & Deployment Controls

Add GitHub integration functionality to allow synchronization of prompt configurations, action definitions, and other code assets with a GitHub repository. This will streamline version control and collaboration workflows. Target completion within two months.

GitHub Integration for Configuration and Code Sync

Develop a peripheral memory or chat context history storage feature that can be configured and associated with unique user IDs. This feature should support maintaining conversation context over time and across sessions, allowing the system to track relevant messages to specific scams and improve scam detection accuracy.

Peripheral Memory / Chat Context History Storage

Help Center

Improved

Fixed

Integrations

lamatic

Rejected

Low Priority

Main Roadmap

Tell us how we could make the product more useful to you. For Support reach out at hello@lamatic.ai.

Lamatic.ai

Integration