Planned
Model Preference & Fallback
A dedicated UI to choose model preferences on project level. There will be different categories of models ( Tabs ) for Text | Embedding| Chat | Image | Audio | Transcript For Each category customer can create 5 Settings and reorder them. In each settings can choose 4 tabs ( Basic | Fallback | Best | A/B Test ) 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
Aman Sharma 11 months ago
💡 Feature Requests
Planned
Model Preference & Fallback
A dedicated UI to choose model preferences on project level. There will be different categories of models ( Tabs ) for Text | Embedding| Chat | Image | Audio | Transcript For Each category customer can create 5 Settings and reorder them. In each settings can choose 4 tabs ( Basic | Fallback | Best | A/B Test ) 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
Aman Sharma 11 months ago
💡 Feature Requests
Planned
Test Case Expected Results
When creating Test Cases, capture expected results (in addition to capturing the name and test conditions as Lamatic does currently). Consider also, Using code checks and AI to find deviation and grade result quality. Use this quality score as an input for deciding whether to deploy an update. Assist in recommending the best option when A|B testing prompts, agents, workflows, models and other settings such as temperature. Using these results to create benchmarks. Measuring and alerting about “model drift” over time. Integrating “human-in-the-loop” strategies to expand Test Cases and quality checking. Added Later (based on feedback from another user): 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.
cwhiteman 12 months ago
💡 Feature Requests
Planned
Test Case Expected Results
When creating Test Cases, capture expected results (in addition to capturing the name and test conditions as Lamatic does currently). Consider also, Using code checks and AI to find deviation and grade result quality. Use this quality score as an input for deciding whether to deploy an update. Assist in recommending the best option when A|B testing prompts, agents, workflows, models and other settings such as temperature. Using these results to create benchmarks. Measuring and alerting about “model drift” over time. Integrating “human-in-the-loop” strategies to expand Test Cases and quality checking. Added Later (based on feedback from another user): 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.
cwhiteman 12 months ago
💡 Feature Requests
In Progress
Add MCP Support
Add Support for connecting custom MCP Server in the Tools section of each AI node and integration Section Desired Flow Adding Directly Go to Integration Choose Add Integration Choose MCP Add MCP Server Credentials Via AI Node Choose Any AI Node Go to tools > Add Tools Choose MCP Select MCP Server / Setup New Via Node Create a new MCP Action Node Choose MCP Node Choose Credentials Define Action to perform Find details here - https://www.perplexity.ai/search/how-can-lamatic-ai-build-mcp-i-9.Gp.fxuQ_WWHzBBqlqFUA
Aman Sharma 3 months ago
High Priority
💡 Feature Requests
In Progress
Add MCP Support
Add Support for connecting custom MCP Server in the Tools section of each AI node and integration Section Desired Flow Adding Directly Go to Integration Choose Add Integration Choose MCP Add MCP Server Credentials Via AI Node Choose Any AI Node Go to tools > Add Tools Choose MCP Select MCP Server / Setup New Via Node Create a new MCP Action Node Choose MCP Node Choose Credentials Define Action to perform Find details here - https://www.perplexity.ai/search/how-can-lamatic-ai-build-mcp-i-9.Gp.fxuQ_WWHzBBqlqFUA
Aman Sharma 3 months ago
High Priority
💡 Feature Requests
Planned
Semantic Caching
Add built in semantic caching that saves on request costs and also make the response more predicatable. Issue reported by the user - Chat response took 12+ seconds to respond to a user question (while also consuming tokens and giving a non-deterministic answer) to answer the identical question that someone else has asked. Is it possible to enable semantic caching for my Lamatic Chat Widget?
AlexBozanic 17 days ago
💡 Feature Requests
Planned
Semantic Caching
Add built in semantic caching that saves on request costs and also make the response more predicatable. Issue reported by the user - Chat response took 12+ seconds to respond to a user question (while also consuming tokens and giving a non-deterministic answer) to answer the identical question that someone else has asked. Is it possible to enable semantic caching for my Lamatic Chat Widget?
AlexBozanic 17 days ago
💡 Feature Requests
Group Results Feature for Search
Add additional option to group results with a particular key to a UI similar to below
Aman Sharma 2 months ago
👏🏻 Feedback
Group Results Feature for Search
Add additional option to group results with a particular key to a UI similar to below
Aman Sharma 2 months ago
👏🏻 Feedback
Planned
Add tries for nodes executions
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.
aruna 11 months ago
High Priority
💡 Feature Requests
Planned
Add tries for nodes executions
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.
aruna 11 months ago
High Priority
💡 Feature Requests
Prompt Users to Connect with Support on Error Dialogues
Summary Add a "Get Help" or "Contact Support" button to all error dialogs to instantly connect users with support when issues occur. Problem When users encounter errors, they must navigate away from the error context to find support, leading to: Lost error context and details Friction in getting help Reduced support ticket quality Proposed Solution Enhance all error dialogs with integrated support options: UI Addition ❌ Error: File processing failed The file could not be processed due to a timeout. [Retry] [Contact Support] [Dismiss] Support Options Live Chat: Instant connection with pre-populated error details Create Ticket: Auto-filled support form with error context Help Center: Direct link to relevant documentation Book Call: Connect with someone Auto-Context Sharing Error message and code User session details Current operation being performed Browser/system information Benefits Faster Resolution: Support gets full error context immediately Better UX: One-click access to help when users need it most Higher Success Rate: Users more likely to get help vs. abandoning Acceptance Criteria [ ] All error dialogs include "Contact Support" option [ ] Support requests auto-populate with error details [ ] Works across all Lamatic components (flows, integrations, etc.) [ ] Respects user privacy preferences for data sharing Priority: Medium Effort: Low-Medium
Aman Sharma 3 days ago
💡 Feature Requests
Prompt Users to Connect with Support on Error Dialogues
Summary Add a "Get Help" or "Contact Support" button to all error dialogs to instantly connect users with support when issues occur. Problem When users encounter errors, they must navigate away from the error context to find support, leading to: Lost error context and details Friction in getting help Reduced support ticket quality Proposed Solution Enhance all error dialogs with integrated support options: UI Addition ❌ Error: File processing failed The file could not be processed due to a timeout. [Retry] [Contact Support] [Dismiss] Support Options Live Chat: Instant connection with pre-populated error details Create Ticket: Auto-filled support form with error context Help Center: Direct link to relevant documentation Book Call: Connect with someone Auto-Context Sharing Error message and code User session details Current operation being performed Browser/system information Benefits Faster Resolution: Support gets full error context immediately Better UX: One-click access to help when users need it most Higher Success Rate: Users more likely to get help vs. abandoning Acceptance Criteria [ ] All error dialogs include "Contact Support" option [ ] Support requests auto-populate with error details [ ] Works across all Lamatic components (flows, integrations, etc.) [ ] Respects user privacy preferences for data sharing Priority: Medium Effort: Low-Medium
Aman Sharma 3 days ago
💡 Feature Requests
File Reading Timeout for Large Files in Deployed Edge Worker
Bug Report: Summary: When attempting to read files that require more than 2 minutes to process, the deployed edge worker times out, resulting in incomplete or failed requests. Steps to Reproduce: Deploy an edge worker that reads a large file (size sufficient to require >2 minutes to process). Initiate the file reading operation. Observe the request timing out before completion. Observed Behavior: Requests for large files (exceeding size or processing time limits) consistently time out after approximately 2 minutes, failing to deliver results. Expected Behavior: Large files should be processed successfully, or clear feedback provided about system limitations. Transparency on Limitations: Maximum file body size: 128 KB when using json() or text() methods for response buffering in EdgeWorkers[3]. Timeouts: Current processing time cap is ~2 minutes for a single request. Token limits: Not documented in the provided sources, but large file size or token count may contribute to timeouts or truncation. Additional: Lamatic’s Extract from File node provides file size and metadata, but does not specify processing time caps[5]. Requested Improvements: Document and Surface Limitations: Clearly state maximum supported file size, processing time, and any token or content size restrictions in user-facing documentation and error messages. Improve Timeout Handling: Implement user-friendly error messaging when a timeout occurs, specifying the cause (e.g., exceeded time or file size limit). Consider chunked or paginated processing for large files to avoid triggering timeouts. Increase Timeout Thresholds: Where feasible, raise timeout limits for file reading operations within edge worker constraints, or provide configurable timeout settings. Impact: Limits the usability of edge workers for processing large files and leads to unclear errors for users unaware of these constraints. References: EdgeWorkers body size and processing limitations[3] Lamatic Extract from File node troubleshooting and file metadata[5]
Aman Sharma 3 days ago
👏🏻 Feedback
File Reading Timeout for Large Files in Deployed Edge Worker
Bug Report: Summary: When attempting to read files that require more than 2 minutes to process, the deployed edge worker times out, resulting in incomplete or failed requests. Steps to Reproduce: Deploy an edge worker that reads a large file (size sufficient to require >2 minutes to process). Initiate the file reading operation. Observe the request timing out before completion. Observed Behavior: Requests for large files (exceeding size or processing time limits) consistently time out after approximately 2 minutes, failing to deliver results. Expected Behavior: Large files should be processed successfully, or clear feedback provided about system limitations. Transparency on Limitations: Maximum file body size: 128 KB when using json() or text() methods for response buffering in EdgeWorkers[3]. Timeouts: Current processing time cap is ~2 minutes for a single request. Token limits: Not documented in the provided sources, but large file size or token count may contribute to timeouts or truncation. Additional: Lamatic’s Extract from File node provides file size and metadata, but does not specify processing time caps[5]. Requested Improvements: Document and Surface Limitations: Clearly state maximum supported file size, processing time, and any token or content size restrictions in user-facing documentation and error messages. Improve Timeout Handling: Implement user-friendly error messaging when a timeout occurs, specifying the cause (e.g., exceeded time or file size limit). Consider chunked or paginated processing for large files to avoid triggering timeouts. Increase Timeout Thresholds: Where feasible, raise timeout limits for file reading operations within edge worker constraints, or provide configurable timeout settings. Impact: Limits the usability of edge workers for processing large files and leads to unclear errors for users unaware of these constraints. References: EdgeWorkers body size and processing limitations[3] Lamatic Extract from File node troubleshooting and file metadata[5]
Aman Sharma 3 days ago
👏🏻 Feedback
Tagging System for better organsation
Summary Implement a comprehensive tagging system across all Lamatic resources (flows, test cases, models, integrations, tools, agents, prompts, etc.) to enable better organization, discovery, and management of assets. Problem As Lamatic workspaces grow, users struggle with: Asset Discovery: Difficulty finding specific resources among hundreds of flows, prompts, and tools Organization: No flexible way to group related resources across different asset types Project Management: Cannot easily identify all components belonging to a specific project or use case Collaboration: Teams can't efficiently share and categorize resources Maintenance: Hard to identify deprecated or experimental assets Proposed Solution Introduce a universal tagging system that allows users to apply custom tags to any resource in Lamatic, with advanced filtering and search capabilities. Core Features 1. Tag Management Create Tags: Custom tag creation with name and optional color coding Tag Hierarchy: Support for nested tags (e.g., project:ecommerce, env:production) Tag Templates: Pre-defined tag sets for common use cases Tag Suggestions: Auto-suggest tags based on resource names and existing tags 2. Tagging Interface Multi-select Tagging: Apply multiple tags to single resources Bulk Tagging: Tag multiple resources simultaneously Quick Tags: One-click application of frequently used tags Tag Inheritance: Option to inherit tags from parent resources (e.g., flows inherit from projects) 3. Search & Filtering Tag-based Search: Find resources by single or multiple tags Advanced Filters: Combine tags with other filters (type, date, owner) Saved Searches: Save frequently used tag combinations Tag Cloud: Visual representation of most-used tags 4. Cross-Resource Organization Examples of tag usage: - project:customer-support (flows, prompts, tools, agents) - env:staging, env:production (test cases, integrations) - team:marketing, team:engineering (all resource types) - status:experimental, status:deprecated (any resource) - priority:high, priority:low (flows, test cases) User Interface Mockup Resource Card with Tags [Flow Name: Customer Onboarding] 🏷️ project:crm team:sales status:active priority:high 📊 Last run: 2 hours ago ✅ Passing Tag Management Panel 🏷️ Manage Tags ├── 📁 Projects │ ├── project:crm (12 resources) │ ├── project:analytics (8 resources) │ └── project:support (15 resources) ├── 👥 Teams │ ├── team:engineering (45 resources) │ └── team:marketing (23 resources) └── 🏷️ Status ├── status:active (67 resources) └── status:deprecated (3 resources) Enhanced Search 🔍 Search: [tag:project:crm AND tag:team:sales] [🎯 Advanced] Filters: [All Types ▼] [All Owners ▼] [All Tags ▼] Results: 12 resources found Technical Implementation 1. Data Model Tags Table: - id, name, color, description, created_by, created_at Resource_Tags Table: - resource_id, resource_type, tag_id, created_by, created_at Tag_Hierarchy Table: - parent_tag_id, child_tag_id 2. API Endpoints POST /api/tags - Create new tag GET /api/tags - List all tags with usage counts POST /api/resources/{id}/tags - Apply tags to resource GET /api/resources/search?tags=tag1,tag2 - Search by tags 3. Performance Considerations Indexing: Optimized database indexes for tag searches Caching: Cache frequently accessed tag combinations Pagination: Efficient pagination for large result sets Benefits For Individual Users Quick Discovery: Find resources instantly using meaningful tags Personal Organization: Create custom categorization systems Context Switching: Easily switch between different projects or contexts For Teams Standardization: Establish consistent tagging conventions Collaboration: Share and discover team resources efficiently Project Management: Track all assets associated with specific initiatives For Organizations Governance: Implement tagging policies and standards Resource Management: Identify unused or deprecated assets Compliance: Tag resources based on data sensitivity or regulatory requirements Acceptance Criteria Core Functionality [ ] Users can create, edit, and delete custom tags [ ] Tags can be applied to all resource types (flows, prompts, tools, etc.) [ ] Search functionality supports single and multiple tag filtering [ ] Bulk operations support tagging multiple resources [ ] Tag usage statistics and analytics available User Experience [ ] Intuitive tag creation and management interface [ ] Auto-complete and suggestions for existing tags [ ] Visual tag indicators on resource cards/lists [ ] Responsive design for desktop and mobile Performance & Scalability [ ] Fast search response times (<200ms for typical queries) [ ] Support for workspaces with 10,000+ resources and 1,000+ tags [ ] Efficient bulk operations for large resource sets Integration [ ] Tags included in export/import functionality [ ] API access for programmatic tag management [ ] Webhook support for tag-related events Future Enhancements Smart Tagging: AI-powered tag suggestions based on resource content Tag Analytics: Insights into tag usage patterns and resource relationships Advanced Permissions: Role-based tag management and visibility controls Tag Automation: Automatic tagging based on rules or triggers This tagging system would transform how users organize and discover resources in Lamatic, making it scalable for teams and organizations of any size.
Aman Sharma 3 days ago
💡 Feature Requests
Tagging System for better organsation
Summary Implement a comprehensive tagging system across all Lamatic resources (flows, test cases, models, integrations, tools, agents, prompts, etc.) to enable better organization, discovery, and management of assets. Problem As Lamatic workspaces grow, users struggle with: Asset Discovery: Difficulty finding specific resources among hundreds of flows, prompts, and tools Organization: No flexible way to group related resources across different asset types Project Management: Cannot easily identify all components belonging to a specific project or use case Collaboration: Teams can't efficiently share and categorize resources Maintenance: Hard to identify deprecated or experimental assets Proposed Solution Introduce a universal tagging system that allows users to apply custom tags to any resource in Lamatic, with advanced filtering and search capabilities. Core Features 1. Tag Management Create Tags: Custom tag creation with name and optional color coding Tag Hierarchy: Support for nested tags (e.g., project:ecommerce, env:production) Tag Templates: Pre-defined tag sets for common use cases Tag Suggestions: Auto-suggest tags based on resource names and existing tags 2. Tagging Interface Multi-select Tagging: Apply multiple tags to single resources Bulk Tagging: Tag multiple resources simultaneously Quick Tags: One-click application of frequently used tags Tag Inheritance: Option to inherit tags from parent resources (e.g., flows inherit from projects) 3. Search & Filtering Tag-based Search: Find resources by single or multiple tags Advanced Filters: Combine tags with other filters (type, date, owner) Saved Searches: Save frequently used tag combinations Tag Cloud: Visual representation of most-used tags 4. Cross-Resource Organization Examples of tag usage: - project:customer-support (flows, prompts, tools, agents) - env:staging, env:production (test cases, integrations) - team:marketing, team:engineering (all resource types) - status:experimental, status:deprecated (any resource) - priority:high, priority:low (flows, test cases) User Interface Mockup Resource Card with Tags [Flow Name: Customer Onboarding] 🏷️ project:crm team:sales status:active priority:high 📊 Last run: 2 hours ago ✅ Passing Tag Management Panel 🏷️ Manage Tags ├── 📁 Projects │ ├── project:crm (12 resources) │ ├── project:analytics (8 resources) │ └── project:support (15 resources) ├── 👥 Teams │ ├── team:engineering (45 resources) │ └── team:marketing (23 resources) └── 🏷️ Status ├── status:active (67 resources) └── status:deprecated (3 resources) Enhanced Search 🔍 Search: [tag:project:crm AND tag:team:sales] [🎯 Advanced] Filters: [All Types ▼] [All Owners ▼] [All Tags ▼] Results: 12 resources found Technical Implementation 1. Data Model Tags Table: - id, name, color, description, created_by, created_at Resource_Tags Table: - resource_id, resource_type, tag_id, created_by, created_at Tag_Hierarchy Table: - parent_tag_id, child_tag_id 2. API Endpoints POST /api/tags - Create new tag GET /api/tags - List all tags with usage counts POST /api/resources/{id}/tags - Apply tags to resource GET /api/resources/search?tags=tag1,tag2 - Search by tags 3. Performance Considerations Indexing: Optimized database indexes for tag searches Caching: Cache frequently accessed tag combinations Pagination: Efficient pagination for large result sets Benefits For Individual Users Quick Discovery: Find resources instantly using meaningful tags Personal Organization: Create custom categorization systems Context Switching: Easily switch between different projects or contexts For Teams Standardization: Establish consistent tagging conventions Collaboration: Share and discover team resources efficiently Project Management: Track all assets associated with specific initiatives For Organizations Governance: Implement tagging policies and standards Resource Management: Identify unused or deprecated assets Compliance: Tag resources based on data sensitivity or regulatory requirements Acceptance Criteria Core Functionality [ ] Users can create, edit, and delete custom tags [ ] Tags can be applied to all resource types (flows, prompts, tools, etc.) [ ] Search functionality supports single and multiple tag filtering [ ] Bulk operations support tagging multiple resources [ ] Tag usage statistics and analytics available User Experience [ ] Intuitive tag creation and management interface [ ] Auto-complete and suggestions for existing tags [ ] Visual tag indicators on resource cards/lists [ ] Responsive design for desktop and mobile Performance & Scalability [ ] Fast search response times (<200ms for typical queries) [ ] Support for workspaces with 10,000+ resources and 1,000+ tags [ ] Efficient bulk operations for large resource sets Integration [ ] Tags included in export/import functionality [ ] API access for programmatic tag management [ ] Webhook support for tag-related events Future Enhancements Smart Tagging: AI-powered tag suggestions based on resource content Tag Analytics: Insights into tag usage patterns and resource relationships Advanced Permissions: Role-based tag management and visibility controls Tag Automation: Automatic tagging based on rules or triggers This tagging system would transform how users organize and discover resources in Lamatic, making it scalable for teams and organizations of any size.
Aman Sharma 3 days ago
💡 Feature Requests
Planned
PostgreSQL Read-Only User Setup Guide
Add comprehensive documentation for creating a dedicated read-only PostgreSQL user before connecting to Lamatic, following security best practices similar to Airbyte's integration guide. Problem Currently, users may connect to Lamatic using admin/superuser credentials, which poses security risks. A clear guide for creating properly scoped read-only users is needed to ensure secure database connections. Proposed Solution Add a pre-setup section to the PostgreSQL integration documentation with step-by-step instructions for creating a dedicated read-only user. Suggested Documentation Structure: Step 1: Create a Dedicated Read-Only PostgreSQL User 1. Connect to your PostgreSQL database as a superuser: psql -h -p -U -d 2. Create a dedicated user for Lamatic: CREATE USER lamatic_user WITH PASSWORD 'your_secure_password'; 3. Grant connection privileges: GRANT CONNECT ON DATABASE TO lamatic_user; 4. Grant schema usage: GRANT USAGE ON SCHEMA TO lamatic_user; 5. Grant read permissions on tables: -- For existing tables GRANT SELECT ON ALL TABLES IN SCHEMA TO lamatic_user; -- For future tables (optional) ALTER DEFAULT PRIVILEGES IN SCHEMA GRANT SELECT ON TABLES TO lamatic_user; 6. Grant read permissions on sequences (if needed): GRANT SELECT ON ALL SEQUENCES IN SCHEMA TO lamatic_user; Step 2: Configure Lamatic Connection Use the newly created lamatic_user credentials in your Lamatic PostgreSQL connection settings. Step 3: Verify Connection Test the connection to ensure the read-only user has appropriate access without write permissions. Benefits Enhanced Security: Limits database access to read-only operations Principle of Least Privilege: Grants only necessary permissions Audit Trail: Clear separation between admin and integration users Risk Mitigation: Reduces potential impact of credential compromise Acceptance Criteria [ ] Documentation includes complete SQL commands [ ] Examples cover common schema configurations [ ] Verification steps are provided [ ] Security considerations are highlighted [ ] Troubleshooting section for common permission issues This addition would significantly improve the security posture of PostgreSQL integrations with Lamatic while providing users with clear, actionable guidance.
Aman Sharma 3 days ago
💡 Feature Requests
Planned
PostgreSQL Read-Only User Setup Guide
Add comprehensive documentation for creating a dedicated read-only PostgreSQL user before connecting to Lamatic, following security best practices similar to Airbyte's integration guide. Problem Currently, users may connect to Lamatic using admin/superuser credentials, which poses security risks. A clear guide for creating properly scoped read-only users is needed to ensure secure database connections. Proposed Solution Add a pre-setup section to the PostgreSQL integration documentation with step-by-step instructions for creating a dedicated read-only user. Suggested Documentation Structure: Step 1: Create a Dedicated Read-Only PostgreSQL User 1. Connect to your PostgreSQL database as a superuser: psql -h -p -U -d 2. Create a dedicated user for Lamatic: CREATE USER lamatic_user WITH PASSWORD 'your_secure_password'; 3. Grant connection privileges: GRANT CONNECT ON DATABASE TO lamatic_user; 4. Grant schema usage: GRANT USAGE ON SCHEMA TO lamatic_user; 5. Grant read permissions on tables: -- For existing tables GRANT SELECT ON ALL TABLES IN SCHEMA TO lamatic_user; -- For future tables (optional) ALTER DEFAULT PRIVILEGES IN SCHEMA GRANT SELECT ON TABLES TO lamatic_user; 6. Grant read permissions on sequences (if needed): GRANT SELECT ON ALL SEQUENCES IN SCHEMA TO lamatic_user; Step 2: Configure Lamatic Connection Use the newly created lamatic_user credentials in your Lamatic PostgreSQL connection settings. Step 3: Verify Connection Test the connection to ensure the read-only user has appropriate access without write permissions. Benefits Enhanced Security: Limits database access to read-only operations Principle of Least Privilege: Grants only necessary permissions Audit Trail: Clear separation between admin and integration users Risk Mitigation: Reduces potential impact of credential compromise Acceptance Criteria [ ] Documentation includes complete SQL commands [ ] Examples cover common schema configurations [ ] Verification steps are provided [ ] Security considerations are highlighted [ ] Troubleshooting section for common permission issues This addition would significantly improve the security posture of PostgreSQL integrations with Lamatic while providing users with clear, actionable guidance.
Aman Sharma 3 days ago
💡 Feature Requests
Save Credentials as draft
Sometime the user credentials fail after test and when the user tries to fix them the form reset. Avoid this by giving option to save the credentials as draft and then later retesting and validating them. Only valid credentials will be allowed to appear in selection menu.
Aman Sharma 3 days ago
UI/UX
👏🏻 Feedback
Save Credentials as draft
Sometime the user credentials fail after test and when the user tries to fix them the form reset. Avoid this by giving option to save the credentials as draft and then later retesting and validating them. Only valid credentials will be allowed to appear in selection menu.
Aman Sharma 3 days ago
UI/UX
👏🏻 Feedback
Support additional params for Postgres Integration
SSL Connection Features SSL Mode Selection: Support multiple SSL modes (disable, allow, prefer, require, verify-ca, verify-full) Force SSL for Cloud: Enforce SSL encryption by default in Airbyte Cloud SSL Certificate Verification: Validate SSL certificates and source identity SSH Tunnel Features SSH Tunnel Methods: Support No Tunnel, SSH Key Authentication, and Password Authentication Bastion Server Connection: Connect to databases via intermediate jump servers SSH Configuration: Configure tunnel host, port, and login credentials Multiple Key Format Support: Accept RSA, Ed25519, and other SSH-compatible key formats Authentication Features Dual Authentication: Handle both SSH (OS-level) and database credentials separately Private Key Management: Copy-paste interface for SSH private keys Password Authentication: Alternative to key-based SSH authentication Security Requirements Encryption Validation: Ensure encrypted communication for cloud deployments Connection Failure Protection: Fail connections when security requirements aren't met
Aman Sharma 3 days ago
💡 Feature Requests
Support additional params for Postgres Integration
SSL Connection Features SSL Mode Selection: Support multiple SSL modes (disable, allow, prefer, require, verify-ca, verify-full) Force SSL for Cloud: Enforce SSL encryption by default in Airbyte Cloud SSL Certificate Verification: Validate SSL certificates and source identity SSH Tunnel Features SSH Tunnel Methods: Support No Tunnel, SSH Key Authentication, and Password Authentication Bastion Server Connection: Connect to databases via intermediate jump servers SSH Configuration: Configure tunnel host, port, and login credentials Multiple Key Format Support: Accept RSA, Ed25519, and other SSH-compatible key formats Authentication Features Dual Authentication: Handle both SSH (OS-level) and database credentials separately Private Key Management: Copy-paste interface for SSH private keys Password Authentication: Alternative to key-based SSH authentication Security Requirements Encryption Validation: Ensure encrypted communication for cloud deployments Connection Failure Protection: Fail connections when security requirements aren't met
Aman Sharma 3 days ago
💡 Feature Requests
Support Subflows / Stages within a flow
Customer want to build multiple agentic flows with different payload and stages within the same flow There are various options we can provide this - Suspend - Resume logic Dedicated Followup Node which sends back the response and takes the input then resumes the flow where it left
Aman Sharma About 2 months ago
💡 Feature Requests
Support Subflows / Stages within a flow
Customer want to build multiple agentic flows with different payload and stages within the same flow There are various options we can provide this - Suspend - Resume logic Dedicated Followup Node which sends back the response and takes the input then resumes the flow where it left
Aman Sharma About 2 months ago
💡 Feature Requests
Add Evals Functionality
Types of Evals - On Test Debug On Deployments Compare On Set of Logs Realtime Sampling Options Build Inhouse with AI Integrations Connect with Langfuse and do it cross directional
Aman Sharma About 2 months ago
💡 Feature Requests
Add Evals Functionality
Types of Evals - On Test Debug On Deployments Compare On Set of Logs Realtime Sampling Options Build Inhouse with AI Integrations Connect with Langfuse and do it cross directional
Aman Sharma About 2 months ago
💡 Feature Requests
Version Control Capability
Add functionality to version control within an environment - Projects Flows Prompts Version control should be visible via - logs API requests Reports
Aman Sharma About 2 months ago
👏🏻 Feedback
Version Control Capability
Add functionality to version control within an environment - Projects Flows Prompts Version control should be visible via - logs API requests Reports
Aman Sharma About 2 months ago
👏🏻 Feedback
