latitude-dev/latitude-llm

Features

• LLM Execution Tracing - Implements specialized tracing for capturing LLM-specific data including prompts, completions, and token usage.
• Agent Execution Traces - Collects granular OpenTelemetry traces of AI agent execution paths for observability and debugging.
• LLM Performance Monitoring - Captures and analyzes LLM traces and sessions to monitor AI agent performance, costs, and failures.
• Tool Exposure Management - Exposes workspace functions as tools for AI agents to read and modify projects and datasets.
• AI Provider Integrations - Provides configuration interfaces to connect with external LLM providers for generation and embeddings.
• Model Context Protocol Integrations - Implements the Model Context Protocol to expose workspace functions as tools for external agents.
• Coding Agent Integrations - Integrates the monitoring workspace with AI-powered IDEs to allow agents to manage projects.
• Trace-to-Dataset Converters - Transforms failing production traces into structured, reusable input-output datasets for agent verification and gating.
• Agent Session Traces - Provides detailed inspection of agent session traces to analyze underlying behaviors and recurring failures.
• Dataset Curation - Collects production traces and expected outputs to create high-quality datasets for regression testing and quality measurement.
• Failure Signal Aggregation - Aggregates similar negative annotations and evaluation failures into prioritized signals with trend tracking and affected-user counts.
• MCP Protocol Integrations - Uses the Model Context Protocol to connect external AI agents for standardized tool sharing.
• LLM Observability - Functions as a specialized platform for capturing and analyzing LLM-specific telemetry and traces.
• AI Evaluation Frameworks - Implements a system for scoring live traffic and executing regression tests using automated semantic judgments.
• Model Behavioral Analysis - Analyzes traces linked to discovered signals to diagnose tool failures, prompt gaps, and model errors.
• Model Context Protocol Servers - Exposes workspace functions as tools for AI coding agents using the Model Context Protocol.
• Model Provider Configurations - Manages credentials and settings for external AI model providers used in evaluations and search.
• AI Model Integrations - Integrates pre-built text generation and embedding models to enable automated quality scoring and semantic search.
• Regression Test Replayers - Replays captured production inputs against agents to verify fixes using existing evaluation signals.
• Observability and Evaluation - Executes automated quality checks against real-time production traffic to score performance and detect regressions.
• Semantic Search - Utilizes vector similarity and exact phrase matching to find relevant interaction traces based on meaning.
• AI Behavior Discovery - Clusters sessions by semantic meaning to surface emerging topics and track interaction trends.
• Granular Operation Tracking - Provides granular logging of individual LLM calls and tool executions including their duration and cost.
• Self-Hosted AI Infrastructure - Provides the infrastructure and tooling to deploy AI monitoring stacks on private hardware.
• Self-Hosted Infrastructure - Supports deploying the full monitoring stack on private infrastructure for total data control.
• Model Context Protocol Servers - Acts as an MCP server to allow AI coding agents to control the monitoring environment.
• Agent Execution Tracing - Captures the complete execution path of interactions to identify the source of latency, cost, or errors.
• Agent Execution Tracing - Records a structured history of every agent decision, tool invocation, and input-output pair for debugging.
• Granular Operation Capture - Captures granular units of work such as LLM calls and tool executions to record inputs and timing.
• Agent Interaction Quality Evaluators - Measures AI interaction quality through automated scoring, human annotations, and semantic judgment.
• Automated Trace Evaluation - Automatically scores production traffic based on defined criteria to detect quality regressions after deployments.
• Agent Failure Tracking - Tracks recurring failure patterns from evaluations and annotations as a set of problems linked to specific traces.
• Failure Signal Tracking - Groups failed scores and human annotations into named signals to track the lifecycle of recurring AI agent issues.
• Failure Pattern Identification - Groups failed evaluation scores from telemetry into named signals to uncover recurring behavioral patterns without predefined categories.
• Failure Signal Aggregations - Groups evaluation failures and human annotations into trackable signals to monitor recurring issues.
• Trace Annotation - Allows users to attach human feedback and failure category labels directly to execution traces to improve evaluation alignment.
• Behavioral Scoring - Assigns verdicts to interactions using human annotations, automated flaggers, and custom domain-specific scores.
• Quality Annotations - Enables marking traces as positive or negative and applying automated flags for tool errors or model refusals.
• AI Model Telemetry - Provides specialized collection of metrics, costs, and session transcripts specifically for large language model interactions.
• Session and Span Analysis - Analyzes AI telemetry spans and clusters user sessions to discover emerging behavior patterns.
• OpenTelemetry Trace Analyzers - Collects and inspects OpenTelemetry-compatible spans to diagnose latency, costs, and tool failures.
• Self-Hosted Infrastructure Platforms - Runs the complete monitoring infrastructure on private hardware, ranging from single hosts to clusters.
• Self-Hosted Monitoring Suites - Offers a containerized monitoring suite deployable on private infrastructure for data residency.
• Session Grouping - Associates multiple traces via a session identifier to track a user's progress through a complex workflow.
• Session Tracking - Groups individual execution traces into logical user sessions using stable identifiers to track multi-turn conversations.
• Telemetry Analysis - Inspects spans and traces to correlate agent behavior with metadata and performance metrics.
• Semantic Session Clustering - Groups AI sessions by meaning into common topics to discover recurring user patterns.
• LLM-As-A-Judge Scoring - Employs LLM-based judges and structural rules to assign quality scores to live traffic.
• Production Trace Regression Testing - Converts failing production traces into test datasets to verify that changes resolve specific issues.
• Trace-Based Regression Testing - Converts failing production traces into datasets to validate code or prompt changes and ensure previous errors do not return.
• Evaluation Alignment Measurement - Compares automated evaluation scores against human annotations to measure how accurately the monitor represents human judgment.
• Evaluation Feedback Aligners - Refines evaluation accuracy by incorporating human feedback and custom scores to align automated judges with human behavior.
• Containerized Deployments - Provides containerized deployment options for the monitoring stack via Docker and Helm.
• AI Tool Usage Analysis - Identifies unused or malformed tool calls by reviewing parameter frequency and call ratios.
• Behavioral - Correlates failure patterns with affected users and costs to prioritize AI agent fixes.
• Execution Cost Analysis - Calculates total, average, and median spend per user to analyze the financial impact of AI accounts.
• Generation Temperature Controls - Allows customization of model temperature and reasoning levels to tune generative AI output.
• Semantic Vector Search - Indexes and retrieves interaction traces using vector embeddings for natural language semantic search.
• Interaction Grouping - Captures and groups individual operations and LLM calls into a single record to analyze end-to-end performance.
• Sensitive Data Redaction - Automatically removes personally identifiable information from conversational text to prevent data leakage.
• Intent-Based Search - Implements search systems that index the reasoning and intent behind agent interactions to uncover behavioral patterns.
• Provider Configuration - Provides configuration for embedding providers to power semantic trace search and issue clustering.
• Behavioral Topic Filtering - Narrows search results by applying discovered behavioral topics alongside semantic and metadata filters.
• Service Scaling - Supports scaling stateless service replicas via Docker Swarm to balance load across machines.
• Helm Chart Deployments - Enables deployment of the application stack on Kubernetes using official Helm charts.
• Single-Node Deployment - Provides a simplified installation path for running the entire stack on a single host.
• Data Residency Controls - Implements configuration mechanisms to restrict telemetry and inference data storage to specific geographic regions.
• Trace Filters - Provides mechanisms for isolating specific execution events using boolean logic based on model, provider, and latency metadata.
• User-to-Error Associations - Links specific user profiles to encountered errors and behavioral triggers to resolve customer complaints.
• User Activity Monitoring - Aggregates AI sessions, errors, and costs by user identity to identify impacted customers.
• Trace Streaming - Streams conversation turns from local AI agent interfaces into a centralized view including prompts and responses.
• Agent Performance Monitoring - Tracks call volume, error rates, and latency for agent tools to identify bottlenecks.
• AI Signal Monitoring - Tracks saved searches and quality regressions to trigger alerts when AI signals require attention.
• AI Traffic Alerting - Triggers incidents when production AI traffic matches specific failure or performance thresholds.
• Alert Triage - Provides a workflow to review failure patterns and example traces to determine if a detected signal requires resolution.
• Evaluator Realignment - Continuously updates automated monitoring logic based on new human annotations to ensure evaluators remain aligned with human judgment.
• Chain Step Visualization - Maps nested execution sequences as child spans to identify bottlenecks or errors within a complex workflow.
• AI Signal Anomaly Detection - Identifies new signals, regressions of resolved issues, or escalation of existing AI failure patterns.
• Conversation Search - Provides the ability to locate specific agent interactions using semantic meaning or literal string matches.
• Tool Invocation Debugging - Isolates failed tool invocations to analyze common error messages and triggering input parameters.
• Execution Path Visualization - Provides tools for visualizing multi-step agent execution paths as trees to improve debuggability and identify latency.
• Signal and Tool Performance Trends - Tracks specific signals and tool health to send notifications when performance incidents occur.
• Application Health Monitors - Monitors agent traffic and signals to trigger health alerts via email or Slack.
• AI Traffic Monitors - Provides alerting based on predefined conditions and saved searches in production AI traffic.
• Trace-Based Alerting Rules - Implements notifications based on exact-match search rules to monitor specific trace patterns in production.
• Project-Based Isolation - Segments observability data, traces, and evaluations into logical projects to ensure data isolation.
• Telemetry Redaction - Masks sensitive PII within telemetry spans using regular expressions before data export.
• Performance Trend Analysis - Evaluates AI behavioral topics using session counts and outcome metrics to identify performance shifts.
• Signal Lifecycle Management - Allows marking discovered issues as resolved or ignored to manage the active set of evaluations and clean up views.
• Stability Regression Alerting - Tracks resolved failure patterns and notifies teams if previously fixed issues reappear.
• Semantic Session Clustering - Groups user interactions into behavioral topics by analyzing the semantic meaning of sessions.
• CI Test Exporting - Exports production failure traces as test datasets for CI gating and regression testing.
• Cohort Performance Analyzers - Compares AI trace duration, cost, and token counts across different tagged cohorts using percentile analysis.
• Metadata Filtering - Allows narrowing down interaction lists by attributes such as status, session identity, or user identifiers.
• Model Evaluation and Benchmarking - Observability platform for AI agents with semantic trace search.

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