Lunary vs promptfoo
Side-by-side comparison to help you choose.
| Feature | Lunary | promptfoo |
|---|---|---|
| Type | Platform | Repository |
| UnfragileRank | 44/100 | 35/100 |
| Adoption | 1 | 0 |
| Quality | 0 | 0 |
| Ecosystem |
| 0 |
| 0 |
| Match Graph | 0 | 0 |
| Pricing | Free | Free |
| Starting Price | $30/mo | — |
| Capabilities | 15 decomposed | 14 decomposed |
| Times Matched | 0 | 0 |
Lunary provides language-specific SDKs (Python, JavaScript) that wrap LLM client libraries (OpenAI, Anthropic, Azure OpenAI, Mistral, Ollama, LiteLLM) using a decorator/monkey-patching pattern. When you call `lunary.monitor(client)`, it intercepts all API calls before they reach the LLM provider, extracts request/response metadata (model, tokens, latency, cost), and asynchronously logs them to Lunary's backend without blocking the application. This enables zero-code instrumentation of existing LLM applications.
Unique: Uses decorator/monkey-patching pattern to intercept calls at the SDK level rather than requiring middleware or proxy layers, supporting 6+ LLM providers with a single `monitor()` call. Integrates with LiteLLM abstraction layer to handle provider-agnostic logging.
vs alternatives: Simpler than Datadog/New Relic for LLM-specific monitoring because it's purpose-built for LLM observability and requires no middleware setup; faster than manual logging because interception is automatic.
Lunary stores complete conversation histories with full message context, user metadata, and timestamps, enabling developers to replay entire multi-turn conversations in the dashboard. The platform reconstructs conversation flow by linking messages via session/thread IDs, preserving the exact sequence of user inputs, LLM responses, and intermediate tool calls. This enables debugging, auditing, and user support without needing to query your application database.
Unique: Reconstructs full conversation context from distributed LLM API logs rather than requiring explicit conversation storage in your application. Automatically links messages via session IDs and timestamps, creating a unified view without needing to query your database.
vs alternatives: More accessible than building custom conversation logging because it works with existing LLM SDKs; more complete than basic request logging because it preserves multi-turn context and user metadata.
Lunary provides a native LangChain integration that automatically instruments LangChain agents, chains, and tools without requiring code changes. The integration hooks into LangChain's callback system to capture chain execution traces, tool calls, and intermediate steps. This enables full visibility into LangChain agent behavior, including tool selection, reasoning steps, and error handling.
Unique: Integrates with LangChain's callback system to automatically capture chain execution traces without requiring code changes. Traces include tool calls, intermediate steps, and reasoning, providing full visibility into agent behavior.
vs alternatives: More integrated than generic LLM monitoring because it understands LangChain-specific concepts (chains, tools, agents); more complete than manual logging because all steps are captured automatically.
Lunary supports OpenTelemetry (OTel) as a standard observability protocol, allowing developers to export LLM traces to any OTel-compatible backend (Jaeger, Datadog, New Relic, etc.). This enables integration with existing observability stacks without vendor lock-in. Lunary can act as an OTel collector or exporter, depending on the application architecture.
Unique: Supports OpenTelemetry as a standard protocol, enabling integration with any OTel-compatible backend without vendor lock-in. Traces can be exported to Lunary or external platforms.
vs alternatives: More flexible than proprietary integrations because it uses open standards; more interoperable than Lunary-only solutions because it works with existing observability stacks.
Lunary offers a self-hosted Community Edition that can be deployed on-premises using Docker or Kubernetes, enabling organizations to keep all data within their infrastructure. The self-hosted version includes core observability features (LLM call logging, dashboards, conversation replay) but may have feature limitations compared to the cloud version. This enables compliance with data residency requirements (GDPR, HIPAA) without relying on cloud infrastructure.
Unique: Offers self-hosted Community Edition for on-premises deployment, enabling data residency compliance without cloud dependency. Deployment is via Docker/Kubernetes, enabling integration with existing infrastructure.
vs alternatives: More compliant than cloud-only solutions for data residency requirements; more flexible than managed-only platforms because organizations can choose cloud or self-hosted.
Lunary provides CSV and JSONL export capabilities for conversations and metrics, enabling integration with external data warehouses, analytics platforms, and BI tools. On Enterprise tier, Lunary offers native connectors to data warehouses (Snowflake, BigQuery, Redshift, etc.), enabling automated data syncing without manual exports. This enables advanced analytics and long-term data retention beyond Lunary's built-in retention limits.
Unique: Provides both manual exports (CSV/JSONL) and automated data warehouse connectors (Enterprise), enabling flexible integration with external analytics platforms. Exports preserve full event context and metadata.
vs alternatives: More flexible than Lunary-only analytics because data can be exported to any BI tool; more automated than manual exports because Enterprise tier offers native connectors.
Lunary provides role-based access control (RBAC) enabling organizations to grant different permissions to team members (e.g., support can view conversations but not edit prompts, developers can edit prompts but not access billing). On Enterprise tier, SSO/SAML integration enables centralized identity management. This enables secure multi-team collaboration without exposing sensitive data to unauthorized users.
Unique: Implements role-based access control at the dashboard and API level, with optional SSO/SAML integration for centralized identity management. Roles control access to conversations, prompts, and settings.
vs alternatives: More secure than shared credentials because roles are granular; more integrated than external access control because RBAC is built into Lunary.
Lunary allows developers to attach custom user IDs, session IDs, and arbitrary metadata (user tier, geography, feature flags) to LLM calls via SDK parameters. The platform aggregates these attributes across all calls from a user, enabling cohort analysis, user-level cost tracking, and behavior segmentation. Custom attributes are indexed and filterable in the dashboard, supporting queries like 'show all conversations from premium users in EU'.
Unique: Embeds user/session context directly into LLM event logs rather than requiring separate user identity service. Attributes are indexed at ingest time, enabling fast filtering and aggregation without joins.
vs alternatives: Simpler than Mixpanel/Amplitude for LLM-specific cohort analysis because it's built into the LLM call pipeline; more flexible than basic request logging because arbitrary custom attributes are supported.
+7 more capabilities
Evaluates prompts and LLM outputs across multiple providers (OpenAI, Anthropic, Ollama, local models) using a unified configuration-driven approach. Supports batch testing of prompt variants against test cases with structured result aggregation, enabling systematic comparison of model behavior without provider lock-in.
Unique: Provides a unified YAML-driven configuration layer that abstracts provider-specific API differences, allowing users to define prompts once and evaluate across OpenAI, Anthropic, Ollama, and custom endpoints without code changes. Uses a plugin-based provider system rather than hardcoding provider logic.
vs alternatives: Unlike Weights & Biases or Langsmith which focus on production monitoring, promptfoo specializes in pre-deployment prompt iteration with lightweight local-first evaluation that doesn't require cloud infrastructure.
Validates LLM outputs against user-defined assertions (exact match, regex, similarity thresholds, custom functions) applied to each test case result. Supports both deterministic checks and probabilistic assertions, enabling automated quality gates that fail evaluations when outputs don't meet specified criteria.
Unique: Implements a composable assertion system supporting exact matching, regex patterns, semantic similarity (via embeddings), and custom functions in a single framework. Assertions are declarative in YAML, allowing non-programmers to define basic checks while enabling advanced users to inject custom logic.
vs alternatives: More flexible than simple string matching but lighter-weight than full LLM-as-judge approaches; combines deterministic assertions with optional LLM-based grading for nuanced evaluation.
Caches LLM outputs for identical prompts and inputs, avoiding redundant API calls and reducing costs. Implements content-based caching that detects duplicate requests across evaluation runs.
Lunary scores higher at 44/100 vs promptfoo at 35/100. Lunary leads on adoption, while promptfoo is stronger on quality and ecosystem.
Need something different?
Search the match graph →© 2026 Unfragile. Stronger through disorder.
Unique: Implements transparent content-based caching at the evaluation layer, automatically detecting and reusing identical prompt/input combinations without user configuration. Cache is persistent across evaluation runs.
vs alternatives: More transparent than manual caching; reduces costs without requiring users to explicitly manage cache keys or invalidation logic.
Supports integration with Git workflows and CI/CD systems (GitHub Actions, GitLab CI, Jenkins) via CLI and configuration files. Enables automated evaluation on code changes and enforcement of evaluation gates in pull requests.
Unique: Designed for CLI-first integration into CI/CD pipelines, with exit codes and structured output formats enabling seamless integration with existing DevOps tools. Configuration files are version-controlled alongside prompts.
vs alternatives: More lightweight than enterprise CI/CD platforms; enables prompt evaluation as a native CI/CD step without requiring specialized integrations or plugins.
Allows users to define custom metrics and scoring functions beyond built-in assertions, implementing domain-specific evaluation logic. Supports JavaScript and Python for custom metric implementation.
Unique: Implements custom metrics as first-class evaluation primitives alongside built-in assertions, allowing users to define arbitrary scoring logic without forking the framework. Metrics are configured declaratively in YAML.
vs alternatives: More flexible than fixed assertion sets; enables domain-specific evaluation without requiring framework modifications, though with development overhead.
Tracks changes to prompts over time, maintaining a history of prompt versions and enabling comparison between versions. Supports reverting to previous prompt versions and understanding how changes affect evaluation results.
Unique: Leverages Git for prompt versioning, avoiding the need for custom version control. Evaluation results can be correlated with Git commits to understand the impact of prompt changes.
vs alternatives: Simpler than dedicated prompt management platforms; integrates with existing Git workflows without requiring additional infrastructure.
Uses a separate LLM instance to evaluate and score outputs from the primary model under test, implementing chain-of-thought reasoning to assess quality against rubrics. Supports custom grading prompts and scoring scales, enabling semantic evaluation beyond pattern matching.
Unique: Implements LLM-as-judge as a first-class evaluation primitive with support for custom grading prompts, chain-of-thought reasoning, and configurable scoring scales. Separates grader model selection from primary model, allowing cost optimization (e.g., using cheaper models for primary task, expensive models for grading).
vs alternatives: More sophisticated than regex assertions but more practical than full human evaluation; enables semantic evaluation at scale without manual review, though with inherent LLM grader limitations.
Supports parameterized prompts with variable placeholders that are substituted with test case values at evaluation time. Uses a simple template syntax (e.g., {{variable}}) to enable prompt reuse across different inputs without code changes.
Unique: Implements lightweight template substitution directly in the evaluation configuration layer, avoiding the need for separate templating engines. Variables are resolved at evaluation time, allowing test case data to drive prompt customization without modifying prompt definitions.
vs alternatives: Simpler than Jinja2 or Handlebars templating but sufficient for most prompt parameterization use cases; integrates directly into the evaluation workflow rather than requiring separate preprocessing.
+6 more capabilities