#1313th of 13 in Observability
Pezzo
Prompt management, observability and caching for LLM apps
Documentation ↗
(opens in a new tab)Website ↗
(opens in a new tab)Repository on GitHub ↗
(opens in a new tab)
- Stars
- 3.3k
- License
- Apache-2.0
- Last commit
- Aug 2026
- Last release
- May 2024
Overview
Stores and versions prompts, logs requests with cost and latency, and caches LLM responses, exposed through Node.js and Python clients and a LangChain integration. Runs on PostgreSQL, ClickHouse, Redis and SuperTokens via Docker Compose, with a GraphQL API server and a console UI. For small teams that want prompt delivery without code changes.
Who it is for: Small teams managing prompts outside application code
Strengths
- Prompts delivered from the console without redeploying application code
- Built-in response caching to cut repeated-call cost and latency
- Node.js and Python clients plus LangChain support
- Apache-2.0; infra is all open source (PostgreSQL, ClickHouse, Redis, SuperTokens)
Weaknesses
- Last commit August 2026 with no release notes in the README
- Four backing services for a modest feature set
- README is thin; features are shown as screenshots, details only in docs
- No evaluation or dataset features mentioned
What it needs
- no GPU
- Compose
- Needs PostgreSQL, ClickHouse, Redis, SuperTokens, Node.js 18+
- port 4200
Also in Observability
See all 13| Rank | Project | Score |
|---|---|---|
| 1 | LangfuseTracing, prompt management and evals for LLM apps on ClickHouse | 74 out of 100 |
| 2 | PhoenixLLM tracing, evals, datasets and prompt playground built on OpenTelemetry | 74 out of 100 |
| 3 | promptfooCLI for evaluating and red-teaming prompts, agents and RAG | 71 out of 100 |
| #4 | MLflowTracing, evals, prompt registry and AI gateway plus classic ML tracking | 70 out of 100 |
| #5 | OpikTrace, evaluate and monitor LLM apps and agents, Apache-2.0 end to end | 64 out of 100 |