PetalTrace

PetalTrace is ranked #8 of 32 in AI agent observability tools on The Geeks Club. It runs on API, Linux, macOS, Self-hosted, Web, Windows. There is a free plan.

PetalTrace plans and pricing

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PetalTrace Free Public repository; no commercial pricing or usage limits stated github.com · 4 Oct 2026

Compared on AI agent observability tools

Session replay: Yes
Prompt and tool tracing: Yes
Deployment options: self_hosted
Agent framework support: open_standard
Cost tracking: Yes

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PetalTrace

Purpose: PetalTrace is an agent observability platform for inspecting AI agent workflows and their execution lifecycle.
Captured data: It captures LLM prompts and completions, tool calls, token usage, costs, and execution timelines.
Access methods: The product exposes its capabilities through a CLI, HTTP API, and MCP server.
Prompt inspection: Full prompt capture includes system prompts, message history, tool definitions, and LLM responses.
Run comparison: It can compare two runs for prompt, output, and cost differences.
Replay: Captured runs can be re-executed with different models or temperatures, or in mocked mode.
OpenTelemetry: PetalTrace accepts standard OTLP traces from any OpenTelemetry-instrumented application, including applications that do not use PetalFlow.
Search and streaming: It supports full-text search across prompts and completions and real-time SSE feeds for active runs.
Integrations: The MCP server lets AI agents query trace history, inspect prompts, analyze costs, compare runs, and trigger replays; the docs include Claude Code configuration.
Storage: The documented architecture stores runs, spans, and LLM interactions in SQLite with full-text search.
Capture modes: PetalFlow integration offers minimal capture for latency, status, and token counts; standard adds prompts, completions, and tool I/O; full adds graph snapshots and edge data.
Product interface: The repository README describes a React web UI for exploring traces, costs, and workflow graphs, alongside the CLI.
Deployment: The repository README documents building PetalTrace from source or downloading a release binary and running its daemon locally.
Maker: Petal Labs' GitHub organization describes the company as building modular, composable tools for agentic AI systems and lists its location as the United States of America.
Price from: Free

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Where it ranks on The Geeks Club

Best AI Agent Observability Tools in 2026#8 of 32

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Sources

docs.petallabs.io/petal-trace/overview/· checked 3 Oct 2026
docs.petallabs.io/petal-trace/guides/mcp-server/· checked 3 Oct 2026
github.com/petal-labs/petaltrace· checked 3 Oct 2026
github.com/petal-labs· checked 3 Oct 2026
docs.petallabs.io/petal-trace/guides/petalflow/· checked 4 Oct 2026
docs.petallabs.io/petal-trace/guides/configuration/· checked 4 Oct 2026
docs.petallabs.io/petal-trace/getting-started/· checked 4 Oct 2026
docs.petallabs.io/petal-trace/concepts/· checked 4 Oct 2026