maple-agent-tracing-litellm

Trace LiteLLM agents with Maple: export LiteLLM's v2 OpenTelemetry spans (Python SDK or self-hosted LiteLLM Proxy) plus your own agent/tool spans so each conversation is one Maple Agent Session with transcript, tool calls, tokens and cost. Triggers on 'trace my litellm agent', 'add Maple to litellm', 'agent sessions for litellm', 'OpenTelemetry for litellm', 'trace the litellm proxy'.

What does this entry provide?

maple-agent-tracing-litellm is an agent skill entry in ChatHome Skills, sourced from GitHub Skill Search and scored 48/100. # Maple agent tracing: LiteLLM Goal: every conversation = one Maple Agent Session. Each agent run = one turn (one trace) with transcript, LiteLLM `chat <model>`. As an agent skill, it packages a reusable instruction set that AI coding agents such as Codex and Claude Code can load to perform this task without hand-written glue code. ChatHome Skills discovered this entry through its daily ingest of official registries and GitHub discovery queries, and re-verifies repository facts, freshness signals, and install metadata on every run. The quality score weighs official-registry presence, a working install command, license clarity, GitHub star trajectory, and repository freshness — the full formula is on the Sources page. The install command above is ready to copy. Review the last-pushed date and repository status before adding maple-agent-tracing-litellm to a production agent workflow; the snapshot facts below are point-in-time values from the most recent ingest.

Snapshot facts

Source: GitHub Skill Search
Quality: 48/100
Install method: codex
Repo: MapleTechLabs/maple
Repo updated: Oct 23, 2026

Install command

codex skill install MapleTechLabs/maple

Install method: codex

Review the upstream source before running this command.

GitHub project

Quality

Repo stars

Repo updated

Unknown

How is this score calculated?

Weighted across six dimensions, scored 0-100.
Provenance (25): listed in an official or curated registry.
Installability (25): a runnable install command beats a declared transport, which beats a source checkout.
Maintenance (20): recency of the last push; archived entries score zero here.
Adoption (20): repository stars on a log curve.

Security signals

mediumMatched in: skillBody
Executes inline Python code, which can hide malicious behavior in a compact payload.
python -c "Dispute this flag?

Scan coverage: Install metadata and the indexed document body.

Automated signals from install metadata and a bounded skill body — not a full code security audit and not a recursive source or dependency review.

Discovery note

# Maple agent tracing: LiteLLM Goal: every conversation = one Maple Agent Session. Each agent run = one turn (one trace) with transcript, LiteLLM `chat <model>` spans with tokens,

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