Five OpenClaw plugins that address core production issues

A Reddit post from r/openclaw details five plugins that address specific production challenges when using the OpenClaw AI coding agent. The author notes that the default setup relies heavily on skills, which are injected into prompts on every run, increasing token usage without solving issues like routing, integrations, or observability. Plugins, which run as separate processes and expose tools only when needed, offer a solution.

Key Plugins and Their Functions

• Manifest: Adds a routing layer between OpenClaw and model providers. It classifies each request and sends it to the cheapest model capable of handling it, preventing simple tool calls from using expensive primary models.
• Composio: Handles integrations through an MCP server. It manages OAuth, token refresh cycles, and rate limits for connected apps like Slack, GitHub, or Gmail, with each integration running in isolation to prevent cascading failures.
• Hyperspell: Replaces the default memory system with a retrieval layer backed by a knowledge graph. It injects only relevant context before each step, keeping prompts smaller and improving recall across longer sessions.
• Foundry: Watches agent usage and turns repeated workflows into executable tools. It detects patterns in sessions and writes new tool definitions with defined inputs and outputs that persist across runs.
• Opik: Adds structured tracing to agent runs. It captures LLM calls, tool inputs/outputs, latency, and token usage as spans, providing a clear execution path to identify slowdowns or failures.

The author states that after adding these plugins, their OpenClaw setup felt much easier to run.