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Configure OpenClaw to use sovseal as its local memory backend. This gives your long-running, autonomous agents the ability to persist context, learned solutions, and execution logs without incurring network round-trips.
There is currently no published openclaw-sovseal plugin in OpenClaw’s plugin registry. OpenClaw’s real memory plugins (e.g. the community Mem0 plugin) are purpose-built — each ships its own config schema — rather than generic wrappers that spawn an arbitrary stdio MCP server via command/args. The configuration below is illustrative of the target shape, modeled on OpenClaw’s real plugins.slots.memory / plugins.entries.<name> architecture, not a confirmed-working, published integration. Treat this page as a build target, not a copy-paste install, until a real openclaw-sovseal plugin ships.
OpenClaw’s plugin system exposes a memory slot (plugins.slots.memory) that exactly one memory provider occupies at a time — see OpenClaw’s memory plugin docs for the current, canonical schema. A real sovseal plugin would need to implement OpenClaw’s memory-provider interface (it would expose memory_search/memory_get-style tools, not just proxy to @sovseal/mcp-server’s store_memory/recall_memory MCP tools directly) and call @sovseal/sdk’s store()/recall() underneath.

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Installation

Install @sovseal/sdk (the underlying calls a real sovseal-backed plugin would make):

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Configuration (illustrative — see warning above)

To register a sovseal-memory plugin in OpenClaw, you would modify your agent’s configuration file (~/.openclaw/config.json) along these lines:

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How it Would Affect the Planning Loop

Once a real sovseal memory plugin exists, integrating local memory would alter OpenClaw’s core execution loop in three key ways:
  1. Context-Aware Initialization: Before commencing a task, the agent queries recall_memory to check for past execution attempts, known file structures, and deprecated functions in the workspace.
  2. Dynamic Skill Loading: Based on retrieved memories, OpenClaw dynamically selects which helper tools to load, reducing LLM token budget waste.
  3. Automatic Fact Extraction: Upon task completion, the agent calls store_memory to write the lessons learned to the local LanceDB store, ensuring state continuity for subsequent runs.

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Command Line Interface (CLI) Usage

The commands below are unconfirmed against OpenClaw’s actual CLI surface — OpenClaw’s documented install path is openclaw plugins install <plugin>, and this page has not verified a openclaw memory <verb> subcommand independent of that. Treat this section as a guess, not a reference.