ByteRover Memory System for OpenClaw

File-based memory for OpenClaw with >92% retrieval accuracy

Website byterover.dev

What it is

ByteRover is a fully local, file-based memory layer for agents with market-best 92.2% retrieval accuracy, that supports cloud portability, and built-in version control. From OpenClaw to Claude Code to Cursor to whatever's next, your own memory travels with you, not trapped in one tool. ByteRover gives your agents stateful memory that keep your context's timeline, facts, and meaning perfectly in place.

Intent

I need it when

Use any LLM provider or model with agent memory without vendor lock-in

ByteRover supports bring-your-own-key (BYOK) for any LLM provider via API, allowing users to maintain full control over model selection, costs, and observability within their existing agentic stack.

Keep agent memory local and private by default while retaining the option to sync across machines or teams

ByteRover runs 100% locally with no telemetry by default. Users can optionally push memory to ByteRover Cloud when needed for team collaboration or multi-machine access, maintaining control over when and how memory leaves the local environment.

Maintain persistent, shared memory across multiple OpenClaw agents without losing context between sessions

ByteRover provides hierarchically structured, version-controlled memory that persists across OpenClaw agents with 92.2% retrieval accuracy. All agents operate with the same knowledge tree, eliminating context loss and enabling coordinated reasoning.

Achieve higher memory retrieval accuracy than vector-based systems for agent decision-making

ByteRover's tiered retrieval pipeline (fuzzy text search → LLM-driven deeper search) achieves 92.2% accuracy on the LoCoMo benchmark, outperforming vector-based alternatives. This precision reduces agent hallucination and improves reasoning quality.

Migrate existing markdown or text-based knowledge systems into a queryable, agent-accessible memory structure

ByteRover ingests markdown files, QMD, and text documents, automatically organizing them into a queryable knowledge tree. Users can run existing systems alongside ByteRover during migration, reducing disruption.

Drop

Not a fit when

User requires on-premises deployment without any cloud option (Free and Pro tiers are local-first but Team/Enterprise may require cloud consideration)
User needs vector-based memory retrieval as primary method (ByteRover replaces vector search with tiered file-search pipeline)
User operates with non-LLM agents that cannot leverage natural language reasoning for memory curation
User requires real-time memory synchronization across 50+ concurrent agents without custom enterprise setup
User has strict data residency requirements outside ByteRover's supported regions (only Enterprise tier offers data residency controls)

Commercials

Pricing

Freemium with tiered paid plans. Free tier includes local-first memory with limited credits. Pro ($19/month) for individual users with 4,500 credits/month and cloud sync. Team ($35/user/month) for collaborative teams with 5 GiB storage and priority support. Enterprise (custom pricing) with SSO, SOC 2, RBAC, and dedicated support. View pricing