Grov

About Grov

Grov is a developer-focused tool that captures and shares AI agent reasoning and memories across team members. It aims to keep important decisions, context and "why" behind solutions available so teammates don't repeat exploration or re-teach an agent each session.

Review

Grov addresses a common pain point for teams using coding agents: knowledge and reasoning getting trapped in ephemeral chats. It captures reasoning traces, generates embeddings, and injects relevant memories into new sessions to reduce redundant work and token use.

Key Features

• Shared synchronized memories: extracts reasoning, decisions and linked commits so the team can reuse past insights.
• Automatic context management: tracks token usage, auto-summarizes near a threshold (~90%), clears chat and re-injects summaries plus team memories.
• Embedding + hybrid search: generates embeddings (OpenAI text-embedding-3-small) and uses semantic + keyword search to inject the top relevant memories into new sessions.
• Model integration roadmap: currently works with Claude Code and plans expanded support (Codex, Gemini, later Cursor).
• Metadata tracking: records files_touched and linked_commit to help connect memories to code changes.

Pricing and Value

Grov launched with a free tier and is listed as open source at launch. Pricing is still being refined based on early feedback, with indications the team is experimenting with team-based plans and special arrangements for small groups. The core value is time and token savings from avoiding repeated re-exploration and by making agent learnings reusable across sessions.

Pros

• Reduces repeated context setup and token waste by surfacing prior reasoning and decisions.
• Automatic summarization preserves long-running context without manual effort.
• Hybrid search + embeddings helps surface the most relevant prior work for a new session.
• Open source / free-at-launch approach lowers the barrier for small teams to try it.

Cons

• Model support is limited right now (primarily Claude Code); broader integrations are planned but not yet available.
• Versioning and automatic invalidation when code changes are still under development, so memories can outlive their context unless managed manually.
• Pricing and scaling details for larger teams remain unclear and may become a factor as usage grows.

Grov is a strong fit for small development teams and early-stage projects that rely on coding agents and want to preserve team knowledge across sessions. It can save time and reduce redundant work during rapid iteration, but teams with strict versioning or enterprise-scale needs should evaluate roadmap progress on version handling and model integrations before committing.

Open 'Grov' Website