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Garry's Opinionated OpenClaw/Hermes Agent Brain
This repository page has enough original analysis, source evidence, and comparison context to be treated as an indexable Git-Stars review.
100
review score
Problem solved
Traditional RAG and personal-knowledge tools return lists of chunks or pages, forcing the user to read and synthesize themselves. GBrain solves this by generating direct, well-cited answers with gap analysis, and by building a typed entity graph (people, companies, deals) without LLM calls, enabling relational queries that vector search alone cannot answer. It also addresses memory persistence for coding agents, which otherwise forget non-code context.
Deployment reality
The available setup signal starts with: bun install -g github:garrytan/gbrain. Treat this as a starting point, then ask Codex or Claude to inspect the README, environment variables, runtime version, and deployment target before production use.
Commercial use
The recorded license is MIT. This is a useful commercial-use signal, but teams should still verify license text, dependencies, model/API terms, and trademark constraints.
Capability ceiling
Developers can build autonomous agents that maintain a long-term institutional memory, answer complex relational questions (e.g., 'who works at Acme?', 'what did Bob invest in?'), and prepare for meetings with synthesized briefs. It supports team-scoped access control for a company brain, and can be wired into Claude Code or Codex as a retrieval layer. The ceiling includes running hundreds of cron jobs for continuous ingestion, enrichment, and overnight memory consolidation, effectively creating a self-improving knowledge base.
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garrytan/gbrain is tracked as a TypeScript project in the AI Agent area. This evaluation combines public GitHub metadata, category signals, short source summaries, and Git-Stars editorial rules rather than copying project documentation.
Momentum check: the repository has 28k total stars, with +78 today, +0 this week, and +0 this month. These growth windows help distinguish durable adoption from short-lived visibility spikes.
Maintenance check: current activity is Moderate; the latest push was 63 days ago, and the open issue queue is 456, about 1.62% of total stars. Treat this as an adoption signal, not a substitute for engineering due diligence.
Adoption check: 4.1k forks and 28k watchers suggest how often the project is reused or followed. License signal: MIT. Always verify license compatibility before commercial or internal use.
Practical fit: this project is most relevant when you need TypeScript teams evaluating ecosystem-native tooling. Be cautious when you need a legal review, security audit, or production SLA.
Source check: Git-Stars currently has 2 explicit source reference(s) for this report and a recent growth signal of 78. Follow the original GitHub repository for final install, security, and release information.
Analysis
ReadyOriginal problem, capability, and verdict guidance are available.
Sources
ReadyRepository metadata and README/source references are attached.
License
ReadyMIT is recorded for review.
Maintenance
ReadyRecent activity is visible in repository metadata.
Alternatives
ReadyEnough nearby projects exist for comparison.
Momentum
28k Stars
Reuse
4.1k Forks
Attention
28k Watchers
Maintenance
moderate
License
MIT
Open issues
456
GBrain is a self-hosted knowledge graph and synthesis engine that turns raw pages into cited answers, with gap analysis, for AI agents. It ingests meetings, emails, and other data, enriches entities, and runs autonomously to keep your memory sharp. It can be used as a personal brain or a team-wide company brain with scoped access.
Key Features
- Synthesis layer that provides actual answers with citations and gap analysis - Self-wiring knowledge graph with typed edges (e.g., works_at, invested_in) extracted without LLM calls - Runs as a 24/7 daemon for continuous ingestion, enrichment, and consolidation - Supports team deployment with per-user scoped access and zero data leaks - Integrates with AI agents like Codex, Claude Code, and OpenClaw
AI Agent
Agent frameworks, autonomous workflows, and tool-use systems
bun install -g github:garrytan/gbrain70
Health Score
Moderate
Commit Activity
Apr 5, 2026
Created
Aug 11, 2026
Last push
Star history will appear after daily syncs accumulate data.
Forks
Open
Watchers
garrytan
GitHub profileobra/superpowers
An agentic skills framework & software development methodology that works.
affaan-m/ECC
The agent harness performance optimization system. Skills, instincts, memory, security, and research-first development for Claude Code, Codex, Opencode, Cursor and beyond.
NousResearch/hermes-agent
The agent that grows with you
mattpocock/skills
Skills for Real Engineers. Straight from my .agents directory.
anomalyco/opencode
The open source coding agent.
✓
License
✓
Forked
~ Moderate
Maintained
Problem Solved
Traditional RAG and personal-knowledge tools return lists of chunks or pages, forcing the user to read and synthesize themselves. GBrain solves this by generating direct, well-cited answers with gap analysis, and by building a typed entity graph (people, companies, deals) without LLM calls, enabling relational queries that vector search alone cannot answer. It also addresses memory persistence for coding agents, which otherwise forget non-code context.
Capabilities
Developers can build autonomous agents that maintain a long-term institutional memory, answer complex relational questions (e.g., 'who works at Acme?', 'what did Bob invest in?'), and prepare for meetings with synthesized briefs. It supports team-scoped access control for a company brain, and can be wired into Claude Code or Codex as a retrieval layer. The ceiling includes running hundreds of cron jobs for continuous ingestion, enrichment, and overnight memory consolidation, effectively creating a self-improving knowledge base.
Bottom Line
GBrain is ideal for developers and teams who want a self-hosted, synthesis-first knowledge layer for their AI agents, especially those building company-brain or personal-memory systems. It is not for those seeking a lightweight, zero-config tool or who are uncomfortable with running a 24/7 daemon. The key trade-off is the setup and maintenance effort versus the powerful, relational, gap-aware answers it provides.