by BennettSchwartz · Codex Skill · ★ 82
Last updated: · Indexed by AgentSkillsHub · Auto-synced every 8h
Membrane Membrane is a selective learning and memory substrate for LLM agents. It gives agents typed, revisable memory instead of an append-only transcript or a flat vector store. Use it when an agent needs to remember facts, retrieve prior context, revise stale knowledge, preserve task state, and cite the records that informed an answer. What It Provides Five memory layers plus entities: episodic, working, semantic, competence, plan graph, and canonical entity nodes that connect them. Entity-connected retrieval: returns ranked records plus bounded graph neighborhoods, not only top-k chunks. Capture-first ingestion: accepts events, observations, tool outputs, working state, and facts, then creates linked records and edges. Revision operations: supersede, fork, retract, merge, and contest records with audit trails. Decay and reinforcement: salience changes over t
| Stars | 82 |
| Forks | 8 |
| Language | Go |
| Category | Codex Skill |
| License | MIT |
| Quality Score | 71.4893625489717/100 |
| Open Issues | 7 |
| Last Updated | 2026-05-02 |
| Created | 2026-01-31 |
| Platforms | go |
| Est. Tokens | ~104k |
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membrane is A selective learning and memory substrate for agentic systems — typed, revisable, decayable memory with competence learning and trust-aware retrieval.. It is categorized as a Codex Skill with 82 GitHub stars.
membrane is primarily written in Go. It covers topics such as agent, agent-framework, agent-memory.
You can find installation instructions and usage details in the membrane GitHub repository at github.com/BennettSchwartz/membrane. The project has 82 stars and 8 forks, indicating an active community.
membrane is released under the MIT license, making it free to use and modify according to the license terms.
The top alternatives to membrane on Agent Skills Hub include membrane, awesome-azure-openai-llm, ClawRecipes. Each offers a different approach to the same problem space — compare them side-by-side by stars, quality score, and community activity.