agent-memory vs teamai-cli
AI agent memory compared with team resource sharing
agent-memory helps coding agents retain project knowledge between sessions, while teamai-cli distributes shared AI resources and context through Git. The main difference is that agent-memory focuses on capturing and organizing persistent memory, whereas teamai-cli focuses on keeping team workflows and resources consistent across people and tools.
agent-memory: Give Coding Agents Persistent, Tiered Memory
agent-memory turns coding-session notes into organized, tiered knowledge and generates an AGENTS.md section with relevant context for new threads. It is a TypeScript tool for teams and developers who want agents to reuse knowledge without relying on manual searches.
teamai-cli: Share AI Workflows Across Teams
TeamAI is a Git-backed CLI for sharing AI agent resources and team context across people, machines, and coding tools. It suits teams that want agents to use consistent skills, rules, and other shared assets without manual syncing.
| agent-memory | teamai-cli | |
|---|---|---|
| Language | TypeScript | TypeScript |
| License | — | NOASSERTION |
| Stars | 7 | 5.1k |
| Forks | 0 | 387 |
| Last analyzed | Oct 4, 2026 | Oct 4, 2026 |
Key differences
- agent-memory consolidates session notes into tiered memory and generates an AGENTS.md section; teamai-cli distributes shared resources such as skills, rules, documentation, and hooks through a Git repository.
- agent-memory stores memory as files and provides a CLI with adapter-based integrations; teamai-cli uses a shared Git repository and supports project- or user-scope initialization.
- agent-memory is aimed at carrying knowledge between coding-agent threads; teamai-cli is aimed at coordinating resources across team members, machines, and coding agents.
- agent-memory’s documented standalone workflow uses Bun and relies on an LLM adapter for consolidation and defragmentation; teamai-cli setup depends on the target agent and shared repository access.
- Both projects are written in TypeScript. The supplied facts list 7 stars and no forks for agent-memory, and 5.1k stars and 387 forks for teamai-cli.
- teamai-cli identifies shared context and team improvement features as beta, while agent-memory’s listed core workflow centers on note capture, consolidation, organization, and context generation.
Choose agent-memory if you…
- want coding agents to reuse project knowledge across sessions without manually searching old notes.
- need tiered memory that can be organized and surfaced in generated AGENTS.md content.
- prefer a standalone CLI with adapter-based integrations and file-based memory storage.
Choose teamai-cli if you…
- need to share and update AI skills, rules, and other resources across a team using Git.
- use multiple coding agents and want common team workflows across them.
- want project- or user-scope setup, with optional beta features for shared context and team improvement.
This comparison is generated with AI from the OSRepos analyses of both projects. Always check each project's repository and documentation before choosing.