by tma1-ai · MCP Server · ★ 101
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TMA1 A monolith for your agent's loop. Silent until it talks back. TMA1 is local-first observability for LLM agents, powered by GreptimeDB. It records every LLM call on your machine, then routes what it sees back into the agent's next turn through hooks and MCP tools. One binary. No Docker. No Grafana. No cloud account. The name comes from TMA-1 (Tycho Magnetic Anomaly-1) in 2001: A Space Odyssey: the monolith buried on the moon, silently recording everything until you dig it out. What TMA1 Does TMA1 has two jobs: Show the human what happened. Tell the agent what it should notice before the next step. The dashboard gives you token usage, cost, latency, tool activity, full conversation replay, anomaly history, prompt evaluation, and SQL access to the underlying data. The agent loop gives Claude Code and Codex a compact block before each turn, can block when a HIGH-severity issue is still unresolved, and exposes seven MCP tools the agent can call on demand. Supported Sources Sessio
| Stars | 101 |
| Forks | 11 |
| Language | Go |
| Category | MCP Server |
| License | Apache-2.0 |
| Quality Score | 69.1209727042815/100 |
| Open Issues | 4 |
| Last Updated | 2026-06-25 |
| Created | 2026-03-13 |
| Platforms | claude-code, cli, codex, go, mcp |
| Est. Tokens | ~18k |
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tma1 is Local-first observability your agent reads back. TMA1 records every LLM call, then routes what it sees into the agent's next turn via hooks and MCP.. It is categorized as a MCP Server with 101 GitHub stars.
tma1 is primarily written in Go. It covers topics such as agent-loop, agent-observability, ai-agents.
You can find installation instructions and usage details in the tma1 GitHub repository at github.com/tma1-ai/tma1. The project has 101 stars and 11 forks, indicating an active community.
tma1 is released under the Apache-2.0 license, making it free to use and modify according to the license terms.
The top alternatives to tma1 on Agent Skills Hub include langfuse-mcp, agent-skills, agent-skills. Each offers a different approach to the same problem space — compare them side-by-side by stars, quality score, and community activity.