by letta-ai · Codex Skill · ★ 193
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trajectory Normalize agent transcripts from different runtimes into one validated, model-ready record format. Agent tools represent the same concepts—messages, reasoning, tool calls, and tool results—in incompatible native formats. provides one TypeScript API that turns those formats into deterministic, structured records for training, evaluation, analysis, and inference. The caller supplies a transcript string and its source. The one exception is Deep Agents, whose sessions reads from its local LangGraph SQLite store by thread ID; see . Installation The TypeScript package is published as : Quick start contains the normalized trajectory. is always present and is empty when the transcript required no recoverable cleanup. json { "records": [ { "role": "meta", "source": "codex" }, { "role": "user", "content": "Check the current directory.", "timestamp": "2026-07-10T12:00:00.000Z" }, { "role": "assistant", "content": null, "toolcalls": [ { "id": "call1", "name": "execcommand",
| Stars | 193 |
| Forks | 12 |
| Language | TypeScript |
| Category | Codex Skill |
| License | Apache-2.0 |
| Quality Score | 70.013971204523/100 |
| Open Issues | 5 |
| Last Updated | 2026-07-28 |
| Created | 2026-07-10 |
| Platforms | claude-code, codex, node |
| Est. Tokens | ~15k |
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trajectory is Convert sessions across harnesses to a unified trajectory format - designed to be consumed by agents (e.g. for memory formation, dreaming, search). It is categorized as a Codex Skill with 193 GitHub stars.
trajectory is primarily written in TypeScript. It covers topics such as agent-harness, agent-memory, claude-code.
You can find installation instructions and usage details in the trajectory GitHub repository at github.com/letta-ai/trajectory. The project has 193 stars and 12 forks, indicating an active community.
trajectory is released under the Apache-2.0 license, making it free to use and modify according to the license terms.
The top alternatives to trajectory on Agent Skills Hub include memorix, signetai, bitrouter. Each offers a different approach to the same problem space — compare them side-by-side by stars, quality score, and community activity.