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by Superone77
Understand Your Agents. Turn agent logs into interactive maps and stories. Explore decisions, replay the work, and get answers linked to the original evidence. For coding, research, and creative workflows.
# Add to your Claude Code skills
git clone https://github.com/Superone77/TraceVistaSee how TraceVista compares with popular alternatives.
TraceVista is an open-source ai agents skill for AI coding assistants such as Claude Code, Codex CLI, and ChatGPT, built by Superone77. Understand Your Agents. Turn agent logs into interactive maps and stories. Explore decisions, replay the work, and get answers linked to the original evidence. For coding, research, and creative workflows. It has 56 GitHub stars.
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Clone the repository with "git clone https://github.com/Superone77/TraceVista" and add it to your Claude Code skills directory (see the Installation section above).
TraceVista is primarily written in TypeScript. It is open-source under Superone77 on GitHub, so you can review or fork the full source.
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Understand Your Agents.
Understand complex agent trajectories. Start with the big picture, unfold the details, and check the original evidence.
For coding, research, and creative workflows.
Open TraceVista · Explore examples · Convert your trajectory · Docs
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https://github.com/user-attachments/assets/2a7f474b-ba02-4f01-85b6-4652edecaf76
Cognitive debt may be more dangerous than technical debt in the age of AI.
Your agent finishes an overnight run. You're ready to ship, but its reasoning, tool calls, edits, and outputs are scattered across thousands of records.
Andrej Karpathy argues that we'll spend more time understanding model outputs. Yet under pressure to ship, we have limited time to understand what our agents did and the code they leave behind:
Those unanswered questions accumulate as cognitive debt, making the project harder to maintain.
That's why we built TraceVista. Start with the big picture, explore the details, and check the evidence wherever something doesn't add up.
Understanding an agent's work shouldn't depend on trusting another AI-generated summary.
TraceVista is open source. Explore a real session, bring your own, and tell us what's still hard to understand.
Open an example and follow its decisions from the overview down to the original records. No sign-up or model key needed.
| Example | Inside the run |
|---|---|
| Pi Agent · Kernel Optimization | DeepSeek Flash optimizes vLLM's multi-LoRA kernels on an A100. Follow grouped Triton kernels, direct launch, an identity-cache regression, and a return to an earlier candidate. |
| Claude Code · Post Training | Fable 5 Max trains Qwen3-4B for AIME 2025 on an H100. Explore fine-tuning, checkpoint averaging, evaluation, and the choice of the final model. |
| Codex · Model Serving | GPT-5.5 High tunes a Mistral-7B server on an H100. Follow scheduler experiments, startup recovery, and a rejected sampling change. |
| OpenHands · Bug Fixing | Qwen3-Coder investigates a Kafka connection retry loop. Trace source edits, revised tests, and the gap between local checks and the benchmark outcome. |
| Tongyi DeepResearch · Literature Research | A teacher trajectory from LiteResearcher SFT data compares two journal articles to identify the authors’ shared institution. Follow an empty PDF, a switch to journal author blocks, and a broader search after zero results. |
| Codex · Garden Animation | GPT-6 Astra turns a ruined hall into a Three.js garden of flowers that sway in the breeze. Follow screenshot checks, a recording timeout and a different save path, and the check of a ten-second MP4. A short excerpt of a longer session. |
Two prompts. A complete map. Copy these into your coding agent.
Install skills/tracevista-skill from https://github.com/Superone77/TraceVista
Use tracevista-skill to convert [trajectory file or folder] into a complete .agent-state.json,
then open https://tracevista.pages.dev/ in my browser and import it.
Use $tracevista-skill in Codex or /tracevista-skill in Claude Code. The skill reads the logs, builds the state map and summaries, validates the export, and opens it in the viewer. You can ask the agent to refine the same map as you explore.
Its writing guide borrows from ASD-STE100: concrete verbs, consistent terms, and one idea per sentence. Explanations connect what the agent did, what the evidence showed, and what it chose next, with the technical conditions intact.
Works with Claude Code, Codex, and Pi session logs, InferenceBench traces, and SWE-rebench OpenHands trajectories. Combine main-agent and reviewer sessions in a single export.
The map, annotations, original records, and source attachments travel together in one .agent-state.json. Reopen it with Import trajectory, send it to a collaborator, or keep refining it with your agent. The conversion scripts use Python 3.10+ and its standard library.
Skill guide · Conversion workflow · Multiple agents
See what the agent tried, what it learned, and what happened next. Decision markers highlight discoveries, rejected ideas, pivots, and open questions. Decisions only folds routine steps so the important turns stand out.
Switch between Version tree and Execution order to follow both code ancestry and the sequence of work. Restored versions, abandoned candidates, and final selections stay visible. Measurements sit alongside the states they describe.
Press Play for a guided walkthrough. Story opens the current step while Map moves to the same place. Pause, move backward or forward, change the pace, or open the original evidence. Exit returns to your previous view.
Explore the same run at four connected levels:
System states → Work phases → Actions → Raw records
| Level | What you see |
|---|---|
| System states | How code and artifacts evolved, where results improved or regressed, and which version was selected. |
| Work phases | The approaches an agent explored, the alternatives it tried, and where it changed direction. |
| Actions | Explore, Plan, Edit, Execute, Verify, and Diagnose—with descriptions, outcomes, and who did the work. |
| Raw records | Original messages, recorded reasoning, tool inputs and outputs, and reviewer feedback. |
Pan and zoom through the same canvas, or jump between levels with the toolbar. Select any card to open its path in Story and follow its evidence.
Select a node, open Chat, and ask about that step or the run around it:
Why did the agent abandon this approach? What evidence led to the next version?
Smart context first reads node summaries and an index of the raw records, then selects the records to read in full. Answers include clickable citations that take you back to the relevant nodes and events.
Ask how a decision happened to get a linked flow diagram, or compare attempts in a table. Click a claim or its evidence to locate it in Story and Map. The answer distinguishes recorded observations, interpretations, and open questions.
Connect Claude, OpenAI, Gemini, DeepSeek, Kimi, GLM, Qwen, MiniMax, OpenRouter, or your own compatible endpoint. Local models work through Ollama and LM Studio.
Expand a card in Story to read the original messages, code, tool calls, and results behind it. The records stay accessible alongside the summaries, so you can check an explanation against its sources and form your own view of what happened.
Bring the main agent and its reviewers into one map. Follow a review request through feedback, revisions, and another round of checks. Inspect skill use, knowledge reuse, TODOs, and measurements in Structure & evidence.
A single agent run can leave more text than anyone will read. The Pi Agent example ran for about 45 minutes an