What is Rungraph? Features, Pricing & Tutorial (2026)

The Problem Rungraph Solves

Modern AI coding sessions have evolved far beyond simple chat conversations. They are now complex asynchronous runs involving orchestrators spawning subagents, parallel testing workflows, frequent tool failures, automatic retries, and occasional human interrupts. Trying to diagnose a failure in a 4,000-line raw transcript is tedious, time-consuming, and inefficient.

Developers and engineers who rely heavily on autonomous coding loops frequently suffer from "transcript blindness," where finding the exact point of failure feels like searching for a needle in a haystack. Rungraph fixes this exact issue by parsing existing session data and rendering it as an intuitive, structured, directed agentic graph where time flows downward and critical events are visually highlighted.

By bringing clarity to chaotic coding runs, Rungraph helps you quickly spot retry storms, unresolved errors, and unexpected course changes without digging through raw logs. In this tutorial, you'll learn exactly how to use Rungraph — step by step.

How to Get Started with Rungraph in 5 Minutes

1. Ensure you have Node.js installed on your local machine to run command-line scripts via npm.
2. Open your terminal and execute the quickstart command npx rungraph to scan your local project directories.
3. Allow the local server to start up and automatically open the interactive visualization dashboard in your default web browser.
4. Select an active or historical agent run from the dashboard interface to view the generated directed graph.
5. Optional: Run npx rungraph mcp --install to wire the MCP server into your local agent environment for direct terminal querying.

How to Use Rungraph: Complete Tutorial

Step 1: Inspecting Historical and Live Agent Sessions

Rungraph works out of the box with zero configuration, hooks, or telemetry wrappers by scanning your existing local project directories such as ~/.claude/projects. Once you launch the tool via the command line, the web dashboard loads a comprehensive run index displaying all your past sessions. You can also pick a session that is actively running; the graph will grow live as the agent executes commands through file watching.

The visual canvas organizes your prompts as the central backbone while parallel agent workflows fan out into side-by-side lanes. Consecutive calls to the same tool automatically collapse into single nodes, preventing repetitive test-fix loops from cluttering your view. You can click on any node to review the complete prompt, response, timing, errors, and the agent's internal narration explaining its reasoning right before executing the call.

Step 2: Triaging Errors Using Signal Strips and Filters

When dealing with extensive coding sessions, a uniform graph offers little assistance in locating root causes. Rungraph solves this by analyzing the run and displaying an opinionated "signal strip" directly above the canvas. This strip highlights specific issues such as retry storms where tools repeatedly fail, unresolved errors that went unfixed, human permission denials, or operational outliers that consumed excessive resources.

Clicking any signal on the strip instantly focuses the graph, lighting up the relevant nodes while dimming the rest of the canvas to a quarter opacity so you never lose the structural context of your session. Additionally, you can press the forward slash key (/) to open the find utility, allowing you to filter node labels and file paths instantly within your browser without relying on external subprocesses or network requests.

Step 3: Querying Runs Directly from Your Agent with MCP

Beyond the browser dashboard, Rungraph features a Model Context Protocol (MCP) server designed to bridge your visual runs directly with your coding terminal. After running the installation command, you can ask your agent contextual questions derived straight from your session data, such as querying why a specific file edit repeatedly failed. The agent utilizes read-only tools like find_nodes and get_detail to inspect errors directly in your terminal.

Once the agent identifies the faulty steps, it can invoke the focus_nodes tool to automatically highlight the corresponding nodes in your open browser dashboard. This creates a tight feedback loop where your graphical visualization and command-line assistant work in tandem. Furthermore, if you manage multiple dashboards or review bundles sent by colleagues, the MCP server automatically aggregates them into a unified query index.

Step 4: Exporting and Sharing Secure Run Files

Collaboration often requires sharing complex agent sessions with team members. Rungraph allows you to export runs securely either directly through the dashboard interface or by commanding your local agent via the CLI. Before any file leaves your machine, the export utility prints a clear inventory detailing total nodes, prompts, and touched files, ensuring full transparency over what data is being bundled.

Crucially, the export engine scans for high-confidence secrets such as AWS keys, GitHub tokens, Slack integrations, and private key blocks using anchored pattern matching calibrated for near-zero false positives. If sensitive data is detected, the export process blocks automatically until you resolve it using flags like --redact-secrets to substitute placeholders, --structure-only to strip out prompts and outputs entirely, or --allow-secrets for verified fixture keys.

Rungraph: Pros & Cons

Rungraph Pricing: Free vs Paid

Rungraph is completely free and open-source software distributed via GitHub under its repository terms. Users can download, execute, and modify the codebase without incurring subscription fees, licensing costs, or hidden enterprise charges. Because it operates entirely locally on your machine via Node.js and npx, there are no cloud tiers, usage caps, or paid seats required to access full functionality.

The free toolset includes retroactive session parsing, live-tail graph growth, signal strip diagnostics, file touch inspectors, the MCP server integration, and secure export filtering tools. There are no paid upgrades or locked features within the current scope of the project, making it fully accessible to individual developers and open-source contributors alike.

👉 Check the latest pricing and repository updates on the official Rungraph website and GitHub repository.

Who is Rungraph Best For?

For Power Developers: Engineers managing complex, multi-step autonomous coding workflows will find immense value in turning dense multi-thousand line logs into clean, structured graphs. It drastically cuts down debugging overhead during heavy refactoring sessions.

For Open-Source Maintainers: Developers collaborating across distributed teams can export redacted session files to show precisely how a feature was built or where an agent went sideways. This makes sharing reproducible debugging runs straightforward and secure.

For Claude Code Users: Engineers heavily invested in local agent environments who want immediate visibility into orchestrator subagents, tool call retries, and file touch histories will appreciate the native alignment with project directory structures.

Who Should Not Use Rungraph?

Rungraph may represent unnecessary overhead for developers who rarely utilize autonomous coding agents or who primarily write code manually without relying on iterative subagent workflows. If your development process consists of simple chat prompts rather than complex multi-step tool runs, a directed graph visualization tool will provide little practical benefit.

Additionally, engineers who do not use Node.js or prefer strictly cloud-hosted web utilities without command-line dependencies might find the local npx execution model misaligned with their standard workflow preferences. Those working exclusively outside of supported local agent project structures will also need to wait for broader tool compatibility.

Alternatives to Rungraph

Reviewing standard raw transcript log viewers provides a baseline option for inspecting raw text output manually, though it lacks visual structure. IDE-native chat history panels offer basic conversational scrolling but fail to map multi-agent branching or tool retry storms. Custom logging scripts written in Python or Node.js can parse JSON transcripts, though they require significant maintenance compared to a dedicated visualization tool. Despite these options, Rungraph remains the superior choice for developers needing instant, zero-setup graphical mapping and deep MCP terminal integration specifically tailored for autonomous coding runs.

How We Evaluated Rungraph

This evaluation and tutorial article was compiled by examining official public documentation, source code repositories, feature launch statements, and architectural guides provided by the creator of Rungraph. Our analysis focuses strictly on verified capabilities, pricing models, setup requirements, and core utility features described in the official product documentation.

Final Verdict: Is Rungraph Worth It?

Rungraph delivers an exceptionally practical, zero-setup solution for developers drowning in complex AI coding agent transcripts. By turning chaotic logs into interactive directed graphs with intelligent error signaling and secure sharing controls, it solves a genuine bottleneck in modern AI-assisted software engineering.