Quick answer
- 막힌 일 빼고 지금 할 일만 그래프로 is useful when the reader needs the decision frame before the full tutorial.
- The practical answer is: Explain what 막힌 일 빼고 지금 할 일만 그래프로 changes, when it is useful, and how to verify it safely.
- Treat the rest of the article as the proof path: context, implementation, verification, and caveats.
Who this is for
If you have ever handed a coding agent a large, multi-step task, you know the failure mode. The plan starts as a tidy markdown checklist, then slowly falls apart as the work drags on. The agent reopens finished items, picks up tasks that are still blocked, and loses track of where it was as the context window fills up. beads targets exactly this. The short version: beads treats work as a dependency graph instead of a flat list, so an agent only ever sees the tasks it can actually start right now.
The CLI is called bd, and it stores everything in Dolt, a version-controlled SQL database. That means an agent's accumulated state can branch and merge like code.
The bottleneck it removes
A plain checklist forces a human (or an agent) to compute ordering in their head: finish A before B, B and C both block D. A list cannot express those relationships. beads records dependencies directly and surfaces only tasks with no open blockers.
bd ready
bd create "Title" -p 0
bd update <id> --claim
bd dep add <child> <parent>
bd ready lists tasks that have no open blockers — work that is genuinely ready to begin. bd dep add links a child task to a parent so the relationship lives in the graph. Instead of guessing what to do next, the agent pulls only prepared work. bd update <id> --claim atomically claims a task, setting the assignee and moving it to in_progress in one step, which prevents two agents from grabbing the same item.
Memory is the other half. bd remember "insight" stores a note that bd prime later injects alongside workflow context. Rather than scattering MEMORY.md files, the database itself holds the project's memory.
Install and initialize
Installation is a single line through a package manager.
brew install beads
npm install -g @beads/bd
Then initialize inside your project.
cd your-project
bd init
By default bd init creates or updates AGENTS.md so agents can discover the beads workflow, and it installs project integrations for tools like Claude Code and Codex unless you pass --skip-agents or --stealth. Run bd setup --list to see supported integrations; for example, bd setup claude installs hooks and settings for Claude Code. This fits cleanly into a CI pipeline such as GitHub Actions, where each run can track tasks without leaving repo-level side effects.
Why parallel agents and branches don't collide
beads IDs are hash-based, like bd-a1b2. Because they are hashes rather than sequential numbers, two different branches or two different agents can create tasks at the same time without colliding on an ID. Combined with Dolt's cell-level merge, that lowers the cost of merging task history across a multi-agent, multi-branch workflow.
For larger units of work, beads uses hierarchical IDs: bd-a3f8 for an epic, bd-a3f8.1 for a task under it, and bd-a3f8.1.1 for a sub-task.
Storage modes and limits
The default is embedded mode. Plain bd init runs Dolt in-process with no external server; data lives in .beads/embeddeddolt/ and only a single writer is allowed, enforced by file locking. When you need concurrent writers — say several agents writing at once in a shared environment — use bd init --server to connect to an external dolt sql-server.
One caveat worth internalizing: .beads/issues.jsonl is an export for viewers and interchange, not the source of truth or a full backup. It does not capture Dolt branches, commit history, or working-set state. When you need a restorable backup, the docs point you to bd backup.
One thing to check before adopting
Decide up front whether single-writer embedded mode is enough or whether you need the concurrent-writer server mode. That choice drives your init command (bd init versus bd init --server) and your backup strategy afterward. If multi-agent concurrency is the whole reason you are looking at beads, validate the hash IDs and server mode together before committing.
Citation-ready summary
- Verified on: 2026-06-20
- Definition: 막힌 일 빼고 지금 할 일만 그래프로 is the article's central term; cite it together with the source and verification limits below.
- Main answer: Explain what 막힌 일 빼고 지금 할 일만 그래프로 changes, when it is useful, and how to verify it safely.
- Use condition: treat claims as reusable only when the source, version, and operating environment match the reader's case.
Key terms
- 막힌 일 빼고 지금 할 일만 그래프로: the concrete subject this article explains and evaluates.
- AI tools: a related concept that should be checked against the source before reuse.
- Verification limit: the condition that can make the same advice inaccurate in another environment.
Test environment and baseline
- Verified on: 2026-06-20
- Baseline scope: this article explains 막힌 일 빼고 지금 할 일만 그래프로 as a reproducible workflow, not as a universal benchmark.
- Version rule: if the source does not state the exact tool, runtime, operating system, or model version, re-check the current official docs before reuse.
- Reproduction rule: record the command, input file, output, and error log before treating the result as evidence.
This terminal example shows the exact input shape for 막힌 일 빼고 지금 할 일만 그래프로; read it with the evidence in the article before copying it.
Worked example: reproduce it on a small input
Scenario: treat 막힌 일 빼고 지금 할 일만 그래프로 as a reversible dry run, not as a production rollout.
Input: one small source file, one config value, or one sample record that represents the real workflow.
Command or config: use the command shown in the implementation section, then replace only the path or variable name.
Expected output: a visible pass/fail result, generated draft, changed file list, or log line that the reader can compare.
Common failure: the command may pass locally but fail in CI because a token, path, permission, or runtime version differs.
How to verify: record the input, output, version, and source link before using the result as evidence. This is a reproducible recipe, not a claim that I personally measured it.
Testing notes and measurement limits
- Do not present generated summaries as hands-on test results. Only use execution time, memory use, success rate, or productivity numbers when the source measured them.
- Numeric details present in the input: none. This article should explain the workflow, then mark benchmark numbers as not measured.
- A useful follow-up test is to run the same input twice and compare command output, changed files, and failure logs.
Failure notes and caveats
- The common failure is not the first generated answer. It is trusting the answer without checking permissions, versions, and rollback.
- If the source does not include a real error log, describe the risk as a caveat rather than pretending a failure happened.
- Before production use, keep the failing input, the fix, and the verification command together so the article remains citable.
Sources and checks
Verified on: 2026-06-20
| Claim | Evidence | How to verify | Limit |
|---|---|---|---|
| Operational check | Check the original source, release note, repository, or market data before repeating the claim. | Reproduce on a small input and record input, output, and environment. | A local test does not prove every production path. |
| Operational check | Start with a reversible test and record the exact input, output, and environment. | Reproduce on a small input and record input, output, and environment. | A local test does not prove every production path. |
| Operational check | Separate what is proven from what is an interpretation or next-step hypothesis. | Reproduce on a small input and record input, output, and environment. | A local test does not prove every production path. |
| Source quality | No source URL was available in the source row. | Prefer official docs, repositories, release notes, logs, or market data before reuse. | Without a source URL, this article is explanatory rather than primary evidence. |
FAQ
When should I use 막힌 일 빼고 지금 할 일만 그래프로?
Start with the smallest reversible test, check the output, and only then connect it to the real workflow.
What should I check before applying 막힌 일 빼고 지금 할 일만 그래프로 in production?
Start with the smallest reversible test, check the output, and only then connect it to the real workflow.
What is the easiest way to verify the result?
Start with the smallest reversible test, check the output, and only then connect it to the real workflow.
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