ThoughtDAG · Open source · MIT

Connect your next question
to your earlier thinking.

ThoughtDAG is an open-source AI conversation canvas. Edit the context sent to your model, explore a side question, and bring selected past discussions into what you ask next.

Get ThoughtDAG Try a tutorial ↓ Explore the interactive story

Select a passage, explore a side question, then return to your plan. Illustrated interaction.

What is getting in the way of your next question?

An old assumption keeps coming back.

Inspect the next request. Remove an unwanted route, keep the earlier discussion, and regenerate.

How to edit AI context →

A side question derails the main thread.

Explore a branch, keep your place in the map, and connect useful findings to a later question.

How to branch an AI conversation →

You remember a conclusion, but not why.

Find supported local conversations, check the original conditions, and cite the parts you need.

How to reuse past AI discussions →

Illustrated workflows. Each tutorial includes steps to try in the app and a downloadable GIF.

Before you start

Can I edit context without deleting the conversation?

Yes. Removing a wire removes that route into a later request while keeping the nodes. Check other input paths, attachments, and Recall too. An existing answer does not change until you edit or regenerate it.

How does branching support nonlinear thinking?

Keep alternative questions on separate branches, compare them on the canvas, and connect selected findings to a new downstream question. A branch inherits its upstream conversation path; selecting a passage does not limit context to those words alone.

Will all my past chats appear automatically?

No. The desktop app indexes supported local records you have connected. It cannot restore unsaved history or automatically access chats stored only in a web account. When you use a remote model, the material included in a request is sent to that service.

The graph is not a picture of context. The graph is the context.

ThoughtDAG walks the incoming edges of a node, orders the relevant ancestors, and builds the message sequence sent to the selected model. The structure stays visible, editable, and inspectable.

Branch

Explore another interpretation without overwriting the path that led you here.

Prune

Keep a useful detour on the canvas while excluding it from the next request.

Merge

Bring selected evidence and reasoning paths back together in one answer.

Inspect

Preview what the model will receive before generation. No hidden memory selection.

$ npx thoughtdag why src/lib/api.ts why src/lib/api.ts · 12 turns in 6 sessions claude-code · ✏ edit Q: Can the API detect vision support? Δ storedProviders → storedProviders, storedVision…

$ npx thoughtdag why src/lib/api.ts why src/lib/api.ts · 12 个相关轮次，来自 6 个会话 claude-code · ✏ 修改 Q: 能否判断模型是否支持多模态？ Δ storedProviders → storedProviders, storedVision…

See your agent context
as a graph

Bring work scattered across different agents into one editable graph. See what each session carries, continue from any node, then bring the new work back to where the thought began.

Local discovery Visible context Cross-agent continuity

Available in the desktop app and the Harness plugin · Claude Code, Codex and DeepSeek Harness sessions.

Choose how to open your sessions →

Why editable context matters

Removing an error at its source does not mean it has left the downstream context. Across nine model endpoints, deleting only the source repaired 152 of 162 derailed cases; removing the contaminated subgraph repaired all 162. And with reasoning switched off, source-only repair collapsed to 2 of 18. In ThoughtDAG, branching, pruning and rebuilding decide what the model actually sees next.

Start with the layer you need

Search with your agent, work inside Harness, or open the standalone app. Choose one to start.

CLI + read-only MCP

Search conversations across supported agents and let your agent call the query tools directly. The desktop app is not required.

CLI and MCP manual →

npm install -g thoughtdag
thoughtdag setup mcp

Already using DeepSeek Harness?

Add the thought graph to the web UI. Use your harness models, run agent turns, and explore sessions in Atlas. No separate ThoughtDAG app or CLI required.

Installation and usage → Plugin file on every release (.tgz): the same command takes its URL →

dsh plugin --profile web add dsh-thoughtdag
dsh web

Connect your next question
to your earlier thinking.