Anyway, great project! Cheers.

A couple of weeks ago I built something very very similar, only for Obsidian, using the Obsidian Canvas and OpenRouter as my baseline components. Works really nicely - handles image uploads, autolayout with dagre.js, system prompts, context export to flat files, etc. Think you've inspired me to actually publish the repo :)

In Solvent, the main utility is allowing forked-off use of the same session without context pollution.

For instance a coding assistant session can be used to generate a checklist as a fork and then followed by the core task of writing code. This allows the human user to see the related flows (checklist gen,requirements gen,coding...etc) in chronological order without context pollution.

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[1] *Mind Map Format Overview* - A graph-based documentation format stored as plain text files where each node is a single line. The format leverages LLM familiarity with citation-style references from academic papers, making it natural to generate and edit [3]. It serves as a superset structure that can represent trees, lists, or any graph topology [4], scaling from small projects (<50 nodes) to complex systems (500+ nodes) [5]. The methodology is fully detailed in PROJECT_MIND_MAPPING.md with bootstrapping tools available at https://gist.github.com/horiacristescu/7942db247fdfb31d7150b....

[2] *Node Syntax Structure* - Each node follows the format: `[N] *Node Title* - node text with [N] references inlined` [1]. Nodes are line-oriented, allowing line-by-line loading and editing by AI models [3]. The inline reference syntax `[N]` creates bidirectional navigation between concepts, with links embedded naturally within descriptive text rather than as separate metadata [1][4]. This structure is both machine-parseable and human-readable, supporting grep-based lookups for quick node retrieval [3].

[3] *Technical Advantages* - The format enables line-by-line overwriting of nodes without complex parsing [2], making incremental updates efficient for both humans and AI agents [1]. Grep operations allow instant node lookup by ID or keyword without loading the entire file [2]. The text-based storage ensures version control compatibility, diff-friendly editing, and zero tooling dependencies [4]. LLMs generate this format naturally because citation syntax `[N]` mirrors academic paper references they've seen extensively during training [1][5].

[4] *Graph Topology Benefits* - Unlike hierarchical trees or linear lists, the graph structure allows many-to-many relationships between concepts [1]. Any node can reference any other node, creating knowledge clusters around related topics [2][3]. The format accommodates cyclic references for concepts that mutually depend on each other, captures cross-cutting concerns that span multiple subsystems, and supports progressive refinement where nodes are added to densify understanding [5]. This flexibility makes it suitable as a universal knowledge representation format [1].

[5] *Scalability and Usage Patterns* - Small projects typically need fewer than 50 nodes to capture core architecture, data flow, and key implementations [1]. Complex topics or large codebases can scale to 500+ nodes by adding specialized deep-dive nodes for algorithms, optimizations, and subsystems [4]. The methodology includes a bootstrap prompt (linked gist) for generating initial mind maps from existing codebases automatically [1]. Scale is managed through overview nodes [1-5] that serve as navigation hubs, with detail nodes forming clusters around major concepts [3][4]. The format remains navigable at any scale due to inline linking and grep-based search [2][3].

... though I honestly do wish that the current LLM interfaces I use would just implement something like this. Maybe they could acquire you guys :D

I find most need for managing context for problem solving. I describe a problem, LLM gives me 5 possible solutions. From those I immediately see 2 of them won't be viable, so I can prune the search. Then it is best to explore the others separately without polluting the context with non-viable solutions.

I saw this problem solving approach described as Tree-based problem management [0]. Often when solving problems there can be some nested problem which can prove to be a blocker and cut off whole branch, so it is effective to explore these first. Another cool attempt was thorny.io [1] (I didn't get to try it, and it is now unfortunately defunct) in which you could mark nodes with metadata like pro/con. Higher nodes would aggregate these which could guide you and give you prioritization which branch to explore next.

Also graph rendering looks cooler, but outliners seem to be more space efficient. I use Logseq, where I apply this tree-based problem solving, but have to copy the context and response back-and-forth manually. Having an outliner view as an alternative for power users would be neat.

[0] https://wp.josh.com/2018/02/11/idea-dump-2018/#:~:text=Tree-... [1] https://web.archive.org/web/20240820171443/http://thorny.io/

I wanted to try something like this from a while. Was excited to see maxly.chat's promotion on X but was disappointed it wasn't ready.

On a side note, do you also have zero data retention agreements with the providers?