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To stop re-explaining a codebase in every new Claude Code session, keep project knowledge outside the chat and load the relevant context when a session starts. In a project write-up, developer Max Małecki describes llmwiki, a command-line tool that builds a Markdown knowledge base from a repository, learns from Claude Code sessions, and injects selected material into CLAUDE.md. The approach is useful because the context becomes inspectable project files rather than conversation history; its freshness and accuracy still depend on how those files are generated and maintained.
Why a new coding session can feel like starting over
A coding assistant can work with the files and conversation available in its current session, but that does not automatically preserve the architectural reasoning, conventions, or decisions discussed in earlier sessions. When that knowledge exists only in chat history, a developer may need to repeat it or spend time asking the assistant to rediscover it.
Małecki’s project write-up frames llmwiki as a way to externalize that knowledge. Rather than treating each session as the sole place where project context lives, the tool stores documentation in a repository-backed Markdown wiki and makes selected material available to a later session. This is a description by the project’s author, not an independent product evaluation.
The Tool Desk
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Małecki describes a command-line tool that scans a code repository and creates Markdown documentation about the system. The reported output includes:
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- Domain concepts, architecture, and a map of services.
- Mermaid diagrams and API documentation derived from OpenAPI specifications.
- Integrations, configuration, feature flags, and runtime modes.
- YAML tags to organize wiki entries, plus a cross-project executive summary and C4 landscape diagram.
The author says a later ingest refines existing entries instead of discarding the wiki and starting from scratch. That incremental approach is intended to make the documentation useful as the codebase changes, although it does not by itself establish that every generated fact remains correct.
How the Claude Code workflow fits together
The described workflow has two distinct jobs: learning from a session and supplying context to another one. Małecki says a Claude Code Stop hook can read qualifying session transcripts, extract analytical responses, and pass them to llmwiki’s absorb command. A separate context command places generated material between marker comments in CLAUDE.md, where Claude Code can receive it at the start of a session.
- Build the initial knowledge base. Run the repository-ingestion workflow to generate documentation from the project.
- Capture useful session knowledge. Configure the described Stop hook to send qualifying transcript material to
absorb. - Prepare session context. Use
contextto insert selected wiki material between the markers inCLAUDE.md. - Refresh as the project changes. Re-run ingestion and review generated or absorbed notes so the knowledge base does not drift away from the code.
The article also describes integrations with the Graymatter memory layer and a NanoClaw Discord bot. Those are optional extensions in the author’s account, not prerequisites for the core repository-to-wiki and context-injection workflow.
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Małecki describes the knowledge base as “plain markdown with YAML front matter.” Because the content is ordinary files, a developer can inspect and edit it, track changes in Git, and open it as an Obsidian vault. The format also makes it easier to audit what the assistant may see than a memory store that is only accessible through an opaque service.
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That transparency comes with a maintenance trade-off: Markdown can be wrong or outdated just as easily as any other documentation. Incremental generation and session absorption may reduce manual note-taking, but the author’s account does not establish an automated guarantee that every statement is current, relevant, or safe to reuse.
What to weigh before adopting persistent project memory
- Setup versus control: A manually maintained project guide or prompt file is simple and explicit; a generated wiki with hooks automates more steps but adds configuration and another workflow to maintain.
- Inspectability: Markdown in Git is readable and reviewable. A managed memory service may require less file-level upkeep, but the reader should check how it exposes, versions, and removes stored information.
- Freshness: Ingestion and session learning can capture changes, but developers still need a way to spot outdated architecture notes, superseded decisions, or assumptions that no longer match the code.
- Retrieval precision: Injecting too much context can burden a session; injecting too little can omit a needed constraint. The useful measure is whether the material supplied is relevant to the task, not simply how much has been stored.
- Data locality and provider choice: The author says an Ollama backend is available for NDA code or air-gapped use cases. This is an option, not a guarantee that every configuration or workflow keeps all data on the machine; verify which components process transcripts and repository content.
- Cost and maintenance: Małecki estimates that
materializeuses approximately 5–15K tokens compared with 50–100K for a full ingest in the workflow he describes. These are the author’s estimates, not controlled measurements or general performance guarantees.
Installation and security claims need current verification
At the time of Małecki’s post, he described llmwiki as written in Go, MIT licensed, and at version 1.0.0. The post lists a shell installation command and go install github.com/emgiezet/llmwiki@latest, with binaries for macOS arm64 and amd64 and Linux arm64 and amd64. Releases and installation instructions can change, so consult the project repository for the current version and supported platforms before installing.
The author also reports that a baseline security audit addressed filesystem path traversal, a fenced LLM prompt pipeline, a loopback-only Ollama default, and symlink time-of-check/time-of-use handling; the post points readers to the project’s SECURITY.md. These are project-reported measures, not evidence of independent certification or a guarantee of security. Review the current code, configuration, and security documentation against your own threat model before processing sensitive repositories or transcripts.
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Who this approach is—and is not—for
A persistent Markdown wiki is a plausible fit if you repeatedly work on the same codebase, want project context to survive between sessions, and value files that can be reviewed and version-controlled. The hook-based learning path may appeal if useful design decisions emerge during coding and you want a way to preserve them.
It may be unnecessary for a small or short-lived project whose context fits in a concise, manually maintained guide. It is also not a substitute for reading the code, validating generated explanations, or deciding which information should be retained. Related projects illustrate other approaches: Repo Brain is described as generating Markdown articles for injection through Claude Code hooks, while Claude Recall focuses on tracking drift between live code and stored context. Those examples show different design choices, not proof that one tool is superior.
Quick Recap
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