• Description
• Features
• Installation
• Getting Started
• Tool usage
• User integrations
• Writing code
• Copyright and License

fnord is a command line tool that uses multiple LLM-powered agents and tools to provide a conversational interface to your codebase, notes, and other (non-binary) files. It can be used to generate on-demand tutorials, playbooks, and documentation for your project, as well as to search for examples, explanations, and solutions to problems in your codebase.

AI-powered tools are limited to the data built into their training data. RAG (Retrieval-Augmented Generation) using tool calls can supplement the training data with information, such as your code base, to provide more accurate and relevant answers to your questions. But even with RAG, the AI still runs up against the context window. This is the conversational "memory" of the AI, often making use of an "attention mechanism" to keep it focused on the current instructions, but causing it to lose track of details earlier in the conversation. If you've ever pasted multiple files into ChatGPT or worked with it iteratively on a piece of code, you've probably seen this in action. It may forget constraints you defined earlier in the conversation or hallucinate entities and functions that don't exist in any of the files you've shown it. fnord attempts to mitigate this with cleverly designed tool calls that allow the LLM to ask other agents to perform tasks on its behalf. For example, it can generate a prompt to ask another agent to read through a file and retrieve specific details it needs, like a single function definition, the declaration of an interface, or whether a specific function behaves in a certain way. This keeps the entire file out of the "coordinating" agent's context window while still allowing it to use the information in the file to generate a response. This allows fnord to conduct more complex research across many files and directories without losing track of the details it needs to provide accurate answers.

• Semantic search
• On-demand explanations, documentation, and tutorials
• Writing code with fancy autocomplete AI assistance
• Git archaeology
• Persistent learning about your projects over time
• Improves its research capabilities with each interaction
• Layered approvals for shell/file operations
• User integrations
• MCP server support

Fnord is written in Elixir and is distributed as an escript.

• Install elixir

# MacOS
brew install elixir

# Debian-based
sudo apt-get install elixir

• Add the escript path to your shell's PATH

echo 'export PATH="$HOME/.mix/escripts:$PATH"' >> ~/.bashrc
source ~/.bashrc

• Install fnord

mix escript.install github sysread/fnord

• Set your OpenAI API key

Fnord reads the key from either FNORD_OPENAI_API_KEY or OPENAI_API_KEY. Create or view your keys here.

• Optional: Install ripgrep

fnord includes tooling for the LLM to use the ripgrep tool in addition to semantic search. This enables the LLM to answer questions about your code base, even if the project has not been indexed yet (with the caveat that the results will be less context-aware).

• Optional: Install a markdown viewer

Markdown seems to be the language of choice for LLMs, so installing something like gum or glow to pipe output to will make the output more readable. You can make your preferred formatter persistent by setting the FNORD_FORMATTER environment variable in your shell.

export FNORD_FORMATTER="gum format"

For the purposes of this guide, we will assume that fnord is installed and we are using it to interact with your project, blarg, which is located at $HOME/dev/blarg.

The first step is to index your project to make it searchable with fnord's semantic search capabilities. The first time you index your project may take a while, especially if there are a lot of files. fnord respects .gitignore, but you may also wish to exclude certain paths or globs from indexing (e.g., -x 'node_modules').

fnord index --project blarg --dir $HOME/dev/blarg --exclude 'node_modules'

If you cancel partway-through (for example, with Ctrl-C), you can resume indexing by running the same command again.

Once indexed, fnord will automatically reindex while the ask command is running if it detects changes to the code base.

You can also manually re-index your project at any time to pick up changes. This is done by running the same command again.

fnord stores its index under $HOME/.fnord/projects/<project>.

Note that semantic search requires an existing index. You can still perform text searches via the shell tool (e.g., ripgrep) if installed and approved, but indexing is recommended for full capabilities.

fnord can generate an initial set of learnings about your project to prime its knowledge base.

fnord prime --project blarg

You can view and edit the configuration for existing projects with the fnord config command.

fnord config list --project blarg
fnord config set --project blarg --root $HOME/dev/blarg --exclude 'node_modules' --exclude 'vendor'

For safety, fnord requires approval for shell commands and file operations. You'll be prompted to approve operations as fnord works. To streamline your workflow, you can pre-approve specific commands using regex patterns. See docs/approval-patterns.md for details.

fnord's semantic search is powered by embeddings generated by OpenAI's text-embedding-3-large model. The indexing process does a lot more than simply generate embeddings for the contents of your files. It also generates summary documentation for ea B1C4 ch file as well as an index of all functions, entities, and symbols in each file to enhance semantic matches for common questions and symbols.

fnord search --project blarg --query "callers of some_function"
fnord search --project blarg --query "some_function definition"
fnord search --project blarg --query "unit tests for some_function"

The summaries generated by the indexing process can also be included in the search results with the --detail flag.

fnord search --project blarg --query "some_function declaration" --detail | glow

If you would like to see more information about a single file, you can use the summary command.

fnord summary --project blarg --file "path/to/some_module.ext" | glow

fnord uses semantic search by default when the project has been indexed. For fnord search, an index is required; without it you won't get semantic results. You can still ask questions, and the AI may use shell_tool-assisted text searches (for example, ripgrep) if installed and approved. For rich, accurate results, index your project first.

Fnord uses LLM-powered agents and tool calls to research your question, including semantic search and git tools (read only).

fnord ask --project blarg --question "Where is the unit test for some_function?"

# Continue a conversation
fnord ask --project blarg --follow <ID> --question "Is some_function still used?"

After each response, you'll see a conversation ID. Use --follow <ID> to continue the conversation or --fork <ID> to branch a new thread.

For advanced options (research rounds, unindexed projects, replaying conversations), see docs/asking-questions.md.

Fnord learns about your project while researching your questions. It saves facts and inferences it makes, building a searchable knowledge base that improves over time. As the knowledge base grows, fnord can answer increasingly complex questions with less research.

You can prime this learning process with:

fnord prime --project blarg

For managing and viewing learned knowledge, see docs/learning-system.md.

fnord is under active development and new features are added regularly. To upgrade to the latest version, simply run:

fnord upgrade

Note that this is just a shortcut for:

mix escript.install github sysread/fnord

• List projects: fnord projects
• List files: fnord files --project <project>
• View file summary: fnord summary --project <project> --file <path>
• View notes: fnord notes --project <project> (use --reset to clear)
• Delete a project: fnord torch --project <project>
• Upgrade fnord: fnord upgrade

Create custom tools (frobs) that fnord can use while researching. Use frobs to query project-specific APIs, check deployment status, retrieve GitHub PR details for review, or gather information from Kubernetes clusters.

fnord frobs create --name my_frob
fnord frobs check --name my_frob
fnord frobs list

Frobs are stored in ~/.fnord/tools. For implementation details, see docs/frobs-guide.md.

MCP servers extend fnord with external tools and data sources. Add servers for GitHub, Kubernetes, project-specific APIs, or any MCP-compatible service.

# Add a stdio server
fnord config mcp add <name> --transport stdio --command ./server

# Add an HTTP server
fnord config mcp add <name> --transport http --url https://api.example.com

# Add with OAuth (auto-configures everything)
fnord config mcp add <name> --transport http --url https://api.example.com --oauth
fnord config mcp login <name>

Advanced Configuration: For complete command reference, custom transport options, and manual configuration, see docs/mcp-advanced.md.

Fnord supports OAuth2 for MCP servers with automatic discovery and registration:

fnord config mcp add myserver --transport http --url https://example.com --oauth
fnord config mcp login myserver

For advanced OAuth options, troubleshooting, and security details, see docs/oauth-advanced.md.

Fnord can (optionally) automate code changes in your project using the ask command with the --edit flag.

• Use --edit with extreme caution.
• AI-driven code modification is unsafe, may corrupt or break files, and must always be manually reviewed.
• Optionally add --yes to pre-approve edits without prompting.

The LLM has access to several tools that allow it to modify code within the project directory and perform basic file management tasks. It cannot perform write operations with git or act on files outside of the project's root and /tmp.

fnord ask --project myproj --edit --question "Add a docstring to foo/thing.ex"

You can also use --worktree to specify a git worktree path to operate within.

fnord ask --project myproj --worktree /path/to/myproj-wt --edit --question "Add a docstring to foo/thing.ex"

Code modification by an LLM is unreliable and is not safe for unsupervised use. The AI may behave unpredictably.

This software is copyright (c) 2025 by Jeff Ober.

This is free software; you can redistribute it and/or modify it under the MIT License.