ArihantDeva / heimdall
A lightweight CPU only memory approach with ranked retrieval. Simple, yet effective.
Why this job: topics: rag, embeddings, semantic-search, knowledge-base
What it does
Heimdall gives AI coding agents persistent memory across every repository and project you work on so the question *"did I already solve this in another project?"* gets answered by one verified search instead of twenty minutes of grep, find, and ls loops.
Get started
npm i -g @arihantdeva/heimdall
heimdall init --harness claude-code
# or pi | codex | cursor | opencode | all You need: Node ≥ 22.5 (the journal uses the built-in node:sqlite), bash, python3; macOS today (launchd daemon management); Linux works with a manual daemon; tree-sitter-capable python for L2/L3 (HEIMDALL_PYTHON env, or ~/.heimdall/venv/bin/python3, or python3 in PATH). The probe extracts a real file and requires symbols back, so this reports what the bridge can actually do rather than what impo
- Maintenance
- Active
- Last commit 7 days ago
- Stars
- 137
- On GitHub
- Licence
- MIT
- Allows business use
- Downloads for
- None listed
- No release files
- Latest version
- v0.9.0
- Released 2 months ago
- Built with
- JavaScript
- 72% of the code
Is it looked after?
- Contributors
- 1
- Last 90 days
- Issues
- 100% closed
- 0 still open
- Pull requests
- 21 merged
- 3 waiting
- Releases
- 1
- 1 in the last year
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More: languages, topics and project record
Languages
- JavaScript 72%
- Python 19%
- Shell 8%
- TypeScript 1%
Topics
- ai-agents
- claude-code
- cli
- code-intelligence
- developer-tools
- embeddings
- knowledge-base
- llm-tools
- mcp
- memory
- rag
- semantic-search
Project record
- JobRAG and search
- StatusListed
- Commits228
- Forks9
- Watching2
- Stars in 28 days+2
- CreatedAug 20, 2026
- First seen by usNov 7, 2026
- CheckedNov 7, 2026