errnest 0.1.0

Local-first, provider-agnostic error/fix memory with a semantic search TUI.

errnest

Local-first, provider-agnostic error/fix memory for the command line.

Capture the errors you hit and how you fixed them. errnest surfaces the past fix the next time a failure looks the same — matched semantically via embeddings, with an offline text-match fallback when no AI backend is reachable. Browse everything in an interactive terminal dashboard.

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Contents

• Why errnest
• Install
• Quickstart
• The dashboard
• AI backends
• CLI reference
• Embedding in other tools
• Configuration
• How it works
• Contributing
• License

Why errnest

Every developer re-solves the same handful of errors over and over — port already in use, a ModuleNotFoundError after switching branches, a Docker permission error. errnest is a small, local database of those errors and their fixes, plus a fast way to ask "have I seen this before?" — from a shell one-liner, a TUI, or your own tooling.

• Local-first. SQLite on disk, no server, no account, no telemetry.
• Provider-agnostic AI. Ollama by default (fully offline); swap in OpenAI, an OpenAI-compatible endpoint, Anthropic, or Gemini via one config key. The rest of the codebase only ever calls embed() / generate().
• Degrades gracefully. No AI backend reachable? Search falls back to text matching instead of failing.
• Redacts before it stores. Secrets and volatile tokens (paths, hex, emails) are stripped/normalized before anything hits disk.
• A real dashboard, not just a CLI — see below.

Install

pip install errnest                 # core (local Ollama backend)
pip install errnest[openai]         # + OpenAI / OpenAI-compatible
pip install errnest[anthropic]      # + Anthropic (generation)
pip install errnest[gemini]         # + Google Gemini
pip install errnest[all-providers]  # everything

Requires Python 3.9+. No provider is required to install or run errnest — with nothing configured it talks to a local Ollama daemon, and even without Ollama running, capture/search/dashboard all still work (offline text matching instead of semantic search).

Quickstart

some-failing-command 2>&1 | errnest capture -c "some-failing-command"
errnest resolve 1 "bumped the timeout in config.yaml"
errnest search "connection refused on startup"
errnest doctor       # check provider + models + credentials
errnest dashboard    # browse everything interactively

The dashboard

errnest dashboard opens an interactive TUI (built with Textual) over your error store — search, filter by status or tag, resolve/dismiss/copy fixes, see semantically-similar past errors, and spot recurring patterns via clustering.

Panes: a filterable, searchable error list on the left; a detail view on the right with metadata, error output, applied fix, an AI explanation (once resolved), and the top 3 semantically similar past errors. A Patterns tab clusters your error history (k-means over embeddings) once you've captured 20+ errors, so you can spot what's actually costing you the most time.

Keybindings:

Key	Action
j / k or ↑ / ↓	Navigate the error list
r	Mark selected error resolved
d	Dismiss selected error (won't-fix)
c	Copy the fix text to your clipboard
s	Focus search
/	Focus filter chips
p or 1–4	Switch tabs (All / Unresolved / Resolved / Patterns)
q or Ctrl-C	Quit

Search is semantic (embeddings + cosine similarity) when an embed backend is reachable, and degrades to a live substring filter when it isn't — with no error shown, just a quieter dashboard.

AI backends

The rest of errnest only calls embed() / generate() — it never references a provider directly. The active provider comes from [ai] provider in ~/.errnest/config.toml:

[ai]
provider = "ollama"        # ollama | openai | openai-compatible | anthropic | gemini
embed_fallback = "ollama"  # used when the provider can't embed (e.g. anthropic)

[ai.ollama]
host = "http://localhost:11434"
embed_model = "nomic-embed-text"
gen_model = "llama3"

[ai.anthropic]
api_key_env = "ANTHROPIC_API_KEY"
gen_model = "claude-haiku-4-5"

Provider SDKs are imported lazily; a provider that's unreachable or missing its SDK degrades to a clear AIUnavailableError, and every caller in errnest (search, the dashboard, doctor) handles that by falling back rather than crashing.

Switch providers without hand-editing TOML:

errnest config set ai.provider anthropic
errnest config show     # effective config, secrets masked

See USAGE.md for the full config reference, every environment variable, and per-provider setup steps.

CLI reference

Command	Description
errnest capture -c "<cmd>"	Capture a failing command + error output (stdin) as unresolved
errnest resolve <id> "<fix>"	Record the fix and mark an error resolved
errnest dismiss <id>	Mark an error won't-fix
errnest search "<query>"	Show the most similar past errors + fixes
errnest show <id>	Full detail for one error
errnest dashboard	Open the interactive TUI
errnest doctor	Check provider/model reachability and credentials
errnest config show	Print effective config (secrets masked)
errnest config set <key> <value>	Set a config value, e.g. ai.provider anthropic

Embedding in other tools

from errnest import ErnestClient

client = ErnestClient()

# Never raises — returns None if there's no confident, resolved match.
fix = client.surface(stderr_text)

# Browse recent errors, optionally scoped to a project directory.
recent = client.search("build failed", cwd="/path/to/project", top_k=3)

Wrap it behind a try/except ImportError in your own tool to make errnest an optional dependency — never import it at module top level, and treat every call as safe-to-fail (it already is, on errnest's side).

Configuration

Environment variables:

Variable	Purpose	Default
ERRNEST_CONFIG	Path to the config file	~/.errnest/config.toml
ERRNEST_DB	Path to the SQLite error store	~/.errnest/errors.db
ERRNEST_SIM_THRESHOLD	Cosine-similarity cutoff for auto-surfacing	0.7
OLLAMA_HOST	Ollama daemon URL	http://localhost:11434

Full config file reference, per-provider setup, and troubleshooting: USAGE.md.

How it works

• Storage: SQLite (~/.errnest/errors.db), one row per captured error, with an optional embedding vector attached at capture time.
• Redaction: API keys, tokens, emails, and provider-specific credential patterns are stripped before storage; paths/hex/numbers are normalized so near-identical errors from different runs still match.
• Search: cosine similarity over embeddings when an embed backend is reachable; Jaccard token overlap over normalized text otherwise.
• Patterns: a small pure-Python k-means (no numpy) over stored embeddings, computed once per dashboard session.

See CHANGELOG.md for release history.

Contributing

Issues and PRs welcome. To run the test suite locally:

pip install -e ".[dev,all-providers]"
pytest
ruff check .

Provider tests mock the SDK/HTTP layer — no real API calls or a running Ollama instance are needed to run the suite.

License

MIT