curo1305
0b0cd07330
Local models (e.g. Gemma on LM Studio) return HTTP 400 when sent a tools-spec request. Catch litellm.BadRequestError in the tool-use loop, inform the user once that tools are disabled, and retry as a plain streaming call so the conversation continues normally. Co-Authored-By: Claude Sonnet 4.6 <noreply@anthropic.com>
Pyra
A personal AI assistant CLI with vault-first security. Combines multi-provider AI chat with long-term memory and (coming) automation skills.
Quick Start
pip install -e . # or: pipx install .
pyra setup # choose your AI provider
pyra chat # start talking
Providers
Local (no API key needed):
- LM Studio —
http://localhost:1234 - Ollama —
http://localhost:11434 - llama.cpp server —
http://localhost:8080
Cloud:
- Anthropic (Claude), OpenAI (GPT), Google (Gemini), DeepSeek, Qwen
Commands
| Command | Description |
|---|---|
pyra setup |
Run the provider setup wizard |
pyra chat |
Start interactive chat |
pyra memory list |
List memory files |
pyra memory read <name> |
Read a memory file |
pyra memory write <name> <content> |
Write a memory file |
pyra memory append <name> <content> |
Append to a memory file |
In-chat slash commands
| Command | Description |
|---|---|
/help |
Show available commands |
/memory list |
List memory files |
/clear |
Clear conversation history |
/quit or /exit |
Exit Pyra |
Security
- API keys live in
~/.pyra/vault/— the AI cannot read this directory config.yamlnever contains credentials — only provider ID, model name, and base URL- Prompt injection scanner — warns on suspicious AI output, logs to
~/.pyra/security.log - Path sandboxing — the AI can only reference memory files by name; traversal is blocked
Memory
Pyra reads your memory files at the start of each session and injects them as context.
Files are plain Markdown stored in ~/.pyra/memory/:
~/.pyra/memory/
├── user/profile.md ← who you are
├── context/ ← ongoing projects
└── knowledge/ ← general notes
~/.pyra/ Directory
~/.pyra/
├── config.yaml ← provider + model (no secrets)
├── security.log ← injection event log
├── memory/ ← AI-readable long-term memory
├── skills/ ← automation scripts (Stage 2)
└── vault/ ← secure, AI-inaccessible storage
└── secrets/api_keys.json
Roadmap
- Stage 1 (now): Core CLI, multi-provider chat, memory, vault security
- Stage 2: Skills — shell/PowerShell/Python automations with user approval gates
- Stage 3: Vault encryption with
age - Stage 4: Security audit sub-agent
- Stage 5: Web UI, embedding-based memory search