PromptManager

PromptManager is a local-first desktop app for capturing, organizing, retrieving, inspecting, and reusing prompt assets in one place.

Its product center is simple: act as a canonical home for prompt assets so useful prompts and LLM queries do not stay scattered across chats, notes, scripts, markdown files, and ad-hoc experiments.

It combines a PySide6 GUI, SQLite persistence, semantic search, prompt editing, and a subordinate operational layer for trustworthy runs, routing, and diagnostics — but the product center stays the prompt catalog itself rather than analytics, chains, or general AI-workstation behavior.

Canonical product direction lives in docs/product-ssot.md.

Who it is for

PromptManager is designed for people who actively work with prompts and want a more structured local-first workflow:

• AI developers who maintain reusable prompts across projects
• prompt engineers who want a durable prompt base instead of scattered snippets
• solo builders who want one local prompt catalog instead of loose Markdown files and chat fragments
• operators and researchers who collect prompts from many places and want to find and reuse them quickly

What it helps with

Use PromptManager when you want to:

• capture useful prompts or LLM queries into a searchable local catalog
• normalize drafts into reusable prompt assets with better titles, metadata, and provenance
• reopen recent work quickly and inspect prompt context without hunting through metadata
• preview templated prompts before reuse or optional validation
• use a lightweight workspace only when prompt validation or reuse benefits from it
• review prompt lineage, reuse context, and light supporting history when needed
• keep prompt work local-first, with optional external providers

Recommended usage path

If you are unsure where to start, use this sequence:

1. Quick Capture a useful prompt or LLM query before it gets lost.
2. Promote Draft once it is worth keeping as a reusable prompt asset.
3. Use Recent or search to get back to it quickly.
4. Inspect the detail view to confirm fit and context.
5. Reuse with Copy Prompt or Open in Workspace when you want the stored prompt body or lightweight validation.

Short version:

Quick Capture → Promote Draft → Recent / search → inspect → Copy Prompt or Open in Workspace

Core capabilities

• Prompt catalog — store, search, tag, edit, fork, and organize prompts as durable assets
• Quick Capture + Draft Promote — capture raw prompt/query text fast, inspect it as a draft, then promote it into a reusable prompt asset with bounded title-quality improvement for weak draft titles
• Recent reopen + semantic retrieval — get back to recent prompts quickly and find prompts by meaning, not only exact text
• Inspect + provenance cues — review draft/source/last-modified context, lineage, and related prompt signals without opening raw metadata first
• Quick reuse actions — copy the real prompt body from detail view with one obvious Copy Prompt action, or open the prompt in the workspace without auto-running it
• Template preview and lightweight validation — render Jinja2 templates with JSON variables and validation feedback when reuse benefits from an extra check

Quick start

1. Create a virtual environment

Use either the existing pip flow or uv.

Option A — pip + venv

python -m venv .venv
source .venv/bin/activate
python -m pip install --upgrade pip
pip install -e .

Option B — uv

uv venv
source .venv/bin/activate
uv pip install -e .

2. Optional: add your API key

Start by copying the example environment file:

cp .env.example .env

If you want to execute prompts with LiteLLM-backed providers, fill in the LiteLLM values in .env or export them directly:

export PROMPT_MANAGER_LITELLM_MODEL="gpt-4o-mini"
export PROMPT_MANAGER_LITELLM_API_KEY="sk-***"

If you skip this step, PromptManager can still be used for local cataloguing, editing, and offline workflows.

3. Validate configuration

python -m main --no-gui --print-settings
# or
uv run python -m main --no-gui --print-settings

4. Launch the app

python -m main
# or
uv run python -m main

Minimal local setup

PromptManager works best as a local-first desktop tool:

• SQLite stores your prompt catalog and execution history
• ChromaDB powers semantic search
• Redis is optional
• LiteLLM is optional for prompt execution
• web search integrations are optional

That means you can start with a local catalog first, then add providers only when you need execution or retrieval enhancements.

Optional integrations

PromptManager supports optional integrations for:

• LiteLLM for prompt execution
• Tavily / Exa / Serper / SerpApi / Google Programmable Search for web search enrichment
• Redis for caching

For developer-oriented setup and deeper configuration notes, see README-DEV.md. The repository root also includes .env.example as a safe starting point for local configuration.

Why PromptManager

Prompt workflows often start in Markdown files, text snippets, chat history, or ad-hoc templates. That works for a while, but it breaks down once you want repeatability, reuse, better retrieval, or versioning.

PromptManager gives you a dedicated local-first home for prompt assets:

• capture-oriented
• structured
• searchable
• reuse-friendly
• validation-aware when needed

Project status

PromptManager is currently in beta and under active development.

Current focus:

• strengthening the core prompt asset loop
• improving low-friction capture, promotion, retrieval, inspection, reuse, and refinement
• making settings, routing, and diagnostics trustworthy before broader automation expansion

What PromptManager is not

PromptManager is not primarily positioned today as:

• a general desktop chatbot
• an agent platform
• a general AI workbench

Developer

See README-DEV.md for development environment setup, testing workflow, configuration details, maintenance notes, and deeper product/documentation links.

Changelog

Track release highlights and historical updates in CHANGELOG.md.

License

PromptManager is licensed under the MIT License.