abstractframework 0.1.6

Unified installer and documentation hub for the AbstractFramework ecosystem

AbstractFramework

Write once. Generate everything.

A modular, open-source ecosystem for building durable, observable, multimodal AI systems. Text, voice, image, video, music — one unified interface, any provider, any model, local or cloud.

AbstractFramework is an ecosystem of composable packages for building AI systems that work in operational reality:

• Durable by default: workflows pause and resume safely (survive crashes and restarts)
• Observable: an append-only ledger so any UI can reconstruct state by replaying history
• Controlled actions: explicit boundaries for tool execution, approvals, and evidence
• Multimodal: capability plugins (voice, vision, music) that stay out of your way until you need them

Think of it as an agentic OS: durable runs + replay-first observability + multimodal capabilities — write once, run across providers and deployment modes.

Prerequisites: Python 3.10+. Node.js 18+ for browser UIs. An LLM backend (Ollama, LM Studio, vLLM, or a cloud API key).

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Two entrypoints

Start lightweight with just the LLM library, or go all-in with a production gateway. Both paths lead to the same ecosystem.

1) AbstractCore — LLM SDK + OpenAI-compatible /v1 server

Start here if you need a lightweight LLM library for scripts, notebooks, or existing applications. No infrastructure required — just pip install and call. Add multimodal capabilities with plugins as you grow.

• 9+ providers with identical API (local + cloud)
• Universal tool calling, structured output, streaming
• Media handling (images, PDFs, audio, video)
• OpenAI-compatible HTTP server mode (/v1)
• Multimodal via capability plugins (Voice, Vision, Music)

pip install abstractcore

from abstractcore import create_llm

llm = create_llm("ollama", model="qwen3:4b-instruct")
resp = llm.generate("Explain durable execution in 3 bullets.")
print(resp.content)

AbstractCore gives you one interface for provider switching, tools, structured output, and media — as a Python SDK or via /v1 for any OpenAI-compatible client.

2) AbstractGateway — durable run control plane (HTTP/SSE APIs)

Start here if you're building persistent AI applications — agents that run for hours, workflows that survive crashes, scheduled tasks. The gateway is your AI control plane: durable runs with ledger replay/streaming and thin clients that can attach/detach across devices.

• Durable execution that survives crashes and restarts
• Append-only ledger (replay-first) for auditability
• Scheduled workflows (cron-style, recurring)
• Multi-client: terminal, browser, tray, Telegram, email
• Start on one device, continue on another

pip install abstractgateway

export ABSTRACTGATEWAY_AUTH_TOKEN="$(python -c 'import secrets; print(secrets.token_urlsafe(32))')"
export ABSTRACTGATEWAY_ALLOWED_ORIGINS="http://localhost:*,http://127.0.0.1:*"
export ABSTRACTGATEWAY_WORKFLOW_SOURCE=bundle
export ABSTRACTGATEWAY_FLOWS_DIR="$PWD/bundles"
export ABSTRACTGATEWAY_DATA_DIR="$PWD/runtime/gateway"

abstractgateway serve --host 127.0.0.1 --port 8080

Monitor runs from a browser:

npx @abstractframework/observer   # open http://localhost:3001

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Author once, run everywhere (AbstractFlow)

AbstractFlow lets you author complex agentic orchestration as portable .flow bundles:

1. Open the Flow Editor (npx @abstractframework/flow)
2. Build a workflow: LLM steps, tool steps, branching, loops, subflows
3. Export a .flow bundle and copy it to ABSTRACTGATEWAY_FLOWS_DIR
4. Run it from any gateway-backed client (Observer, AbstractAssistant, Code Web UI, your app)

AbstractAgent provides ready-made agent patterns (ReAct, CodeAct, MemAct) that can be used inside flows or standalone.

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Monitor and schedule with AbstractObserver

• Observe: replay the full ledger of any run, or watch one live over SSE
• Control: cancel, resume, or inspect runs from the browser
• Schedule: durable schedules (cron-style) owned by the gateway — they survive restarts

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Example apps

App	What it does	Install
AbstractCode	Terminal agentic dev client — durable sessions, tool approvals, /workflow support	pip install abstractcode
AbstractAssistant	macOS tray client — gateway-first, workflow picker per session, voice support	pip install abstractassistant
AbstractObserver	Browser UI — monitor, control, and schedule gateway runs	npx @abstractframework/observer
Code Web UI	Browser coding assistant (gateway-backed)	npx @abstractframework/code

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Install the pinned ecosystem profile

Light / Apple / GPU profiles

Choose how the framework runs based on your hardware and constraints. All profiles keep the same interfaces; they mainly change which local inference stacks are available.

Light (default) — endpoint-only inference (cloud APIs or local OpenAI-compatible servers), no in-process ML engine stacks:

pip install abstractframework

Apple — native Apple Silicon local stacks (MLX/Metal) in addition to endpoint providers:

pip install "abstractframework[apple]"

GPU — native GPU local stacks (CUDA/ROCm) in addition to endpoint providers:

pip install "abstractframework[gpu]"

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Documentation

Page	What it covers
docs/README.md	Documentation hub — pick your starting point
docs/getting-started.md	Two entry points + first end-to-end run
docs/architecture.md	Layered model, durable execution primitives, comparisons
docs/configuration.md	Minimal config, where defaults live, Core vs Gateway
docs/glossary.md	Shared terminology (run, ledger, effect, wait, bundle, …)
docs/faq.md	Common questions, comparisons, troubleshooting
docs/api.md	Meta-package API (pins, helpers, re-exports)

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Developer setup (from source)

Clone all sibling repos and build everything in editable mode:

./scripts/clone.sh           # clone 14 repos as siblings
source ./scripts/build.sh    # editable installs into .venv (use `source` to stay in the venv)

Then configure:

abstractcore --config
abstractcore --install

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License

MIT. See LICENSE.