AI Frameworks · lastmile-ai

aiconfig

AIConfig is an open-source Python/Node framework that separates AI prompts, model parameters, and settings from application code by storing them as JSON configs. It provides a visual editor for prompt iteration and SDKs to execute those configs in production applications.

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Source: GitHub — github.com/lastmile-ai/aiconfig

1.1k

GitHub stars

Forks

Python

Primary language

MIT

License (OSI-approved)

Key facts

Objective fields from the source. Values we can't verify are shown as “Unknown” rather than guessed.

Field	Value
Repository	lastmile-ai/aiconfig
Owner	lastmile-ai
Primary language	Python
License	MIT — OSI-approved
Stars	1.1k
Forks	90
Open issues	166
Latest release	v1.1.32 (2024-03-18)
Last updated	2026-02-10
Source	https://github.com/lastmile-ai/aiconfig

What aiconfig is

AIConfig decouples generative AI logic into versioned JSON artifacts using a standardized schema, with model-agnostic SDKs (Python/Node), a local editor for prototyping, and extensibility for custom models. Supports prompt chaining, multimodal workflows, and API-based model execution.

Quickstart

Get the aiconfig source

Clone the repository and explore it locally.

terminalbash

1git clone https://github.com/lastmile-ai/aiconfig.git2cd aiconfig3# follow the project's README for install & configuration

Need it deployed, integrated, or customized instead? DEV.co ships production installs.

Best use cases

Rapid prompt experimentation and iteration

Visual editor enables non-engineers and engineers to prototype, test, and version control prompt chains without modifying application code.

Complex multi-model/multi-step workflows

Manage prompt routing, chaining, RAG pipelines, and function calling across different LLMs (OpenAI, etc.) in a single declarative JSON config.

Separating AI artifact development from engineering

Enables data scientists/prompt engineers to iterate on prompts independently while software engineers integrate via stable SDK interface.

Implementation considerations

Requires explicit API key management (e.g., OpenAI) for editor and runtime; ensure secrets handling follows your deployment policy.
Schema versioning and config format lock-in risk; review migration path if schema evolves or project is abandoned.
Model support extensibility is documented but requires custom handler code; confirm target models (non-OpenAI) have examples or active community support.
Editor dependency on Python CLI (`aiconfig edit`) for non-VS-Code workflows; evaluate tooling fit for your team's development environment.
Streaming and async support available but verify compatibility with your application's concurrency model and error handling.

When to avoid it — and what to weigh

Real-time, latency-critical inference — JSON config loading and serialization overhead may not suit sub-millisecond response requirements; evaluate performance under your SLA.
Deeply embedded AI logic in complex application flows — If AI calls are tightly intertwined with transaction control, state machines, or conditional business logic, decoupling via config may add complexity.
Offline-only or air-gapped deployment — Framework is oriented toward API-based model services; local model integration requires custom extension work.
Minimal dependencies or resource-constrained environments — Python/Node runtime and SDK footprint may be unsuitable for embedded or serverless edge environments.

License & commercial use

MIT License. Permissive OSI-approved license allowing commercial use, modification, and distribution with attribution.

MIT permits commercial use without royalty or license fee. However, review dependencies for compatible licenses. No warranty provided; users bear operational and liability risk. Consider support model (community vs. commercial) based on production SLA requirements.

DEV.co evaluation signals

Editorial assessment — not user reviews. Directional, with an explicit confidence level.

Signal	Assessment
Maintenance	Active
Documentation	Adequate
License clarity	Clear
Deployment complexity	Low
DEV.co fit	Strong
Assessment confidence	High

Security considerations

API keys must be managed securely (environment variables, secrets manager); no built-in encryption for stored configs. JSON configs may expose model settings; evaluate if prompt/parameter exposure is acceptable for your threat model. No explicit security audit, vulnerability disclosure policy, or SBOM provided in data. Dependency on third-party model APIs (OpenAI, etc.) inherits their security posture. Validate SDK and dependency versions regularly.

Alternatives to consider

LangChain

Broader ecosystem for LLM application development with more model integrations, memory/caching, and agent frameworks. More mature but less focused on config-based prompt versioning.

Prompt Flow (Microsoft)

Low-code workflow builder with Azure integration, MLOps observability, and enterprise support. More tightly coupled to Azure/Windows ecosystem.

PromptBase / Manual versioning

Minimal framework approach using Git + JSON; suits teams preferring lightweight tooling and full control over serialization and schema.

Software development agency

Build on aiconfig with DEV.co software developers

Explore AIConfig to version, iterate, and scale generative AI applications with separation of concerns. Start with pip install and the visual editor.

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aiconfig FAQ

Does AIConfig support models other than OpenAI?

Yes, it is model-agnostic and extensible, but OpenAI is emphasized in quickstart. Other models require custom model handler implementation; review cookbooks and documentation for examples.

Can I use AIConfig in a production application?

Yes. MIT license permits commercial use. Ensure API key management, error handling, monitoring, and testing are production-grade. No explicit SLA or managed service provided by vendor.

Is the aiconfig file format locked-in?

Configs are stored as JSON with a 'latest' schema version. Unknown if schema is backward-compatible across major versions; test migrations or lock to a specific framework version.

What's the overhead of loading and running an aiconfig?

Not explicitly benchmarked in provided data. JSON deserialization and model API calls dominate latency. Test under your load to confirm SLA compliance.

Work with a software development agency

From first prototype to production, DEV.co delivers software development services around tools like aiconfig. Our software development agency staffs experienced software developers and web developers for custom software development, web development, integrations, and ongoing support across ai frameworks and beyond.

Decouple Your AI Logic from Code

Explore AIConfig to version, iterate, and scale generative AI applications with separation of concerns. Start with pip install and the visual editor.

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