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AgentStack

Build, deploy, and scale AI agents with ease.

The PersonaFlow platform aims to provide highly personalized user experiences fueled by Generative AI. With a focus on scalability and efficiency, AgentStack offers a suite of tools and APIs to create complex, database-backed configurable agents backed by an API.

Key features include:

  • Context optimization: Intelligent partitioning of ingested data based on the semantic similarity of surrounding content (see: Document Processing).
  • Enterprise feasibility: focus on scalability, cost efficiency, and privacy
  • Agentic assistants: Quickly build and evaluate custom assistants from complex agent architectures (see: Assistants)
  • Evaluation: Deep integration with evaluation tools to score and optimize agents and RAG configurations
  • Build and deploy: Easily deploy agents and RAG configurations to production environments
  • Multiple built-in auth options (see: Auth Guide)

Upcoming features:

  • New agent-backed assistant architectures including adaptive RAG and multi-agent collaboration.
  • New RAG performance optimization features: hybrid search support and knowledge graph generation based on GraphRAG
  • Evaluation and scoring of assistants and RAG configurations with batch runs (Currently done through Arize Phoenix and LangFuse integrations.)
  • Client SDKS: Easy integration of generative AI features into applications via the AgentStack client SDK

Overview

Much of the API and business language is modeled after the OpenAI Assistants API and uses LangGraph under the hood for complex agent architectures that can be accessed via the assistants API along with a modular RAG system, and a suite of agent-based retrieval and tool use features. It is currently being expanded with additional RAG optimization techniques and new agent architectures.

Note: This project is in the very early stages of development and testing. Breaking changes should therefore be expected until the first stable release.

OpenAPI spec can be found on SwaggerHub.

Roadmap

  • Assistants API
  • File management
  • Advanced RAG with adaptive chunking and summarization
  • Advanced RAG assistants integration
  • More LLMs, embedding options
  • Local LLMs and embeddings (Ollama, Huggingface)
  • Auth
  • Assistant builder UI
  • More agent types (self-reflection, etc.)
  • Evaluation and scoring of assistants and RAG configurations
  • Python and TypeScript SDKs

Technology Stack

  • Programming Language
    • Server and backend libraries: Python
    • Admin UI: TypeScript/Next.js
  • Relational Database: PostgreSQL
  • Vector Database: Qdrant (more will be supported) - Easily add new database integrations by extending the base class
  • Low-level Agentic Framework: LangGraph
  • Document Processing: Semantic-Router, Unstructured, LlamaIndex, LangChain
  • Mono-repo manager: Pants
  • ORM: SQLAlchemy
  • Database Migration Tool: Alembic

Quickstart (Docker)

Follow these instructions if you are only running from Docker and do not need to set up the environment for development.

  1. Clone the repo and navigate to the root directory
  2. Create .env file using the .env.example template.
  3. Run docker-compose up -d from terminal.

Dev Setup

Builds and dependencies are managed by Pantsbuild. Pants is a fast, scalable, user-friendly rust-based build system.

  1. install Pants on your system.
  2. Clone the repo and make sure you have Python 3.11.* installed and the interpreter selected in your IDE.
  3. Create .env file using the .env.example template.
  4. Open docker-compose.yaml and make sure the stack block under services is commented out. Optionally uncomment the langfuse and phoenix blocks if you plan to run the evaluation services.
  5. Open docker on your machine if it is not already running and run docker-compose up -d. This will download and start the images.
  6. Install dependencies by running poetry install --no-root.
  7. When that is fiinished, run make migrate. This will run the migrations and seed the database with initial data.
  8. To run the backend with hot reload, use make stack-dev, otherwise you can use pants run stack:local if you do not need the backend server to hot reload.
  9. Navigate to http://localhost:9000/docs to see the API documentation.

Useful Pants Commands

  • Lint: pants lint ::
  • Test: pants test ::
  • Run the stack server: pants run stack:local (use --ldebug for debug logging)
  • Run the dev server with hot reload: make stack-dev

Note: :: means all files in project. For more information on targeting, see: Targets and BUILD files.

Pants uses a constraints.txt as the lock file for dependencies, which is exported from the from poetry.lock. If you add a new dependency, you will need to run poetry lock to update the poetry.lock, followed by poetry export --format constraints.txt --output constraints.txt to regenerate the constraints file which will lock the new dependency to the version in the poetry.lock file.

API Usage

To run the APIs, you can use the Swagger UI at http://localhost:9000/docs or via the Postman collection.

UI Setup

Dependencies are managed by NPM

  1. Install node or make sure you have it installed (node -v).
  2. Navigate to ui folder.
  3. Run npm install to install dependencies.
  4. copy .env.local.example into .env.local file (e.g., cp .env.local.example .env.local).
  5. Update .env.local and uncomment the line where NEXT_PUBLIC_BASE_API_URL is defined. Make sure that the key value is correct and you have backend running under the same address.
  6. Run npm run dev and open the link from the console in a browser.

Documentation

Contributing

Contributions are welcome! If you have a feature request, bug report, or a pull request, please feel free to open an issue or submit a PR.

📝 License

This project is licensed under the MIT License - see the LICENSE file for details.