manifold
Manifold is a platform for enabling workflow automation using AI assistants.
Stars: 328
Manifold is a powerful platform for workflow automation using AI models. It supports text generation, image generation, and retrieval-augmented generation, integrating seamlessly with popular AI endpoints. Additionally, Manifold provides robust semantic search capabilities using PGVector combined with the SEFII engine. It is under active development and not production-ready.
README:
Manifold is a powerful platform designed for workflow automation using AI models. It supports text generation, image generation, and retrieval-augmented generation, integrating seamlessly with popular AI endpoints including OpenAI, llama.cpp, Apple's MLX LM, Google Gemini, Anthropic Claude, ComfyUI, and MFlux. Additionally, Manifold provides robust semantic search capabilities using PGVector combined with the SEFII (Semantic Embedding Forest with Inverted Index) engine.
Note: Manifold is under active development, and breaking changes are expected. It is NOT production-ready. Contributions are highly encouraged!
Ensure the following software is installed before proceeding:
- Chrome Browser: Required for web tools. Used as a headless browser and managed by Manifold. WebPGU support should be enabled for text-to-speech to work.
- Python: Version 3.10 or newer (Download).
- Docker: Recommended for easy setup of PGVector (Download).
For development, you'll also need:
- Go: Version 1.21 or newer (Download).
-
Node.js: Version 20 managed via
nvm(Installation Guide).
The easiest way to get started with Manifold is to download a pre-built binary from the releases page.
-
Download the appropriate binary for your platform:
- macOS:
manifold-darwin-universal.zip(Universal binary for both Intel and Apple Silicon) - Linux:
manifold-linux-amd64.zipormanifold-linux-arm64.zip - Windows:
manifold-windows-amd64.zip
- macOS:
-
Extract the zip file and navigate to the extracted directory.
-
Create a
config.yamlfile in the same directory as the binary (a templateconfig.yaml.exampleis included). -
Run the binary:
# On macOS/Linux $ chmod +x manifold-* $ ./manifold-* # On Windows $ manifold-windows-amd64.exe
$ git clone https://github.com/intelligencedev/manifold.git
$ cd manifoldAfter cloning the repository, initialize and update the git submodules:
$ git submodule update --init --recursiveThis will fetch the required dependencies:
- llama.cpp for local model inference
- pgvector for vector similarity search in PostgreSQL
Manifold will automatically manage the lifecycle of the PGVector container using Docker. Ensure Docker is installed and running on your system.
- Follow the official ComfyUI installation guide.
- No extra configuration needed; Manifold connects via proxy.
- Follow the MFlux installation guide.
Use the provided config.yaml.example template to create a new config.yaml file. This file must be placed in the same path as the main.go file if running in development mode, or in the same path as the manifold binary if you build the project.
Ensure to update the values to match your environment.
For development it is not necessary to build the application. See development notes at the bottom of this guide.
Execute the following commands:
$ cd frontend
$ nvm use 20
$ npm install
$ npm run build
$ cd ..
$ go build -ldflags="-s -w" -trimpath -o ./dist/manifold .
$ cd dist
# 1. Place config.yaml in the same path as the binary
# 2. Run the binary
$ ./manifoldThis sequence will:
- Switch Node.js to version 20.
- Build frontend assets.
- Compile the Go backend, generating the executable.
- Launch Manifold from the
distdirectory.
Upon first execution, Manifold creates necessary directories and files (e.g., data).
Note that Manifold builds the frontend and embeds it in its binary. When building the application, the frontend is not a separate web server.
- On first boot, the application will take longer as it downloads the required models for completions, embeddings, and reranker services.
- The application defaults to a single node instance configuration, managing the lifecycle of services using the llama-server backend and bootstrapping PGVector.
- Services can be configured to run on remote hosts to alleviate load on a single host, but users must manage the lifecycle of remote services manually.
Create or update your configuration based on the provided config.yaml.example in the repository:
host: localhost
port: 8080
data_path: ./data
jaeger_host: localhost:6831 # Optional Jaeger tracing
# API Keys (optional integrations)
anthropic_key: "..."
openai_api_key: "..."
google_gemini_key: "..."
hf_token: "..."
# Database Configuration
database:
connection_string: "postgres://myuser:changeme@localhost:5432/manifold"
# Completion and Embedding Services
completions:
default_host: "http://localhost:32186/v1/chat/completions" # Default: local llama-server
api_key: ""
embeddings:
host: "http://localhost:32184/v1/embeddings" # Default: local llama-server
api_key: ""
dimensions: 768
embed_prefix: "search_document: "
search_prefix: "search_query: "
reranker:
host: "http://localhost:32185/v1/rerank" # Default: local llama-serverCrucial Points:
- Update database credentials (
myuser,changeme) according to your PGVector setup. - Adjust
default_hostandembeddings.hostbased on your chosen model server.
Launch your browser and navigate to:
http://localhost:8080
Replace
8080if you customized your port inconfig.yaml.
Manifold is compatible with OpenAI-compatible endpoints:
-
Port Conflict: If port 8080 is occupied, either terminate conflicting processes or choose a new port in
config.yaml. -
PGVector Connectivity: Confirm your
database.connection_stringmatches PGVector container credentials. -
Missing Config File: Ensure
config.yamlexists in the correct directory. Manifold will not launch without it.
Ensure config.yaml is present at the root of the project by using the provided config.yaml.example template and configuring your values.
Run the Go backend:
$ go mod tidy
$ go run .
Run the frontend:
$ cd frontend
$ nvm use 20
$ npm install
$ npm run dev
Manifold uses GitHub Actions to automatically build and publish releases. To create a new release:
- Update version references in the codebase as needed
- Create and push a new tag with the version number (e.g.,
v0.1.0) - GitHub Actions will automatically build binaries for all supported platforms and publish them as a GitHub release
Manifold welcomes contributions! Check the open issues for tasks and feel free to submit pull requests.
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