Visit the Releases page to grab the latest assets: https://github.com/ttcs60cntt3/meta-ai-orchestrator/releases
- Overview
- Why this project
- Key concepts
- Features at a glance
- Architecture and design
- Core modules
- Supported integrations
- Getting started
- Deployment models
- Configuration and run-time
- Command line and API
- Observability and reliability
- Security and governance
- Development and testing
- Contributing
- Roadmap
- FAQ
- License
- Acknowledgments
Meta AI Orchestrator is an enterprise-grade AI orchestration platform designed to coordinate and optimize AI workloads across multiple large language models (LLMs) and knowledge sources. It provides retrieval-augmented generation (RAG) capabilities, end-to-end workflow orchestration, and deep observability to ensure performance, reliability, and governance in complex AI deployments. Built with a focus on scale, security, and interoperability, it helps teams reduce latency, improve decision quality, and accelerate AI-driven business processes.
This platform supports multiple LLM providers, including major players in the space, and offers pluggable components for model selection, routing, prompt engineering, data access, and vector database integration. It includes a robust workflow engine, orchestration primitives, an instrumentation layer for telemetry, and a simple yet powerful API for integration with existing systems and pipelines. The goal is to give enterprises a reliable, auditable, and scalable means to run AI at scale with confidence.
Topics covered by this project include ai, async, automation, claude, copilot, enterprise, llm, machine-learning, microservices, multi-llm, observability, openai, opentelemetry, orchestration, performance, prometheus, rag, rust, vector-database, and workflow.
In modern enterprises, AI workloads live at the intersection of multiple teams, services, and data stores. Teams need a single orchestration surface that can:
- Coordinate several LLMs and AI agents to solve problems that are bigger than any single model.
- Access, fuse, and reason over data using retrieval-augmented generation (RAG) patterns.
- Ensure performance, reliability, and governance with strong observability, traceability, and audit trails.
- Scale across microservices architectures, containers, and cloud-native environments.
- Provide a clean interface for developers, data scientists, and operators to plug in new models, data sources, or deployment targets.
Meta AI Orchestrator is designed to meet these needs. It provides predictable behavior, transparent decision paths, and measurable outcomes. It is built to support enterprise deployment requirements, including security controls, RBAC, and traceable workflows.
- Multi-LLM orchestration: Route requests to the best model for the task, possibly combining outputs from several models to form a single answer.
- RAG (Retrieval-Augmented Generation): Bring in external knowledge via vector stores and data connectors to ground model outputs in facts.
- Observability: End-to-end telemetry, metrics, traces, and logs to understand system behavior and detect anomalies early.
- Workflow orchestration: Define workflows as code with deterministic steps, retry policies, dependencies, and parallelism.
- Vector databases: Integrate with vector stores to perform efficient similarity search for context retrieval.
- Microservices and containers: A modular, service-oriented design that scales in cloud-native environments.
- Security and governance: Role-based access, secrets management, auditing, and policy-based controls.
- Zero-downtime deployment and rolling upgrades
- Seamless support for multiple LLM providers and local models
- Flexible routing policies for model selection
- RAG pipelines with pluggable retrievers and data sources
- Observability by design: metrics, traces, logs, dashboards
- Extensible with adapters for popular toolchains and data stores
- Deterministic workflow execution with retries and timeouts
- Lightweight core in Rust with safe FFI exposure for extensions
- Rich APIs and SDKs to embed orchestration capabilities in your apps
- Security-first defaults with built-in secrets and RBAC
The platform is built around a modular, service-oriented architecture. At its core, a lightweight orchestrator coordinates the lifecycle of tasks, stakes decisions on model routing, and wires data flows through retrieval, reasoning, and output stages. The monolith is broken into microservices to enable independent scaling, independent deployment, and safe upgrades.
Key architectural ideas:
- Rust-based core for performance and safety, with clear boundaries to higher-level components.
- Plug-in adapters for LLM providers and vector stores to minimize vendor lock-in.
- Event-driven communication with a message bus to decouple components and enable asynchronous processing.
- A workflow engine that expresses orchestration logic as directed graphs of steps, decisions, and retries.
- An observability plane that collects metrics, traces, metrics, and logs, and presents them in dashboards.
- Data privacy and governance baked into the configuration and policy model.
A high-level diagram would show the LLM adapter layer, the RAG engine, the vector store connectors, the workflow engine, and the observability bus all connected to a central orchestrator. In practice, you can view the architecture as:
- Clients and services call the orchestrator API.
- The orchestrator assigns tasks to LLM adapters.
- Outputs are processed by prompt engineering components and post-processing steps.
- The RAG layer fetches relevant context from vector stores or data sources.
- Results are aggregated, routed, and delivered to downstream systems.
- Telemetry and logs flow to the observability stack.
To illustrate, imagine a use case where a customer support flow is triggered. The orchestrator asks multiple LLMs for guidance, retrieves relevant knowledge from a product database, fuses model outputs with retrieved context, and returns a final answer to the customer channel. The same flow can be reused for compliance checks, technical reports, or automated decision-making in enterprise processes.
- Orchestrator engine: The brain that schedules tasks, routes to LLMs, coordinates sub-tasks, and preserves deterministic semantics.
- LLM adapters: Small, safe wrappers around external model APIs, with retry logic, rate limiting, and authentication handling.
- RAG orchestrator: Manages retrieval pipelines, prompts, and context planning. Supports multiple retrievers and context stores.
- Vector store adapters: Connectors for Pinecone, Weaviate, Milvus, and other vector databases. Includes pruning and indexing strategies.
- Data connectors: Access to internal knowledge bases, messages queues, file systems, and databases.
- Observability stack: Metrics, traces, and logs collection, plus dashboards for operational insight.
- Security and policy layer: Secrets management, RBAC, and policy evaluation hooks.
- Packaging and distribution: Dockerfiles, Helm charts, and build pipelines to ease deployment.
- LLM providers: OpenAI, Claude, Copilot, and other major players. The system is built to add more adapters with minimal changes.
- Data sources: Document stores, SQL databases, NoSQL stores, and file systems for retrieval contexts.
- Vector databases: Pinecone, Weaviate, Milvus, and other vector engines.
- Telemetry: OpenTelemetry, Prometheus, Grafana for dashboards and alerts.
- Observability: Tracing, metrics, logs, service maps, dashboards.
- Monitoring: Health checks, readiness probes, and canary deployments.
This ecosystem lets enterprises mix and match components to meet requirements for latency, cost, and model behavior while maintaining a unified operational view.
Prerequisites
- A modern Linux or macOS environment
- Rust toolchain (rustc, cargo)
- Docker and optional Kubernetes cluster for orchestration
- Python 3.x for client tools and scripting (optional)
- A cloud account for hosting models and data sources (optional)
Quick start with Docker
- This quick start is designed to get you up and running quickly. It sets up a minimal in-container environment, runs a basic workflow, and exposes a minimal API surface for testing.
Steps:
- Clone the repository
- Build the image
- Run the container with a sample configuration
- Verify that the API is responsive and the telemetry is visible
Notes:
- This quick start uses a small, local test model and a small in-memory vector store for demonstration purposes.
- For production, you should use external vector stores and real model providers.
From source
- If you prefer to build from source, you can compile the core and run locally. This gives you a hands-on view of the inner workings, helps you extend the core, and lets you tailor components to your needs.
Commands (illustrative):
- cargo build --release
- cargo test
- bin/meta-ai-orchestrator --help
You will find example configuration files under docs or config directories in the repository. Use those as starting points and adapt them to your environment.
Release download and install
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The repository exposes release artifacts in the Releases page. From the Releases page, download the appropriate asset for your platform and run the installer or follow the install instructions included in the release notes. The Releases page is here: https://github.com/ttcs60cntt3/meta-ai-orchestrator/releases
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Note: When you download the latest release asset, you should pick the asset that matches your environment (Linux, macOS, Windows, containerized form, etc.). After downloading, run the installer to set up the core services and dependent components. The exact steps vary by asset and platform; please refer to the release notes for precise commands and prerequisites.
Kubernetes
- Use Kubernetes to deploy the orchestrator as a set of microservices. The deployment includes:
- A controller that schedules tasks and routes workloads
- A set of model adapters that talk to HSMs or external model endpoints
- A RAG pipeline service that fetches context and constructs prompts
- A vector store service that manages indexes
- An observability sidecar or daemonset to collect metrics and traces
- The deployment is designed for rolling upgrades, zero-downtime deployments, and easy rollback.
Helm charts
- Helm charts simplify deployment to Kubernetes with sensible defaults. They expose configuration knobs for:
- LLM provider credentials
- Vector store endpoints
- Telemetry and logging backends
- Security policies and RBAC
- Resource requests and limits
- The charts are designed to be composable so you can add or remove components without disrupting running workloads.
Standalone and edge
- For edge deployments or standalone experiments, you can run a compact version of the orchestrator with a reduced feature set. This is useful for pilot projects, offline testing, and education.
Configuration in Meta AI Orchestrator is explicit. It uses environment variables and, optionally, a configuration file. This makes it easy to manage across environments and maintain reproducible builds.
Environment variables
- Define core options such as:
- ORCHESTRATOR_BIND_ADDRESS
- ORCHESTRATOR_PORT
- LOG_LEVEL
- METRICS_ENABLED
- TRACING_ENABLED
- VECTOR_STORE_ENDPOINT
- LLM_PROVIDER_CREDENTIALS
- You should also set policy and security options to align with your governance requirements.
Configuration file
- A YAML or JSON configuration file can define:
- Global settings
- Workflow definitions
- Retrievers and vector store adapters
- Model routing policies
- Data source mappings
- The schema is documented in the repository with examples. You can copy a sample and adapt it to your environment.
Runtime considerations
- Performance: The orchestrator is designed to minimize latency by parallelizing model calls and retrieving context in parallel when possible.
- Consistency: Workflows are executed with deterministic semantics to reduce divergence and ensure auditability.
- Resilience: Timeouts and retries are configurable to handle transient failures without cascading errors.
CLI reference
- The command line interface exposes:
- Init and bootstrap commands
- Workflow creation and management
- Model adapter configuration
- Telemetry and diagnostics commands
- Import/export of configurations
- Example:
- meta-ai orchestrator init
- meta-ai orchestrator deploy --config config.yaml
- meta-ai orchestrator status
REST API overview
- The orchestrator exposes a REST API that mirrors core functionality:
- Define new workflows
- Submit tasks
- Retrieve results
- Query metrics and health endpoints
- The API is designed to be stable and backward compatible. It is authenticated using API keys or OAuth tokens as configured.
SDKs and client libraries
- Client libraries in popular languages make it easy to integrate with existing apps:
- Python SDK for scripting and orchestration automation
- TypeScript/JavaScript SDK for web apps and services
- Potentially other languages as the community grows
- The SDKs provide:
- Methods to create and run workflows
- Helpers to connect to LLM adapters and vector stores
- Utilities to handle authentication and error handling
Code samples
- Basic flow with the Python SDK:
- from meta_ai_orchestrator import Client
- client = Client(api_key="...")
- resp = client.run_workflow("customer-support-qa", inputs={"question": "How can I track my order?"})
- print(resp["answer"])
- Basic flow with the TypeScript SDK:
- import { Client } from "meta-ai-orchestrator-sdk"
- const client = new Client({ apiKey: "..." })
- const result = await client.runWorkflow("ticket-resolution", { subject: "...", body: "..." })
- console.log(result.answer)
Documentation
- The docs site contains:
- API references
- Quickstart guides
- Tutorials for building RAG pipelines
- Tutorials for adding new LLM adapters
- Security and governance guides
Telemetry and metrics
- The platform ships with an integrated telemetry layer. It collects:
- Request latency per workflow step
- Queue depth and throughput for the orchestrator
- Error rates per adapter and per workflow
- Resource usage across containers and nodes
- Metrics are exposed to Prometheus-compatible endpoints and can be scraped by Grafana dashboards.
Tracing
- Distributed tracing helps you understand end-to-end flow across services.
- Each workflow step emits trace spans with identifiers to correlate events across components.
- Traces help you pinpoint where delays occur and how data moves through the system.
Logs
- Structured logs provide context for operations, errors, and important state transitions.
- Logs can be aggregated to central logging backends and retained for compliance purposes.
Dashboards
- Prebuilt dashboards present at-a-glance views of latency, throughput, and health.
- Dashboards are customizable to align with your organization’s KPIs and SLAs.
Performance considerations
- The system is designed to minimize end-to-end latency through parallelism and efficient data retrieval.
- Bottlenecks are typically model response times or vector store lookups; the architecture enables independent scaling of these components.
- You can tune the system by adjusting timeouts, concurrent execution limits, and batching strategies.
Reliability
- The orchestrator includes failure handling strategies such as timeouts, retries, circuit breakers, and fallback paths.
- It supports graceful degradation, where non-critical steps can be skipped when a component is unavailable.
- Observability data is essential to maintain reliability; ensure telemetry backends are properly configured.
- Secrets management: Use a secure store for credentials and tokens. Never embed secrets in code.
- RBAC: Role-based access controls govern who can deploy, modify, or execute workflows.
- Audit trails: All changes to configurations and workflow executions are logged for compliance.
- Data security: Access to internal data must respect data governance policies and data residency requirements.
- Network security: Use TLS for all communications and enforce strict firewall rules between components.
- Secrets rotation: Implement automated rotation of credentials and keys with minimal downtime.
- Compliance: Ensure workflows and data handling align with applicable regulations.
Development workflow
- Contribute code in isolated branches and submit pull requests for review.
- Run unit tests locally to verify correctness before pushing changes.
- Use the provided CI pipeline to run integration tests and ensure compatibility with target environments.
Testing strategy
- Unit tests verify individual components in isolation.
- Integration tests exercise interactions between modules, including LLM adapters and the RAG pipeline.
- End-to-end tests simulate real-world scenarios to validate workflow correctness and telemetry capture.
Quality gates
- Tests, static analysis, and code style checks form the quality gate.
- Performance tests simulate workloads to ensure latency and throughput targets are met.
Debugging
- Debugging tools and logs help identify issues in orchestration, data retrieval, and model outputs.
- The observability stack makes it easier to see where delays occur.
Documentation
- Documentation is kept up to date with examples, configuration details, and troubleshooting guides.
- Follow clear, concise naming for modules, functions, and variables.
- Write tests for new features and edge cases.
- Document new modules and public APIs with examples.
- Keep dependencies to a minimum and prefer stable, well-supported libraries.
- Respect backward compatibility where possible and plan for deprecation gracefully.
We welcome collaboration. If you want to contribute, follow these guidelines:
- Start with the issues or roadmap to find a good first task.
- Create a feature branch with a descriptive name.
- Open a pull request with a clear description of what you changed and why.
- Include tests that cover the new behavior.
- Update or add documentation to reflect changes.
License and attribution
- The project uses an open-source license that aligns with the community and enterprise use cases.
- We value attribution and acknowledge contributors in release notes and documentation.
Community guidelines
- Be respectful and constructive in discussions.
- Share findings and resources that help others learn.
- Respect privacy and security constraints when sharing data or code.
- Expand multi-LLM routing policies to include more dynamic model selection.
- Improve RAG pipelines by integrating more retrievers and context enrichment strategies.
- Add advanced prompt engineering templates and prompt evaluation tooling.
- Enhance observability with more granular traces and anomaly detection.
- Expand deployment options for edge and offline environments.
- Integrate with more vector stores, including new and upcoming options.
- Strengthen security features with policy enforcement points and automated audits.
- Support more languages and localization in documentation.
We are committed to iterative improvement and value community input to shape the future of enterprise AI orchestration.
Q: What is the primary use case for Meta AI Orchestrator? A: It coordinates multiple LLMs and RAG pipelines to solve complex tasks with data-backed reasoning, while offering observability, scalability, and governance.
Q: Do I need to run all components in one cluster? A: No. The architecture supports a modular deployment with independent services that can scale as needed.
Q: Can I use this in production? A: Yes. The system is designed for enterprise deployments, with security, governance, and observability features suitable for production use.
Q: How do I add a new LLM provider? A: Implement a new adapter following the existing adapter interface. The core provides a clean abstraction to plug in your own provider.
Q: Where can I find tutorials and examples? A: The documentation site and repository contain tutorials, examples, and reference configurations to get you started quickly.
Q: What if I need help or want to report a bug? A: Open an issue in the repository or join the community chat if available. Provide a minimal reproduction and logs to help triage.
Q: How do I upgrade to a new release? A: Use the Releases page to obtain the latest assets and follow the upgrade instructions in the release notes. The page is accessible at the link above and used again here for convenience: https://github.com/ttcs60cntt3/meta-ai-orchestrator/releases
Q: Is there a security roadmap? A: Yes. Security and governance are core to the project. Review the security guidelines in the documentation and follow best practices for authentication and data handling.
Q: How do I contribute to testing? A: Run the unit and integration tests locally, and contribute test fixtures for common workflows to help coverage improve.
Q: Can I deploy without Kubernetes? A: Yes, you can run a standalone variant for testing and small-scale workloads. For large-scale deployments, Kubernetes is recommended for reliability and scaling.
Meta AI Orchestrator is released under an open-source license. See LICENSE file for details. The project encourages reuse and contribution in accordance with the license terms.
- Contributions from the early adopters and the community
- Partners and model providers who helped shape the integration approach
- Open-source projects that inspired the design and tooling
- Teams that helped test and validate performance and reliability under real workloads
This section supports readers who want quick visual cues about the architecture and workflow:
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Architecture overview badges:
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Flow diagrams can be embedded using lightweight diagrams or simple SVG illustrations. Use the existing repository structure to add visual assets and diagrams that explain the end-to-end data flow, including:
- Client -> Orchestrator -> LLM adapters
- RAG layer -> Context retrieval
- Output formatting and delivery
- Telemetry and observability loop
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Sample prompt templates and example workflows can be stored under docs/prompts as YAML or JSON templates to help teams begin quickly.
The Releases page hosts executable assets and installers designed to set up the platform in a given environment. If you encounter issues, check the asset notes and the accompanying release notes. For quick access, the link is integrated above in the badge and repeated in the Downloads section. The Releases page is the primary source of truth for installers, upgrade notes, and compatibility matrices. If the link stops working, you should visit the repository’s Releases section to locate the latest release notes and assets.