orchestr

[!CAUTION] Alpha software. This package is part of a broader effort by Ian Flores Siaca to develop proper AI infrastructure for the R ecosystem. It is under active development and should not be used in production until an official release is published. APIs may change without notice.

Graph-based multi-agent workflow orchestration for R. Built on ellmer for LLM chat and optionally securer for sandboxed code execution.

When to use orchestr

Use orchestr when a single ellmer chat isn’t enough – when you need multi-step reasoning (ReAct loops), parallel tool execution, supervisor-routed agent teams, or persistent memory across turns. If your workflow fits in one LLM call, use ellmer directly. If it needs orchestration, use orchestr.

Part of the secure-r-dev Ecosystem

orchestr is part of a 7-package ecosystem for building governed AI agents in R:

                    ┌─────────────┐
                    │   securer    │
                    └──────┬──────┘
          ┌────────────────┼─────────────────┐
          │                │                  │
   ┌──────▼──────┐  ┌─────▼──────┐  ┌───────▼────────┐
   │ securetools  │  │ secureguard│  │ securecontext   │
   └──────┬───────┘  └─────┬──────┘  └───────┬────────┘
          └────────────────┼─────────────────┘
                    ┌──────▼────────┐
                    │>>> orchestr<<<│
                    └──────┬────────┘
          ┌────────────────┼─────────────────┐
          │                                  │
   ┌──────▼──────┐                    ┌──────▼──────┐
   │ securetrace  │                   │ securebench  │
   └─────────────┘                    └─────────────┘

orchestr is the orchestration hub that wires agents into workflows. It sits below the tool/guardrail/context layer and above the observability and benchmarking layers, coordinating agents that use securer for execution, secureguard for safety, and securecontext for memory.

Package	Role
securer	Sandboxed R execution with tool-call IPC
securetools	Pre-built security-hardened tool definitions
secureguard	Input/code/output guardrails (injection, PII, secrets)
orchestr	Graph-based agent orchestration
securecontext	Document chunking, embeddings, RAG retrieval
securetrace	Structured tracing, token/cost accounting, JSONL export
securebench	Guardrail benchmarking with precision/recall/F1 metrics

Installation

# install.packages("pak")
pak::pak("ian-flores/orchestr")

Setup

orchestr uses ellmer for LLM access. You’ll need an API key for your chosen provider:

# For Anthropic (Claude)
Sys.setenv(ANTHROPIC_API_KEY = "your-key-here")

# For OpenAI
Sys.setenv(OPENAI_API_KEY = "your-key-here")

See ellmer’s documentation for all supported providers.

Features

• Agent – wraps an ellmer Chat with tools and optional secure execution
• GraphBuilder – fluent API for constructing agent graphs with typed state
• Conditional routing – route between agents based on state
• Human-in-the-loop – interrupt graph execution for human approval
• State management – typed state schemas with reducers, snapshots
• Memory & checkpointing – persist state across invocations
• Visualization – render graphs as Mermaid diagrams

Quick Start

Single Agent (ReAct)

The react_graph() function wraps a single agent with state management and checkpointing. ellmer’s Chat class handles tool call loops internally – when an agent has registered tools, they are executed automatically during $chat(). The graph wraps this with state management and checkpointing.

library(orchestr)
library(ellmer)

analyst <- agent("analyst", chat = chat_anthropic(
  system_prompt = "You analyze data. Use your tools to compute results."
))
graph <- react_graph(analyst, max_iterations = 5)
result <- graph$invoke(list(messages = list("What is the mean of c(1,2,3,4,5)?")))

Agent Pipeline

pipeline_graph() chains agents in sequence. Each agent processes the state and passes it to the next. One LLM call per agent in the pipeline.

drafter <- agent("drafter", chat = chat_anthropic(
  system_prompt = "Write a short draft on the given topic."
))

editor <- agent("editor", chat = chat_anthropic(
  system_prompt = "Improve the following draft."
))

pipeline <- pipeline_graph(drafter, editor)
result <- pipeline$invoke(list(messages = list("Benefits of open source.")))

Supervisor Routing

supervisor_graph() creates a supervisor that routes tasks to specialized workers. The supervisor decides which worker to invoke (or to finish) by calling an automatically injected route tool.

supervisor <- agent("supervisor", chat = chat_anthropic(
  system_prompt = "You coordinate workers to solve tasks."
))

math_worker <- agent("math", chat = chat_anthropic(
  system_prompt = "You are a math expert. Solve math problems step by step."
))

writing_worker <- agent("writing", chat = chat_anthropic(
  system_prompt = "You are a writing expert. Help with writing tasks."
))

graph <- supervisor_graph(
  supervisor = supervisor,
  workers = list(math = math_worker, writing = writing_worker),
  max_iterations = 10
)
result <- graph$invoke(list(messages = list("Calculate the integral of x^2 from 0 to 1.")))

Cost Awareness

Each node in a graph that calls an LLM makes an API request. Be mindful of costs:

• react_graph(): The agent runs once, with ellmer handling any tool calls internally
• pipeline_graph(): One LLM call per agent in the pipeline
• supervisor_graph(max_iterations = 50): Up to 50+ LLM calls (supervisor routing + worker execution). Start with low max_iterations values
• Use verbose = TRUE when compiling graphs to see execution flow: compile(verbose = TRUE)

Documentation

• Getting Started
• Multi-Agent Workflows
• Secure Execution

Contributing

Contributions are welcome! Please file issues on GitHub and submit pull requests.

License

MIT