This directory contains the core components of OpenDevin.

This diagram provides an overview of the roles of each component and how they communicate and collaborate.

OpenDevin System Architecture Diagram (July 4, 2024)

The key classes in OpenDevin are:

• LLM: brokers all interactions with large language models. Works with any underlying completion model, thanks to LiteLLM.
• Agent: responsible for looking at the current State, and producing an Action that moves one step closer toward the end-goal.
• AgentController: initializes the Agent, manages State, and drive the main loop that pushes the Agent forward, step by step
• State: represents the current state of the Agent's task. Includes things like the current step, a history of recent events, the Agent's long-term plan, etc
• EventStream: a central hub for Events, where any component can publish Events, or listen for Events published by other components
  • Event: an Action or Observeration
    • Action: represents a request to e.g. edit a file, run a command, or send a message
    • Observation: represents information collected from the environment, e.g. file contents or command output
• Runtime: responsible for performing Actions, and sending back Observations
  • Sandbox: the part of the runtime responsible for running commands, e.g. inside of Docker
• Server: brokers OpenDevin sessions over HTTP, e.g. to drive the frontend
  • Session: holds a single EventStream, a single AgentController, and a single Runtime. Generally represents a single task (but potentially including several user prompts)
  • SessionManager: keeps a list of active sessions, and ensures requests are routed to the correct Session

Here's the basic loop (in pseudocode) that drives agents.

while True:
  prompt = agent.generate_prompt(state)
  response = llm.completion(prompt)
  action = agent.parse_response(response)
  observation = runtime.run(action)
  state = state.update(action, observation)

In reality, most of this is achieved through message passing, via the EventStream. The EventStream serves as the backbone for all communication in OpenDevin.

flowchart LR
  Agent--Actions-->AgentController
  AgentController--State-->Agent
  AgentController--Actions-->EventStream
  EventStream--Observations-->AgentController
  Runtime--Observations-->EventStream
  EventStream--Actions-->Runtime
  Frontend--Actions-->EventStream

The Runtime class is abstract, and has a few different implementations:

• We have a LocalRuntime, which runs commands and edits files directly on the user's machine
• We have a DockerRuntime, which runs commands inside of a docker sandbox, and edits files directly on the user's machine
• We have an E2BRuntime, which uses e2b.dev containers to sandbox file and command operations