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* finish is working * start reworking main_goal * remove main_goal from microagents * remove main_goal from other agents * fix issues * revert codeact line * make plan a subclass of task * fix frontend for new plan setup * lint * fix type * more lint * fix build issues * fix codeact mgs * fix edge case in regen script * fix task validation errors * regenerate integration tests * fix up tests * fix sweagent * revert codeact prompt * update integration tests * update integration tests * handle loading state * Update agenthub/codeact_agent/codeact_agent.py Co-authored-by: Engel Nyst <enyst@users.noreply.github.com> * Update opendevin/controller/agent_controller.py Co-authored-by: Engel Nyst <enyst@users.noreply.github.com> * Update agenthub/codeact_agent/codeact_agent.py Co-authored-by: Engel Nyst <enyst@users.noreply.github.com> * Update opendevin/controller/state/plan.py Co-authored-by: Engel Nyst <enyst@users.noreply.github.com> * update docs * regenerate tests * remove none from state type * revert test files * update integration tests * rename plan to root_task * revert plugin perms * regen integration tests * tweak integration script * prettier * fix test * set workspace up for regeneration * regenerate tests * Change directory of copy * Updated tests * Disable PlannerAgent test * Fix listen * Updated prompts * Disable planner again * Make codecov more lenient * Update agenthub/README.md * Update opendevin/server/README.md * re-enable planner tests * finish top level tasks * regen planner * fix root task factory --------- Co-authored-by: Engel Nyst <enyst@users.noreply.github.com> Co-authored-by: Xingyao Wang <xingyao6@illinois.edu> Co-authored-by: Graham Neubig <neubig@gmail.com> Co-authored-by: Boxuan Li <liboxuan@connect.hku.hk>
OpenDevin Server
This is a WebSocket server that executes tasks using an agent.
Install
Follow the instructions in the base README.md to install dependencies and set up.
Start the Server
uvicorn opendevin.server.listen:app --reload --port 3000
Test the Server
You can use websocat to test the server: https://github.com/vi/websocat
websocat ws://127.0.0.1:3000/ws
{"action": "start", "args": {"task": "write a bash script that prints hello"}}
Supported Environment Variables
LLM_API_KEY=sk-... # Your OpenAI API Key
LLM_MODEL=gpt-3.5-turbo # Default model for the agent to use
WORKSPACE_BASE=/path/to/your/workspace # Default path to model's workspace
API Schema
There are two types of messages that can be sent to, or received from, the server:
- Actions
- Observations
Actions
An action has three parts:
action: The action to be takenargs: The arguments for the actionmessage: A friendly message that can be put in the chat log
There are several kinds of actions. Their arguments are listed below. This list may grow over time.
initialize- initializes the agent. Only sent by client.model- the name of the model to usedirectory- the path to the workspaceagent_cls- the class of the agent to use
start- starts a new development task. Only sent by the client.task- the task to start
read- reads the content of a file.path- the path of the file to read
write- writes the content to a file.path- the path of the file to writecontent- the content to write to the file
run- runs a command.command- the command to runbackground- if true, run the command in the background
kill- kills a background commandid- the ID of the background command to kill
browse- opens a web page.url- the URL to open
recall- searches long-term memoryquery- the query to search for
think- Allows the agent to make a plan, set a goal, or record thoughtsthought- the thought to record
finish- agent signals that the task is completed
Observations
An observation has four parts:
observation: The observation typecontent: A string representing the observed dataextras: additional structured datamessage: A friendly message that can be put in the chat log
There are several kinds of observations. Their extras are listed below. This list may grow over time.
read- the content of a filepath- the path of the file read
browse- the HTML content of a urlurl- the URL opened
run- the output of a commandcommand- the command runexit_code- the exit code of the command
recall- the result of a searchquery- the query searched for
chat- a message from the user