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Agent Backends

Configured via --backend flag or AGENT_BACKEND env var. Default: claudecode.

Backend Architecture Auth Status
claudecode Single claude -p subprocess Subscription or API key stable
pydantic Parallel sub-agents via pydantic-ai + summary agent API key required stable
codex Single codex exec in sandbox OAuth or API key stable
deepagents LLM orchestrator + subagents via langchain API key required unstable

How each backend works

pydantic claudecode codex deepagents
Prompts 1 agent per prompt, parallel All concatenated into system prompt All concatenated into system prompt 1 subagent per prompt, orchestrator coordinates
Diff in user message Yes No — reads files via tools No — reads files via sandbox Yes
File tools read_file, list_dir, grep (Python) Read, Grep, Glob, Bash(git...) (built-in) Sandbox filesystem access read_file, ls, grep, glob (via deepagents)
Summary Separate summary agent call Single response Single response Orchestrator synthesizes
Output SubAgentFindings → merged → determine_recommendation() ReviewResult via --json-schema ReviewResult via --output-schema submit_review tool captures ReviewResult

When to use which

Scenario Backend Why
Local review (default) claudecode Git tools (log, blame, show), explores beyond diff, no API key needed
CI pipeline pydantic Parallel, structured, predictable cost
OpenAI sandbox codex Isolated exec, file tools
Experimental deepagents Orchestrator pattern — unstable, may skip submit_review

Detailed docs: - Pydantic AI - Claude Code - Codex - DeepAgents

Adding a new backend: see Adding backends.