AgentTester

Send a single prompt to multiple coding agents running in parallel and compare the results. Each agent works in its own git worktree on a separate branch so they never interfere with each other. Optionally, configure LLM evaluators to review each agent's diff and drive an iterative refinement loop.

How It Works

1. You provide a prompt and select agents
2. AgentTester creates a git worktree + branch for each agent from the current HEAD
3. All agents run concurrently, each in its own worktree
4. Agent output streams to the terminal with colored prefixes
5. A markdown comparison report is generated with diff stats and timing
6. Worktrees are cleaned up (branches are preserved for git diff)

Branches are named agenttester/<agent-name>/<run-name> so you can compare results:

git diff agenttester/claude/auth-refactor agenttester/aider/auth-refactor

For agent-tester run, when no --name is given, a slug is derived from the first six words of the prompt plus a short hash (e.g. add-unit-tests-for-the-auth-a3f2c1).

Install

uv pip install -e ".[dev]"

Configuration

Copy config.example.yaml to agent-tester.yaml (or agent-tester.yml) in your target repo to customize agents. Built-in presets are available for claude, aider, and codex.

Config file discovery

Auto-detected local config files must use a .yml or .yaml extension. The following names are checked in order:

agent-tester.yaml
agent-tester.yml
.agent-tester.yaml
.agent-tester.yml

You can also pass a config file explicitly:

agent-tester run "Fix the bug" --agents claude --config /path/to/myconfig

A global config at ~/.config/agenttester/config.yml is merged automatically. Local project config takes precedence over global, which takes precedence over built-in presets.

Reports

Reports are written to ~/.config/agenttester/projects/<repo-name>/ by default. Override per-project:

Local config (agent-tester.yaml in your repo):

reports_dir: ~/my-reports/myproject

Global config (~/.config/agenttester/config.yml):

projects:
  myproject:
    reports_dir: ~/my-reports/myproject

Agent Settings

Agents are configured under an agents: block. Each entry defines a shell command to run.

Command placeholders

• {prompt} — replaced with the shell-escaped prompt text
• {prompt_file} — replaced with a path to a temp file containing the prompt
• If neither is present, the prompt is piped via stdin

Field	Description	Default
command	Shell command template	(required)
commit_style	auto (agent commits) or manual (agenttester commits)	auto
timeout	Max seconds before the agent is killed	600
env	Extra environment variables	{}

Skills

Skills are markdown instruction files prepended to every agent prompt. AgentTester ships with four built-in skills:

Skill	Description
editing.md	Permission to read and edit files freely; look for reusable code before writing new code
testing.md	Run the test suite and linter after making changes; don't mark complete until tests pass
git.md	Permitted git operations; never push to the default branch
bash.md	Permitted bash operations scoped to the worktree

Override any built-in or add new skills at two levels:

Global (~/.config/agenttester/skills/): applies to all projects.

Local (.agent-tester/skills/ inside your repo): applies to this project only.

A skill file with the same name as a built-in replaces it entirely. New filenames add additional instructions. Skills are output in priority order — built-ins first, global second, local last.

~/.config/agenttester/skills/testing.md   # overrides built-in testing skill globally
your-repo/.agent-tester/skills/testing.md # overrides for this project only
your-repo/.agent-tester/skills/style.md   # adds a new skill for this project

Authentication

Define a providers block to share credentials across evaluators and REPL model agents. Each provider type reads credentials from a standard environment variable automatically — no api_key_env required unless you want to override the default.

type	Description	Default env var	Install
openai	Any OpenAI-compatible endpoint (vLLM, etc.)	OPENAI_API_KEY	built-in
anthropic	Direct Anthropic Messages API	ANTHROPIC_API_KEY	built-in
bedrock	AWS Bedrock Converse API	BEDROCK_API_KEY (api_key mode)	built-in; pip install agenttester[aws] for boto3 modes
azure	Azure AI Foundry / Azure OpenAI Service	AZURE_OPENAI_API_KEY	built-in
vertex	GCP Vertex AI (OpenAI-compatible endpoint)	GOOGLE_API_KEY	built-in

Override the default for any provider or evaluator with api_key_env: MY_CUSTOM_VAR.

OpenAI-compatible

providers:
  my-openai:
    type: openai
    endpoint: http://localhost:8004
    # reads OPENAI_API_KEY automatically; set api_key_env to override

AWS Bedrock

Four auth modes via auth_method:

• auth_method: api_key — reads BEDROCK_API_KEY as Authorization: Bearer. Use with AWS Bedrock API keys or Bedrock-compatible proxies. No boto3 required.
• auth_method: profile — uses a named ~/.aws/config entry (SSO, assumed roles, etc.). Requires pip install agenttester[aws].
• auth_method: keys — reads aws_access_key_id_env / aws_secret_access_key_env. Requires pip install agenttester[aws].
• auth_method: default (default) — standard boto3 credential chain. Requires pip install agenttester[aws].

providers:
  # API key — reads BEDROCK_API_KEY; no boto3 required
  bedrock-apikey:
    type: bedrock
    region: us-east-1
    auth_method: api_key

  # Named AWS CLI profile (SSO, assumed roles, etc.)
  bedrock-sso:
    type: bedrock
    region: us-east-1
    auth_method: profile
    aws_profile: my-sso-profile

  # Explicit credentials from environment variables
  bedrock-keys:
    type: bedrock
    region: us-east-1
    auth_method: keys
    aws_access_key_id_env: MY_AWS_KEY_ID
    aws_secret_access_key_env: MY_AWS_SECRET
    aws_session_token_env: MY_AWS_TOKEN   # optional

  # Default boto3 credential chain
  bedrock-default:
    type: bedrock
    region: us-east-1

Azure AI Foundry

• auth_method: api_key (default) — reads AZURE_OPENAI_API_KEY and sends it as an api-key header.
• auth_method: cli — runs az account get-access-token for an Entra ID Bearer token. Requires the Azure CLI and az login.

providers:
  my-azure:
    type: azure
    endpoint: https://my-resource.openai.azure.com
    # reads AZURE_OPENAI_API_KEY automatically; use auth_method: cli for Entra ID

GCP Vertex AI

• auth_method: api_key (default) — reads GOOGLE_API_KEY as Authorization: Bearer.
• auth_method: cli — runs gcloud auth print-access-token. Requires the Google Cloud SDK and gcloud auth login.

providers:
  my-vertex:
    type: vertex
    endpoint: https://us-central1-aiplatform.googleapis.com/v1beta1/projects/my-project/locations/us-central1/endpoints/openapi
    # reads GOOGLE_API_KEY automatically; use auth_method: cli for ADC

CLI tokens (Azure and GCP) are cached for 55 minutes to avoid extra subprocesses on every request.

Evaluators and REPL models

Providers are referenced by name in evaluators: (for diff review) and models: (for the REPL):

providers:
  anthropic:
    type: anthropic
  my-azure:
    type: azure
    endpoint: https://my-resource.openai.azure.com
  bedrock-sso:
    type: bedrock
    region: us-east-1
    auth_method: profile
    aws_profile: my-sso-profile

evaluators:
  - name: claude
    provider: anthropic
    model: claude-opus-4-7
  - name: gpt-4o
    provider: my-azure
    model: gpt-4o

evaluation:
  inject_raw_reports: false   # true → send raw reports instead of aggregate
  max_aggregate_tokens: 2000  # aggregate is summarized before injection if too long

models:
  claude-bedrock:
    provider: bedrock-sso
    model: anthropic.claude-3-5-sonnet-20241022-v2:0
  azure-gpt4o:
    provider: my-azure
    model: gpt-4o
  local-llm:
    endpoint: http://localhost:8001
    model: meta-llama/Meta-Llama-3-8B-Instruct
    api_key_env: MY_KEY   # optional; overrides the default OPENAI_API_KEY

The REPL

Open an interactive multi-model session:

agent-tester                               # open REPL (auto-discovers agent-tester.yaml)
agent-tester --resume <SESSION_ID>         # resume a previous session
agent-tester repl --config custom.yaml    # explicit config path
agent-tester repl --workdir /path/to/repo # enable tool use with a target repo

The REPL fans out each prompt to all configured models in parallel and maintains separate conversation history per model. Tab-completes model names after @ and slash-commands. Prompt history is persisted across invocations in ~/.config/agenttester/repl_history.

Slash commands

Command	Description
/reset	Clear conversation history for all models
/status	Show which models are running or idle
/stop [@model …]	Cancel a running model. Without a tag, stops all busy models.
/interrupt [@model …] <message>	Cancel and immediately re-dispatch with <message>. Without a tag, targets all busy models.
/report	Show each model's git commits, diff stats, and token usage. Report data is persisted in the session file (~/.config/agenttester/sessions/<name>.yaml).
/evaluate [m1,m2,…]	Cross-evaluate: each model reviews the others' work. Evaluation documents are saved to .agenttester/evaluations/<session>/. Eval results are also persisted in the session file.
/iterate <prompt>	After /evaluate, inject peer evaluations as context and send an iteration prompt. Requires y confirmation.

Use @modelname message to address a single model. Use exit or Ctrl-C to quit.

Sessions

A session UUID is generated automatically and printed at startup:

Session: 3f2a1b4c-8d9e-4f0a-b1c2-d3e4f5a6b7c8
...
bye  —  agent-tester --resume 3f2a1b4c-8d9e-4f0a-b1c2-d3e4f5a6b7c8

Each model's conversation history is saved on exit and restored on resume.

agent-tester sessions          # list sessions, newest first
agent-tester sessions --yaml   # machine-readable YAML

Each session entry shows its date, start/end times, and associated branches with their availability (local, remote, local,remote, or unknown).

Watcher

The main REPL shows brief per-model status. To see the full context — every prompt, tool call, and response — open a second terminal:

agent-tester watch --session <SESSION_ID> --model <MODEL_NAME>

The watcher tail-follows the model's event log and renders each event with Rich as it arrives. Open one watcher per model while keeping the main REPL for sending prompts.

Tool use and branches

Pass --workdir <dir> to enable an agent loop for OpenAI-compatible and Anthropic models. Each model gains access to bash, read_file, write_file, git_clone, git_commit, and git_push tools. When --workdir is a git repo, each model works in its own clone under .agenttester/worktrees/<session-id>/ on a dedicated branch.

Before the first prompt is dispatched, all models negotiate a branch name (silent LLM calls that don't affect conversation history). The agreed name is combined with a short session hash:

agenttester/<model-name>/<8-char-session>-<feature-name>

The branch is created lazily on the first write and reused for the session. On resume, previously negotiated branch names are restored so models continue on the same branches without re-negotiating.

If a model hits its output token limit mid-generation, the loop automatically sends "Continue from where you left off." and appends the continuation.

Use --pem <path> to authenticate git operations over SSH:

agent-tester repl \
  --session sprint-42 \
  --workdir ~/dev/my-project \
  --pem ~/.ssh/deploy_key

Evaluation

Each evaluator independently critiques every model's diff for accuracy, readability, code smells, and correctness. An aggregate assessment is synthesized across evaluators and shown in the terminal; raw per-evaluator reports are preserved in the markdown report.

Run /evaluate once models have committed work, then /iterate <prompt> to send the peer evaluations back as context for the next round. New commits are appended to the same branch so git log shows the full progression.

Cleanup

Remove branches from old sessions interactively:

agent-tester cleanup               # scans CWD repo for agenttester/* branches
agent-tester cleanup --workdir /path/to/repo

The command walks through two phases — select sessions to delete entirely, then pick individual model branches from remaining sessions — then asks whether to delete locally, remotely, or both. Session records (history, reports, eval results) are preserved unless you explicitly approve their deletion.

Docker

Provider API keys are forwarded automatically from the host environment — set any of ANTHROPIC_API_KEY, AZURE_OPENAI_API_KEY, GOOGLE_API_KEY, BEDROCK_API_KEY, or the standard AWS_* variables before running.

# Open REPL against the current directory
docker compose run --rm agent-tester repl --workdir /repo

# Open REPL against a different repo
REPO_PATH=/path/to/repo docker compose run --rm agent-tester repl --workdir /repo

# Pass a custom config
REPO_PATH=/path/to/repo docker compose run --rm agent-tester repl \
  --workdir /repo --config /repo/agent-tester.yaml

CLI Reference

Command	Description
agent-tester / agent-tester repl	Open the interactive REPL
agent-tester run	Run shell agents against a prompt in parallel
agent-tester sessions	List previous REPL sessions
agent-tester watch	Stream a model's event log from a running or past session
agent-tester cleanup	Interactively prune agenttester branches from a repo
agent-tester agents	List configured agents and built-in presets

Use --help on any subcommand for full options.