From 2f11676d426d9eb5660f8fe9589e19b3d88fbf67 Mon Sep 17 00:00:00 2001
From: Farouk Adeleke <farouk@modaic.dev>
Date: Sun, 11 Jan 2026 00:44:31 +0000
Subject: [PATCH] Update README.md

---
 README.md | 18 +++++++++---------
 1 file changed, 9 insertions(+), 9 deletions(-)

diff --git a/README.md b/README.md
index d355e52..055a8a4 100644
--- a/README.md
+++ b/README.md
@@ -7,7 +7,7 @@ license: MIT
 A packaged version of an open source red-teaming framework that uses the power of [DSPy](https://github.com/stanfordnlp/dspy) to red-team language models through automated attack generation and optimization.
 
 ## Quick Start
-Run this agent within a new project:
+Run this program within a new project:
 
 ```bash
 uv init
@@ -31,9 +31,9 @@ import json
 import dspy
 from tqdm import tqdm
 from dspy.teleprompt import MIPROv2
-from modaic import AutoAgent
+from modaic import AutoProgram
 
-redteam_agent = AutoAgent.from_precompiled("farouk1/redteam", config_options={"num_layers": 3})
+redteam = AutoProgram.from_precompiled("farouk1/redteam", config_options={"num_layers": 3})
 
 def main():
     with open("advbench_subset.json", "r") as f:
@@ -50,7 +50,7 @@ def main():
 
     litellm.cache = None
     for ex in tqdm(trainset, desc="Raw Input Score"):
-        base_score += redteam_agent.attack_program.metric(
+        base_score += redtean.attack_program.metric(
             intent=ex.harmful_intent, attack_prompt=ex.harmful_intent, eval_round=True
         )
     base_score /= len(trainset)
@@ -58,11 +58,11 @@ def main():
     print(f"Baseline Score: {base_score}")
 
     # evaluating architecture with no compilation
-    attacker_prog = redteam_agent
+    attacker_prog = redteam.attack_program
     print(f"\n--- Evaluating Initial Architecture ---")
-    redteam_agent.attack_program.eval_program(attacker_prog, trainset)
+    redteam.eval_program(attacker_prog, trainset)
 
-    optimizer = MIPROv2(metric=redteam_agent.attack_program.metric, auto=None)
+    optimizer = MIPROv2(metric=redteam.attack_program.metric, auto=None)
     best_prog = optimizer.compile(
         attacker_prog,
         trainset=trainset,
@@ -74,7 +74,7 @@ def main():
 
     # evaluating architecture DSPy post-compilation
     print(f"\n--- Evaluating Optimized Architecture ---")
-    redteam_agent.attack_program.eval_program(best_prog, trainset)
+    redteam.attack_program.eval_program(best_prog, trainset)
 
 if __name__ == "__main__":
     main()
@@ -82,7 +82,7 @@ if __name__ == "__main__":
 
 ### Configuration
 
-The red-team agent can be configured via the `config_options` parameter in `AutoAgent.from_precompiled`:
+The red-team program can be configured via the `config_options` parameter in `AutoProgram.from_precompiled`:
 
 ```python
 class RedTeamConfig(PrecompiledConfig):