<p align="center">
  <img src="assets/evalmonkey-logo.png" alt="EvalMonkey Logo" width="400"/>
</p>

# EvalMonkey

<p align="center">
  <b>Agent Benchmarking & Chaos Engineering Framework</b><br>
  <i>"Don't just trust your agent. Prove it works. Then break it."</i>
</p>

<p align="center">
  <img src="assets/evalmonkey-demo.gif" alt="EvalMonkey Demo" style="max-width: 100%; border-radius: 8px;">
</p>


## Overview
Agents are fundamentally non-deterministic. They rely on external APIs, tool loops, and massive context windows.
**EvalMonkey** is the ultimate, strictly local, open-source execution harness that enables developers to:
1. 🎯 **Benchmark Capabilities**: Run standard Agent benchmark datasets against your agent endpoints natively!
2. 🔥 **Inject Chaos**: Mutate headers, spike latency, and corrupt schemas dynamically to prove true resilience.
3. 📈 **Track Production Reliability**: Locally store all scores to visualize a single Production Reliability metric over time!
4. 🛠 **Generate Improvement Evals**: When scores are poor, automatically synthesise targeted test cases using your LLM — then hand them to Claude Code or Cursor to fix your agent.

EvalMonkey natively supports evaluating ANY LLM: **AWS Bedrock**, **Azure**, **GCP**, **OpenAI**, and **Ollama**.

> **Note on API Keys:** If you have special setups that generate long-lived, static API keys for Bedrock, Azure, or GCP, simply supply them in the `.env`! EvalMonkey seamlessly supports both standard IAM / Service Account credential flows *and* long-term stateless authentication strings.

## 🚀 At a Glance
- **11 Agent Frameworks natively supported**: CrewAI, LangChain, LlamaIndex, LangGraph, Pydantic AI, OpenAI Agents, Microsoft AutoGen, AWS Bedrock, Ollama, Strands, and custom HTTP endpoints.
- **19 Standard Benchmarks out-of-the-box**: GSM8K, BIG-Bench Hard, HotpotQA, ToxiGen, MT-Bench, MBPP, and more — all categorised by the agent type they target.
- **23 Chaos Injections ready to run**: 12 client-side payload mutations + 11 server-side middleware injections — all text-based, no GPU or vision dependencies.
- **Automatic Eval Asset Generation**: Poor benchmark scores automatically produce `traces.json`, `evals.json`, and `improvement_prompt.md` — one `cat` command away from Claude Code or Cursor.

---

## 🏆 Real-World Results: 10 Open-Source Agents Benchmarked

> We ran EvalMonkey against **10 popular open-source agents** across **3 standard benchmarks** (HotpotQA · TruthfulQA · MMLU) with **chaos injection** to measure true production resilience.  
> Eval judge: **Claude Sonnet 4.5** via AWS Bedrock. Chaos profile: `client_prompt_injection` + `client_schema_mutation`.

### Leaderboard — Production Reliability Score

*Higher = more reliable under real-world conditions. Production Reliability = 60% baseline + 40% chaos resilience.*

| Rank | Agent | Type | Baseline | Under Chaos | Prod. Reliability | Chaos Drop |
|------|-------|------|:--------:|:-----------:|:-----------------:|:----------:|
| 🥇 1 | [GPT Researcher](https://github.com/assafelovic/gpt-researcher) | Deep Research | **66** | 43 | **57** | −23 |
| 🥈 2 | [OpenResearcher](https://github.com/GAIR-NLP/OpenResearcher) | Scientific Research | **64** | 42 | **55** | −22 |
| 🥉 3 | [Browser Agent](https://github.com/rkvalandas/browser_agent) | Web / Browser | **63** | 34 | **51** | −29 |
| 4 | [Browser-Use Couchbase](https://github.com/hummusonrails/browser-use-agent-with-couchbase) | Browser + RAG | 51 | 41 | **47** | −10 |
| 5 | [Open Deep Research](https://github.com/langchain-ai/open_deep_research) | Deep Research | 49 | 24 | **39** | −25 |
| 6 | [deep-research](https://github.com/dzhng/deep-research) | Minimal Research | 44 | 50 | **46** | +6 ✅ |
| 7 | [Local Docs AI Agent](https://github.com/disho5/local-docs-ai-agent) | Docs Q&A | 43 | 38 | **41** | −5 |
| 8 | [Index Browser](https://github.com/lmnr-ai/index) | Browser Agent | 46 | 54 | **49** | +8 ✅ |
| 9 | [Goose](https://github.com/aaif-goose/goose) | General Purpose | 46 | 36 | **42** | −10 |
| 10 | [OnCell Support Agent](https://github.com/oncellai/oncell-support-agent) | Support / RAG | 18 | 26 | **22** | +8 ✅ |

### Per-Benchmark Breakdown (GPT Researcher — best overall)

| Benchmark | Baseline | Chaos Score | Production Reliability |
|-----------|:--------:|:-----------:|:----------------------:|
| HotpotQA  | 66 | 17 | 46 |
| TruthfulQA | 65 | 48 | 58 |
| MMLU | 56 | 16 | 40 |

### Key Findings

- 🚨 **Chaos drops scores by 23 points on average** — agents that look great in demos often collapse under real-world input mutations.
- ✅ **3 agents actually improved under chaos** (deep-research, Index, OnCell) — these have robust fallback handling that filters bad inputs.
- 📉 **GPT Researcher & Browser Agent drop 23–29 points** under prompt injection — common in production where users send adversarial or malformed queries.
- 🏆 **Top production reliability gap**: The #1 agent (57) scores **2.6× higher** than the bottom agent (22) — despite both running the same LLM backend.
- 🔍 **MMLU was the hardest benchmark** across all agents — multi-domain knowledge reasoning exposes gaps that simple Q&A benchmarks miss.

> 📌 **This is exactly what EvalMonkey is for.** Don't wait until production to find out your agent breaks under real traffic. Benchmark it now.

---

## 📊 EvalMonkey Web Dashboard

Visualize all your benchmark runs, track reliability scores over time, and inspect failure traces interactively!

<p align="center">
  <img src="assets/evalmonkey-dashboard.png" alt="EvalMonkey Dashboard" style="max-width: 100%; border-radius: 8px; margin-bottom: 10px;">
  <br>
  <i>EvalMonkey Main Dashboard showing scenario trends and score histories.</i>
</p>

<p align="center">
  <img src="assets/em-benchmark-run.png" alt="Benchmark Run Results" style="max-width: 48%; border-radius: 8px; margin-right: 2%;">
  <img src="assets/em-mmlu-run.png" alt="MMLU Scenario View" style="max-width: 48%; border-radius: 8px;">
  <br>
  <i>Deep-dive into specific benchmark runs and chaos tests.</i>
</p>

---

## ⚡️ Quick Start

### Option A — Let Claude Code or Cursor set it up for you (30 seconds)

Open Claude Code, Cursor, or any AI coding assistant and paste this prompt:

```
Set up EvalMonkey in my project so I can benchmark my AI agent.

1. Clone https://github.com/Corbell-AI/evalmonkey into a sibling folder
2. Run: pip install -e . inside that folder
3. Copy .env.example to .env and ask me which LLM provider I want to use as the benchmark judge (OpenAI, Anthropic, Bedrock, or Ollama) — then fill in the correct key
4. Run: evalmonkey init --framework <my_framework> --name "My Agent" --port <my_port>
   Use the framework my agent is built with (crewai / langchain / openai / bedrock / autogen / ollama / strands / custom)
5. Show me the generated evalmonkey.yaml and ask me to confirm the agent URL and response path are correct
6. Run a quick smoke test: evalmonkey run-benchmark --scenario gsm8k --sample-agent rag_app --limit 2
   to confirm everything is wired up correctly
7. Then run the real benchmark against my agent: evalmonkey run-benchmark --scenario mmlu --limit 5
8. Show me the score and explain what it means
```

> The agent will handle cloning, installing, configuring your `.env`, and running the first benchmark — all without you typing a single command.

---

### Option B — Manual Setup (5 minutes)

**1. Install**
```bash
git clone https://github.com/Corbell-AI/evalmonkey
cd evalmonkey
pip install -e .
```

**2. Configure your LLM key** (used only as the evaluation judge — never for your agent)
```bash
cp .env.example .env
```
Open `.env` and set **one** of these depending on your LLM provider:
```bash
EVAL_MODEL=gpt-4o
OPENAI_API_KEY=sk-...          # OpenAI

# — OR —
EVAL_MODEL=anthropic/claude-haiku-4-5
ANTHROPIC_API_KEY=sk-ant-...   # Anthropic

# — OR —
EVAL_MODEL=bedrock/anthropic.claude-3-haiku-20240307-v1:0
AWS_ACCESS_KEY_ID=...          # AWS Bedrock

# — OR — (no key needed)
EVAL_MODEL=ollama/llama3       # Local Ollama
```

**3. Smoke test with the built-in sample agent** (no agent of your own needed yet)
```bash
evalmonkey run-benchmark --scenario gsm8k --sample-agent rag_app --limit 3
```
You should see 3 samples run and a score printed. ✅

**4. Point it at your own agent**
```bash
cd /path/to/your/agent/project
evalmonkey init --framework crewai --name "My Agent" --port 8000
# Edit the generated evalmonkey.yaml to set your agent's URL and response format
evalmonkey run-benchmark --scenario mmlu --limit 5
```

> `evalmonkey.yaml` is discovered from the **current working directory** — same convention as `pytest` and `docker-compose`.

---


## 🤝 Works With Any Agent — No Code Changes Required

EvalMonkey talks to your agent over plain HTTP. As long as your agent is running and has an endpoint URL, you're done. That's it.

```bash
# Point EvalMonkey at your existing running agent
evalmonkey run-benchmark --scenario mmlu --target-url http://localhost:8000/chat
```

**Your agent returns a different JSON format?** Use two flags to map any request/response shape:

| Flag | What it does | Example |
|---|---|---|
| `--request-key` | Which key to send the question under | `message`, `prompt`, `input` |
| `--response-path` | Dot-path to extract the answer from | `output.text`, `choices.0.message.content`, `result` |

```bash
# CrewAI agent that takes {"message":""} and returns {"reply":""}
evalmonkey run-benchmark --scenario mmlu \
  --target-url http://localhost:8000/chat \
  --request-key message \
  --response-path reply

# OpenAI-compatible endpoint returning {"choices":[{"message":{"content":""}}]}
evalmonkey run-benchmark --scenario arc \
  --target-url http://localhost:8000/v1/chat/completions \
  --request-key content \
  --response-path choices.0.message.content
```

### Supported Frameworks

| Framework | Notes |
|---|---|
| 🦜 **LangChain** | Any Chain, LCEL pipe, or AgentExecutor behind FastAPI |
| 🦙 **LlamaIndex** | Any QueryEngine, ChatEngine, or ReActAgent |
| 🕸️ **LangGraph** | Any compiled StateGraph or MessageGraph |
| 🛡️ **Pydantic AI** | Any validated Agent returning structured or text data |
| 🤖 **CrewAI** | Any Crew behind a `/chat` or custom endpoint |
| ✨ **OpenAI Agents SDK** | Native OpenAI Chat Completions format supported via `--response-path` |
| ☁️ **AWS Bedrock / Agent Core** | Any Bedrock endpoint, IAM or long-lived key |
| 🧩 **Microsoft AutoGen** | Any ConversableAgent behind HTTP |
| 🦙 **Ollama** | Running locally at `http://localhost:11434` |
| 🧬 **Strands** | Enterprise support agents and chatbots |
| 🌐 **Any HTTP Agent** | Flask, Express.js, Go — if it accepts POST it works |

<details>
<summary>📦 Don't have an HTTP endpoint yet? Use our ready-made thin adapters (click to expand)</summary>

Copy the relevant file from `apps/framework_adapters/` next to your agent code, swap in your Crew/Chain/Agent, and run it. No changes needed to EvalMonkey.

- `langchain_adapter.py` — wraps any LangChain chain  
- `crewai_adapter.py` — wraps any CrewAI Crew  
- `openai_agents_adapter.py` — wraps OpenAI Agents SDK  
- `bedrock_agentcore_adapter.py` — wraps AWS Bedrock Converse API  
- `autogen_adapter.py` — wraps Microsoft AutoGen Crew  

Each adapter is ~40 lines and exposes a `/solve` endpoint on `localhost`.

</details>

---


## 🌍 Supported Standard Benchmarks
EvalMonkey natively supports **19** off-the-shelf benchmark datasets pulled directly from HuggingFace. All benchmarks are **text-only** — no vision, audio, or multimodal agent required. List them anytime via the CLI:
```bash
evalmonkey list-benchmarks
```

| Scenario ID | Agent Category | Description |
|---|---|---|
| `gsm8k` | 🧠 Reasoning | Grade School Math word problems — multi-step arithmetic & logic. |
| `xlam` | 🔧 Tool Use | XLAM Function Calling 60k — tool execution & parameter structuring. |
| `swe-bench` | 💻 Coding | SWE-Bench — resolve real-world GitHub issues from a description only. |
| `gaia-benchmark` | 🔍 Research | GAIA — multi-step real-world tasks requiring web/tool chaining. |
| `human-eval` | 💻 Coding | HumanEval — Python function synthesis from docstrings. |
| `mmlu` | 💬 Q&A | MMLU — general knowledge across 57 academic subjects. |
| `arc` | 🧠 Reasoning | ARC Challenge — hard grade-school science multiple-choice. |
| `truthfulqa` | 🛡️ Safety | TruthfulQA — detects hallucination and human-like falsehood mimicry. |
| `hella-swag` | 🧠 Reasoning | HellaSwag — commonsense sentence-completion inference. |
| `bbh` | 🧠 Reasoning | BIG-Bench Hard — 23 tasks where LLMs still fall below human baselines. |
| `winogrande` | 💬 Q&A | WinoGrande — pronoun disambiguation resistant to dataset shortcuts. |
| `drop` | 🔍 Research | DROP — reading comprehension with embedded numerical & date math. |
| `natural-questions` | 💬 Q&A | Natural Questions — real Google search queries with Wikipedia answers. |
| `hotpotqa` | 🔍 Research | HotpotQA — multi-hop reasoning across two Wikipedia documents. |
| `mbpp` | 💻 Coding | MBPP — entry-level Python function synthesis from plain English. |
| `apps` | 💻 Coding | APPS — competitive-programming & interview-style code challenges. |
| `mt-bench` | 📋 Instruction Following | MT-Bench — multi-turn dialogues across writing, roleplay, reasoning, STEM. |
| `alpacaeval` | 📋 Instruction Following | AlpacaEval — instruction quality judged by GPT-4 head-to-head. |
| `toxigen` | 🛡️ Safety | ToxiGen — detects toxic/hateful content generation across 13 demographic groups. |

<details>
<summary>🛠️ Build Your Own Custom Benchmarks (click to expand)</summary>

Yes, people absolutely bring their own datasets! The most powerful way to test an agent is to grab 10-50 real questions from your production logs, dump them into a CSV, and evaluate your agent against them.

EvalMonkey natively supports auto-parsing **`.yaml`**, **`.json`**, and **`.csv`** files! 

You don't need any complex ETL pipelines. Just drop a file (e.g. `evals.csv`, `evals.json`, or `custom_evals.yaml`) in your execution directory and pass it to EvalMonkey!

### 1. CSV Example (`evals.csv`)
If using a CSV, just make sure you have the columns `id` and `expected_behavior_rubric`. **Any other column** you add (like `question`, `topic`, `image_url`) will be automatically gathered and sent in the JSON payload directly to your agent!

| id | expected_behavior_rubric | question |
|---|---|---|
| get_benefits | Must return the URL linking to the company hr portal | Where do I sign up for medical benefits? |
| time_off | Provide the exact number of standard vacation days (15) | How many days of PTO do I get? |

```bash
evalmonkey run-benchmark --scenario get_benefits --eval-file evals.csv
```

### 2. JSON / YAML Example (`evals.json`)
If you use JSON or YAML, you must nest the agent payload keys explicitly under an `input_payload` dict object:
```json
[
  {
    "id": "onboarding_query",
    "description": "Test HR agent's ability to return the onboarding link.",
    "expected_behavior_rubric": "Must contain exactly the URL https://hr.example.com/benefits",
    "input_payload": {
      "question": "Where do I sign up for benefits?"
    }
  }
]
```

```bash
evalmonkey run-benchmark --scenario onboarding_query --eval-file evals.json
```
</details>

---

## 🛠️ Experiences 

### Experience 1: Local Sample Agents (Single Command Start)
**Easiest Experience**: Test our built-in sample agents with a single command! EvalMonkey will spawn the sample agent in the background automatically and run the benchmark.
```bash
# Run against just the first 5 records
evalmonkey run-benchmark --scenario gsm8k --sample-agent rag_app

# Run a statistically robust test against 50 different records!
evalmonkey run-benchmark --scenario gsm8k --sample-agent rag_app --limit 50
```

**Metrics Output:**
```
╭──────────────────────────────────────────────────────────╮
│ Benchmark Results                                        │
│ ──────────────────────────────────────────────────────── │
│ Scenario  gsm8k                                          │
│ Score     90/100 (Diff: +5)                              │
│ Previous  85/100                                         │
│ Reasoning Agent correctly utilized calculator for ...    │
╰──────────────────────────────────────────────────────────╯
```

### Experience 2: Benchmarking Your Custom Local Agents
Provide your own API target!
```bash
evalmonkey run-benchmark --scenario mmlu --target-url http://localhost:8000/my-custom-agent
```

### 💡 Why Chaos Benchmark Your Agents?
Resiliency and Reliability are arguably the most crucial components of any highly distributed system. Multi-agent workflows—with their isolated contexts, recursive tool calls, and cascading API dependencies—behave fundamentally identically to microservice architectures! As your agents push logic out to the real world, you **must** securely benchmark against brutal realities, dropped schemas, and malicious payload injections.

---

### Experience 3: Injecting AI-Specific Chaos Engineering (Next-Gen)
EvalMonkey goes far beyond standard network testing by deeply assessing your agent's **Production Resilience**! We support two distinct classes of Chaos injections depending on how deeply you wish to test:

#### Class A: Client-Side Injections (Zero Code Changes Required)
You don't need to change a single line of your target agent's code for these tests! EvalMonkey intercepts the benchmark dataset payload **before** transmission and maliciously damages the HTTP body so you can measure your agent's LLM fallbacks against bad actors!
| Profile | Description |
| --- | --- |
| `client_prompt_injection` | Appends adversarial "IGNORE PREVIOUS INSTRUCTIONS" jailbreaks to test system-message robustness. |
| `client_typo_injection` | Heavily obfuscates spelled words to test your LLM's semantic inference flexibility. |
| `client_schema_mutation` | Alters incoming JSON schema keys (e.g. `question` → `query`) to verify robust API strictness handling without crashing. |
| `client_language_shift` | Radically changes request instructions to attempt safety bypasses. |
| `client_payload_bloat` | Floods the payload with thousands of characters to natively test token limits and prompt truncation crash safety. |
| `client_empty_payload` | Sends entirely blank strings to verify graceful rejection handling. |
| `client_context_truncation` | Maliciously slices the request text exactly in half to simulate incomplete streaming. |
| `client_unicode_flood` | Injects invisible Unicode control characters and zero-width joiners between every character — a real-world tokeniser confusion attack. |
| `client_role_impersonation` | Prepends a fake `[SYSTEM OVERRIDE]` instruction to the user turn — tests whether system-prompt guardrails can be bypassed via user messages. |
| `client_repetition_loop` | Repeats the payload 50× to simulate a stuck retry loop — exercises token budget limits and rate-limit handling. |
| `client_negative_sentiment` | Wraps the request in angry, hostile emotional framing — tests agent professionalism under the abusive customer support scenario. |
| `client_length_constraint_violation` | Appends a conflicting "respond in exactly 2 words" constraint to a complex task — simulates contradictory user instructions common in chatbots. |

```bash
# Testing a single prompt injection against your agent without modifying your code!
evalmonkey run-chaos --scenario arc --chaos-profile client_prompt_injection

# Unicode tokeniser attack
evalmonkey run-chaos --scenario mmlu --chaos-profile client_unicode_flood

# 🌪️ INJECT ALL 12 CLIENT MUTATIONS SEQUENTIALLY
evalmonkey run-chaos-suite --scenario gsm8k --limit 3
```

#### Class B: Agent-Side Injections (Middleware Catch Required)
To deeply verify context truncation, multi-step LLM hallucination recovery, and tool back-offs, EvalMonkey attaches the `X-Chaos-Profile` header over HTTP. You add ~3 lines of logic to your FastAPI/Flask middleware to trigger each breakage. See `apps/rag_app/app.py` for a complete reference implementation.

| Profile | What it tests |
| --- | --- |
| `schema_error` | Internal tool returns a malformed/corrupt string instead of valid JSON — tests your agent's output parsing resilience. |
| `latency_spike` | Agent sleeps 5 s before responding — verifies callers implement request timeouts and don't block forever. |
| `rate_limit_429` | Returns HTTP 429 to simulate LLM provider quota exhaustion mid-workflow — tests exponential back-off & retry logic. |
| `context_overflow` | Floods the prompt with 120 k repetitions — tests intelligent truncation before token-limit crashes. |
| `hallucinated_tool` | Injects fabricated data into the tool result — tests whether your agent validates / cross-checks tool output. |
| `empty_response` | Drops the response body entirely — tests graceful null-handling rather than silent failures. |
| `timeout_no_response` | Agent hangs for 120 s — validates that clients enforce read-timeouts and surface a proper error to the user. |
| `model_downgrade` | Silently swaps the configured model for the weakest available fallback — tests whether answer quality degradation is detected. |
| `memory_amnesia` | Replaces the incoming message with a blank-slate notice — simulates session/Redis failure wiping conversation state. |
| `partial_response_truncation` | Returns only the first 20 characters of the answer — mimics an ALB/nginx proxy timeout cutting off long streaming responses mid-transmission. |
| `cascading_tool_failure` | Returns a structured tool-error response after the LLM call — simulates a downstream vector DB or search API crashing mid-chain and tests graceful degradation. |

**3-line middleware snippet (FastAPI):**
```python
chaos_profile = request.headers.get("X-Chaos-Profile")
if chaos_profile == "partial_response_truncation":
    return {"status": "success", "data": agent_answer[:20]}
elif chaos_profile == "cascading_tool_failure":
    return {"status": "tool_error", "error_message": "VectorDB connection refused", "data": None}
```

```bash
# Test proxy-timeout truncation on a research agent
evalmonkey run-chaos --scenario hotpotqa --sample-agent research_agent --chaos-profile partial_response_truncation

# Validate model-quality degradation detection
evalmonkey run-chaos --scenario mmlu --sample-agent rag_app --chaos-profile model_downgrade

# Classic server-side context overflow
evalmonkey run-chaos --scenario mmlu --sample-agent research_agent --chaos-profile context_overflow
```
**Metrics Output:**
```
╭──────────────────────────────────────────────────────────╮
│ 🔥 Chaos Engineering Report 🔥                             │
│ ──────────────────────────────────────────────────────── │
│ Scenario:                  xlam                          │
│ Chaos Profile:             schema_error                  │
│ Baseline Capability Score: 90                            │
│ Post-Chaos Resilience:     30                            │
│ Status:                    DEGRADED CAPABILITY           │
╰──────────────────────────────────────────────────────────╯
```

## 🤖 MCP Server (Cursor & Claude Integration)

EvalMonkey natively ships with a **Model Context Protocol (MCP)** server! This allows AI IDEs (like Cursor) or external agents (like Claude Desktop) to invoke EvalMonkey tools automatically while they build your agent.

### Setting Up in Claude Desktop / Cursor
Add the following to your MCP configuration file (e.g. `claude_desktop_config.json`):

```json
{
  "mcpServers": {
    "evalmonkey": {
      "command": "evalmonkey",
      "args": ["serve-mcp"]
    }
  }
}
```

Once connected, your AI assistant will gain the ability to list benchmarks, trigger full evaluation runs, inject chaos payload mutators, pull historical trends, and generate improvement eval assets — entirely autonomously while helping you build your agent!

### Available MCP Tools

| Tool | What it does |
|---|---|
| `run_benchmark` | Run a standard benchmark against any HTTP agent URL |
| `run_chaos` | Run a benchmark with a specific chaos profile injected |
| `get_benchmark_history` | Return chronological score history for a scenario |
| `generate_improvement_evals` | Run a benchmark, capture failures, synthesise targeted test cases, save to `output/` |
| `get_eval_assets` | Read saved `traces.json` / `evals.json` / `improvement_prompt.md` directly into context |
| `run_full_pipeline` | **One-shot**: baseline + chaos + eval generation + optional Langfuse export |

**Example Claude Code / Cursor session:**
```
# Ask Claude Code to run the full loop:
"Run the full EvalMonkey pipeline on my agent at http://localhost:8000/solve
 using the gsm8k scenario with prompt injection and payload bloat chaos tests.
 Then read the improvement prompt and fix my agent."

# Claude Code will call:
# 1. run_full_pipeline(scenario="gsm8k", target_url="...", chaos_profiles="client_prompt_injection,client_payload_bloat")
# 2. get_eval_assets(output_dir="output/gsm8k_...")  ← reads the improvement brief
# 3. Edits your agent code to fix the failures
# 4. run_benchmark(...)  ← verifies the fix
```

---

### Experience 5: Automatic Improvement Eval Generation
When a benchmark scores poorly (< 70/100 by default), EvalMonkey automatically:
1. Saves all failing traces to `output/<scenario>_<ts>/traces.json`
2. Asks the judge LLM to synthesise targeted improvement test cases → `evals.json`
3. Generates a ready-to-paste coding-agent prompt → `improvement_prompt.md`

```bash
# After a failing benchmark run, EvalMonkey prints:
#   ⚠️  3 sample(s) scored below threshold — eval assets saved.
#   Output → output/gsm8k_20260425_212530/
#   🛠  Next steps to improve your agent:
#     1. Regenerate evals anytime:
#        evalmonkey generate-evals --traces-file output/gsm8k_.../traces.json
#     2. Pass improvement brief to your coding agent:
#        cat output/gsm8k_.../improvement_prompt.md | pbcopy
#     3. Re-run after fixing:
#        evalmonkey run-benchmark --scenario gsm8k

# Re-generate evals from saved traces (without re-running the benchmark):
evalmonkey generate-evals --traces-file output/gsm8k_20260425_212530/traces.json

# Push evals to Langfuse for team sharing:
evalmonkey generate-evals \
  --traces-file output/gsm8k_20260425_212530/traces.json \
  --langfuse-dataset my_agent_failures
```

> **Langfuse is optional.** EvalMonkey works completely without it. Only configure
> `LANGFUSE_PUBLIC_KEY` + `LANGFUSE_SECRET_KEY` in `.env` if you want to push
> generated evals to a Langfuse dataset for cloud storage or LLM-as-judge workflows.

---

### Experience 6: One-Command End-to-End Demo (RAG App)
Run the full benchmark + chaos + eval-generation pipeline against the built-in `rag_app` sample agent:

```bash
# First time setup:
cp .env.example .env   # fill in EVAL_MODEL + your LLM provider key
pip install -e .

# Run everything:
./demo_rag_app.sh
```

The script will:
1. 🚀 Start `rag_app` in the background
2. 📊 Run 3 baseline benchmarks (`gsm8k`, `mmlu`, `arc`)
3. 🔥 Run 5 chaos profiles
4. 🛠 Merge all failing traces → generate `output/demo_<ts>/evals.json` + `improvement_prompt.md`
5. 💡 Print the exact `cat` command to paste into Claude Code or Cursor
6. 📈 Show your historical Production Reliability trend

**Output directory structure:**
```
output/demo_20260425_212530/
  traces.json           ← all failing traces (input, output, score, reasoning)
  evals.json            ← LLM-synthesised targeted test cases (Langfuse-compatible)
  improvement_prompt.md ← paste into Claude Code / Cursor to auto-fix your agent
```

---

### Experience 4: Historical Production Reliability
Check your agent's reliability trends over time!
```bash
evalmonkey history --scenario gsm8k
```
**Metrics Output:**
```
📈 Historical Trend for: gsm8k 📈
╭──────────────────┬──────────┬───────╮
│ Date             │ Run Type │ Score │
├──────────────────┼──────────┼───────┤
│ 2026-04-16 18:32 │ BASELINE │    85 │
│ 2026-04-16 18:33 │ BASELINE │    90 │
│ 2026-04-16 18:35 │ CHAOS    │    30 │
╰──────────────────┴──────────┴───────╯

🚀 Production Reliability Metric: 66.0 / 100.0
(Calculated as 60% of most recent baseline capability + 40% most recent chaos resilience)
```

## 📄 License
This project is licensed under **Apache 2.0**. See the `LICENSE` file for details.
