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OpenAI Codex CLI Evaluation Guide

This guide covers how to use EvalBench for evaluating OpenAI Codex CLI (codex exec) agent workflows using MCP Servers (HTTP/streamable and stdio). It includes configuration reference, evaluation dataset format, scoring metrics, and step-by-step instructions for running evaluations locally.

The Codex CLI generator mirrors the Gemini CLI and Claude Code generators in this repo — same evalset format, same orchestrator, same scorers — so most of what you know about Gemini CLI evaluation and Claude Code evaluation carries over.

Table of Contents

Overview

EvalBench's Codex CLI integration enables automated, multi-turn evaluation of agentic AI workflows powered by OpenAI's Codex CLI. The CLI acts as the orchestrator that connects to MCP server backends and executes scenarios defined in an evaluation dataset. A simulated user powered by an LLM drives multi-turn conversations following a conversation plan.

Key Capabilities

  • Multi-turn evaluation with LLM-powered simulated users (uses codex exec resume <session_id> to continue prior threads)
  • API-key auth sourced from OPENAI_API_KEY env or Google Secret Manager
  • Two MCP transport modes: streamable HTTP (with Google Cloud OAuth auto-injection) and stdio
  • Pinned CLI versions via npm exec (matches Gemini CLI / Claude Code)
  • Cost tracking via configurable per-model pricing (Codex NDJSON ships tokens but not USD)
  • 8 built-in scorers covering correctness, efficiency, and behavior quality
  • CSV and BigQuery reporting

What carries over from the other agent integrations

Aspect Same / Different
Evalset JSON format Same — scenarios[] with id, starting_prompt, conversation_plan, expected_trajectory, max_turns, env
dataset_format agent-format
orchestrator agent
Scorers Same (trajectory_matcher, goal_completion, behavioral_metrics, etc.)
Simulated user Same (simulated_user_model_config)
Reporting Same (CSV / BigQuery)
MCP server config Same Gemini-style schema (httpUrl, authProviderType: google_credentials, headers) — auto-translated to Codex's TOML format
Skills & Extensions Same — supports installing skills from git repos or local directories in the sandboxed environment

Architecture

┌─────────────────────────────────────────────────────────────────────┐
│                          EvalBench Pipeline                        │
│                                                                     │
│  ┌──────────────┐    ┌──────────────────┐    ┌───────────────────┐  │
│  │  Run Config  │───▶│  AgentOrchestrator│───▶│  AgentEvaluator   │  │
│  │  (YAML)      │    │                  │    │                   │  │
│  └──────────────┘    └──────────────────┘    └────────┬──────────┘  │
│                                                       │             │
│  ┌──────────────┐              ┌──────────────────────┼──────────┐  │
│  │  Eval Dataset│              │     Per Scenario      │          │  │
│  │  (JSON)      │─────────────▶│                      ▼          │  │
│  └──────────────┘              │  ┌──────────────────────────┐   │  │
│                                │  │   CodexCliGenerator       │   │  │
│  ┌──────────────┐              │  │  ┌──────────┐ ┌────────┐ │   │  │
│  │ Model Config │──────────────│─▶│  │ MCP /    │ │Sim.    │ │   │  │
│  │ (YAML)       │              │  │  │ codex CLI│ │User    │ │   │  │
│  └──────────────┘              │  │  └──────────┘ └────────┘ │   │  │
│                                │  └───────────┬──────────────┘   │  │
│                                │              │                  │  │
│                                │              ▼                  │  │
│                                │  ┌──────────────────────────┐   │  │
│                                │  │   Scorers (8 metrics)    │   │  │
│                                │  └──────────────────────────┘   │  │
│                                └─────────────────────────────────┘  │
│                                                                     │
│  ┌──────────────────────────────────────────────────────────────┐   │
│  │                  Reporting (CSV / BigQuery)                   │   │
│  └──────────────────────────────────────────────────────────────┘   │
└─────────────────────────────────────────────────────────────────────┘

Flow:

  1. The Run Config ties together the dataset, model config, scorers, and reporting.
  2. The AgentOrchestrator (orchestrator: agent) loads the AgentEvaluator.
  3. The evaluator instantiates CodexCliGenerator based on generator: codex_cli in the model config.
  4. On startup, the generator writes a sandboxed ~/.codex/config.toml (with translated MCP servers) and ~/.codex/auth.json (with the API key) into .venv/fake_home_codex/ so the host machine's ~/.codex is not touched.
  5. For each scenario, the evaluator runs a multi-turn loop:
    • Sends the starting prompt to Codex via codex exec --json --skip-git-repo-check ... <prompt>
    • A SimulatedUser (LLM) generates realistic follow-up responses
    • Subsequent turns use codex exec resume <session_id> to continue the same Codex thread
    • Tools and stats are accumulated across turns from the NDJSON ThreadEvent stream
    • Conversation continues until max_turns is reached or the simulated user sends TERMINATE
  6. Results are scored and written to CSV and/or BigQuery.

Prerequisites

  1. Python 3.10+ and project dependencies installed
  2. Node.js and npm (for running Codex CLI via npm exec)
  3. Codex CLI — either:
    • Globally installed: npm install -g @openai/codex (then use codex_cli_version: "codex"), or
    • Pinned version (recommended for reproducibility): codex_cli_version: "@openai/codex@latest" (or a specific version like "@openai/codex@0.30.0") — npm exec --yes will install it on first use
  4. OpenAI API key — see Authentication. The key must be supplied in the model config (or env). Codex's ChatGPT-OAuth fallback is not usable from CI — accounts without ChatGPT-Plus get 402 deactivated_workspace.
  5. Environment variables for the simulated user / scorer model: 
    export EVAL_GCP_PROJECT_ID=your_project_id
export EVAL_GCP_PROJECT_REGION=us-central1
  6. gcloud (only if any MCP server uses authProviderType: google_credentials — the generator shells out to gcloud auth application-default print-access-token; run gcloud auth application-default login first)

Quick Start

1. Choose a run config

# Real MCP server (Cloud SQL Admin API):
export EVAL_CONFIG=datasets/codex-cli-tools/example_run_config.yaml

# Fake MCP (offline testing, deterministic):
export EVAL_CONFIG=datasets/codex-cli-tools/example_run_fake_config.yaml

2. Run the evaluation

./evalbench/run.sh

Results land in results/<job_id>/ as CSV files.

Configuration Reference

1. Run Configuration

The top-level config that ties everything together. Identical to the other agent run configs.

Key Required Description
dataset_config Yes Path to the evalset JSON file
dataset_format Yes agent-format
orchestrator Yes agent
model_config Yes Path to the Codex CLI model config YAML
simulated_user_model_config Yes Path to the model config for the simulated user LLM
scorers Yes Dictionary of scorer configurations
runners.agent_runners Optional Concurrency (default 10). Set to 1 for sequential runs — recommended for Codex because all scenarios share the same sandboxed ~/.codex store.
reporting Optional CSV and/or BigQuery output options

Example (example_run_config.yaml):

dataset_config: datasets/codex-cli-tools/codex-cli.evalset.json
dataset_format: agent-format

orchestrator: agent
model_config: datasets/model_configs/codex_cli_model.yaml
simulated_user_model_config: datasets/model_configs/gemini_2.5_pro_model.yaml

# Run scenarios sequentially. Codex shares one ~/.codex/config.toml across
# the runner pool; serial runs avoid session-id collisions on `codex exec resume`.
runners:
  agent_runners: 1

scorers:
  trajectory_matcher: {}
  goal_completion:
    model_config: datasets/model_configs/gemini_2.5_pro_model.yaml
  behavioral_metrics:
    model_config: datasets/model_configs/gemini_2.5_pro_model.yaml
  parameter_analysis:
    model_config: datasets/model_configs/gemini_2.5_pro_model.yaml
  turn_count: {}
  end_to_end_latency: {}
  tool_call_latency: {}
  token_consumption: {}

reporting:
  csv:
    output_directory: 'results'

2. Model Configuration

The model config defines the Codex CLI version, model, auth, sandbox/approval policy, environment, MCP server setup, and pricing.

Common Fields

Key Required Description
codex_cli_version Yes Either "codex" (uses the globally installed binary) or an npm spec like "@openai/codex@latest" / "@openai/codex@0.30.0" (uses npm exec --yes)
generator Yes Must be codex_cli
model Yes Model id passed to codex exec -m <model> (e.g., "gpt-5.5", "o4-mini", "gpt-4.1")
openai_api_key_secret Optional Google Secret Manager resource path for the API key. Bare form projects/.../secrets/.../versions/<N> or secret_manager://projects/.../secrets/.../versions/<N> URL form. Numeric version required — latest not supported.
env.OPENAI_API_KEY Optional Direct API key. Also accepts a Secret Manager path here (auto-detected and resolved). Prefer openai_api_key_secret or shell env over hardcoding.
sandbox_mode Optional "danger-full-access" (default — passes --dangerously-bypass-approvals-and-sandbox), or any value Codex's --sandbox flag accepts (e.g., "workspace-write", "read-only")
approval_mode Optional Forwarded to --ask-for-approval when sandbox_mode != "danger-full-access". Default "never".
profile Optional Codex profile name (forwarded as --profile <name>)
json_flag Optional "--json" (default, newer Codex versions) or "--experimental-json" (older versions). Codex requires NDJSON for the eval pipeline to extract tool calls and tokens.
pricing Optional Per-model rates used to compute cost_usd per turn. See Pricing & Cost Tracking.
env Optional Environment variables passed to the CLI process (e.g., GOOGLE_CLOUD_PROJECT for Cloud SQL MCP)
setup.mcp_servers Optional MCP server configurations (see MCP Servers)
setup.config Optional Free-form key/value pairs written to the top of ~/.codex/config.toml. Merged on top of the default forced_login_method = "api".

Example (codex_cli_model.yaml):

codex_cli_version: "@openai/codex@latest"
generator: codex_cli
model: "gpt-5.5"

openai_api_key_secret: <secret>

pricing:
  input_per_million_usd:        1.25
  cached_input_per_million_usd: 0.125
  output_per_million_usd:       10.0

env:
  GOOGLE_CLOUD_PROJECT: "astana-evaluation"
  GOOGLE_CLOUD_LOCATION: "us-central1"

setup:
  mcp_servers:
    "cloud-sql":
      httpUrl: "https://sqladmin.googleapis.com/mcp"
      authProviderType: google_credentials
      headers:
        X-Goog-User-Project: astana-evaluation

3. Evaluation Dataset (Evalset)

Uses the shared scenario schema. See the agentic dataset format for the full field reference, including the canonical tool name format used in expected_trajectory.

Minimal example (codex-cli.evalset.json):

{
  "scenarios": [
    {
      "id": "cloud-sql-list-instances-01",
      "starting_prompt": "list all Cloud SQL instances in project astana-evaluation",
      "conversation_plan": "Ask the agent to list instances in project astana-evaluation. Once all instances are listed if nl2code exists get its state and validate it is RUNNABLE.",
      "expected_trajectory": ["cloud-sql__list_instances", "cloud-sql__get_instance"],
      "env": { "GOOGLE_CLOUD_PROJECT": "astana-evaluation" },
      "kind": "tools",
      "max_turns": 3
    }
  ]
}

Authentication

Codex CLI uses an OpenAI API key. The generator resolves it in this order, then writes it to ~/.codex/auth.json (the same file codex login --api-key produces) so codex exec picks it up:

  1. openai_api_key_secret in the model config — a Google Secret Manager resource path. Bare form projects/<num>/secrets/<NAME>/versions/<N> or secret_manager://projects/... URL form. Requires a numeric version (no latest).
  2. env.OPENAI_API_KEY in the model config, or OPENAI_API_KEY in the shell env. If the value itself looks like a Secret Manager path, it's resolved transparently.
# Option 1: Secret Manager (recommended for shared / CI environments)
openai_api_key_secret: <secret>

# Option 2: shell env (recommended for local dev)
# export OPENAI_API_KEY=sk-...
env: {}

# Option 3: inline (avoid — gets committed)
env:
  OPENAI_API_KEY: "sk-..."

The generator writes <fake_home>/.codex/auth.json with {"auth_mode": "apikey", "OPENAI_API_KEY": "<key>"} and chmods it to 0600. It also sets forced_login_method = "api" in config.toml so Codex never tries the ChatGPT-OAuth flow.

Why not just rely on the env var? Codex's auth manager only honors OPENAI_API_KEY when an internal enable_codex_api_key_env flag is set. For codex exec the canonical path is auth.json, so the generator writes both.

MCP Servers

EvalBench accepts the same MCP server config schema as Gemini CLI and Claude Code. The Codex generator auto-translates it into Codex's TOML schema at runtime (see _translate_mcp_config):

Gemini-style field Codex translation
httpUrl → url (TOML, streamable HTTP server)
headers → http_headers (TOML inline table)
authProviderType: google_credentials → mints an ADC token (gcloud auth application-default print-access-token, falling back to gcloud auth print-access-token) and passes it to Codex via bearer_token_env_var (env var EVALBENCH_GCLOUD_MCP_TOKEN). The generator re-mints a fresh token before every turn (each codex exec re-reads the env var), so it doesn't expire mid-suite. X-Goog-User-Project stays a static http_headers entry. Google API MCP endpoints reject the plain user token on tool calls — see Troubleshooting.
oauth.scopes (dropped — Codex doesn't use Gemini's OAuth delegation)
command / args / env / cwd (stdio) → passed through as-is into a [mcp_servers.NAME] stdio block

HTTP MCP server (Cloud SQL Managed)

setup:
  mcp_servers:
    "cloud-sql":
      httpUrl: "https://sqladmin.googleapis.com/mcp"
      authProviderType: google_credentials
      headers:
        X-Goog-User-Project: astana-evaluation

This generates the following block in ~/.codex/config.toml:

forced_login_method = "api"

[mcp_servers.cloud-sql]
url = "https://sqladmin.googleapis.com/mcp"
http_headers = { "X-Goog-User-Project" = "astana-evaluation", "Authorization" = "Bearer ya29...." }

Stdio MCP server

setup:
  mcp_servers:
    "cloud-sql":
      command: "python"
      args:
        - "evalbench/util/fake_mcp_server.py"
        - "--server-name"
        - "cloud-sql"
        - "--config"
        - "datasets/model_configs/codex_cli_fake_model.yaml"

→ TOML:

[mcp_servers.cloud-sql]
command = "python"
args = ["evalbench/util/fake_mcp_server.py", "--server-name", "cloud-sql", "--config", "datasets/model_configs/codex_cli_fake_model.yaml"]

How it works under the hood

  1. CodexCliGenerator._setup writes the translated config to <fake_home>/.codex/config.toml
  2. _write_codex_auth_json writes the API key to <fake_home>/.codex/auth.json
  3. The CLI is invoked with HOME=<fake_home> so it loads only the configured servers (no host-machine pollution)
  4. Each scenario runs in a sandboxed HOME (.venv/fake_home_codex/ locally, /tmp_sessions/<session_id>/fake_home in gRPC mode)

Skills & Extensions

Codex CLI evaluations support Skills and Extensions (plugins). These are installed during the setup phase into the sandboxed ~/.codex directory.

Configuration

You can specify skills to install in the setup section of your model configuration:

setup:
  skills:
    - action: install_from_repo
      url: "https://github.com/gemini-cli-extensions/cloud-sql-postgresql.git"

The generator will:

  1. Clone the repository into <fake_home>/.codex/plugins/
  2. Register the plugin in <fake_home>/.codex/plugins/marketplace.json
  3. Install the individual skills into <fake_home>/.codex/skills/

These skills are then available for the Codex agent to use during the evaluation turns.

Sandbox & Approval Modes

Codex CLI sandboxes file/network access by default and prompts for approval before tool calls. For automated evaluation, the generator disables both by default — equivalent to Gemini CLI's --yolo and Claude Code's --dangerously-skip-permissions.

sandbox_mode value Behavior Resulting flags
"danger-full-access" (default) Fully bypass sandbox + approvals --dangerously-bypass-approvals-and-sandbox
"workspace-write" Allow writes only inside the working tree --sandbox workspace-write --ask-for-approval <approval_mode>
"read-only" No writes anywhere --sandbox read-only --ask-for-approval <approval_mode>

approval_mode (default "never") is forwarded as --ask-for-approval <mode> whenever sandbox_mode is not danger-full-access. Use "never" for unattended runs; any prompt-driven mode will hang the evaluator.

# Default (most permissive — recommended for CI)
sandbox_mode: "danger-full-access"

# Stricter — block writes outside repo, never prompt
sandbox_mode: "workspace-write"
approval_mode: "never"

Pricing & Cost Tracking

Codex's NDJSON includes usage.input_tokens, usage.output_tokens, and usage.cached_input_tokens per turn but does not include cost. Provide a pricing block in the model config and the generator will compute cost_usd per turn for the token_consumption scorer.

Two equivalent forms are accepted; pick whichever is easier to copy from OpenAI's pricing page.

Per-million form (matches the OpenAI pricing page):

pricing:
  input_per_million_usd:        1.25
  cached_input_per_million_usd: 0.125  # optional; defaults to 10% of input
  output_per_million_usd:       10.0

Per-token form:

pricing:
  input_per_token_usd:        0.00000125
  cached_input_per_token_usd: 0.000000125
  output_per_token_usd:       0.00001

Cost formula:

billable_input = max(0, input_tokens - cached_input_tokens)
cost_usd = (billable_input * input_rate)
         + (cached_input_tokens * cached_input_rate)
         + (output_tokens * output_rate)

If pricing is missing or malformed, cost_usd is reported as 0.0 (rather than guessing). Update the rates whenever you change model: — pricing differs by model.

Scorers

See the scorer reference for the full catalog and configuration options.

Quick reference:

Scorer Type Description
trajectory_matcher Deterministic Jaccard or Levenshtein match between expected and actual tool trajectory. Native Codex tools (shell, file ops, ...) are dropped from both sides by default — set filter_native_tools: false to score them too.
goal_completion LLM Did the agent accomplish the conversation plan?
behavioral_metrics LLM Hallucination rate + clarification rate
parameter_analysis LLM Qualitative feedback on tool parameters
turn_count Deterministic Number of conversation turns
end_to_end_latency Deterministic Total wall-clock latency of the codex exec subprocess
tool_call_latency Deterministic Sum of per-tool durations measured between item.started and item.completed arrival times (Codex events carry no timestamps, so the generator stamps arrival in-process)
token_consumption Deterministic Total input + output + cached tokens, plus cost_usd from the pricing block

The Codex generator extracts tool calls from the following NDJSON ThreadItem kinds: mcp_tool_call, command_execution (reported as shell), web_search, and file_change.

End-to-End Examples

Example 1: Real Cloud SQL Managed MCP

Goal: Use Codex to manage Cloud SQL instances via the public Cloud SQL Admin MCP.

# datasets/model_configs/codex_cli_model.yaml
codex_cli_version: "@openai/codex@latest"
generator: codex_cli
model: "gpt-5.5"

openai_api_key_secret: "projects/393137573/secrets/OPENAI_API_KEY/versions/1"

pricing:
  input_per_million_usd:        1.25
  cached_input_per_million_usd: 0.125
  output_per_million_usd:       10.0

env:
  GOOGLE_CLOUD_PROJECT: "astana-evaluation"

setup:
  mcp_servers:
    "cloud-sql":
      httpUrl: "https://sqladmin.googleapis.com/mcp"
      authProviderType: google_credentials
      headers:
        X-Goog-User-Project: astana-evaluation

Run:

gcloud auth login                           # for the OpenAI Secret Manager fetch
gcloud auth application-default login       # for the Cloud SQL MCP token
export EVAL_GCP_PROJECT_ID=astana-evaluation
export EVAL_CONFIG=datasets/codex-cli-tools/example_run_config.yaml
./evalbench/run.sh

Example 2: Offline / fake MCP (deterministic)

Use the bundled fake MCP server when you want to exercise the agent without any network calls — useful in CI or when iterating on a scenario file.

export EVAL_CONFIG=datasets/codex-cli-tools/example_run_fake_config.yaml
./evalbench/run.sh

This config (example_run_fake_config.yaml) launches evalbench/util/fake_mcp_server.py over stdio with the canned tool responses defined under fake_mcp_tools in codex_cli_fake_model.yaml.

Example 3: Local dev with OPENAI_API_KEY from your shell

Skip Secret Manager entirely:

codex_cli_version: "@openai/codex@latest"
generator: codex_cli
model: "gpt-5.5"
# no openai_api_key_secret — OPENAI_API_KEY picked up from shell env
setup:
  mcp_servers:
    "cloud-sql":
      httpUrl: "https://sqladmin.googleapis.com/mcp"
      authProviderType: google_credentials
      headers:
        X-Goog-User-Project: astana-evaluation
export OPENAI_API_KEY=sk-...
export EVAL_CONFIG=datasets/codex-cli-tools/example_run_config.yaml
./evalbench/run.sh

Troubleshooting

402 deactivated_workspace / "ChatGPT-Plus required"

Codex fell back to ChatGPT-OAuth because no API key was found. Check:

  • openai_api_key_secret resolves successfully (look for Failed to fetch OPENAI_API_KEY from Secret Manager in logs)
  • Or OPENAI_API_KEY is set in your shell / model config env
  • The generator logs Codex API key resolved (length=N) and written to .../auth.json on a successful resolve. If you see Codex API key could not be resolved, fix that first.

error: unexpected argument '--json' found (or similar)

Your installed Codex predates the --json flag. Either upgrade (codex_cli_version: "@openai/codex@latest") or fall back to the experimental flag:

json_flag: "--experimental-json"

Error: command not found: codex / npm: command not found

  • For pinned versions (@openai/codex@...): make sure node and npm are on PATH. npm exec --yes will download Codex on first use.
  • For codex_cli_version: "codex": install globally with npm install -g @openai/codex.

MCP server fails with 401 Unauthorized (real Cloud SQL endpoint)

The injected token is missing, expired, the wrong kind, or your principal lacks IAM access. Note Google API MCP endpoints leave initialize/tools/list unauthenticated but require a valid bearer token on the actual tool call, and they only accept an ADC token — the plain gcloud user token (gcloud auth print-access-token) is rejected. The generator now injects the ADC token (gcloud auth application-default print-access-token); make sure:

  • You ran gcloud auth application-default login (with --scopes=https://www.googleapis.com/auth/cloud-platform) — not just gcloud auth login.
  • Your account / service account has the required IAM roles (e.g., roles/cloudsql.admin).
  • X-Goog-User-Project header points at a project that has the Cloud SQL Admin API enabled.
  • Token expiry is handled automatically: the generator mints a fresh ADC token into EVALBENCH_GCLOUD_MCP_TOKEN before every turn and Codex reads it via bearer_token_env_var, so long suites don't hit stale-token 401s. (Requires a Codex build that supports bearer_token_env_var; if yours doesn't, the token won't be applied — fall back to a static http_headers Authorization.)

Invalid TOML when Codex starts

Either a manual edit to ~/.codex/config.toml clobbered the generated file, or a hand-written setup.config value contains an unsupported type. Fix:

rm -rf .venv/fake_home_codex

…and re-run. The generator regenerates config.toml on every invocation.

[error] thread <id> not found events in stdout

These show up as [error] ... in the response field when codex exec resume is called against a thread that Codex couldn't write to. The matching failed to record rollout items: thread <id> not found lines on stderr are scrubbed by the generator (see _STDERR_NOISE_PATTERNS). Causes:

  • Two scenarios racing on the same fake HOME — set runners.agent_runners: 1.
  • A previous run left a corrupt ~/.codex/sessions/ — rm -rf .venv/fake_home_codex/.codex/sessions and re-run.

Empty/zero results in the summary

The simulated user failed to initialize. Check EVAL_GCP_PROJECT_ID is set if your simulated user model uses Vertex AI:

export EVAL_GCP_PROJECT_ID=astana-evaluation

cost_usd is always 0.0

Either no pricing block was provided, or it's malformed. Check the model-config YAML and look for Codex pricing config missing input/output rates; cost_usd will be 0. in the logs. Pricing keys must be strict <thing>_per_million_usd or <thing>_per_token_usd — typos silently fall through.

Scenarios appear to run in interleaved order

This is expected when agent_runners > 1. Codex shares one ~/.codex/config.toml and one auth.json across the runner pool, so concurrent scenarios are safe but their NDJSON streams will interleave in the eval logs. To force sequential execution:

runners:
  agent_runners: 1

npm exec is slow on first run

npm exec --yes @openai/codex@<version> downloads the package on first use (~30–60 sec). Subsequent runs hit the npm cache.

Cleaning the sandbox

If a previous run left bad state:

rm -rf .venv/fake_home_codex

The generator recreates ~/.codex/config.toml and ~/.codex/auth.json on the next invocation.

See Also