AgentDiff

AgentDiff is a developer-first Python library and CLI designed to solve the hardest problem in agent engineering: regression testing multi-turn, tool-using AI agents by comparing execution paths (trajectories) head-to-head.

What AgentDiff Is

• A Trajectory Diff Engine: Compares Run A (Baseline) against Run B (Candidate) across their execution Directed Acyclic Graphs (DAGs).
• A Local-First CI/CD Gate: Runs locally in your terminal or inside pytest and GitHub Actions, raising errors or exit codes on regression violations.
• A Universal Comparator: Ingests telemetry run files from OpenInference/OTel, Langfuse, LangSmith, OpenAI Agents SDK, or raw/custom JSON.

Installation

Install the PyPI package:

pip install agent-trajectory-diff

Or using uv:

uv add agent-trajectory-diff

Quickstart

1. CLI Usage

Compare two trajectory JSON traces from your terminal:

agentdiff baseline_run.json candidate_run.json --fail-on-regression --max-divergence 0.25

Options:

• --adapter: Telemetry parser to use (auto, generic, openinference, langfuse, langsmith, openai_agents).
• --format: Format for the output (terminal, json, markdown).
• --fail-on-regression: Return exit code 1 if thresholds are violated.
• --max-loops: Maximum loops allowed.
• --max-divergence: Maximum Trajectory Divergence Index (TDI) allowed.
• --max-cost-delta: Maximum cost increase percentage allowed.
• --baseline, -b PATH: Compare against a persistent baseline trace file (see Baseline workflow).
• --update-baseline: Overwrite the persistent baseline with the candidate after a clean diff.
• --config PATH: Load defaults from an agentdiff.toml (auto-discovered if not given).

Config-as-code (agentdiff.toml)

Commit your thresholds, adapter, and baseline path next to your traces instead of repeating CLI flags. Explicit flags always win over config.

[compare]
detect_loops = true
strict_tool_signatures = false

[adapter]
name = "auto"            # auto, generic, openinference, langfuse, langsmith, openai_agents

[cli]
format = "terminal"      # terminal, json, markdown, pr
baseline = "baselines/current.json"
max_loops = 0
max_divergence = 0.3
max_cost_delta = 10.0

[assertions]             # defaults used by assert_no_regressions / pytest plugin
max_divergence = 0.25
max_cost_increase_pct = 5.0
allow_loops = false
max_wasted_effort = 0.1

AgentDiff auto-discovers agentdiff.toml from the current directory upward, or you can point at it explicitly with --config.

Baseline workflow

Keep a single baseline.json file committed to your repo instead of hand-managing two trace files. The first run establishes the baseline; later runs compare against it and advance it only on clean diffs.

# First run: stores candidate as the baseline, exits 0
agentdiff baseline.json today.json --baseline baseline.json --update-baseline

# Later runs: compare today's run against the stored baseline
agentdiff baseline.json today.json --baseline baseline.json --update-baseline --fail-on-regression

• If baseline.json does not exist and --update-baseline is set, the candidate is copied in as the baseline and the command exits 0.
• If it does not exist and --update-baseline is omitted, the command exits 2 with a helpful message.
• On a regression the baseline is never overwritten, and --fail-on-regression exits 1.

2. Python SDK & Pytest Integration

Catch agent loop regressions or token cost spikes in your test suites:

import pytest
from agentdiff import load_trace, compare
from agentdiff.testing import assert_no_regressions

def test_agent_refactor_efficiency():
    # Load traces from disk (auto-detects the telemetry format)
    baseline = load_trace("tests/traces/baseline.json")
    candidate = load_trace("tests/traces/candidate.json")

    # Run the comparison
    report = compare(baseline, candidate)

    # Expressive assertion helper that raises detailed error messages on regression
    assert_no_regressions(
        report,
        max_divergence=0.25,        # TDI threshold [0.0 - 1.0]
        max_cost_increase_pct=5.0,  # Max cost increase allowed
        allow_loops=False,           # Reject if tool loops are detected
        max_wasted_effort=0.10      # Max Wasted Effort Index (WEI) allowed
    )

3. GitHub Action

Gate a PR on agent trajectory regressions with the reusable composite action. Pin it to a release tag and point package at the published package (or a git+ path / local directory for pre-release testing):

name: AgentDiff Gate
on:
  pull_request:

jobs:
  agentdiff:
    runs-on: ubuntu-latest
    steps:
      - uses: actions/checkout@v4
      - uses: actions/setup-python@v5
        with:
          python-version: "3.11"
      - uses: lostmartian/agentdiff/.github/actions/agentdiff-check@v0.2.2
        with:
          baseline: traces/baseline.json   # committed baseline trace
          candidate: traces/candidate.json # generated by an earlier step
          update-baseline: "false"
          max-divergence: "0.3"
          max-cost-delta: "10.0"

The action installs the package (default agent-trajectory-diff from PyPI), runs agentdiff --fail-on-regression, and fails the job when divergence, loops, or cost spikes exceed the thresholds.

Available inputs:

Core Metrics

Development & Operations

This project utilizes uv to manage environments and dependencies. Automation tasks are defined in the Makefile:

• make lint / make format: Run Ruff linter checks and formatter.
• make test: Run pytest suite (including style & formatting assertions).
• make build: Package the library into source and wheel distributions in dist/.
• make website-dev: Start the Next.js landing and documentation site local server.
• make website-build: Build the Next.js static output in website/out/.

Repository Layout

• src/: Python source code package modules.
• tests/: Quality assurance unit tests.
• website/: Next.js web application and documentation pages.