git diff for how your AI agent thinks.

Detect the exact step where two agent runs diverged — which tool it switched to, when its reasoning changed, what prompt edit caused the fork. Built for CI/CD on AI agents.

Quick Start · How It Works · CLI Reference · GitHub Action · vs. Alternatives · Contributing · Changelog

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Note

agentdelta evaluates behavior, not output. Two runs can produce identical final answers while the agent took completely different paths — calling different tools, in different orders, with different reasoning chains. agentdelta catches that.

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Why

Most LLM evaluations check: did the agent get the right answer? They miss the harder question: did it get there the same way?

• Prompt changes are invisible — tweaking a system prompt can silently flip which tool an agent calls first, changing latency, cost, and reliability without touching the output
• Model upgrades change behavior — moving from GPT-4o-mini to GPT-4o or Claude 3.5 Sonnet → Opus changes reasoning paths even when benchmark scores stay flat
• Tool-calling regressions are silent — an agent that starts calling web_search instead of read_database may produce correct answers today and fail tomorrow when the web page moves

agentdelta gives every agent deployment a behavioral fingerprint so you can detect divergence in CI before it reaches production.

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Quick start

Install:

pip install agentdelta
# or zero-install with pipx:
pipx run agentdelta --help

Note: First install downloads PyTorch and CUDA libs (~2.5 GB). For CPU-only systems: pip install agentdelta --extra-index-url https://download.pytorch.org/whl/cpu

With LangChain/LangGraph:

pip install "agentdelta[langchain]"

The [langchain] extra is only needed if instrumenting a real LangChain agent. AgentdeltaCallback can be used directly with any framework.

Capture two runs (self-contained — no LangChain required):

from agentdelta.instrument import AgentdeltaCallback
from agentdelta import AgentTrace, diff_traces
from agentdelta.report import print_diff

class _FakeLLMResponse:
    def __init__(self, text):
        self.generations = [[type("G", (), {"text": text})()]]

def build_trace(run_id, tool_name):
    cb = AgentdeltaCallback(run_id=run_id)
    cb.on_chain_start({}, {"input": "What is the weather in Tokyo?"})
    cb.on_llm_end(_FakeLLMResponse("I should look up the weather."))
    cb.on_tool_start({"name": tool_name}, "location='Tokyo'")
    cb.on_tool_end('{"temp": 22, "condition": "sunny"}')
    cb.on_llm_end(_FakeLLMResponse("The weather in Tokyo is 22C and sunny."))
    cb.on_chain_end({"output": "Tokyo: 22C, sunny."})
    return cb.trace

# Baseline run (before your change)
trace_a = build_trace("v1.0", tool_name="get_weather")
trace_a.save("baseline.jsonl")

# Candidate run (after your change — switched to a different tool)
trace_b = build_trace("v1.1", tool_name="web_search")
trace_b.save("candidate.jsonl")

Diff them:

agentdelta diff baseline.jsonl candidate.jsonl

╭───────────────────────────────────────────────╮
│ agentdelta  v1.0 vs v1.1                      │
╰───────────────────────────────────────────────╯
  🔴 REGRESSION DETECTED  3/6 steps matched (50.0%)  1 changed  +1 added  -1 removed

╭────────────────────── Fork Point ──────────────────────╮
│ ⚡ First fork at step 3                               │
│ Tool selection changed: 'get_weather' → 'web_search'  │
│                                                        │
│   Before: get_weather(location='Tokyo')                │
│   After:  web_search(query='Tokyo weather today')      │
╰────────────────────────────────────────────────────────╯

 Step   Status    Type          Detail
    3   CHANGED   🔧 tool_call  Tool selection changed: 'get_weather' → 'web_search'
    4   REMOVED   ↩ tool_return  - [tool_return] {"temp": 22, "condition": "sunny"}
    5   CHANGED   🧠 llm        Reasoning path diverged (similarity: 0.85)

---

How it works

flowchart LR
    A[Agent Run A\nbaseline.jsonl] --> E[embed_trace\nall-MiniLM-L6-v2]
    B[Agent Run B\ncandidate.jsonl] --> E
    E --> AL[align_traces\nsliding-window cosine similarity]
    AL --> D[diff_traces\nfork threshold = 0.70]
    D --> FP[ForkPoint\nfirst divergent step]
    D --> R[Report\nRich · JSON · Markdown]

Loading

1. Embed — each node's content (LLM reasoning, tool calls, tool outputs) is embedded with all-MiniLM-L6-v2 (22M params, runs locally, no API key)
2. Align — sliding-window cosine similarity matches nodes by meaning, not by position — insertions and deletions are handled gracefully
3. Fork — the first aligned pair whose similarity falls below fork_threshold (default 0.70) becomes the ForkPoint
4. Report — Rich terminal table, JSON for programmatic use, or Markdown for GitHub PR comments

See ARCHITECTURE.md for the full data flow and algorithm details.

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Features

Feature	Description
Semantic step alignment	Matches steps by meaning, not index — handles insertions and deletions
Fork point detection	Pinpoints the first divergent step with a human-readable explanation
Tool change detection	Identifies when the agent switched tools, even with identical arguments
Reasoning path diff	Detects LLM reasoning divergence, not just output changes
LangChain instrumentation	One-line record() context manager — no agent code changes
Offline inference	Runs entirely locally — no OpenAI/Anthropic API calls for the diff itself
CI/CD integration	--exit-code flag for pipeline failures; GitHub Action available
Multiple output formats	Rich terminal · JSON · GitHub PR Markdown
JSONL trace format	Human-readable, git-diffable, framework-agnostic
Content-addressed IDs	Same reasoning step → same node ID across runs

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Python API

from agentdelta import AgentTrace, diff_traces
from agentdelta.report import print_diff, to_json, to_markdown

trace_a = AgentTrace.load("baseline.jsonl")
trace_b = AgentTrace.load("candidate.jsonl")

result = diff_traces(trace_a, trace_b, fork_threshold=0.70, match_threshold=0.85)

# Terminal output
print_diff(result)

# Programmatic access
if result.has_regression:
    fp = result.fork_point
    print(f"Fork at step {fp.step_a}: {fp.description}")
    print(f"Similarity: {fp.similarity:.2f}")

# CI/CD JSON
json_str = to_json(result)

# GitHub PR comment
markdown_str = to_markdown(result)

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CLI Reference

agentdelta diff TRACE_A TRACE_B [OPTIONS]

Option	Default	Description
--format	rich	Output format: rich | json | markdown
--fork-threshold	0.70	Similarity below this marks a fork point
--match-threshold	0.85	Similarity above this is a match (no change)
--show-matches	false	Include unchanged steps in terminal output
--exit-code	false	Exit 1 if regression detected (for CI)

agentdelta inspect TRACE_FILE

Prints a step-by-step summary of a single trace file.

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Trace format

Traces are .jsonl files — one JSON object per line. Human-readable and git-diffable.

{"type": "trace_meta", "run_id": "v1.0"}
{"type": "node", "step": 1, "node_type": "start",      "content": "What is the weather in Tokyo?", ...}
{"type": "node", "step": 2, "node_type": "llm",        "content": "I should look up the current weather.", ...}
{"type": "node", "step": 3, "node_type": "tool_call",  "content": "get_weather(location='Tokyo')", ...}
{"type": "node", "step": 4, "node_type": "tool_return","content": "{\"temp\": 22, \"condition\": \"sunny\"}", ...}
{"type": "edge", "source_step": 1, "target_step": 2, "edge_type": "sequence", ...}

You can generate traces from any agent framework by writing nodes and edges directly, or use the LangChain callback for automatic capture.

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GitHub Action

Use agentdelta directly in your GitHub Actions workflow:

# .github/workflows/agent-regression.yml
- name: Behavioral diff
  uses: sandeep-alluru/agentdelta@v0.1.0
  with:
    baseline: traces/baseline.jsonl
    candidate: traces/candidate.jsonl
    fail-on-regression: "true"

- name: Post diff as PR comment
  uses: marocchino/sticky-pull-request-comment@v2
  with:
    path: agentdelta-diff.md

Or use the CLI directly:

- name: Install agentdelta
  run: pip install agentdelta

- name: Behavioral diff
  run: |
    agentdelta diff traces/baseline.jsonl traces/candidate.jsonl \
      --format markdown --exit-code > diff.md

- name: Post comment
  uses: marocchino/sticky-pull-request-comment@v2
  with:
    path: diff.md

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OpenAI integration

Codex CLI — the CODEX.md file at repo root gives OpenAI Codex full project context (architecture, invariants, build commands). Clone the repo and Codex is immediately project-aware.

Assistants API / Responses API — paste tools/openai-tools.json directly into your assistant definition to give it diff_traces, inspect_trace, and record_snippet as callable functions:

import json, openai

tools = json.loads(open("tools/openai-tools.json").read())

response = openai.chat.completions.create(
    model="gpt-4o",
    messages=[{"role": "user", "content": "Did my agent regress?"}],
    tools=tools,
)

GPT Actions / Custom GPTs — the openapi.yaml at repo root is a complete OpenAPI 3.1 spec. To register agentdelta as a ChatGPT Action:

1. Run pip install "agentdelta[api]" && uvicorn agentdelta.api:app (or deploy to any host)
2. In ChatGPT → My GPTs → Create → Add Action → import from openapi.yaml

---

Claude / MCP integration

Install the MCP server to use agentdelta as a native Claude tool — no CLI needed:

pip install "agentdelta[mcp]"

Add to your Claude Desktop config (~/.config/claude/claude_desktop_config.json on Linux, ~/Library/Application Support/Claude/claude_desktop_config.json on macOS):

{
  "mcpServers": {
    "agentdelta": {
      "command": "agentdelta-mcp"
    }
  }
}

Claude then has three tools: diff_traces, inspect_trace, record_snippet — callable directly in conversation with no shell commands.

Claude Code slash commands are included in the repo. After cloning, type /project: to see:

Command	What it does
/project:diff	Diff two trace files and explain the fork
/project:inspect	Summarise a single trace's execution path
/project:record	Generate copy-paste recording boilerplate
/project:add-adapter	Scaffold a new framework instrumentation adapter
/project:pr-prep	Run lint + types + tests + CHANGELOG check

---

vs. Alternatives

	agentdelta	LangSmith	Arize / Phoenix	Weave (W&B)
Behavioral diff (two runs)	✅ core feature	❌	❌	❌
Fork point detection	✅ step-level	❌	❌	❌
Offline / local	✅ no API key	❌ SaaS	❌ SaaS	❌ SaaS
CI exit code on regression	✅ --exit-code	❌	❌	❌
Git-diffable trace format	✅ JSONL	❌ proprietary	❌ proprietary	❌ proprietary
GitHub Action	✅	❌	❌	❌
Trace collection	LangChain/custom	✅ full platform	✅ full platform	✅ full platform
Eval / scoring	planned	✅	✅	✅
Cost	free / MIT	free tier + paid	free tier + paid	free tier + paid

agentdelta is not an observability platform — it is a diff tool. Use it alongside LangSmith or Phoenix for collection and scoring, and agentdelta for behavioral regression detection in CI.

---

Real-World Scenario

Fintech: Catching Silent Behavioral Regression in Loan Underwriting

A compliance team upgrades their loan-underwriting agent from model=claude-sonnet-3-5 to claude-sonnet-3-7. Benchmarks stay flat. But the agent now calls verify_income before pull_credit_score — reordering the decision chain in a way that violates Regulation B for 18% of borderline applications.

agentdelta catches this in CI before it reaches production:

from agentdelta.instrument import AgentdeltaCallback
from agentdelta import AgentTrace, diff_traces
from agentdelta.report import print_diff


class _FakeLLMResponse:
    def __init__(self, text):
        self.generations = [[type("G", (), {"text": text})()]]


def build_loan_trace(run_id, first_tool, second_tool):
    """Simulate a loan underwriting agent run with two sequential tool calls."""
    cb = AgentdeltaCallback(run_id=run_id)
    cb.on_chain_start({}, {"input": "Underwrite loan application #LN-8821."})
    cb.on_llm_end(_FakeLLMResponse("I need to gather applicant data to assess creditworthiness."))

    # First tool call — this is what changed between model versions
    cb.on_tool_start({"name": first_tool}, "applicant_id='LN-8821'")
    cb.on_tool_end('{"status": "ok", "result": "retrieved"}')
    cb.on_llm_end(_FakeLLMResponse("First check complete. Running second verification."))

    # Second tool call
    cb.on_tool_start({"name": second_tool}, "applicant_id='LN-8821'")
    cb.on_tool_end('{"status": "ok", "result": "retrieved"}')
    cb.on_llm_end(_FakeLLMResponse("All checks complete. Loan approved."))

    cb.on_chain_end({"output": "approved"})
    return cb.trace


# Baseline: claude-sonnet-3-5 called pull_credit_score first (Regulation B compliant)
baseline = build_loan_trace(
    run_id="sonnet-3-5-baseline",
    first_tool="pull_credit_score",
    second_tool="verify_income",
)
baseline.save("loan_baseline.jsonl")

# Candidate: claude-sonnet-3-7 now calls verify_income first (Regulation B violation)
candidate = build_loan_trace(
    run_id="sonnet-3-7-candidate",
    first_tool="verify_income",
    second_tool="pull_credit_score",
)
candidate.save("loan_candidate.jsonl")

# Load saved traces and diff
trace_a = AgentTrace.load("loan_baseline.jsonl")
trace_b = AgentTrace.load("loan_candidate.jsonl")

result = diff_traces(trace_a, trace_b)
print_diff(result)

# Compliance gate: fail CI if the first tool call changed
if result.has_regression and result.fork_point is not None:
    fork = result.fork_point
    baseline_content = fork.node_a.content if fork.node_a else None
    candidate_content = fork.node_b.content if fork.node_b else None
    if baseline_content != candidate_content:
        print(
            f"\n[COMPLIANCE ALERT] Tool call order changed at step {fork.step_a}:\n"
            f"  baseline : {baseline_content}\n"
            f"  candidate: {candidate_content}\n"
            "Regulation B requires credit score to be evaluated before income verification.\n"
            "18% of borderline approvals may now violate fair-lending rules.\n"
            "Block this deployment and review the model upgrade."
        )
        raise SystemExit(1)

What this catches that unit tests miss: Unit tests verify the final answer ("approved"/"denied"). agentdelta catches how the agent got there — the tool calling order that determines whether Regulation B is satisfied.

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Case Studies

See how teams are using agentdelta in production:

• Catching Invisible Behavioral Regressions in a Fraud Detection Agent
• Behavioral Fingerprinting for FDA-Regulated AI Model Upgrades

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Repository structure

agentdelta/
├── src/agentdelta/
│   ├── trace.py          # Data model: TraceNode, TraceEdge, AgentTrace
│   ├── embed.py          # Embeddings + sliding-window alignment
│   ├── diff.py           # Fork detection → DiffResult, ForkPoint
│   ├── instrument.py     # LangChain callback + record() context manager
│   ├── report.py         # Rich / JSON / Markdown output formatters
│   └── cli.py            # Click CLI (diff, inspect)
├── tests/                # 43 unit tests — pytest
├── examples/
│   └── demo.py           # Runnable end-to-end demo
├── assets/               # Logo, banner, demo GIF
├── .github/
│   ├── workflows/
│   │   ├── ci.yml        # Lint + test + coverage on push/PR
│   │   └── release.yml   # PyPI publish on tag push
│   ├── ISSUE_TEMPLATE/   # Bug report + feature request templates
│   └── PULL_REQUEST_TEMPLATE.md
├── action.yml            # Use agentdelta as a GitHub Action
├── ARCHITECTURE.md       # Full data flow + algorithm details
├── CONTRIBUTING.md       # How to contribute
├── CHANGELOG.md          # Release history
└── SECURITY.md           # Vulnerability reporting

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Development

git clone https://github.com/sandeep-alluru/agentdelta
cd agentdelta
python -m venv .venv && source .venv/bin/activate
pip install -e ".[dev]"
pre-commit install

make test       # run the full test suite (43 tests)
make lint       # ruff check + format
make typecheck  # mypy
make all        # lint + typecheck + test

See CONTRIBUTING.md for the full guide including how to add output formats and instrumentation adapters.

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License

MIT — see LICENSE.

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GitHub Topics

If you're adding this repo to GitHub, set these topics for maximum discoverability:

llm  agents  langchain  diff  regression-testing  mcp  behavioral-testing  ci-cd  openai  python

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