Time-travel debugging for AI agents.

pip install ai-agent-vcr

No API keys. No cloud. Runs entirely locally.

LangSmith shows you what happened. Agent VCR lets you change it.

from agent_vcr import VCRRecorder

recorder = VCRRecorder()
recorder.start_session("my_run")

# Your existing agent code — unchanged
state = {"query": "build a REST API"}
state = planner(state)          # step 1
recorder.record_step("planner", input_state, state)

state = coder(state)            # step 2
recorder.record_step("coder", input_state, state)

recorder.save()                 # → .vcr/my_run.vcr

with VCRRecorder() as recorder:
    recorder.start_session("my_run")
    # ... your agent code ...
# auto-saved on exit

from agent_vcr import VCRPlayer
from agent_vcr.models import ResumeConfig

player = VCRPlayer.load(".vcr/my_run.vcr")

# Inspect any step
print(player.goto_frame(0))     # {'query': 'build a REST API', ...}
print(player.goto_frame(1))     # {'plan': '...', 'steps': [...], ...}
print(player.get_errors())      # see what failed

# Diff two frames
diff = player.compare_frames(0, 1)
# {'added': {'plan': ...}, 'modified': {'query': ...}, ...}

# Fix and resume from step 1 with a different plan
player.resume(
    agent_callable=coder,
    config=ResumeConfig(
        from_frame=1,
        state_overrides={"plan": "use FastAPI instead of Flask"}
    )
)

from langgraph.graph import StateGraph
from agent_vcr import VCRRecorder
from agent_vcr.integrations.langgraph import VCRLangGraph

graph = StateGraph(MyState)
graph.add_node("planner", planner_node)
graph.add_node("coder", coder_node)
graph.add_edge("planner", "coder")

recorder = VCRRecorder()
graph = VCRLangGraph(recorder).wrap_graph(graph)  # one line

result = graph.invoke({"query": "Build a todo app"})
recorder.save()

from crewai import Crew
from agent_vcr import VCRRecorder
from agent_vcr.integrations.crewai import VCRCrewAI

recorder = VCRRecorder()
recorder.start_session("crew_run")

crew = Crew(agents=[researcher, writer], tasks=[task1, task2])
result = VCRCrewAI(recorder).kickoff(crew)

recorder.save()

pip install "ai-agent-vcr[crewai]"
pip install "ai-agent-vcr[langgraph]"

from agent_vcr import VCRRecorder
from agent_vcr.integrations.langgraph import vcr_record

recorder = VCRRecorder()

@vcr_record(recorder, node_name="research_step")
def research(state: dict) -> dict:
    return {"findings": search(state["query"])}

from agent_vcr import VCRRecorder
from agent_vcr.integrations.openhands import ACIDWorkspace

recorder = VCRRecorder()
acid = ACIDWorkspace("/my/workspace", recorder=recorder)

acid.begin(session_id="task-001")        # isolated git branch
acid.savepoint(state, node_name="coder") # checkpoint state + filesystem
acid.savepoint(state, node_name="tester")

# Agent writes bad code at step 4 — rollback
acid.rollback(to_frame_index=1)
# git reset --hard → bad files are GONE from disk, not just hidden

acid.commit()                            # merge clean branch into main

python examples/acid_golden_run.py

from agent_vcr.golden_cache import GoldenRunCache

cache = GoldenRunCache()

# After a successful run:
cache.save_golden_run("Build a REST API with JWT auth", recorder)

# Next time — instant, $0.00:
outputs, ledger = cache.replay("Build a REST API with JWT auth")
print(ledger)
# CostLedger(saved=100% | $0.0123 | 4,100 tokens | 2,349ms)

python examples/acid_golden_run.py

RUN 1: Original            RUN 2: Ghost Replay
Tokens:    4,100           Tokens:    0
Cost:    $0.0123           Cost:    $0.00
Latency: 2,350ms           Latency:  1ms

💰 Savings: 100% · $0.0123 · 4,100 tokens · 2,349ms

vcr-tui .vcr/my_run.vcr

┌──────────────────────────────────────────────────────────┐
│ 📼 Agent VCR TUI              Session: my_run · 8 frames │
├──────────────────────────────────────────────────────────┤
│ ▶ Frame 0  │ planner     │ 100ms  │ ●                    │
│   Frame 1  │ researcher  │  250ms │ ●                    │
│   Frame 2  │ coder       │  480ms │ ✗ ERROR              │
│   Frame 3  │ tester      │   80ms │ ●                    │
├──────────────────────────────────────────────────────────┤
│  State at frame 0:                                       │
│  { "query": "build a todo app",                          │
│    "context": "...",                                     │
│    "plan": null }                                        │
├──────────────────────────────────────────────────────────┤
│ ← → navigate  │ e edit  │ d diff  │ r resume  │ q quit   │
└──────────────────────────────────────────────────────────┘

vcr-server .vcr/
# Open localhost:8000

original_run ────────────────────────────────────────────► [done]
               │ frame 3
               ╰──► fork_v1 ──► [coder] ──► [tester] ──► [done]
               │
               ╰──► fork_v2 ──► [coder] ──► [done]

from openhands_sentinel import Sentinel
from agent_vcr import VCRRecorder

recorder = VCRRecorder()
sentinel = Sentinel(recorder=recorder)
sentinel.attach(runtime.event_stream)  # 3 lines, auto-intercepts every file write

python examples/sentinel_demo.py

STEP 1: Agent writes auth/utils.py
🛡️ SENTINEL: auth/utils.py — CLEAN ✓

STEP 2: Agent writes handlers.py
🛡️ SENTINEL: VIOLATIONS DETECTED!
  CRITICAL  hash_password() already exists in auth/utils.py:8 — reuse it
  CRITICAL  handle_auth_request() is 109 lines (max 40) — break it up
  CRITICAL  Cyclomatic complexity 32 (max 8) — simplify
  WARNING   9 parameters (max 5) — use a config object

STEP 3: Agent self-corrects
🛡️ SENTINEL: handlers.py — CLEAN ✓ All issues resolved!

📼 Audit trail: .vcr/sentinel-demo.vcr

sentinel scan ./my-ai-project

recorder = VCRRecorder(
    output_dir=".vcr",     # where to save sessions
    auto_save=True,        # flush frames to disk as you go
    diff_mode=False,       # also store state diffs (jsonpatch)
)

recorder.start_session(session_id="my_run", tags=["prod"])
recorder.record_step(node_name, input_state, output_state, metadata)
recorder.record_llm_call(node_name, prompt, response, tokens, cost_usd)
recorder.record_tool_call(node_name, tool_name, args, result)
recorder.record_error(node_name, input_state, error)
recorder.save() -> Path
recorder.fork(from_frame=3) -> VCRRecorder  # branch from a frame

# Context manager — auto-saves on exit
with VCRRecorder() as r:
    r.start_session("run")
    ...

player = VCRPlayer.load(".vcr/my_run.vcr")
player = VCRPlayer.load_by_id("my_run")

player.goto_frame(index)           # → dict (output state at frame N)
player.get_frame(index)            # → Frame object
player.get_input_state(index)      # → dict (input state at frame N)
player.list_nodes()                # → ['planner', 'coder', ...]
player.get_errors()                # → [Frame, ...]
player.compare_frames(a, b)        # → {'added': {}, 'removed': {}, 'modified': {}}
player.get_total_latency()         # → float (ms)
player.get_total_tokens()          # → int
player.get_total_cost()            # → float (USD)

player.resume(
    agent_callable,                # your agent function
    config=ResumeConfig(
        from_frame=7,              # rewind to BEFORE step 7 ran
        state_overrides={"k": "v"},# apply these before re-running
        mode=ResumeMode.FORK,      # FORK | REPLAY | MOCK
    )
) -> str                           # new session ID

acid = ACIDWorkspace("/workspace", recorder=recorder)
acid.begin(session_id="task-001")
acid.savepoint(state, node_name="coder")
acid.rollback(to_frame_index=2)    # git reset --hard
acid.commit()                      # merge to main

from agent_vcr.golden_cache import GoldenRunCache

cache = GoldenRunCache(cache_dir=".vcr/golden")
cache.save_golden_run(task_description, recorder) -> str  # fingerprint
cache.replay(task_description)    -> (outputs, CostLedger)
cache.invalidate(task_description) -> bool
cache.list_runs()                  -> list[dict]

# Basic recording and playback
python examples/basic_usage.py

# Time-travel: rewind, edit state, resume (with assertion)
python examples/time_travel_demo.py

# LangGraph auto-instrumentation
python examples/langgraph_integration.py

# ACID transactions + Ghost Replay (most impressive demo)
python examples/acid_golden_run.py

# OpenHands Sentinel: agent self-correction live
python examples/sentinel_demo.py

# Async recording
python examples/async_example.py

{"type": "session", "data": {"session_id": "my_run", "created_at": "2024-01-01T00:00:00Z", ...}}
{"type": "frame", "data": {"node_name": "planner", "input_state": {...}, "output_state": {...}, "metadata": {"latency_ms": 120}}}
{"type": "frame", "data": {"node_name": "coder", ...}}

pytest tests/benchmarks/ -v --benchmark-only

git clone https://github.com/ixchio/agent-vcr.git
cd agent-vcr
pip install -e ".[dev,tui]"
pytest tests/unit/ -v