TimeTravel

Time-travel debugging for AI agents — an OTel-in / replay-out engine

TimeTravel an agent to any span, change a prompt, and re-run live from there — branching a new timeline you can diff against the original. Consumes standard OpenTelemetry / OpenInference traces. No cloud, no API keys, no persistent production proxy, no data leaving the machine.

What it does

A developer running an agent on qwen3:32b via Ollama captures a run with any OpenInference/OTel instrumentor, timetravels to span 4, edits the system prompt, branches the execution forward live against the same local model, and sees a side-by-side diff of what changed — all offline, in under a minute of setup.

Architecture (the key insight)

Capture = PASSIVE   ->  an OTel span only exists *after* a call completes.
                        OpenTelemetry + OpenInference solve this. We ingest.
Replay  = ACTIVE    ->  to timetravel we *inject* the cached response during a
                        re-run. That is runtime patching, not observability.

So TimeTravel does not need its own capture proxy. It needs:

1. A local OTLP receiver that stores traces into SQLite (production path, zero agent-side lock-in).
2. A decorator-first workbench that invokes registered agents with typed inputs and a TimeTravelContext only during a debug session.
3. An opt-in replay-time LLM-client wrapper (timetravel.replay()) — only active during a debug session, never in production.

Status

Quick start

pip install agent-timetravel
# The wheel includes the built timeline UI; no separate frontend build is needed.
agent-timetravel serve --port 4318 --db ./timetravel.db
# Point your OTel/OpenInference-instrumented agent at:
#   OTEL_EXPORTER_OTLP_ENDPOINT=http://127.0.0.1:4318
# Run a trace, then inspect it in the browser:
agent-timetravel ui --port 8484 --db ./timetravel.db
# → http://127.0.0.1:8484/ui

Decorator-first agents

The current workbench entry point is a TimeTravel object with typed agent inputs:

from agent_timetravel import TimeTravelContext, timetravel

@timetravel.agent(description="Answer a question")
async def answer(question: str, context: TimeTravelContext | None = None) -> str:
    return question

Run agent-timetravel dev app:timetravel to expose the agent list and interactive sessions at the local UI. Direct calls to answer(...) remain ordinary pass-through calls; TimeTravelContext is injected only for workbench runs. For a custom title or separate registry, use:

from agent_timetravel import TimeTravel, TimeTravelContext

timetravel = TimeTravel(title="Research")

Existing names such as debugger remain supported.

During a workbench run, TimeTravel auto-activates interception for the frameworks below — no manual model wrapping required:

• OpenAI — official OpenAI Python SDK Chat Completions calls (chat.completions.create, sync and async), including when the SDK is configured for an OpenAI-compatible endpoint.
• LangGraph / langchain — every BaseChatModel / BaseTool invoke/ainvoke (which bind_tools bindings and ToolNode calls route through), so graphs that construct models inside their nodes are stepped, replayed, and captured unchanged. This includes graphs built with deepagents, LangGraph's prebuilt create_react_agent, and similar frameworks on top of langchain-core.

Other framework replay adapters (CrewAI, PydanticAI, ADK, SmolAgents) remain explicit: use the factories in docs/replay-adapters.md when needed. Generic decorator auto-activation for those frameworks is currently unavailable, and the workbench reports the actionable adapter/wrapper instead of claiming the framework is installed.

Run any LangGraph app

A foreign LangGraph project needs no TimeTravel-specific code. Install agent-timetravel[langgraph] alongside the app, then point the CLI at the exported graph:

pip install agent-timetravel[langgraph]   # or: pip install -e /path/to/timetravel[langgraph]
agent-timetravel app:main                          # ≡ agent-timetravel dev app:main

app:main may be a timetravel.TimeTravel registry, a compiled LangGraph graph / langchain runnable (wrapped into a one-agent registry automatically), or a plain callable. The workbench opens in your browser (--no-open to suppress) with the graph registered as an interactive agent: start it from the form, and every LLM and tool call pauses in the step-by-step debugger.

Replay a recorded trace

# Read-only inspection (prints cursor + branch info):
agent-timetravel replay <trace_id> --mode frozen --db ./timetravel.db

# Branch from span index 4 and go live from there:
agent-timetravel replay <trace_id> --branch-at 4 --mode branch --db ./timetravel.db

From Python (the load-bearing integration point)

from agent_timetravel.replay import replay
from agent_timetravel.storage import TraceStore

store = TraceStore("~/.agent-timetravel/timetravel.db")

# Frozen replay — zero outbound calls, deterministic:
with replay(store, trace_id="<trace>", mode="frozen"):
    agent.run()  # every LLM call served from the recorded spans

# Branch from span 4, then go live:
with replay(store, trace_id="<trace>", branch_at=4, mode="branch"):
    agent.run()  # spans 0-3 from recording, span 4+ calls your live model

Wrap a framework model (Phase 6 adapters)

One import + one wrapper call per agent — no upstream framework changes:

# Google ADK
from agent_timetravel.adapters.adk import replay_llm
agent = Agent(model=replay_llm(real_adk_llm))

# CrewAI
from agent_timetravel.adapters.crewai import replay_llm
crew.llm = replay_llm(real_crewai_llm)

# PydanticAI
from agent_timetravel.adapters.pydantic_ai import replay_model
agent = Agent(model=replay_model(real_model))

# HuggingFace SmolAgents
from agent_timetravel.adapters.smolagents import replay_model
agent.model = replay_model(real_smol_model)

# LangGraph (Phase 3 — adapter pattern origin)
from agent_timetravel.adapters.langgraph import replay_chat_model
graph.compiled = replay_chat_model(real_chat_model)

Install the optional extras as needed:

pip install agent-timetravel[adk]              # one framework
pip install agent-timetravel[adk,pydantic-ai] # several TimeTravel-managed frameworks
pip install crewai                             # CrewAI adapter dependency
pip install agent-timetravel[adapters]         # all four

Without an extra installed, the corresponding factory raises agent_timetravel.adapters.<fw>.AdapterError with an actionable install hint at call time. agent-timetravel --version and import agent_timetravel.adapters.<fw> both succeed without any framework installed.

Eval a replay candidate against a baseline (Phase 5.5)

agent-timetravel eval suite.yaml --db ./timetravel.db --suite-name my-suite
# exit 0 = PASS, 1 = FAIL, 2 = ERROR/validation

Run the live decorator-first workbench

The verified local demo uses the decorator-first registry and a seeded OpenAI-compatible Gemma/Unsloth endpoint. Start the backend with examples/start_deep_research_stepping.py, run Vite on port 5174, and open the workbench on port 8484. The complete commands and acceptance walkthrough are in docs/interactive-workbench-testing.md.

The current walkthrough verifies fresh sessions, automatic PROCEED advancement, substantive-call pauses after the final response, separate thinking, token/cost/latency/context panels, checkpoints, saved-step navigation, no-call timetravel/forward, continue-from-checkpoint, and the available edit, variant, assertion, review, and regression flows. It does not claim that every planned recording or framework-integration feature is implemented or demonstrated.

The older register_runner/POST /api/v1/sessions surface remains available as an advanced escape hatch for custom runners and explicit framework wiring; it is not the primary decorator-first usage path.

Development

# from agent_timetravel/
python -m venv .venv && source .venv/bin/activate
pip install -e ".[dev]"

# full quality gate (run before commit)
ruff check src/agent_timetravel tests
pylint src/agent_timetravel/
mypy --strict src/agent_timetravel
python -m pytest tests --no-cov -q

# per-phase security scan (ruff S-rules + bandit, deepsec if available)
python scripts/security_scan.py --phase <N>

# frontend dev server (Vite 6 — must run from web/; --host avoids IPv6 trap)
cd web && pnpm dev   # or:  node_modules/.bin/vite --host 127.0.0.1

Latest verified full suite: 527 passed, 13 skipped, 49 deselected, and 3 warnings. Frontend TypeScript typecheck and production build passed, and git diff --check passed. See docs/interactive-workbench-testing.md for the live workbench checks.

Layout

timetravel/
  src/agent_timetravel/          Python package
    adapters/          Phase 6 — per-framework replay wrappers (adk, crewai,
                       pydantic_ai, smolagents, langgraph) + shared _common.py
    receiver.py        OTLP/HTTP ingest (Phase 1)
    replay.py          Frozen / branch / full replay engine (Phase 3)
    checkpoint.py      State snapshot/restore (Phase 4)
    diff.py            Trace diff (Phase 5)
    eval_api.py        Suite runner + baseline diff (Phase 5.5)
    cli.py             Click-based CLI: serve / ui / replay / eval / version
  tests/               pytest suites (latest full suite: 527 passed)
  web/                 React + Vite + TypeScript timeline UI (P2)
  docs/
    phases/            Per-phase: QA, security, dev-handoff, design
    diagrams/          Architecture + sequence (.mmd) for each phase
  scripts/
    dev_seed_serve.py  Local dev harness
    security_scan.py   ruff S + bandit per-phase vulnerability scan
  .deepsec/            Vulnerability scan reports (when deepsec available)
  pyproject.toml       Strict ruff + pylint + mypy config; optional extras

See docs/README.md for a navigable index of all phase docs and diagrams. For wheel builds, installation requirements, and release publishing, see docs/packaging-release.md.

Out of scope (v1)

• A bespoke capture proxy / capture decorator SDK (OTel + OpenInference solve this; Phase 6 adds a thin adapter factory for replay, not capture).
• Cloud / multi-user / team / sync (a trace is a local SQLite file).
• MCP security sandbox, model routing, mobile/remote access.

See plan.md (parent dir) for the full phased plan.