Frameworks

Frameworks build their own SDK clients internally, so there's nothing to wrap — PatchSet patches the underlying SDK classes (sync and async Anthropic/OpenAI chat, plus the OpenAI Responses API), so any framework built on top of them is captured automatically.

Framework	How	CI-verified
Pydantic AI	PatchSet — async OpenAI/Anthropic clients	Yes
OpenAI Agents SDK	PatchSet — Responses API	Yes
LangChain	PatchSet — sync + async chat	Yes
LangGraph	LangGraphAdapter for node-level events, or PatchSet	Yes (existing)
CrewAI	Works via LiteLLM's OpenAI-compatible sync path	Documented, not CI-verified

The universal pattern — wrap the framework call, nothing else changes:

from agentsnap import PatchSet
from agentsnap.core.asserter import AgentAsserter

with PatchSet():
    with AgentAsserter("my_framework_agent") as a:
        a.output = my_pydantic_ai_agent.run_sync("What is Python?").output

Caveats

• Streamed OpenAI Responses-API runs (responses.create(stream=True)) pass through unrecorded this iteration — non-streaming Responses calls and all chat-completions streaming (sync + async) are recorded and replayable.
• The model-tools check (see Model tools) is gated trace-wide: if any call in the trace is a streamed call or a non-Anthropic/OpenAI provider, the whole run's model_tools/model_tool_args comparison is skipped.

Real-framework verification tests live in tests/frameworks/ (marker frameworks, pytest.importorskip-guarded, run via a separate CI job with .[dev,frameworks] installed) — they drive each framework's real code path through an offline mock transport, asserting on agentsnap's recorded trace, not framework internals.

LangGraph

Wrap your compiled graph with LangGraphAdapter. It injects a callback handler that captures LLM and tool events at the node level, not just the top-level graph invocation:

from agentsnap.adapters.langgraph import LangGraphAdapter

graph = build_my_graph()   # your compiled StateGraph
agent = LangGraphAdapter(graph)

with AgentRecorder("langgraph_agent") as rec:
    result = agent.invoke({"messages": [HumanMessage(content="Hello")]})
    rec.output = str(result)

Node-level capture means the snapshot reflects what each node in your graph actually called — tool calls within nodes, intermediate LLM calls, and their responses — not just the final output.