Messages view trace format reference

If you trace with a supported integration, your traces render in the Messages view automatically.

Use this reference when you’re tracing an agent framework or LLM client that isn’t on the supported list, emitting runs manually through RunTree or the REST API, or diagnosing a trace that does not render correctly in the Messages view. Extraction strategy refers to the per-integration logic that reads the LLM and tool runs in a trace and produces the ordered conversation the Messages view renders. For each supported integration, this page documents the metadata keys that determine which strategy LangSmith applies, the JSON shape the strategy expects on inputs and outputs, and how tool calls are paired with their results.

Tracing default on this page means the LangSmith SDK sets the relevant metadata key automatically when you use the documented entry point. Anything else is the integration vendor’s own instrumentation or a user override.

Extraction strategy resolution

For each trace, the first matching extraction strategy wins. Each strategy’s detection explicitly defers to others when it sees markers from another integration. The most common collision: ls_provider: "openai" paired with a LangChain-shaped payload; the OpenAI strategy defers to LangChain in that case. If no strategy matches the first run of the trace, the messages API returns 400 no adapter pair found for trace format.

Integration	Strategy	Primary metadata signal	Tracing default (LangSmith SDK)
Vercel AI SDK	vercel	`ls_integration: "vercel-ai-sdk"` or `ai_sdk_method`	Yes (`wrapAISDK`)
OpenAI Chat Completions	openai	`ls_provider: "openai"` or `"azure"` (no `use_responses_api`)	Yes (`wrap_openai`)
OpenAI Responses	openai	`ls_provider` plus `ls_invocation_params.use_responses_api: true`	Yes (`wrap_openai` responses)
OpenAI Agents SDK	openai	`ls_integration: "openai-agents-sdk"`	Yes (Python tracing processor)
Anthropic Messages (`wrap_anthropic`)	anthropic	`ls_message_format: "anthropic"` (must be set explicitly today)	Partial: provider set, format key is opt-in
Claude Agent SDK (Python)	anthropic	`ls_integration: "claude-agent-sdk"`	Yes
Claude Code	anthropic	`ls_integration: "claude-code"`	Set by claude-code itself
Claude Agent SDK (JS)	anthropic	Currently does not auto-match (emits `"claude-agent-sdk-js"`)	Provider yes, format no (gap)
LangChain chat models	langchain	`ls_integration: "langchain_chat_model"`	Yes (langchain-core)
LangGraph	langchain	`graph_id` or `langgraph_node`	Yes (langgraph)
`langchain.create_agent`	langchain	`ls_integration: "langchain_create_agent"` (falls through to other signals)	Yes (langchain)
Deep Agents	langchain	`ls_integration: "deepagents"` or `"deepagents-cli"`	Yes (deepagents)

LangChain / LangGraph

Covers BaseChatModel-derived LLM runs, LangGraph graphs, deepagents, and langchain.create_agent.

Detection

Any of the following on metadata triggers extraction:

ls_message_format is "langchain" (explicit override)
ls_integration is "langchain_chat_model", "deepagents", or "deepagents-cli"
graph_id is present (LangGraph root)
langgraph_node is present (LangGraph sub-run; sub-runs carry this even when graph_id is only on the root)

OpenAI detection explicitly defers to LangChain when these markers are present, so LangChain extraction wins even when ls_provider: "openai".

Tracing defaults

langchain-core BaseChatModel sets the following on every chat-model run:

metadata.ls_integration: "langchain_chat_model"
Plus the provider-specific _get_ls_params() output (ls_provider, ls_model_name, ls_model_type, ls_temperature, ls_max_tokens, ls_stop)

langchain.create_agent sets metadata.ls_integration: "langchain_create_agent" on the agent config.

langchain_create_agent is not in the explicit detection allowlist. It’s claimed today via the ls_provider/ls_message_format fallthroughs in the matching OpenAI/Anthropic detection, or via graph_id / langgraph_node when the agent runs inside LangGraph. If routing is unreliable, set metadata.ls_message_format: "langchain" explicitly.

LangGraph sets graph_id (root) and langgraph_node (every sub-run). Deep Agents sets metadata.ls_integration: "deepagents" on the root chain run; the CLI variant uses "deepagents-cli".

Run shape

LangChain serializes messages with its constructor format. The id array’s last element identifies the class:

{
  "lc": 1,
  "type": "constructor",
  "id": ["langchain", "schema", "messages", "AIMessage"],
  "kwargs": {
    "content": "...",
    "id": "run-abc-123",
    "type": "ai",
    "tool_calls": [
      {"name": "search", "args": {}, "id": "call_1", "type": "tool_call"}
    ],
    "tool_call_id": "call_1"
  }
}

Role mapping (last element of id → canonical role):

SystemMessage → system
HumanMessage → human
AIMessage → ai
ToolMessage, FunctionMessage → tool
ChatMessage → human

Inputs:

inputs.messages is [[msg, msg, ...]]. LangChain wraps in an extra array for batched generations; the outer array is unwrapped during extraction.

Outputs (multiple paths, tried in order):

outputs.generations[0][*].message: standard chat-model output
outputs.messages[]: direct messages (e.g., LangGraph state outputs)
outputs.output.update.messages[]: deepagents-cli subagent outputs; the inner messages carry tool_call_id linking to the subagent invocation
outputs.output (object): fallback for tool runs

Tool-call matching

tool_calls[*].id on the assistant message matches either kwargs.tool_call_id (constructor format) or top-level tool_call_id (flat format) on the tool-result message.

Dedup

Prefer kwargs.id (constructor format), then top-level id (flat format), then fall back to a role + content hash. Stable LangChain run IDs make dedup cheap across re-emissions.

Example trace

A LangChain chat-model run that issues a tool call, the tool run, and the follow-up. Note the double-nesting on inputs.messages ([[ ... ]]) and the generations envelope on outputs.

[
  {
    "id": "0001",
    "trace_id": "trace-0005",
    "run_type": "llm",
    "name": "ChatOpenAI",
    "metadata": {
      "ls_integration": "langchain_chat_model",
      "ls_provider": "openai",
      "ls_model_name": "gpt-4o"
    },
    "inputs": {
      "messages": [[
        {"lc": 1, "type": "constructor", "id": ["langchain","schema","messages","SystemMessage"],
         "kwargs": {"content": "You are a helpful assistant.", "id": "sys-1"}},
        {"lc": 1, "type": "constructor", "id": ["langchain","schema","messages","HumanMessage"],
         "kwargs": {"content": "what is the weather in paris?", "id": "hu-1"}}
      ]]
    },
    "outputs": {
      "generations": [[{
        "message": {
          "lc": 1, "type": "constructor",
          "id": ["langchain","schema","messages","AIMessage"],
          "kwargs": {
            "content": "",
            "id": "ai-1",
            "tool_calls": [
              {"name": "get_weather", "args": {"city": "Paris"}, "id": "call_abc", "type": "tool_call"}
            ]
          }
        }
      }]]
    }
  },
  {
    "id": "0002",
    "trace_id": "trace-0005",
    "parent_run_id": "0001",
    "run_type": "tool",
    "name": "get_weather",
    "metadata": {"ls_integration": "langchain_chat_model"},
    "inputs": {"city": "Paris"},
    "outputs": {
      "output": {
        "lc": 1, "type": "constructor",
        "id": ["langchain","schema","messages","ToolMessage"],
        "kwargs": {"content": "Sunny, 22C", "tool_call_id": "call_abc", "id": "tool-1"}
      }
    }
  },
  {
    "id": "0003",
    "trace_id": "trace-0005",
    "run_type": "llm",
    "name": "ChatOpenAI",
    "metadata": {
      "ls_integration": "langchain_chat_model",
      "ls_provider": "openai",
      "ls_model_name": "gpt-4o"
    },
    "inputs": {
      "messages": [[
        {"lc": 1, "type": "constructor", "id": ["langchain","schema","messages","SystemMessage"],
         "kwargs": {"content": "You are a helpful assistant.", "id": "sys-1"}},
        {"lc": 1, "type": "constructor", "id": ["langchain","schema","messages","HumanMessage"],
         "kwargs": {"content": "what is the weather in paris?", "id": "hu-1"}},
        {"lc": 1, "type": "constructor", "id": ["langchain","schema","messages","AIMessage"],
         "kwargs": {"content": "", "id": "ai-1",
                    "tool_calls": [{"name": "get_weather", "args": {"city": "Paris"}, "id": "call_abc", "type": "tool_call"}]}},
        {"lc": 1, "type": "constructor", "id": ["langchain","schema","messages","ToolMessage"],
         "kwargs": {"content": "Sunny, 22C", "tool_call_id": "call_abc", "id": "tool-1"}}
      ]]
    },
    "outputs": {
      "generations": [[{
        "message": {
          "lc": 1, "type": "constructor",
          "id": ["langchain","schema","messages","AIMessage"],
          "kwargs": {"content": "It's sunny and 22°C in Paris.", "id": "ai-2"}
        }
      }]]
    }
  }
]

LangGraph traces look the same but add graph_id / langgraph_node to the metadata, and may use outputs.messages[] directly instead of outputs.generations.

OpenAI (`wrap_openai`)

One extraction strategy covers both OpenAI API shapes (Chat Completions and Responses) and the OpenAI Agents SDK (Responses shape). Detection picks the API shape from metadata.

Detection

Decision tree, in order. The first rule that matches wins.

metadata.ls_integration:
- "openai-agents-sdk" → Responses
- "langchain_chat_model", "deepagents", "deepagents-cli" → not claimed (these emit LangChain-shaped payloads with ls_provider: "openai"; LangChain extraction takes them)
metadata.ls_message_format (explicit override, wins over the ls_provider heuristic):
- "responses" → Responses
- "completions" → Completions
- "langchain", "anthropic" → not claimed
- Unknown values fall through to rule 3 so future format strings keep working
metadata.graph_id or metadata.langgraph_node present → not claimed (LangGraph subtree; LangChain extraction takes it even when ls_provider: "openai")
metadata.ls_provider is "openai" or "azure":
- If metadata.ls_invocation_params.use_responses_api == true → Responses
- Otherwise → Completions

Tracing defaults

wrap_openai sets the following on every LLM run:

metadata.ls_provider: "openai" (or "azure" if the client is AzureOpenAI / AsyncAzureOpenAI)
metadata.ls_model_type: "chat" or "llm"
metadata.ls_model_name, ls_temperature, ls_max_tokens, ls_stop
metadata.ls_invocation_params: an allowlisted subset of the SDK call kwargs. When the call goes through client.responses.create or client.responses.parse, this dict contains use_responses_api: true.
run_type: "llm"
name: "ChatOpenAI", "OpenAI", "AzureChatOpenAI", or "AzureOpenAI" (overridable)

wrap_openai does not emit ls_integration or ls_message_format. Detection falls through to rule 4 (ls_provider). OpenAI Agents SDK uses a tracing processor (not a wrapper). On every span it sets:

metadata.ls_integration: "openai-agents-sdk"
metadata.ls_integration_version: package version
metadata.ls_agent_type: "root" on the root span
LLM spans also carry metadata.openai_trace_id and openai_span_id

The Agents SDK emits Responses-API-shaped payloads but with outputs = {"output": [...]} only. The rest of the Response envelope (id, model, tools, usage) lives in extra.metadata, not in outputs.

Run shape

Completions:

inputs.messages is an array of {role, content, tool_calls?, tool_call_id?, refusal?, name?, id?}
outputs.choices[*].message has the same shape (typically role: "assistant")
Tool call IDs live on tool_calls[*].id; tool-result messages link back via top-level tool_call_id

Responses:

Optional top-level inputs.instructions (string) is promoted to a synthetic system message and prepended.
inputs.input is an array of items where each item is one of:
- A simple {role, content} message
- A typed {type: "message", role, content}
- {type: "function_call", call_id, name, arguments} (assistant role)
- {type: "function_call_output", call_id, output} (tool role)
- {type: "reasoning", ...} (assistant role)
outputs.output has the same array shape for LLM runs. For tool runs it can be a string, an object, or a bare top-level object (Agents SDK case); each is wrapped as a tool-role message with content set to the JSON text.

Tool-call matching

Completions: tool_call_id on the tool-result message matches tool_calls[*].id on the prior assistant message.
Responses: call_id on function_call matches call_id on function_call_output. Bare-object tool-run outputs may carry call_id at the top level of outputs.

Dedup

Responses items dedup by id when present, else by item-type-specific content: call_id|arguments for function_call, call_id|output for function_call_output, and content for messages.

Example traces

Chat Completions (wrap_openai): three runs: a tool-calling assistant turn, the tool run, and the follow-up assistant turn with the final answer.

[
  {
    "id": "0001",
    "trace_id": "trace-0002",
    "run_type": "llm",
    "name": "ChatOpenAI",
    "metadata": {"ls_provider": "openai", "ls_model_name": "gpt-4o"},
    "inputs": {
      "messages": [
        {"role": "system", "content": "You are a helpful assistant."},
        {"role": "user", "content": "what is the weather in paris?"}
      ]
    },
    "outputs": {
      "choices": [{
        "index": 0,
        "finish_reason": "tool_calls",
        "message": {
          "role": "assistant",
          "content": null,
          "tool_calls": [{
            "id": "call_abc123",
            "type": "function",
            "function": {"name": "get_weather", "arguments": "{\"city\":\"Paris\"}"}
          }]
        }
      }]
    }
  },
  {
    "id": "0002",
    "trace_id": "trace-0002",
    "parent_run_id": "0001",
    "run_type": "tool",
    "name": "get_weather",
    "inputs": {"city": "Paris"},
    "outputs": {"tool_call_id": "call_abc123", "role": "tool", "content": "Sunny, 22C"}
  },
  {
    "id": "0003",
    "trace_id": "trace-0002",
    "run_type": "llm",
    "name": "ChatOpenAI",
    "metadata": {"ls_provider": "openai", "ls_model_name": "gpt-4o"},
    "inputs": {
      "messages": [
        {"role": "system", "content": "You are a helpful assistant."},
        {"role": "user", "content": "what is the weather in paris?"},
        {"role": "assistant", "content": null, "tool_calls": [
          {"id": "call_abc123", "type": "function", "function": {"name": "get_weather", "arguments": "{\"city\":\"Paris\"}"}}
        ]},
        {"role": "tool", "tool_call_id": "call_abc123", "content": "Sunny, 22C"}
      ]
    },
    "outputs": {
      "choices": [{
        "index": 0,
        "finish_reason": "stop",
        "message": {"role": "assistant", "content": "It's sunny and 22°C in Paris."}
      }]
    }
  }
]

Responses API (OpenAI Agents SDK): items are typed (function_call, function_call_output, message) rather than role-keyed. inputs.instructions is promoted to a synthetic system message.

[
  {
    "id": "0001",
    "trace_id": "trace-0003",
    "run_type": "llm",
    "name": "Helpful Assistant Response",
    "metadata": {"ls_integration": "openai-agents-sdk", "ls_model_name": "gpt-4.1"},
    "inputs": {
      "instructions": "You are a helpful assistant.",
      "input": [{"role": "user", "content": "what time is it in san francisco?"}]
    },
    "outputs": {
      "output": [{
        "type": "function_call",
        "call_id": "call_LVsl",
        "name": "get_time",
        "arguments": "{\"timezone\":\"America/Los_Angeles\"}",
        "id": "fc_0ed8"
      }]
    }
  },
  {
    "id": "0002",
    "trace_id": "trace-0003",
    "parent_run_id": "0001",
    "run_type": "tool",
    "name": "get_time",
    "metadata": {"ls_integration": "openai-agents-sdk"},
    "inputs": {"timezone": "America/Los_Angeles"},
    "outputs": {"output": "12:00 PM (America/Los_Angeles)", "call_id": "call_LVsl"}
  },
  {
    "id": "0003",
    "trace_id": "trace-0003",
    "run_type": "llm",
    "name": "Helpful Assistant Response",
    "metadata": {"ls_integration": "openai-agents-sdk", "ls_model_name": "gpt-4.1"},
    "inputs": {
      "instructions": "You are a helpful assistant.",
      "input": [
        {"role": "user", "content": "what time is it in san francisco?"},
        {"type": "function_call", "call_id": "call_LVsl", "name": "get_time", "arguments": "{\"timezone\":\"America/Los_Angeles\"}", "id": "fc_0ed8"},
        {"type": "function_call_output", "call_id": "call_LVsl", "output": "12:00 PM (America/Los_Angeles)"}
      ]
    },
    "outputs": {
      "output": [{
        "type": "message",
        "role": "assistant",
        "status": "completed",
        "content": [{"type": "output_text", "text": "It is currently 12:00 PM in San Francisco.", "annotations": []}]
      }]
    }
  }
]

Vercel AI SDK

Detection

Either of the following on extra.metadata of any LLM run triggers extraction:

ai_sdk_method key is present (any value), or
ls_integration is "vercel-ai-sdk"

Both are set automatically by the LangSmith Vercel AI SDK wrapper. The underlying language-model provider (OpenAI, Anthropic, Google, etc.) is irrelevant; the wrapper normalizes all of them into a single message envelope.

Tracing defaults

When the Vercel AI SDK wrapper is in use, the SDK sets on every LLM run:

metadata.ls_integration: "vercel-ai-sdk"
metadata.ai_sdk_method: "ai.doGenerate" or "ai.doStream"
metadata.ls_model_name: the model id
run_type: "llm"
name: "ai.doGenerate" or "ai.doStream" (overridable)

Run shape

LLM runs:

run_type: "llm"
inputs.messages or inputs.prompt contains the conversation
outputs.role is one of assistant, tool, user
outputs.content contains the emitted content. Tool calls are blocks within content carrying toolCallId and toolName.

Tool runs:

run_type: "tool"
inputs.toolCallId matches the LLM output’s toolCallId
inputs.toolName or name identifies the tool
outputs.output or outputs.result holds the tool result

Tool-call matching

Prefer matching by toolCallId; fall back to tool name when the ID is unavailable.

Example trace

A two-run trace: one LLM run that asks for a tool call, one tool run that returns the result. Each entry is one LangSmith run; inputs, outputs, and metadata are JSON-encoded strings on the wire (shown unescaped here for readability).

[
  {
    "id": "0001",
    "trace_id": "trace-0001",
    "run_type": "llm",
    "name": "ai.doGenerate",
    "metadata": {
      "ls_integration": "vercel-ai-sdk",
      "ai_sdk_method": "ai.doGenerate",
      "ls_model_name": "gpt-4o"
    },
    "inputs": {
      "prompt": [
        {"role": "user", "content": [{"type": "text", "text": "what's the weather in paris?"}]}
      ]
    },
    "outputs": {
      "role": "assistant",
      "content": [
        {"type": "tool-call", "toolCallId": "call_abc", "toolName": "get_weather", "input": {"city": "Paris"}}
      ]
    }
  },
  {
    "id": "0002",
    "trace_id": "trace-0001",
    "parent_run_id": "0001",
    "run_type": "tool",
    "name": "get_weather",
    "inputs": {"toolCallId": "call_abc", "toolName": "get_weather", "args": {"city": "Paris"}},
    "outputs": {"result": "Sunny, 22C"}
  }
]

Anthropic Messages (`wrap_anthropic`)

Covers wrap_anthropic (the Messages-API wrapper) and the Claude Agent SDK / Claude Code integrations.

Detection

Any of the following on metadata triggers extraction:

ls_message_format is "anthropic"
ls_integration is "claude-agent-sdk" or "claude-code"

Tracing defaults

wrap_anthropic sets the following on every LLM run:

metadata.ls_provider: "anthropic"
metadata.ls_model_type: "chat"
metadata.ls_model_name, ls_temperature, ls_max_tokens, ls_stop
metadata.ls_invocation_params: an allowlisted subset (mcp_servers, service_tier, tool_choice, top_k, top_p, stream, thinking)
run_type: "llm", name: "ChatAnthropic" (overridable)

wrap_anthropic does not emit ls_integration or ls_message_format.

Known limitation: with only the wrapper, detection does not match today. Set ls_message_format: "anthropic" explicitly for the run to be claimed.

Claude Agent SDK (Python) sets the following on the root chain run:

metadata.ls_integration: "claude-agent-sdk"
metadata.ls_integration_version: package version
Optional metadata.model, permission_mode, max_turns

Its synthetic LLM child runs get metadata.ls_provider: "anthropic" and optionally ls_model_name, but no ls_integration. They ride along on the parent chain’s claim (the first run of the trace drives the choice). Claude Agent SDK (JS) sets metadata.ls_integration: "claude-agent-sdk-js" and ls_agent_type: "root".

Known limitation: only "claude-agent-sdk" and "claude-code" are auto-detected today, not "claude-agent-sdk-js". JS traces need ls_message_format: "anthropic" set explicitly to be picked up.

Run shape

wrap_anthropic Messages API:

inputs.messages: [{role, content}, ...]
Optional inputs.system: string OR array of content blocks; becomes a prepended system message
content may be a string or an array of {type, ...} content blocks (text, tool_use, tool_result, image, thinking, redacted_thinking)
Outputs preserve the full Anthropic Message object. Content is found at one of:
- outputs.message.content (most common, wrapped)
- outputs.content when outputs.type == "message" (bare)
- outputs.content when outputs.role == "assistant" (Agents SDK bare)
- outputs.output.messages[0].content (JS SDK)
- outputs.messages[0].content (Claude Code)

Claude Agent SDK / Claude Code:

inputs.input (array of messages, same Anthropic message shape). inputs.messages takes precedence when both are present and non-empty.
Tool-run outputs use either outputs.output (object) or outputs.content (top-level array, subagent-style).

Tool-call matching

tool_use_id on the assistant tool_use block matches tool_result.tool_use_id on the corresponding result block within the next user message.

Example trace

wrap_anthropic Messages API with a tool call and follow-up. The assistant turn returns a content array containing a tool_use block; the tool result is sent back as a tool_result block inside the next user message.

[
  {
    "id": "0001",
    "trace_id": "trace-0004",
    "run_type": "llm",
    "name": "ChatAnthropic",
    "metadata": {
      "ls_provider": "anthropic",
      "ls_model_name": "claude-opus-4-7",
      "ls_message_format": "anthropic"
    },
    "inputs": {
      "system": "You are a helpful assistant.",
      "messages": [
        {"role": "user", "content": "what is the weather in paris?"}
      ]
    },
    "outputs": {
      "message": {
        "id": "msg_01",
        "role": "assistant",
        "type": "message",
        "content": [
          {"type": "text", "text": "Let me check."},
          {"type": "tool_use", "id": "toolu_01", "name": "get_weather", "input": {"city": "Paris"}}
        ]
      }
    }
  },
  {
    "id": "0002",
    "trace_id": "trace-0004",
    "parent_run_id": "0001",
    "run_type": "tool",
    "name": "get_weather",
    "inputs": {"city": "Paris"},
    "outputs": {"output": {"temperature": 22, "condition": "Sunny"}}
  },
  {
    "id": "0003",
    "trace_id": "trace-0004",
    "run_type": "llm",
    "name": "ChatAnthropic",
    "metadata": {
      "ls_provider": "anthropic",
      "ls_model_name": "claude-opus-4-7",
      "ls_message_format": "anthropic"
    },
    "inputs": {
      "system": "You are a helpful assistant.",
      "messages": [
        {"role": "user", "content": "what is the weather in paris?"},
        {"role": "assistant", "content": [
          {"type": "text", "text": "Let me check."},
          {"type": "tool_use", "id": "toolu_01", "name": "get_weather", "input": {"city": "Paris"}}
        ]},
        {"role": "user", "content": [
          {"type": "tool_result", "tool_use_id": "toolu_01", "content": "Sunny, 22C"}
        ]}
      ]
    },
    "outputs": {
      "message": {
        "id": "msg_02",
        "role": "assistant",
        "type": "message",
        "content": [{"type": "text", "text": "It's sunny and 22°C in Paris."}]
      }
    }
  }
]

Claude Agent SDK / Claude Code traces look similar but use inputs.input instead of inputs.messages and set ls_integration: "claude-agent-sdk" (or "claude-code") on the root chain run.

Connect these docs to Claude, VSCode, and more via MCP for real-time answers.

Edit this page on GitHub or file an issue.

Tracing setup

Configuration & troubleshooting

Viewing & managing traces

Automations

Feedback & evaluation

Monitoring & alerting

Data type reference

Messages view trace format reference

Extraction strategy resolution

LangChain / LangGraph

Detection

Tracing defaults

Run shape

Tool-call matching

Dedup

Example trace

OpenAI (`wrap_openai`)

Detection

Tracing defaults

Run shape

Tool-call matching

Dedup

Example traces

Vercel AI SDK

Detection

Tracing defaults

Run shape

Tool-call matching

Example trace

Anthropic Messages (`wrap_anthropic`)

Detection

Tracing defaults

Run shape

Tool-call matching

Example trace

Tracing setup

Configuration & troubleshooting

Viewing & managing traces

Automations

Feedback & evaluation

Monitoring & alerting

Data type reference

Documentation Index

​Extraction strategy resolution

​LangChain / LangGraph

​Detection

​Tracing defaults

​Run shape

​Tool-call matching

​Dedup

​Example trace

​OpenAI (wrap_openai)

​Detection

​Tracing defaults

​Run shape

​Tool-call matching

​Dedup

​Example traces

​Vercel AI SDK

​Detection

​Tracing defaults

​Run shape

​Tool-call matching

​Example trace

​Anthropic Messages (wrap_anthropic)

​Detection

​Tracing defaults

​Run shape

​Tool-call matching

​Example trace

Extraction strategy resolution

LangChain / LangGraph

Detection

Tracing defaults

Run shape

Tool-call matching

Dedup

Example trace

OpenAI (`wrap_openai`)

Detection

Tracing defaults

Run shape

Tool-call matching

Dedup

Example traces

Vercel AI SDK

Detection

Tracing defaults

Run shape

Tool-call matching

Example trace

Anthropic Messages (`wrap_anthropic`)

Detection

Tracing defaults

Run shape

Tool-call matching

Example trace