hermes-agent/docs/observability

Hermes Observer Hooks

Hermes observer hooks are the read-only telemetry contract for plugins that need to reconstruct agent execution without changing runtime behavior. This contract supports trace, metrics, audit, replay, and export integrations such as Langfuse, OpenTelemetry-style collectors, and NeMo Relay.

Observer hooks are intentionally backend-neutral. They expose stable lifecycle events, correlation IDs, sanitized payloads, timing, status, and error fields. They do not replace Hermes' planner, model providers, memory, tool registry, approval UX, CLI, gateway behavior, or execution semantics.

Behavior-changing request or execution wrappers are outside this observer contract. Observer hooks should report what happened; they should not replace provider requests, tool arguments, or execution callbacks.

Hermes also has a first-party NeMo Relay shared-metrics path. It uses these lifecycle boundaries directly and does not require enabling an observability plugin. See Relay shared metrics.

Contract

Plugins register observer callbacks from register(ctx):

def register(ctx):
    ctx.register_hook("pre_api_request", on_pre_api_request)
    ctx.register_hook("post_api_request", on_post_api_request)
    ctx.register_hook("pre_tool_call", on_pre_tool_call)
    ctx.register_hook("post_tool_call", on_post_tool_call)

Every hook callback receives keyword arguments. Plugins should accept **kwargs so additive fields remain backward-compatible:

def on_post_tool_call(**kwargs):
    tool_name = kwargs.get("tool_name")
    status = kwargs.get("status")
    result = kwargs.get("result")

The plugin manager injects this field into every hook payload:

telemetry_schema_version = "hermes.observer.v1"

Hook callbacks are fail-open. Hermes catches callback exceptions, logs a warning, and keeps the agent loop running.

Most observer hook return values are ignored. The exceptions are older behavior-affecting hooks:

Hook	Return behavior
pre_llm_call	May return a string or {"context": "..."} to inject ephemeral context into the current user message.
pre_tool_call	May return {"action": "block", "message": "..."} to block a tool before execution, or {"action": "modify", "args": {...}} to transform the tool's input arguments.
transform_tool_result	May return a replacement tool result string after post_tool_call.
transform_llm_output	May return a replacement final assistant text string.

Telemetry plugins should treat these behavior-affecting returns as optional compatibility features, not as observability requirements.

Correlation IDs

Observer payloads use stable IDs so plugins can join events without relying on callback order alone.

Field	Meaning
session_id	Conversation/session identity.
task_id	Task identity, especially useful for subagents and isolated execution.
turn_id	User-turn identity shared by API attempts and tool calls in a turn.
api_request_id	Opaque provider-attempt identity. Do not parse its string format.
api_call_count	Numeric API attempt count within the agent loop.
tool_call_id	Provider-supplied tool call ID when available.
parent_session_id / child_session_id	Session link for delegated subagents.
parent_subagent_id / child_subagent_id	Subagent link when available.
parent_turn_id	Parent turn that spawned delegated work.

Consumers should prefer explicit fields over parsing compound IDs. In particular, api_request_id is an opaque correlation value.

Event Families

Session Lifecycle

Session hooks describe conversation boundaries and resets:

Hook	When it fires
on_session_start	A brand-new session starts after the system prompt is built.
on_session_end	A run_conversation call ends, including interrupted or incomplete turns.
on_session_finalize	CLI or gateway tears down an active session identity.
on_session_reset	CLI or gateway moves from an old session identity to a new one.

Common fields include session_id, completed, interrupted, reason, old_session_id, and new_session_id where available.

on_session_end is turn/run scoped. It is not necessarily the final lifetime boundary for a chat identity. Use on_session_finalize and on_session_reset for lifecycle cleanup that must happen once per session identity.

Turn-Scoped LLM Hooks

These hooks frame the user turn, not individual provider API attempts:

Hook	When it fires
pre_llm_call	Before the tool loop begins for a user turn.
post_llm_call	After the turn completes with final assistant output.

Common pre_llm_call fields include session_id, turn_id, user_message, conversation_history, is_first_turn, model, platform, and sender_id.

Common post_llm_call fields include session_id, turn_id, user_message, assistant_response, conversation_history, model, and platform.

Use request-scoped API hooks for LLM span telemetry. Use pre_llm_call and post_llm_call for turn-level context, compatibility, and final turn summary.

Request-Scoped API Hooks

API hooks describe provider attempts inside the agent loop:

Hook	When it fires
pre_api_request	Immediately before a provider API request.
post_api_request	After a successful provider response.
api_request_error	After a failed provider request or retryable error path.

pre_api_request includes:

• identity: session_id, task_id, turn_id, api_request_id
• runtime: platform, model, provider, base_url, api_mode
• attempt metadata: api_call_count, message_count, tool_count, approx_input_tokens, request_char_count, max_tokens
• timing: started_at
• sanitized request payload: request

post_api_request includes the same identity/runtime fields plus:

• api_duration, started_at, ended_at
• finish_reason, message_count, response_model
• usage
• assistant_content_chars, assistant_tool_call_count
• sanitized response payload: response
• compatibility object: assistant_message

api_request_error includes the same identity/runtime fields plus:

• api_duration, started_at, ended_at
• status_code, retry_count, max_retries, retryable, reason
• structured error = {"type": ..., "message": ...}
• sanitized failed request payload: request

The sanitized request, response, and error fields are the canonical observer inputs for new consumers.

Tool Lifecycle

Tool hooks describe individual tool calls:

Hook	When it fires
pre_tool_call	Before guardrail-approved tool dispatch.
post_tool_call	After tool dispatch, cancellation, block, or error completion.
transform_tool_result	After post_tool_call, before the result is appended to model context.

pre_tool_call includes tool_name, args, task_id, session_id, tool_call_id, turn_id, and api_request_id.

post_tool_call includes the same identity fields plus result, duration_ms, status, error_type, and error_message.

status is the observer-grade lifecycle outcome. Common values include:

Status	Meaning
ok	Tool completed normally.
error	Tool ran and returned or raised an error outcome.
blocked	A pre_tool_call hook blocked execution.
cancelled	Execution was cancelled before normal completion.

post_tool_call is emitted for blocked and cancelled paths so telemetry plugins can close spans cleanly.

Approval Lifecycle

Approval hooks describe dangerous-command approval prompts:

Hook	When it fires
pre_approval_request	Before the approval request is shown or sent.
post_approval_response	After the user responds or the request times out.

Common fields include command, description, pattern_key, pattern_keys, session_key, and surface.

post_approval_response also includes choice, with values such as once, session, always, deny, and timeout.

Approval hooks are observer-only. Plugins cannot pre-answer or veto approvals from these hooks. To prevent a tool from reaching approval, use pre_tool_call blocking.

Subagent Lifecycle

Subagent hooks describe delegated child-agent work:

Hook	When it fires
subagent_start	A delegated child agent is created.
subagent_stop	A delegated child agent returns or fails.

subagent_start fields include parent_session_id, parent_turn_id, parent_subagent_id, child_session_id, child_subagent_id, child_role, and child_goal.

subagent_stop fields include parent/child session IDs, role/status fields, child_summary, duration_ms, and a metadata-only tool_call_history. Each history entry contains the tool name, argument names, bounded side-effect targets, input/output byte counts, and outcome. URL query strings and fragments are removed; raw arguments, prompts, commands, contents, headers, and results are intentionally excluded.

Observers can use these hooks to model nested trajectories while keeping child agent execution linked to the parent turn that spawned it.

Payload Safety

Observer payloads are designed for telemetry consumers, not raw object access. New consumers should use the sanitized API payloads:

• pre_api_request.request
• post_api_request.response
• api_request_error.request
• api_request_error.error

Sanitization converts provider objects to JSON-compatible structures, bounds large payloads, redacts sensitive keys, and avoids exposing raw response objects in sanitized fields.

Legacy compatibility fields such as request_messages, conversation_history, and assistant_message may still be present for existing plugins. New observability consumers should prefer the sanitized payloads.

Performance

The default uninstrumented path should stay cheap. Expensive request/response payload construction is gated behind has_hook(...), so Hermes only builds sanitized API telemetry payloads when at least one plugin registered the relevant hook.

Plugin authors should preserve this property:

• Register only hooks the plugin actually consumes.
• Avoid deep-copying or re-sanitizing already sanitized payloads.
• Keep hook callbacks fast and fail-open.
• Offload network export or batch writes when practical.

Writing An Observer Plugin

Minimal observer plugin:

def register(ctx):
    ctx.register_hook("pre_api_request", on_pre_api_request)
    ctx.register_hook("post_api_request", on_post_api_request)
    ctx.register_hook("pre_tool_call", on_pre_tool_call)
    ctx.register_hook("post_tool_call", on_post_tool_call)


def on_pre_api_request(**kwargs):
    start_llm_span(
        request_id=kwargs.get("api_request_id"),
        turn_id=kwargs.get("turn_id"),
        request=kwargs.get("request"),
        model=kwargs.get("model"),
    )


def on_post_api_request(**kwargs):
    finish_llm_span(
        request_id=kwargs.get("api_request_id"),
        response=kwargs.get("response"),
        usage=kwargs.get("usage"),
        duration=kwargs.get("api_duration"),
    )


def on_pre_tool_call(**kwargs):
    start_tool_span(
        call_id=kwargs.get("tool_call_id"),
        name=kwargs.get("tool_name"),
        args=kwargs.get("args"),
    )


def on_post_tool_call(**kwargs):
    finish_tool_span(
        call_id=kwargs.get("tool_call_id"),
        result=kwargs.get("result"),
        status=kwargs.get("status"),
        duration_ms=kwargs.get("duration_ms"),
    )

Use session_id, turn_id, api_request_id, and tool_call_id for span correlation. Use subagent and approval hooks when the export format supports nested agent work or security lifecycle events.

Existing Consumers

The bundled Langfuse plugin demonstrates direct hook-based observability for turns, provider requests, and tool calls.

The native NeMo Relay SDK integration maps Hermes session, turn, LLM, and tool lifecycles to Relay. Explicit Relay plugin configuration can add ATOF, ATIF, or OTEL exporters and execution middleware; see Relay shared metrics.