Rerun and Manual Generation Operations

Profile and playbook generation can be triggered in different modes:

Key Differences:

• Rerun: Uses ALL interactions, outputs PENDING status (requires upgrade workflow), supports wait_for_response
• Manual: Uses window_size from config, outputs CURRENT status directly, always fire-and-forget

rerun_profile_generation

Regenerate user profiles from ALL existing interactions. Creates profiles with PENDING status.

response = client.rerun_profile_generation(
    user_id="user_123",
    wait_for_response=True
)

manual_profile_generation

Manually trigger profile generation with window-sized interactions (fire-and-forget). Creates profiles with CURRENT status directly (no upgrade needed).

client.manual_profile_generation(user_id="user_123")

Returns: None (fire-and-forget operation)

Prerequisites:

• window_size must be configured in your config
• Extractors must have allow_manual_trigger=True to be included

Example:

# Force regeneration for a user (CURRENT status, fire-and-forget)
client.manual_profile_generation(user_id="user_123")

# Regenerate for all users with specific source
client.manual_profile_generation(source="chat")

# Legacy clients may still send extractor_names, but they no longer select extractors.
client.manual_profile_generation(user_id="user_123")

rerun_playbook_generation

Regenerate playbook entries from ALL interactions for a specific agent version. Creates entries with PENDING status.

response = client.rerun_playbook_generation(
    agent_version="v2.1.0",
    wait_for_response=True
)

manual_playbook_generation

Manually trigger playbook generation with window-sized interactions (fire-and-forget). Creates entries with CURRENT status directly (no upgrade needed).

client.manual_playbook_generation(agent_version="v2.1.0")

Returns: None (fire-and-forget operation)

Prerequisites:

• window_size must be configured in your config
• Playbook configs must have allow_manual_trigger=True to be included

Example:

# Force regeneration for an agent version (CURRENT status, fire-and-forget)
client.manual_playbook_generation(agent_version="v2.1.0")

# Regenerate with specific source filter
client.manual_playbook_generation(
    agent_version="v2.1.0",
    source="chat"
)

# Legacy clients may still send playbook_name, but it no longer selects a playbook config.
client.manual_playbook_generation(agent_version="v2.1.0")

upgrade_profiles

Promote PENDING profiles to CURRENT, archive old CURRENT profiles, and delete old ARCHIVED profiles. Used after rerun_profile_generation to apply the new generation results.

response = client.upgrade_profiles(user_id="user_123")

upgrade_user_playbooks

Promote PENDING user playbooks to CURRENT, archive old CURRENT, and delete old ARCHIVED. Used after rerun_playbook_generation to apply the new generation results.

response = client.upgrade_user_playbooks(agent_version="v2.1.0")

run_playbook_aggregation

Aggregate user playbooks into consolidated insights using clustering.

response = client.run_playbook_aggregation(
    agent_version="v2.1.0"
)

clear_user_data

Delete all rows scoped to a single user_id — the user's interactions, user playbooks, profiles, and requests. Does not touch agent playbooks, which are intentionally shared cross-project. Useful for isolating per-task data on a shared backend (e.g. paired-protocol harnesses) without a full clear-all.

response = client.clear_user_data(user_id="user_123")
print(response.deleted_counts)

Learning-Stall State

When a configured LLM provider returns an auth or billing error, Reflexio records a "learning stall" and pauses background extraction. These two methods let a client read and acknowledge that state.

get_stall_state

Read the current learning-stall state from the server.

state = client.get_stall_state()
if state.stalled:
    print(state.reason, state.error_message)

mark_stall_notified

Idempotently mark the current stall as notified (sets notified_in_cc=True).

result = client.mark_stall_notified()

Returns: MarkNotifiedResponse with the new notified_in_cc value.