reflexio-client 0.2.3

Lightweight Python client for the Reflexio API

Reflexio Client

Python SDK for interacting with the Reflexio API. Provides type-safe, sync-first access to user profiles, interactions, playbooks, evaluations, and configuration.

Installation

pip install reflexio-client

With LangChain integration:

pip install reflexio-client[langchain]

Quick Start

from reflexio import ReflexioClient

# API key from constructor or REFLEXIO_API_KEY env var
client = ReflexioClient(api_key="your-api-key")

# Or connect to a self-hosted instance
client = ReflexioClient(
    api_key="your-api-key",
    url_endpoint="http://localhost:8081",
)

Authentication

The client authenticates via Bearer token. Provide your API key in one of two ways:

1. Constructor: ReflexioClient(api_key="your-key")
2. Environment variable: Set REFLEXIO_API_KEY (auto-detected)

The base URL defaults to https://www.reflexio.ai/ and can be overridden with url_endpoint or the REFLEXIO_API_URL env var.

Publishing Interactions

Publish user interactions to trigger profile extraction, playbook generation, and evaluation:

from reflexio import InteractionData

# Fire-and-forget (non-blocking, returns None)
client.publish_interaction(
    user_id="user-123",
    interactions=[
        InteractionData(
            request_id="req-001",
            user_request="How do I reset my password?",
            agent_response="Go to Settings > Security > Reset Password.",
        )
    ],
    source="support-bot",
    agent_version="v2.1",
    session_id="session-abc",
)

# Wait for server to finish processing
response = client.publish_interaction(
    user_id="user-123",
    interactions=[
        InteractionData(
            request_id="req-002",
            user_request="Thanks, that worked!",
            agent_response="Glad I could help!",
        )
    ],
    agent_version="v2.1",
    wait_for_response=True,
)
print(response.success, response.msg)

Profiles

# Semantic search for profiles
results = client.search_profiles(user_id="user-123", query="password preferences")
for profile in results.profiles:
    print(profile.profile_name, profile.profile_content)

# Get all profiles for a user
profiles = client.get_profiles(user_id="user-123")

# Filter by status
from reflexio import Status
profiles = client.get_profiles(user_id="user-123", status_filter=[Status.CURRENT])

# Get all profiles across all users
all_profiles = client.get_all_profiles(limit=50)

# Delete a specific profile
client.delete_profile(user_id="user-123", profile_id="prof-456", wait_for_response=True)

# Get profile change history
changelog = client.get_profile_change_log()

# Rerun profile generation from existing interactions
response = client.rerun_profile_generation(
    user_id="user-123",
    extractor_names=["preferences"],
    wait_for_response=True,
)
print(f"Generated {response.profiles_generated} profiles")

Interactions

# Semantic search
results = client.search_interactions(user_id="user-123", query="password reset")

# List interactions for a user
interactions = client.get_interactions(user_id="user-123", top_k=50)

# Get all interactions across all users
all_interactions = client.get_all_interactions(limit=100)

# Delete a specific interaction
client.delete_interaction(
    user_id="user-123", interaction_id="int-789", wait_for_response=True
)

Requests & Sessions

# Get requests grouped by session
requests = client.get_requests(user_id="user-123")

# Delete a request and its interactions
client.delete_request(request_id="req-001", wait_for_response=True)

# Delete all requests in a session
client.delete_session(session_id="session-abc", wait_for_response=True)

Playbooks

User Playbooks (extracted from interactions)

# Get user playbooks
raw = client.get_raw_feedbacks(feedback_name="usability", limit=50)

# Search user playbooks
results = client.search_raw_feedbacks(query="slow response", agent_version="v2.1")

# Add user playbook directly
from reflexio import RawFeedback
client.add_raw_feedback(raw_feedbacks=[
    RawFeedback(
        agent_version="v2.1",
        request_id="req-001",
        feedback_content="User found the response helpful",
        feedback_name="satisfaction",
    )
])

# Rerun playbook generation
client.rerun_feedback_generation(
    agent_version="v2.1",
    feedback_name="usability",
    wait_for_response=True,
)

Agent Playbooks (clustered insights)

# Get agent playbooks
from reflexio import PlaybookStatus
agent_playbooks = client.get_agent_playbooks(
    playbook_name="usability",
    playbook_status_filter=PlaybookStatus.APPROVED,
)

# Search agent playbooks
results = client.search_agent_playbooks(query="response quality", agent_version="v2.1")

# Add agent playbook directly
from reflexio import AgentPlaybook
client.add_agent_playbooks(agent_playbooks=[
    AgentPlaybook(
        agent_version="v2.1",
        content="Users prefer concise answers",
        playbook_status=PlaybookStatus.APPROVED,
        playbook_metadata="Aggregated from 15 user playbooks",
        playbook_name="style",
    )
])

# Run playbook aggregation
client.run_playbook_aggregation(
    agent_version="v2.1",
    playbook_name="usability",
    wait_for_response=True,
)

Unified Search

Search across profiles, agent playbooks, user playbooks, and skills in one call:

from reflexio import ConversationTurn

results = client.search(
    query="user prefers dark mode",
    top_k=5,
    agent_version="v2.1",
    user_id="user-123",
    enable_reformulation=True,
    conversation_history=[
        ConversationTurn(role="user", content="What themes are available?"),
        ConversationTurn(role="assistant", content="We support light and dark themes."),
    ],
)

print(results.profiles)
print(results.feedbacks)       # agent playbooks
print(results.raw_feedbacks)   # user playbooks

Evaluation

# Get agent success evaluation results
results = client.get_agent_success_evaluation_results(
    agent_version="v2.1",
    limit=50,
)

Skills

# Search skills
skills = client.search_skills(request={"query": "data export", "agent_version": "v2.1"})

# Get skills
skills = client.get_skills(request={"agent_version": "v2.1"})

Configuration

from reflexio import Config

# Get current config
config = client.get_config()
print(config)

# Update config
client.set_config(Config(
    profile_extractor_config=[...],
    playbook_config=[...],
))

Bulk Delete Operations

# Delete by IDs
client.delete_requests_by_ids(["req-001", "req-002"])
client.delete_profiles_by_ids(["prof-001", "prof-002"])
client.delete_feedbacks_by_ids([1, 2, 3])
client.delete_raw_feedbacks_by_ids([4, 5, 6])

# Delete all
client.delete_all_interactions()
client.delete_all_profiles()
client.delete_all_feedbacks()

Fire-and-Forget vs Blocking

Most write/delete methods support wait_for_response:

• wait_for_response=False (default): Non-blocking fire-and-forget. Returns None. Efficient for high-throughput pipelines.
• wait_for_response=True: Blocks until the server finishes processing. Returns the full response.

In async contexts (e.g., FastAPI), fire-and-forget uses the existing event loop. In sync contexts, it uses a shared thread pool.

API Reference

Method	Description
publish_interaction()	Publish interactions (triggers profile/playbook/evaluation)
search_profiles()	Semantic search for profiles
get_profiles()	Get profiles for a user
get_all_profiles()	Get all profiles across users
delete_profile()	Delete profiles by ID or search query
get_profile_change_log()	Get profile change history
rerun_profile_generation()	Regenerate profiles from interactions
manual_profile_generation()	Trigger profile generation with window-sized interactions
search_interactions()	Semantic search for interactions
get_interactions()	Get interactions for a user
get_all_interactions()	Get all interactions across users
delete_interaction()	Delete a specific interaction
get_requests()	Get requests grouped by session
delete_request()	Delete a request and its interactions
delete_session()	Delete all requests in a session
get_raw_feedbacks()	Get user playbooks
search_raw_feedbacks()	Search user playbooks
add_raw_feedback()	Add user playbook directly
get_feedbacks()	Get agent playbooks
search_feedbacks()	Search agent playbooks
add_feedbacks()	Add agent playbook directly
rerun_feedback_generation()	Regenerate playbooks for an agent version
manual_feedback_generation()	Trigger playbook generation with window-sized interactions
run_feedback_aggregation()	Cluster user playbooks into agent playbooks
search()	Unified search across all entity types
get_agent_success_evaluation_results()	Get evaluation results
search_skills()	Search skills
get_skills()	Get skills
set_config()	Update org configuration
get_config()	Get current configuration
delete_requests_by_ids()	Bulk delete requests
delete_profiles_by_ids()	Bulk delete profiles
delete_feedbacks_by_ids()	Bulk delete agent playbooks
delete_raw_feedbacks_by_ids()	Bulk delete user playbooks
delete_all_interactions()	Delete all interactions
delete_all_profiles()	Delete all profiles
delete_all_feedbacks()	Delete all playbooks

Requirements

• Python >= 3.11
• pydantic >= 2.0.0
• requests >= 2.25.0
• aiohttp >= 3.12.9
• python-dateutil >= 2.8.0
• python-dotenv >= 0.19.0