seclai 1.3.0

Seclai Python SDK

Seclai Python SDK

The official Python SDK for the Seclai API. Provides typed wrappers for the Seclai API, file uploads, SSE streaming, polling helpers, and full async support.

Requires Python 3.11+.

Install

pip install seclai

Exports

All public symbols are available from the top-level seclai package:

from seclai import (
    Seclai,                     # Synchronous client
    AsyncSeclai,                # Asynchronous client
    SeclaiError,                # Base exception
    SeclaiConfigurationError,   # Missing API key / invalid config
    SeclaiAPIStatusError,       # Non-2xx HTTP response
    SeclaiAPIValidationError,   # HTTP 422 validation error
    SeclaiStreamingError,       # SSE stream error event
    AgentRunStreamRequest,      # TypedDict for streaming run requests
    JSONValue,                  # Recursive JSON type alias
)

Quick start

from seclai import Seclai

client = Seclai(api_key="...")

# List agents
agents = client.list_agents()
print(agents)

# Run an agent and stream the result
from seclai import AgentRunStreamRequest

run = client.run_streaming_agent_and_wait(
    "agent_id",
    body=AgentRunStreamRequest(input="Summarize the latest uploads", metadata={}),
    timeout=60.0,
)
print("run:", run.run_id, "status:", run.status)

Async client

import asyncio
from seclai import AsyncSeclai

async def main():
    async with AsyncSeclai(api_key="...") as client:
        agents = await client.list_agents()
        print(agents)

asyncio.run(main())

Configuration

Option	Environment variable	Default
api_key	SECLAI_API_KEY	—
access_token	—	—
profile	SECLAI_PROFILE	"default"
config_dir	SECLAI_CONFIG_DIR	~/.seclai
auto_refresh	—	True
account_id	—	—
timeout	—	30.0 (seconds)
api_key_header	—	x-api-key
default_headers	—	None
http_client	—	None (auto-created httpx.Client)

Set SECLAI_API_URL to point at a different API host (e.g., staging):

export SECLAI_API_URL="https://staging-api.seclai.com"

Authentication

Credentials are resolved via a chain (first match wins):

1. Explicit api_key option
2. Explicit access_token option (string or callable)
3. SECLAI_API_KEY environment variable
4. SSO — cached tokens from ~/.seclai/sso/cache/ (always available as fallback)

# API key
client = Seclai(api_key="sk-...")

# Static bearer token
client = Seclai(access_token="eyJhbGciOi...")

# Dynamic bearer token provider (sync callable, called per request)
client = Seclai(access_token=lambda: get_token_from_vault())

# Async provider — use AsyncSeclai for async callables
client = AsyncSeclai(access_token=get_token_async)

# SSO profile (uses cached tokens, auto-refreshes)
client = Seclai(profile="my-profile")

# Environment variable (no options needed)
# export SECLAI_API_KEY="sk-..."
client = Seclai()

SSO authentication

SSO is the default fallback when no explicit credentials are provided. The SDK includes built-in production SSO defaults, so no configuration is needed:

npx @seclai/cli auth login    # authenticate via browser — works immediately

To customize SSO settings (e.g. for a staging environment), use seclai configure sso or set environment variables:

Variable	Description	Default
SECLAI_SSO_DOMAIN	Cognito domain	auth.seclai.com
SECLAI_SSO_CLIENT_ID	Cognito app client ID	4bgf8v9qmc5puivbaqon9n5lmr
SECLAI_SSO_REGION	AWS region	us-west-2

API documentation

Online API documentation (latest):

https://seclai.github.io/seclai-python/1.3.0/

Resources

Agents

# CRUD
agents = client.list_agents(page=1, limit=20)
agent = client.create_agent({"name": "My Agent", "description": "..."})
fetched = client.get_agent("agent_id")
updated = client.update_agent("agent_id", {"name": "Renamed"})
client.delete_agent("agent_id")

# Definition (step workflow)
definition = client.get_agent_definition("agent_id")
client.update_agent_definition("agent_id", {
    "change_id": definition["change_id"],
    "steps": [{"type": "llm", "config": {}}],
})

# Export / import an agent
exported = client.export_agent("agent_id")

# Validate the payload first to surface unresolved entity refs in this account
preview = client.preview_import_agent({"agent_definition": exported})
entity_remap = {
    ref["ref_id"]: ""  # pick a target uuid from ref["alternatives"]
    for ref in preview.get("unresolved_refs", [])
}

# Commit — `entity_remap` substitutes workflow refs before save
imported = client.create_agent({
    "name": "Imported",
    "agent_definition": exported,
    "entity_remap": entity_remap,
})
# `imported["import_warnings"]` lists any items that couldn't be applied.

Agent runs

from seclai._generated.models.agent_run_request import AgentRunRequest

# Start a run
run = client.run_agent("agent_id", AgentRunRequest(input_="Hello"))

# List & search runs
runs = client.list_agent_runs("agent_id")
search = client.search_agent_runs({"query": "test"})

# Fetch run details (optionally with step outputs)
detail = client.get_agent_run("run_id", include_step_outputs=True)

# Cancel or delete
client.cancel_agent_run("run_id")
client.delete_agent_run("run_id")

Streaming

The SDK provides two streaming patterns over the SSE /runs/stream endpoint.

Block until done — returns the final done payload or raises on timeout:

from seclai import AgentRunStreamRequest

run = client.run_streaming_agent_and_wait(
    "agent_id",
    body=AgentRunStreamRequest(input="Hello from streaming", metadata={}),
    timeout=60.0,
)

Generator-based — yields every SSE event as (event_type, data) tuples:

for event_type, data in client.run_streaming_agent(
    "agent_id",
    body=AgentRunStreamRequest(input="Hello", metadata={}),
):
    print(event_type, data)

Async:

async for event_type, data in client.run_streaming_agent(
    "agent_id",
    body=AgentRunStreamRequest(input="Hello", metadata={}),
):
    print(event_type, data)

Polling

For environments where SSE is not practical, poll for a completed run:

from seclai._generated.models.agent_run_request import AgentRunRequest

result = client.run_agent_and_poll(
    "agent_id",
    AgentRunRequest(input_="Hello"),
    poll_interval=2.0,
)

Agent input uploads

# Discover which files (if any) the agent expects before staging uploads
refs = client.get_agent_attachment_references("agent_id")
# refs["requires_uploads"] -> bool; refs["agent"] lists the exact_names /
# indexes_max / patterns a run-time upload batch must satisfy.

upload = client.upload_agent_input("agent_id", file=b"data", file_name="input.pdf")
status = client.get_agent_input_upload_status("agent_id", upload["upload_id"])

Agent run attachments

# Download a file emitted by a step in an agent run. attachment_id is the
# URL-safe-base64 storage_key surfaced in run output manifests / webhooks.
response = client.download_agent_run_attachment("run_id", "attachment_id")  # raw httpx.Response
with response:
    for chunk in response.iter_bytes():
        ...  # write to disk

Agent AI assistant

steps = client.generate_agent_steps("agent_id", {"user_input": "Build a RAG pipeline"})
config = client.generate_step_config("agent_id", {"step_type": "llm", "user_input": "..."})

# Conversation history
history = client.get_agent_ai_conversation_history("agent_id")
client.mark_agent_ai_suggestion("agent_id", "conversation_id", {"accepted": True})

Agent evaluations

# CRUD
criteria_list = client.list_evaluation_criteria("agent_id")
criteria = client.create_evaluation_criteria("agent_id", {"name": "accuracy"})
detail = client.get_evaluation_criteria("criteria_id")
client.update_evaluation_criteria("criteria_id", {"name": "updated"})
client.delete_evaluation_criteria("criteria_id")

# Test a draft
client.test_draft_evaluation("agent_id", {"criteria": {}, "run_id": "run_id"})

# Results & summaries
results = client.list_evaluation_results("criteria_id")
summary = client.get_evaluation_criteria_summary("criteria_id")
client.create_evaluation_result("criteria_id", {"run_id": "run_id", "score": 0.9})

# Results by run
run_results = client.list_run_evaluation_results("agent_id", "run_id")
non_manual = client.get_non_manual_evaluation_summary("agent_id")
compatible = client.list_compatible_runs("criteria_id")

Knowledge bases

kbs = client.list_knowledge_bases()
kb = client.create_knowledge_base({"name": "My KB"})
fetched = client.get_knowledge_base("kb_id")
client.update_knowledge_base("kb_id", {"name": "Renamed"})
client.delete_knowledge_base("kb_id")

Memory banks

banks = client.list_memory_banks()
bank = client.create_memory_bank({"name": "Chat Memory", "type": "conversation"})
fetched = client.get_memory_bank("mb_id")
client.update_memory_bank("mb_id", {"name": "Updated"})
client.delete_memory_bank("mb_id")

# Stats & compaction
stats = client.get_memory_bank_stats("mb_id")
client.compact_memory_bank("mb_id")

# Test compaction
test = client.test_memory_bank_compaction("mb_id", {"entries": []})
standalone = client.test_compaction_prompt_standalone({"prompt": "test"})

# Templates & agents
templates = client.list_memory_bank_templates()
agents = client.get_agents_using_memory_bank("mb_id")

# AI assistant
suggestion = client.generate_memory_bank_config({"user_input": "Create a bank"})
last_conv = client.get_memory_bank_ai_last_conversation()
client.accept_memory_bank_ai_suggestion("conversation_id", {"accepted": True})

# Source management
client.delete_memory_bank_source("mb_id")

Sources

sources = client.list_sources(page=1, limit=20)
source = client.create_source({"name": "My Source"})
fetched = client.get_source("source_id")
client.update_source("source_id", {"name": "Updated"})
client.delete_source("source_id")

File uploads

Upload a file to a source (max 200 MiB):

upload = client.upload_file_to_source(
    "source_connection_id",
    file="./document.pdf",
    title="Q4 Report",
    metadata={"department": "finance"},
)

Upload inline text:

upload = client.upload_inline_text_to_source("source_connection_id", {
    "title": "Greeting",
    "content": "Hello, world!",
})

Replace a content version with a new file:

upload = client.upload_file_to_content(
    "source_connection_content_version",
    file="./updated.pdf",
    metadata={"revision": 2},
)

Replace a content version with inline text:

client.replace_content_with_inline_text("source_connection_content_version", {
    "title": "Updated",
    "content": "New content text",
})

Source exports

exports = client.list_source_exports("source_id")
export = client.create_source_export("source_id", {"format": "json"})
status = client.get_source_export("source_id", "export_id")
estimate = client.estimate_source_export("source_id", {"format": "json"})
response = client.download_source_export("source_id", "export_id")  # raw httpx.Response
client.delete_source_export("source_id", "export_id")
client.cancel_source_export("source_id", "export_id")

Source embedding migrations

migration = client.get_source_embedding_migration("source_id")
client.start_source_embedding_migration("source_id", {"target_model": "v2"})
client.cancel_source_embedding_migration("source_id")

Content

detail = client.get_content_detail("source_connection_content_version")
embeddings = client.list_content_embeddings("source_connection_content_version")
client.delete_content("source_connection_content_version")

Solutions

solutions = client.list_solutions()
sol = client.create_solution({"name": "My Solution"})
fetched = client.get_solution("solution_id")
client.update_solution("solution_id", {"name": "Renamed"})
client.delete_solution("solution_id")

# Link / unlink resources
client.link_agents_to_solution("solution_id", {"agent_ids": ["a1"]})
client.unlink_agents_from_solution("solution_id", {"agent_ids": ["a1"]})
client.link_knowledge_bases_to_solution("solution_id", {"kb_ids": ["kb1"]})
client.unlink_knowledge_bases_from_solution("solution_id", {"kb_ids": ["kb1"]})
client.link_source_connections_to_solution("solution_id", {"sc_ids": ["sc1"]})
client.unlink_source_connections_from_solution("solution_id", {"sc_ids": ["sc1"]})

# AI assistant
plan = client.generate_solution_ai_plan("solution_id", {"user_input": "Build it"})
client.accept_solution_ai_plan("solution_id", "conversation_id", {})
client.decline_solution_ai_plan("solution_id", "conversation_id")

# AI-generated resources
client.generate_solution_ai_knowledge_base("solution_id", {"user_input": "..."})
client.generate_solution_ai_source("solution_id", {"user_input": "..."})

# Conversations
convs = client.list_solution_conversations("solution_id")
client.add_solution_conversation_turn("solution_id", {"user_input": "..."})
client.mark_solution_conversation_turn("solution_id", "conversation_id", {"accepted": True})

Governance AI

plan = client.generate_governance_ai_plan({"user_input": "Create a content policy"})
convs = client.list_governance_ai_conversations()
client.accept_governance_ai_plan("conversation_id")
client.decline_governance_ai_plan("conversation_id")

Alerts

alerts = client.list_alerts(status="active")
alert = client.get_alert("alert_id")
client.change_alert_status("alert_id", {"status": "resolved"})
client.add_alert_comment("alert_id", {"text": "Investigating"})

# Subscriptions
client.subscribe_to_alert("alert_id")
client.unsubscribe_from_alert("alert_id")

# Alert configs
configs = client.list_alert_configs()
client.create_alert_config({"name": "Config"})
config = client.get_alert_config("config_id")
client.update_alert_config("config_id", {"name": "Updated"})
client.delete_alert_config("config_id")

# Organization preferences
prefs = client.list_organization_alert_preferences()
client.update_organization_alert_preference("org_id", "anomaly", {"enabled": True})

Models

alerts = client.list_model_alerts()
client.mark_model_alert_read("alert_id")
client.mark_all_model_alerts_read()
unread = client.get_unread_model_alert_count()
recs = client.get_model_recommendations("model_id")

# Model playground experiments
experiment = client.create_experiment({"model_ids": ["model_id"], "prompt": "..."})
experiments = client.list_experiments()
detail = client.get_experiment("experiment_id")
client.cancel_experiment("experiment_id")
client.delete_experiment("experiment_id")  # soft-delete, preserves audit history

Search

results = client.search(query="quarterly report")
filtered = client.search(query="my agent", entity_type="agent", limit=5)

Top-level AI assistant

# Generate plans for different resource types
kb_plan = client.ai_assistant_knowledge_base({"user_input": "Create a product FAQ KB"})
source_plan = client.ai_assistant_source({"user_input": "Set up a docs source"})
solution_plan = client.ai_assistant_solution({"user_input": "Build a support bot"})
mb_plan = client.ai_assistant_memory_bank({"user_input": "Create a chat memory bank"})

# Accept or decline
client.accept_ai_assistant_plan("conversation_id", {"accepted": True})
client.decline_ai_assistant_plan("conversation_id")

# Memory bank conversation history
history = client.get_ai_assistant_memory_bank_history()
client.accept_ai_memory_bank_suggestion("conversation_id", {"accepted": True})

# Feedback
client.submit_ai_feedback({"rating": 5, "comment": "Helpful!"})

Pagination

All list methods accept page and limit parameters. For auto-pagination across all pages, use the paginate helper:

# Sync — yields items one by one (generator)
for agent in client.paginate("GET", "/agents"):
    print(agent["name"])

# With a custom items key
for alert in client.paginate("GET", "/alerts", items_key="items"):
    print(alert["id"])

# Async — also an async generator
async for agent in client.paginate("GET", "/agents"):
    print(agent["name"])

Error handling

All SDK errors inherit from SeclaiError. Use specific exception types for targeted handling:

from seclai import (
    Seclai,
    SeclaiAPIStatusError,
    SeclaiAPIValidationError,
    SeclaiConfigurationError,
    SeclaiStreamingError,
)

client = Seclai(api_key="...")

try:
    from seclai._generated.models.agent_run_request import AgentRunRequest

    result = client.run_agent("agent_id", AgentRunRequest(input_="Hello"))
except SeclaiAPIValidationError as e:
    print("Validation error:", e.status_code, e.validation_error)
except SeclaiAPIStatusError as e:
    print("API error:", e.status_code, e.response_text)
except SeclaiStreamingError as e:
    print("Streaming error:", e.message, "run:", e.run_id)
except SeclaiConfigurationError as e:
    print("Config error:", e)

Error type	When
SeclaiConfigurationError	Missing API key, invalid configuration
SeclaiAPIStatusError	Non-2xx HTTP response
SeclaiAPIValidationError	HTTP 422 (inherits SeclaiAPIStatusError)
SeclaiStreamingError	SSE stream error event received

Low-level access

Use client.request() for direct API requests:

result = client.request("GET", "/custom/endpoint", params={"key": "value"})

Development

Testing

make test

To pass args through to pytest:

make test ARGS='-k auth'

Formatting

make format

Linting

make lint

OpenAPI spec & regenerating the client

Copy the OpenAPI JSON file into openapi/seclai.openapi.json, then run:

make generate

Generate docs

make docs

Reporting issues

If you hit a bug or have a feature request, please open an issue and include:

• what you were trying to do
• a minimal repro snippet (if possible)
• the exception / traceback
• your environment (Python version, OS)

License

MIT — see LICENSE for details.