Python client for Iranti memory infrastructure

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  • License Expression: AGPL-3.0-or-later
    SPDX License Expression
  • Author: NF
  • Tags iranti , ai , agents , memory , chatbot
  • Requires: Python >=3.9
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Project description

Iranti Python Client

Python client for the Iranti REST API.

Installation

pip install iranti

For local development from this repository:

cd clients/python
pip install -e .

Setup

Start the Iranti API server first:

# In your Iranti directory
npm run api

Usage

from iranti import IrantiClient

client = IrantiClient(
    base_url="http://localhost:3001",
    api_key="replace-with-real-iranti-key"
)

# Or use environment variables
# IRANTI_URL=http://localhost:3001
# IRANTI_API_KEY=replace-with-real-iranti-key
client = IrantiClient()

# Check server is running
print(client.health())

# Write a fact
result = client.write(
    entity="researcher/jane_smith",
    key="affiliation",
    value={"institution": "MIT"},
    summary="Affiliated with MIT",
    confidence=85,
    source="OpenAlex",
    agent="my_agent"
)
print(result.action)  # created / updated / rejected / escalated

# Ingest raw text
result = client.ingest(
    entity="researcher/jane_smith",
    content="Dr. Jane Smith has 24 publications and is at MIT CSAIL.",
    source="OpenAlex",
    confidence=80,
    agent="my_agent"
)
print(
    f"Extracted: {result.extracted_candidates}, "
    f"Written: {result.written}, "
    f"Skipped malformed: {result.skipped_malformed}"
)

# Get working memory before a task
brief = client.handshake(
    agent="my_agent",
    task="Research publication history",
    recent_messages=["Starting research..."]
)
print(brief.inferred_task_type)

# Per-turn memory decision (inject only when needed)
turn = client.attend(
    agent_id="my_agent",
    latest_message="What is my favorite snack?",
    current_context="User: What is my favorite snack?\nAssistant:",
    entity_hints=["user/main"]
)
print(turn["shouldInject"], turn["reason"])

# Save task progress so a later handshake can recommend resuming it
brief = client.checkpoint(
    agent_id="my_agent",
    task="Research publication history",
    recent_messages=["Still comparing two source timelines."],
    checkpoint={
        "currentStep": "Review affiliation conflict",
        "nextStep": "Write corrected fact",
        "openRisks": ["Two sources disagree on start year"],
    },
)

if brief.session_recovery and brief.session_recovery.available:
    client.resume_session(
        agent_id="my_agent",
        session_id=brief.session_recovery.session_id
    )

session = client.inspect_session("my_agent")
print(session.has_checkpoint)

sessions = client.list_sessions(operator_state="interrupted", sort="operator")
print([(item.agent_id, item.operator_state) for item in sessions])

# Query facts
result = client.query("researcher/jane_smith", "affiliation")
if result.found:
    print(result.value)

Error Handling

from iranti import IrantiClient, IrantiAuthError, IrantiValidationError, IrantiError

try:
    result = client.write(...)
except IrantiAuthError:
    print("Invalid API key")
except IrantiValidationError as e:
    print(f"Bad input: {e}")
except IrantiError as e:
    print(f"API error: {e}")

Environment Variables

Variable Description
IRANTI_URL API server URL (default: http://localhost:3001)
IRANTI_API_KEY API token (keyId.secret) or legacy shared server key

License

AGPL-3.0-or-later.

Project details

Verified details

These details have been verified by PyPI
Project links
GitHub Statistics
Maintainers
Avatar for nfemmanuel from gravatar.com nfemmanuel

Unverified details

These details have not been verified by PyPI
Meta
  • License Expression: AGPL-3.0-or-later
    SPDX License Expression
  • Author: NF
  • Tags iranti , ai , agents , memory , chatbot
  • Requires: Python >=3.9
Classifiers

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