gigaevo-memory 0.2.1

Python client for GigaEvo Memory Module — persistent storage for CARL artifacts

gigaevo-memory

Python client for the GigaEvo Memory Module: persistent storage for CARL artifacts such as chains, steps, agents, and memory cards.

mmar-carl is a required dependency and is installed automatically with the client.

Installation

pip install gigaevo-memory

For vector or hybrid search with the default local embedding provider, also install sentence-transformers:

pip install gigaevo-memory sentence-transformers

If you are running against this repository's local Docker stack, bring the API up and apply migrations first:

make up
make migrate

Quick Start

Save and load a chain

from mmar_carl import ContextSearchConfig, LLMStepDescription, ReasoningChain
from gigaevo_memory import MemoryClient

chain = ReasoningChain(
    steps=[
        LLMStepDescription(
            number=1,
            title="Analyze text",
            aim="Summarize the input text",
            reasoning_questions="What is the main topic? What are the key points?",
            stage_action="Read the input and produce a concise summary",
            example_reasoning="The text is about X, with key points A, B, and C.",
        )
    ],
    max_workers=1,
    metadata={"name": "Simple Analysis Chain"},
    search_config=ContextSearchConfig(strategy="substring"),
)

with MemoryClient(base_url="http://localhost:8000") as client:
    ref = client.save_chain(
        chain=chain,
        name="simple_analysis_chain",
        tags=["demo", "analysis"],
    )

    loaded_chain = client.get_chain(ref.entity_id, channel="latest")
    loaded_chain_dict = client.get_chain_dict(ref.entity_id, channel="latest")
    versions = client.list_versions(ref.entity_id, entity_type="chain")

    print(ref.entity_id)
    print(len(loaded_chain.steps))
    print(loaded_chain_dict["metadata"]["name"])
    print([v.version_number for v in versions])

Save and search memory cards

from gigaevo_memory import MemoryClient, SearchType

memory_card = {
    "description": "Batch Processing Pattern",
    "explanation": "Use when work can be split into independent chunks.",
    "keywords": ["batch", "parallel", "etl"],
    "category": "pattern_optimization",
}

with MemoryClient(base_url="http://localhost:8000") as client:
    ref = client.save_memory_card(
        memory_card=memory_card,
        name="Batch Processing Pattern",
        tags=memory_card["keywords"],
        when_to_use=memory_card["explanation"],
    )

    card = client.get_memory_card(ref.entity_id)
    results = client.search(
        query="batch processing",
        search_type=SearchType.BM25,
        entity_type="memory_card",
        top_k=5,
    )

    print(card.description)
    print([item.description for item in results])

Search APIs

The client exposes unified search for memory-card retrieval:

• search(query, search_type=..., top_k=..., entity_type="memory_card") Unified BM25, vector, or hybrid search. Returns list[MemoryCardSpec].

Use unified search for memory-card retrieval:

from gigaevo_memory import MemoryClient, SearchType

with MemoryClient() as client:
    bm25_hits = client.search(
        query="batch processing",
        search_type=SearchType.BM25,
        entity_type="memory_card",
    )

    hybrid_hits = client.search(
        query="performance optimization",
        search_type=SearchType.HYBRID,
        entity_type="memory_card",
        hybrid_weights=(0.3, 0.7),
    )

Batch search is also available for memory cards:

from gigaevo_memory import MemoryClient, SearchType

with MemoryClient() as client:
    results = client.batch_search(
        queries=["batch processing", "etl pipeline", "parallel execution"],
        search_type=SearchType.BM25,
        top_k=3,
    )

Vector and hybrid search requirements

Vector-capable search has two runtime requirements:

• The client must be able to generate embeddings. By default this means installing sentence-transformers, or passing a custom embedding_provider.
• The server must have vector search enabled. If the API is started with vector search disabled, vector and hybrid requests return 503.

Version management

The client includes helpers for versioned entities and channel management:

from gigaevo_memory import MemoryClient

with MemoryClient() as client:
    entity_id = "your-chain-id"
    version_id = "your-version-id"
    from_version = "older-version-id"
    to_version = "newer-version-id"

    versions = client.list_versions(entity_id, entity_type="chain")
    detail = client.get_version(entity_id, version_id, entity_type="chain")
    diff = client.diff_versions(entity_id, from_version, to_version, entity_type="chain")
    client.pin_channel(entity_id, channel="stable", version_id=version_id, entity_type="chain")
    client.promote(entity_id, from_channel="latest", to_channel="stable", entity_type="chain")

Watching for updates

Use watch_chain() to subscribe to SSE updates for a chain:

from gigaevo_memory import MemoryClient

with MemoryClient() as client:
    entity_id = "your-chain-id"

    sub = client.watch_chain(
        entity_id,
        callback=lambda new_chain: print(f"Chain updated: {len(new_chain.steps)} steps"),
    )

    # ... later ...
    sub.stop()

Cache policies

from gigaevo_memory import CachePolicy, MemoryClient

# TTL-based cache (default: 300 seconds)
client = MemoryClient(cache_policy=CachePolicy.TTL, cache_ttl=300)

# Conditional GET using ETag when a cached entry exists
client = MemoryClient(cache_policy=CachePolicy.FRESHNESS_CHECK)

CachePolicy.SSE_PUSH exists as a cache policy enum, but normal entity reads do not automatically attach an SSE listener. For push-style updates today, use watch_chain() explicitly.

Development

make client-install
make client-test
make client-lint
make client-build

Examples

Runnable example scripts live in examples/:

• upload_chain.py
• download_chain.py
• update_chain.py
• run_chain.py
• upload_memory_card.py
• memory_cards_demo.py