gigaevo-client 0.3.0

Unified GigaEvo SDK — persistent memory for CARL artifacts + Platform API surface

gigaevo-client

Unified Python SDK for the GigaEvo ecosystem: persistent memory for CARL artifacts (chains, steps, agents, memory cards, agent skills) plus the GigaEvo Platform API surface.

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

Renamed in 0.3.0. The legacy distribution name gigaevo-memory is now a meta-package that depends on gigaevo-client. Existing from gigaevo_memory import ... call sites keep working through a shim that emits one DeprecationWarning per process. New projects should depend on gigaevo-client and import from gigaevo_client.

Installation

pip install gigaevo-client

Legacy installs (pip install gigaevo-memory) still work and transitively pull in gigaevo-client.

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

The compose stack in this repository publishes the API at http://localhost:8002. MemoryClient() without an explicit base_url still defaults to http://localhost:8000, which is intended for a standalone local API process or an equivalent reverse-proxied endpoint.

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:8002") 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:8002") 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:

The following examples use MemoryClient() without base_url, so they assume a standalone API on http://localhost:8000 unless you provide a different endpoint.

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