neruva-crewai 0.1.2

Drop-in CrewAI memory integration for Neruva. Replaces CrewAI's default storage with Records + KG federation. Substrate (v0.5.7) adds typed-shape context dispatch, tenant-specific PII rules, depth-unlimited nested-belief tracking, counterfactual rollouts, EFE planner, continual learning. Deterministic from seed (bit-identical replay). Persists across crew runs, supports semantic search via agent_recall, optional entity tracking via the HD knowledge graph. One-line install.

neruva-crewai

Drop-in CrewAI memory integration for Neruva. Three memory flavors backed by the Neruva substrate.

pip install neruva-crewai

What's new in the substrate (v0.5.7, May 2026)

The substrate this adapter wraps has gained a lot since the last release. Existing code keeps working — new capabilities are just available.

• Deterministic replay — every query is bit-identical across reruns from the same seed. Replay any past crew state for audit or debugging.
• Typed-shape context — pull structured JSON from records with per-field citations. {question, shape: {field: type}} → typed result without an LLM at query time. Natural fit for crew tool calls that need a specific output schema.
• Tenant-specific PII rules — register your custom ID formats (employee codes, patient codes, order IDs) from 3-5 examples. The substrate redacts them automatically.
• Depth-unlimited nested-belief tracking — store and retrieve chains like Alice → Bob → Carol thinks X at any depth, with inner-position-swap rejection. Useful for multi-agent crew sims.
• Counterfactual rollouts — "what if action k had been a' instead?" Replay an action sequence with one step substituted.
• Active inference planning — score candidate action sequences by KL distance to a goal-marginal.
• Continual K-gram learning — provable no-forgetting via integer-add commutativity.

Quick start

from neruva_crewai import NeruvaLongTermMemory
from crewai import Crew

memory = NeruvaLongTermMemory(
    namespace="my_project",         # one per crew / domain
    api_key="nv_...",               # or env NERUVA_API_KEY
)

crew = Crew(
    agents=[...],
    tasks=[...],
    memory=True,
    long_term_memory=memory,        # plugged in here
)

Three memory flavors

Class	What it backs	Underlying substrate
NeruvaShortTermMemory	CrewAI ShortTermMemory (per-run scratchpad)	Records, kind="short_term"
NeruvaLongTermMemory	CrewAI LongTermMemory (cross-run persistent)	Records, kind="long_term"
NeruvaEntityMemory	CrewAI EntityMemory (named entities + relationships)	Records + HD KG triples

All three persist across process restarts via GCS — no Redis or Postgres setup required.

Entity memory with triple binding

Use the canonical extraction prompt to extract triples in your own LLM turn (Claude / GPT / etc), then pass them as metadata:

from neruva_crewai import NeruvaEntityMemory

entity_mem = NeruvaEntityMemory(namespace="my_project", api_key="nv_...")

# After your agent observes a fact about an entity:
entity_mem.save(
    value="Caroline researches adoption agencies",
    metadata={
        "triples": [
            ["caroline", "researches", "adoption_agencies"],
            ["caroline", "works_at", "charity"],
        ],
    },
)

# Later — sub-ms KG entity recall:
results = entity_mem.search("What did Caroline research?", limit=5)

Or use Neruva's agent_remember(extract="managed") to have the substrate run extraction for you on every save (server-side, sub-$0.001/turn typical).

Why use Neruva instead of CrewAI's default storage?

Feature	CrewAI default	Neruva
Persists across process restart	ChromaDB local file	GCS-backed, multi-machine
Cross-crew recall	Manual setup	One namespace per crew, instant federation
Knowledge-graph entity tracking	LLM-based, opaque	HD KG, sub-ms cosine, deterministic
Causal queries / Pearl do-operator	Not offered	agent_causal_query
Provable replay	Not offered	agent_snapshot + agent_restore
GDPR forget by user	Manual	user_id auto-folds, one-call forget
Portability	None	.neruva zip container

Get an API key · Docs