hebbs 0.1.2

Python SDK for HEBBS cognitive memory engine

HEBBS Python SDK

Python client for the HEBBS cognitive memory engine. Provides an async gRPC client with a Pythonic interface -- no protobuf in the public API.

HEBBS gives your AI agents real memory: sub-10ms writes, sub-10ms recalls, four recall strategies, entity-scoped multitenancy, and GDPR-compliant erasure. This SDK wraps the gRPC API in idiomatic Python so you can integrate HEBBS in minutes.

Quick Start

1. Install the HEBBS Server

curl -sSf https://hebbs.ai/install | sh

This installs hebbs-server, hebbs-cli, and hebbs-bench to ~/.hebbs/bin/. The server runs BGE-small-en-v1.5 (ONNX) for embeddings internally -- no external embedding API needed.

2. Start the Server

hebbs-server

The server listens on localhost:6380 (gRPC) and localhost:6381 (REST) by default. On first start it prints a bootstrap API key -- save it.

3. Install the Python SDK

pip install hebbs

To include the demo app and all LLM providers:

pip install "hebbs[demo]"

4. Use the SDK

import asyncio
from hebbs import HebbsClient

async def main():
    # api_key falls back to HEBBS_API_KEY env var if not passed
    async with HebbsClient("localhost:6380", api_key="hb_...") as h:
        # Store a memory (entity-scoped)
        mem = await h.remember(
            content="Acme Corp uses Salesforce CRM and has 200 engineers",
            importance=0.8,
            context={"company": "Acme Corp", "topic": "tech_stack"},
            entity_id="acme_corp",
        )

        # Recall by semantic similarity
        results = await h.recall(
            cue="What CRM does Acme use?",
            strategies=["similarity"],
            top_k=5,
            entity_id="acme_corp",
        )
        for r in results.results:
            print(f"  [{r.score:.2f}] {r.memory.content}")

        # Prime a session (load relevant context for an entity)
        prime = await h.prime(entity_id="acme_corp", max_memories=50)
        print(f"Primed {len(prime.results)} memories")

        # Generate insights from memory clusters (uses LLM server-side)
        reflect = await h.reflect(entity_id="acme_corp")
        print(f"Created {reflect.insights_created} insights")

        # GDPR-compliant cryptographic erasure
        forget = await h.forget(entity_id="acme_corp")
        print(f"Forgot {forget.forgotten_count} memories")

asyncio.run(main())

SDK Reference

HebbsClient

Method	Description
remember(content, importance, context, entity_id, edges)	Store a memory
get(memory_id)	Retrieve a memory by ID (bytes)
recall(cue, strategies, top_k, entity_id, scoring_weights, cue_context)	Multi-strategy recall
prime(entity_id, max_memories, similarity_cue, scoring_weights)	Session warm-up
revise(memory_id, content, importance, context, entity_id)	Update a memory
forget(entity_id, memory_ids)	GDPR-compliant erasure (by entity or by IDs)
set_policy(max_snapshots_per_memory, auto_forget_threshold, decay_half_life_days)	Configure tenant policies
subscribe(entity_id, confidence_threshold)	Real-time memory surfacing
reflect(entity_id)	Generate insights from clusters (LLM server-side)
insights(entity_id, max_results)	Retrieve accumulated insights
health()	Server health check
count()	Total memory count

Recall Strategies

• similarity -- semantic vector search (HNSW, entity-filtered)
• temporal -- time-ordered retrieval
• causal -- cause-and-effect graph traversal
• analogical -- cross-domain pattern matching (blends embedding + structural similarity)

Pass strategy names as strings for basic usage. For advanced tuning, pass RecallStrategyConfig objects. You can mix both in the same call:

results = await h.recall(
    cue="What happened with Acme?",
    strategies=["temporal", RecallStrategyConfig("similarity", top_k=3, ef_search=200)],
    entity_id="acme_corp",
)

RecallStrategyConfig

Per-strategy tuning parameters for advanced recall. Most users should just pass strategy names as strings. Use this when you need fine-grained control.

Field	Type	Default	Used By	Description
strategy	str	(required)	all	Strategy name: "similarity", "temporal", "causal", "analogical"
entity_id	str | None	None	all	Override entity scope for this strategy
top_k	int | None	None	all	Per-strategy result limit (separate from the top-level top_k)
ef_search	int | None	50	similarity	HNSW candidate count. Higher = more accurate, slower.
time_range	tuple[int, int] | None	None (unbounded)	temporal	(start_us, end_us) microsecond timestamps. When omitted, returns all memories newest-first.
seed_memory_id	bytes | None	None (auto)	causal	Starting node for graph traversal. When omitted, the engine picks the best seed.
max_depth	int | None	5 (max 10)	causal	Maximum hops in graph traversal.
edge_types	list[EdgeType] | None	None (all)	causal	Restrict traversal to specific edge types.
analogical_alpha	float | None	0.5	analogical	Blend weight: 0.0 = pure structural, 1.0 = pure embedding similarity.

Causal recall -- trace cause-and-effect chains from a seed memory:

from hebbs import RecallStrategyConfig, EdgeType

results = await h.recall(
    cue="What led to the pricing pushback?",
    strategies=[
        RecallStrategyConfig(
            "causal",
            seed_memory_id=mem.id,
            max_depth=3,
            edge_types=[EdgeType.CAUSED_BY, EdgeType.FOLLOWED_BY],
        )
    ],
)

Analogical recall -- find structurally similar patterns across entities:

results = await h.recall(
    cue="enterprise CRM evaluation",
    strategies=[RecallStrategyConfig("analogical", analogical_alpha=0.7)],
    cue_context={"industry": "technology", "stage": "evaluation"},
    top_k=5,
)

Scoring Weights

Recall results are ranked by a composite score blending relevance, recency, importance, and reinforcement. Pass scoring_weights to tune the blend:

from hebbs import ScoringWeights

# Pure semantic match
results = await h.recall(
    cue="competitor pricing",
    scoring_weights=ScoringWeights(w_relevance=1.0, w_recency=0.0, w_importance=0.0, w_reinforcement=0.0),
)

# Recency-biased -- "what just happened?"
results = await h.recall(
    cue="latest updates",
    scoring_weights=ScoringWeights(w_relevance=0.2, w_recency=0.8, w_importance=0.0, w_reinforcement=0.0),
)

# Also works as a plain dict
results = await h.recall(
    cue="latest updates",
    scoring_weights={"w_relevance": 0.2, "w_recency": 0.8, "w_importance": 0.0, "w_reinforcement": 0.0},
)

Omit scoring_weights for the default blend (relevance 0.5, recency 0.2, importance 0.2, reinforcement 0.1).

Authentication

The server generates a bootstrap API key on first start and prints it to stderr. Pass it to the client:

async with HebbsClient("localhost:6380", api_key="hb_...") as h:
    ...

Or set the HEBBS_API_KEY environment variable and omit api_key -- the SDK picks it up automatically. To explicitly connect without auth, pass api_key="".

Entity Isolation (Multitenancy)

All HEBBS operations are scoped by entity_id. Memories stored under one entity are never returned when querying a different entity -- this applies to all four recall strategies, prime, reflect, and insights. No configuration needed; isolation is structural.

await h.remember(content="Uses Salesforce", entity_id="acme_corp")
await h.remember(content="Uses HubSpot", entity_id="techflow_inc")

results = await h.recall(cue="What CRM?", entity_id="acme_corp")
# Only returns "Uses Salesforce" -- techflow_inc data is invisible

Demo App

The demo ships an AI sales agent ("Atlas") that uses HEBBS for memory-augmented conversations. It shows every HEBBS operation in real-time panels: remember latency, recall scores, prime context, and session metrics.

Configure an LLM

# Pick one:
export GEMINI_API_KEY="your-key"        # Gemini (default)
export OPENAI_API_KEY="your-key"        # OpenAI
export ANTHROPIC_API_KEY="your-key"     # Anthropic
# Or use Ollama / mock (no keys needed)

Run Interactive Chat

hebbs-demo interactive

Or specify a config:

hebbs-demo interactive --config gemini-vertex  # Gemini via Vertex AI
hebbs-demo interactive --config gemini         # Gemini via API key
hebbs-demo interactive --config openai         # GPT-4o
hebbs-demo interactive --config local          # Ollama (no API key)
hebbs-demo interactive --mock-llm              # Mock LLM (no API key)

Switch entities mid-session to demonstrate multitenancy:

hebbs-demo interactive --entity acme_corp
# In-session: /session techflow_inc

Run Scenarios

hebbs-demo scenarios --all                # Run all 7 scenarios
hebbs-demo scenarios --run discovery_call # Run a specific one

Available scenarios: discovery_call, objection_handling, multi_session, reflect_learning, subscribe_realtime, forget_gdpr, multi_entity.

LLM Providers

Provider	Config	Env Variable
Gemini (Vertex AI)	gemini-vertex.toml	GOOGLE_APPLICATION_CREDENTIALS, GOOGLE_CLOUD_PROJECT
Gemini (API key)	gemini.toml	GEMINI_API_KEY
OpenAI	openai.toml	OPENAI_API_KEY
Anthropic	(custom toml)	ANTHROPIC_API_KEY
Ollama	local.toml	(none -- Ollama must be running)

Requirements

• Python >= 3.10
• A running HEBBS server (gRPC on port 6380)

Contributing

Contributions are welcome. By submitting a pull request, you agree to the Contributor License Agreement.

License

Copyright 2025 Parag Arora. Apache 2.0 -- see LICENSE.