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Flumes SDKs

Flumes gives your agents durable, structured memory via a simple, entity‑first SDK. You get a one‑line client, hybrid retrieval with sane presets, explicit budgeting, and optional helpers for grounded chat.

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What you get

  • Entity‑first design: always scope by entity_id (user/session) and optional namespace.
  • Add → Extract → Store in a single call using context assemble (no separate summarize step).
  • Hybrid retrieval with presets (balanced, factual, recent, graphy) or explicit weights.
  • Budget caps with pack telemetry on every add.
  • Explicit LLM integration: the Agent helper requires an openai_api_key (or a custom backend).

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Install

pip install flumes-ai           # core SDK
# optional Agent helper – requires OpenAI client
pip install "flumes-ai[agent]"   # or: pip install openai
Python ≥ 3.9.

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Minimal example (Python)

from flumes import MemoryClient

client = MemoryClient(api_key="FLUMES_KEY", agent_id="travel-bot", namespace="prod")
u = client.for_entity("user_001")

res = u.add("Planning a trip to France. Like wine and quiet places.", budget="standard")
print("pack=", res.get("pack"))

hits = u.search("trip recommendations", top_k=8)
print("matches=", len(hits.get("matches", [])))

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What happens on add()

  • Flumes extracts structured facts/events from your turn.
  • It stores them and returns pack telemetry (target/hard cap/used/dropped).
  • You can later retrieve with search() or assemble full context via the API.

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Optional Agent helper (explicit key)

from flumes import MemoryClient, Agent

client = MemoryClient(api_key="FLUMES_KEY", agent_id="travel-bot", namespace="prod")
agent = Agent(
    agent_id="travel-bot",
    entity_id="user_001",
    memory_client=client,
    openai_api_key="OPENAI_KEY",  # required explicitly
)

print(agent.chat("Plan my trip."))
The Agent helper assembles context with defaults (preset='factual' + small predicate boost for preferences) and composes a grounded prompt. Provide your own llm_backend to integrate a different LLM.

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Next: Python details

  • Dive into the Python specifics, including retrieval knobs and budgeting, in the Python tab.