Context intelligence
for AI agents.
Files, CSV, JSON, markdown, web results, logs — auto-classified and externalized before they bloat your LLM context. Memories and entities extracted from the content itself. One Python SDK.
CSV, JSON, code, logs —
externalized, not ingested.
Tool results are auto-classified and stored as retrievable artifacts. The LLM sees a compact summary. Memories and entities are extracted and queryable across sessions. Your token bill drops. Your agent remembers.
They extract after. We intercept before.
Context intelligence at the tool-execution boundary — not a post-hoc memory layer. Every tool result flows through MemoSift on its way to your LLM.
First-class adapters.
No rewrites.
Decorate your tools, wrap your client, or install hooks. MemoSift meets your agent where it already lives.
1from memosift import MemoSift2from claude_agent_sdk import Agent34ms = MemoSift(api_key="msk_...")5agent = ms.wrap(Agent(tools=[...]))67# every tool result is now sifted.8agent.run("summarize Q3 revenue")
Recall that doesn't miss.
47 probes across 170-turn sessions. Hybrid of vector + BM25 + entity-graph overlap, rerank by cross-encoder.
Two-stage deterministic compaction, type-aware across 23 content types. Tool-call integrity preserved.
Intercept, classify, and route at the tool boundary. Extraction runs async; your agent never waits.
PII, secrets, injection —
caught before the LLM sees it.
Three-tier compliance pipeline: per-turn findings, session digests with risk trajectory, project-wide reports with executive summaries.