Every session starts from zero. Agents forget what happened last week. Can't share learnings. Can't coordinate. Stateless by design — by necessity — but that necessity is now solvable.
Contexte gives agent fleets persistent, queryable, shareable memory across sessions. Not a chatbot. An infrastructure layer.
Today's AI agents are stateless. Every conversation begins fresh. Every task starts from zero context. This isn't a bug — it's how LLMs work by default.
But it becomes a critical failure mode as you scale from one agent to a fleet of autonomous agents coordinating on complex, multi-step work.
Conversations exceed context windows. Agents forget critical details from earlier in the session. Work gets repeated. Errors get repeated.
Agent A has no idea what Agent B learned last week. Different sessions, different contexts, different capabilities. A team of isolated individuals.
Agents can't improve from experience. A month of valuable learnings evaporates every session. Every agent starts at the same competency level, always.
Every agent gets unlimited persistent memory that survives session boundaries. Store facts, learnings, preferences, and context — retrieved intelligently when relevant, not manually requested.
agent.memory.store(
key: "customer_preferences",
value: { format: "markdown", ... },
ttl: "30d"
)
Agents share a common memory pool. One agent learns something — every agent can retrieve it. Fleet-wide context that compounds over time.
Agents query memory by meaning, not keyword. Vector embeddings power natural-language retrieval — ask "what did we learn about this customer?" and get relevant facts.
Automatic intelligent compression of session context. The system learns what's worth remembering and optimizes context windows automatically — no manual curation required.
Built on the Model Context Protocol. Works with LangChain, CrewAI, AutoGen, and any MCP-compatible framework. Drop in, start remembering.
Memory has a lifecycle. Set time-to-live on facts, auto-expire stale learnings, keep memory fresh. Agents only retrieve current, relevant information.
During any task, the agent calls memory.store() with a key, value, and optional TTL. Facts are embedded and indexed immediately.
await contexte.store(
agent: "agent-alpha",
key: "pricing_changed",
value: "Competitor reduced prices by 20% on Q2 plan",
ttl: "90d"
)
Later, an agent — even a different agent — queries memory with a question. Contexte searches the memory pool semantically and returns ranked, relevant facts.
const facts = await contexte.query(
agent: "agent-beta",
question: "what happened with competitor pricing?"
)
// Returns: [{ score: 0.94, key: "pricing_changed", ... }]
Facts compound. Every task contributes to the fleet's knowledge base. Over weeks and months, the agent fleet becomes genuinely intelligent — not just stateless pattern matching.
The companies that deploy agent fleets with persistent memory will have a compounding intelligence advantage over those running stateless agents. Every task, every failure, every success — remembered. Leveraged. Compounded.
Contexte is that layer.