Compatible with everything you already use
Why agents forget.
Three architectural failures every team hits once they go past a demo. Threadline eliminates all three.
Context windows blow up
Stuffing every chat into the prompt burns tokens and degrades quality after ~8 turns.
Threadline distills sessions into compact, recallable facts.
RAG retrievals go stale
Vector search returns yesterday's answer, not what the user said five minutes ago.
Real-time memory updates with versioning and recency scoring.
Sessions die at logout
Switch models, switch products, restart the app — your agent forgets the user.
One context object, portable across every LLM and surface.
~38ms p50 retrieval means zero-latency injection before every LLM call — your agent never waits.
Your Agent
user interaction
Threadline
threadline.to
Your Product
already knows
One context object · every agent · no repeated conversation
1. inject() enriches your prompt → 2. LLM call with context → 3. update() captures new facts
Two lines. That's it.
Choose your integration method and start giving your agents memory in under a minute.
01npm install threadline-sdk02 03import { Threadline } from "threadline-sdk"04 05const tl = new Threadline({06 apiKey: process.env.THREADLINE_API_KEY07})08 09const { injectedPrompt } = await tl.inject(userId, basePrompt)10 11await tl.update({ userId, userMessage, agentResponse })Everything your agent needs to know.
Seven scopes of context, automatically extracted and maintained.
preferences
How they want the agent to behave
e.g. "No code examples unless asked"
goals
What they're working toward
e.g. "Launching a SaaS product, targeting developers"
knowledge
Their technical background and expertise
e.g. "5 years TypeScript, Next.js, Supabase"
history
What's happened across past sessions
e.g. "Last session: deploying to Cloudflare Workers"
relationships
People and roles they mention
e.g. "Co-founder Sarah handles design"
communication_style
How they like to be spoken to
e.g. "Prefers concise, bullet-point answers"
general
Core identity and context
e.g. "Maya, San Francisco, building a fintech app"
User‑owned context. Not yours. Not ours. Theirs.
Built for the world where users demand control over their AI data.
OAuth‑style grants
Users approve what each agent can see
Hard delete
Users can permanently erase their context
Full audit trail
Every read and write is logged
Context Dashboard
Manage what agents can access
preferences
Granted
goals
Granted
history
Granted
relationships
Revoked
Auth0 solved identity. Threadline solves context.
| Feature | Threadline | Mem0 | Supermemory | Zep | Letta |
|---|---|---|---|---|---|
| Persistent memory | |||||
| Works with any LLM | |||||
| MCP compatible | |||||
| User‑owned context | |||||
| OAuth‑style grant system | |||||
| Scoped agent access | |||||
| Idempotent grants / scope expansion | |||||
| Full audit trail | |||||
| Hard delete by user | |||||
| Retrieval latency | ~38ms p50* | ~200ms | <300ms | ~300ms | N/A |
| Free tier | 10K mem/mo | 1K/mo | Limited | None | None |
* Threadline's inject() is a direct database lookup, not vector search. Intelligence happens at update() time, not retrieval time — which is why retrieval is this fast. Competitors perform full semantic search at retrieval time.
Start free. Scale when you're ready.
Free up to 10,000 memories a month — no credit card. Builder and Scale tiers when your agent grows up.
See full pricingShip agent memory in production. Two lines, any LLM, free for 10K memories.
Free to start. No credit card. Works with everything you already use.
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