Last updated Apr 9th 2026
OpenAI-compatible API gateway for 200+ models from 30+ providers. One integration gives you smart routing, persistent memory, web search, context compression, and input security. All features activate per request by adding a single parameter. No new SDKs, no infrastructure changes.
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Sansa Benchmark data released to the public in December 2025.
Sansa Censorship Benchmark covered by online media world wide in December 2025. (gazeta.ru, substack.com, etc.)
Sansa Benchmarks go viral on X and Reddit in December 2025.
Sansa reaches first 100 waitlist signups in December 2025.
Sansa surpasses 12 Billion tokens processed in January 2026.
Sansa launched to the public on February 17, 2026.
Sansa releases OpenClaw installer for the open source AI agent framework: https://github.com/sansa-ai/openclaw-installer
Sansa adds Text and Audio processing to their OpenAI compatible API in March 2026.
Sansa is launching Memory, Input Guard (input security), Context Compression, AI Web Search, and Smart Routing as gateway features in 2026.
Sansa is a unified LLM gateway. One API, one key, one base URL. OpenAI-compatible. 200+ models from 30+ providers (OpenAI, Anthropic, Google, Meta, Mistral, and more). The gateway automatically routes between providers for uptime. What you get depends on what you pass in the request:
- Any model by name: pass claude-3.5-sonnet, gpt-4o, gemini-2.5-pro, or any of the 200+ supported models. The gateway handles provider-level failover automatically. - Smart Model Routing: pass sansa-auto as the model and a routing model trained on 20+ Billion tokens selects the best model for the prompt. Better than frontier quality at roughly half the cost. - Memory: add a memory parameter and the model remembers users across sessions. No vector database or retrieval pipeline required. - AI Web Search: append :search to the model name and the model gets grounded with live web results and inline citations. - Context Compression: add a compression parameter to strip low-value tokens from long inputs. ~40% faster latency, ~30% fewer input tokens. - Input Guard: add input_guard to detect PII and prompt injections before they reach the model.
The Sansa gateway is OpenAI-compatible. Change the base URL and API key and existing OpenAI SDK code works immediately. Pass the model you want, or pass sansa-auto to let the routing model choose. Add parameters to the request body to turn on memory, search, compression, or input guard. Everything is one API call.
- 200+ models supported - 30+ inference providers with automatic failover - 99.9% uptime - 20+ Billion training tokens for the smart routing model - 10-15ms latency added by smart routing (sansa-auto) - ~40% token savings with context compression - 98% accuracy for input guard detection - Sub-50ms latency for memory and search - $5 per million tokens with smart routing vs $10-25 for frontier models
Compatible with OpenAI SDK (three line code change) and the Sansa SDK. No new SDKs, middleware, or infrastructure changes required. Every feature is opt-in per request.
Free community benchmarking tool. Tests individual models from various providers on real-world tasks. Does not test the sansa-auto endpoint. Results published at trysansa.com/benchmark
SOC 2 Type II ready. Data never sold or shared with third parties. All requests encrypted in transit and at rest. Opt out of training data. US-hosted infrastructure. Full user control over data at all times.
Last updated Apr 9th 2026
Memory is a feature of the Sansa AI gateway. It gives AI assistants persistent, cross-session memory without requiring a vector database, an embedding pipeline, or retrieval code.
Add a user_id or agent_id to the sansa.memory object in your request and the gateway remembers context across conversations. Before each request, Sansa searches the memory store for that user or agent and injects relevant facts into context. After the model responds, new information is extracted and stored automatically.
1. You include sansa.memory with a user_id or agent_id in the request body. 2. The gateway retrieves stored memories relevant to the current prompt and adds them to the context window. 3. The model generates a response using the enriched context. 4. New facts from the conversation are extracted and persisted to the memory store for future use.
No vector database setup. No embedding pipeline. No retrieval code on your side.
A separate Memory API provides full CRUD access to stored memories. Use it to build admin dashboards, handle deletion requests, audit stored context, or pre-populate memory before a user's first conversation.
- Sub-50ms latency for memory retrieval - 90 day default retention - Unlimited memories per user or agent - Works with any of the 200+ models on the gateway
One field in the request body. The gateway is OpenAI-compatible. Change the base URL and API key and existing OpenAI SDK code works. Memory activates per request. No new SDKs, no middleware.
Memory is one feature of the Sansa gateway. Other features include smart model routing (sansa-auto), AI web search, context compression, input guard, and automatic provider failover. All activate through the same API by adding parameters to the request.
Learn more at trysansa.com/product/memory
Memory
Add a memory parameter to your chat completion request and your AI remembers users across sessions.
LATENCY
RETENTION
MEMORIES
Add user_id or agent_id to the sansa.memory object and every model call starts remembering. Without having to set up a vector database, an embedding pipeline, or retrieval code.
Before each request, Sansa searches the memory store for that user_id or agent_id and injects relevant facts into context. After the model responds, new information is extracted and stored automatically.
Use the Memory API to get more control over stored memories to build admin dashboards, handle deletion requests, or pre-populate context before a user's first conversation.
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