Give your AI a memory that remembers your users across every session — and lets you rewind to exactly what it knew at any moment in the past. Two endpoints, synthesized profiles that drop straight into a system prompt, open-source and docker compose up if you'd rather run it yourself. Under the hood: a bi-temporal knowledge graph.
Two endpoints on the outside. Three stages on the inside. One synthesized artifact your model can actually use.
Think of Anansi as a notebook your AI keeps about each user. Every time someone talks to your app, the message gets handed to Anansi. Anansi quietly figures out what's worth remembering — who the user is, what they're working on, what they prefer — and writes it down. Next time the user shows up, your app asks for the notebook and pastes it into the prompt. The AI greets them like an old friend.
Two HTTP surfaces, three internal stages, one versioned artifact (static_documents) per user.
SDKs are convenience wrappers. The contract is REST, the auth is a bearer token, the response is JSON. Two calls do the job; the other two surfaces are there when you need them.
Store a conversation turn, document, note, or meeting transcript. Sanitization + chunking + embedding happen async — the request returns 202 before any of it runs.
Returns synthesized static + dynamic arrays plus optional relevant chunks. Drop both into a system prompt.
Raw hybrid search (vector + BM25 + RRF). For when you want the chunks, not the profile.
Bi-temporal entity graph extracted during synthesis. People, projects, decisions — with valid-time and knowledge-time edges, queryable via asOf / asOfKnowledge. Pro+.
Memory is a crowded space. Here's what Anansi does that's genuinely distinct — each one backed by code in the repo, not a slide.
Entity edges track two time axes — valid-time (when it was true) and knowledge-time (when we learned it). Query the graph asOf a date, or as we knew it. Ask what was true when.
Open-source under MIT. docker compose up brings up Postgres + Redis; the API ships its own Dockerfile. Run the hosted service or host it yourself — your users' memory, your infra.
An LLM pass distills accumulated chunks into static facts and dynamic context. Drop both arrays into a system prompt — no chunks to dedupe, rank, or trim yourself.
Swap the embedding provider (Nomic or local Ollama), or skip it entirely — pass a pre-computed embedding on ingest and we store your vectors. Model-agnostic on the LLM side too.
Not "memory for everyone" — memory for teams whose problem shape matches what this engine actually does.
"What did we know on March 3rd?" is an audit question, and most memory stores can't answer it — they overwrite. Anansi's knowledge-time axis reconstructs the graph as you knew it at any instant, self-hosting keeps memory on your infrastructure, and DELETE /v1/user gives you a GDPR hard-delete that cascades through chunks, profile, and graph.
Install the Slack app and the workspace becomes shared memory: messages ingest automatically, /ask answers from a two-layer profile (curated static facts + current context), and /memory forget-me lets anyone opt out. No pipeline to build — the bot runs on the same engine as the API.
MIT license, no open-core asterisks. docker compose up starts Postgres + Redis, and with local Ollama serving both embeddings and synthesis, the whole stack runs on your laptop with zero external API calls. Read the retrieval code before you trust it with your users' memory.
An honest read of the memory landscape. Mem0, Supermemory, and Zep are strong tools with graph memory, temporal reasoning, and dashboards of their own — these are the axes where Anansi is genuinely differentiated.
| capability | anansi | supermemory | mem0 |
|---|---|---|---|
| Knowledge-time queries — the graph "as we knew it" (asOfKnowledge) | ✓ | — | — |
| MIT license, full stack self-hostable via docker compose | ✓ | hosted | OSS core |
| Graph / temporal memory | ✓ | ✓ | ✓ |
| Synthesized profile (system-prompt ready) | ✓ | ✓ | — |
| Hybrid retrieval (BM25 + vector) | ✓ | ✓ | ✓ |
| First-party Slack app with /ask team memory | ✓ | — | — |
// based on each product's public docs as of July 2026. Spotted an error? Open an issue — we'll fix the table.
Usage-metered. Cancel any time via /portal. Free tier is real — 1,000 ingest calls per month, no card.
Anansi didn't weave a new web for every story. Your agent shouldn't build a new mind for every session.
Two function calls. Any LLM. Free tier, no card. Five minutes from signed-up to remembering users in production.
We're hardening the API before opening general access. Drop your email and we'll reach out when you're in — no spam, one email when beta opens.
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