Get This Tool
Emem
Pricing
- Model
- Free
- Free Tier
- No key required for reads
Summary
Ask two models about the same river and you get two rivers — each plausible, neither verifiable, and completely useless the moment the agents need to compare notes. emem is the shared memory layer built for exactly that breakdown.
emem stores facts as short, signed tokens — each one a content-addressed handle that any agent can carry through a summarization pass, hand to another agent on a different model or vendor, and resolve back to the exact signed bytes without trusting whoever sent them. The verify step is offline: recompute the hash and ed25519 signature yourself, no server call required. Cold resolution runs around 180 ms; warm cache hits around 10 ms, with every receipt reporting its own latency stats. The honest caveat from the vendor's own benchmarks: against a bare inline number, a single emem token costs 5.8x more context — the savings only appear when you bundle multiple facts into one round trip.
Bottom line: Pick emem when two agents need to reason from the same signed measurement without sharing a database; skip it when your agent fleet exchanges simple scalar values one at a time and raw context cost matters more than provenance.
Community Performance Report Card
No community ratings yet. Be the first to rate this tool!
Community Benchmarks Community
Sign in to submit a benchmarkNo community benchmarks yet. Be the first to share a real-world data point.
Pros
Sign in to edit- Content-addressed tokens survive summarization passes, so a fact stored at the start of a long session is still resolvable after the model compresses its context — no data lost to context window limits.
- Offline ed25519 verification means a receiving agent can confirm the exact signed bytes without trusting the sender or calling back to the server, which removes the 'garbage-in from an upstream agent' failure mode in multi-agent pipelines.
- No shared database required for cross-vendor handoff — one agent on OpenAI passes a token, another agent on a different model at a different company resolves it directly by content hash, so inter-company agent collaboration needs no joint infrastructure agreement.
- Pre-filled earth observation substrate (NDVI, rasters, spatiotemporal cubes) means geospatial multi-agent applications start with real, checkable data rather than synthetic test fixtures, cutting the time from integration to a meaningful demo.
- MCP connection requires no API key to read, so the barrier to wiring an existing agent into shared verifiable memory is a single config block — no credential provisioning, no onboarding flow.
Cons
Sign in to edit- A single emem token costs 5.8x more context than inlining the bare number — the vendor's own benchmark confirms this. For agents that exchange many small scalar values in tight context windows, the overhead accumulates fast and teams revert to direct inline values, surrendering cross-agent verifiability entirely.
- The self-hosted option does not exist: emem runs on Vortx AI's hosted infrastructure. Teams with data-residency requirements or air-gapped deployment mandates cannot run emem on their own infrastructure and must switch to a different architecture — likely a combination of a local vector store and a custom signing layer.
- The token family (fact, cell, entity, bundle, raster, cube) covers structured geospatial and observational facts well, but unstructured conversational memory or arbitrary document chunks have no native type. Teams building document-grounded agents that need the same cross-vendor verifiability have to map their content into the closest available shape or build a wrapper, adding integration work the SDK does not currently absorb.
Community Reviews
Sign in to write a reviewNo reviews yet. Be the first to share your experience.
About
- Platforms
- Web, API, MCP
- API Available
- Yes
- Self-Hosted
- No
- Last Updated
- 2026-07-23T08:02:49.068Z
Best For
Who it's for
- Multi-agent system builders
- Applications requiring tamper-proof provenance
What it does well
- Multi-agent fact sharing without shared databases
- Verifiable citations across different models and vendors
- Long-term memory that survives context compression
- Earth observation and geospatial data grounding for agents
Integrations
Discussion Community
Sign in to commentNo discussion yet. Sign in to start the conversation.
Compare Emem
Spotted incorrect or missing data? Join our community of contributors.
Sign Up to ContributeCommunity Notes & Tips Community
Sign in to contributeBe the first to contribute. General notes, observations, gotchas, and tips from people who use this tool day-to-day.
Frequently Asked Questions
- Is Emem free?
- Emem has a permanent free tier alongside paid upgrades. You can keep using a baseline version indefinitely without paying.
- Is Emem open source?
- Yes. Emem is open source.
- Does Emem have an API?
- Yes. Emem exposes a developer API. See the official documentation at https://emem.dev for details.
- When was Emem released?
- Emem was first released in 2026.
- What platforms does Emem support?
- Emem is available on: Web, API, MCP.
Hours Saved & ROI Stories Community
Sign in to contributeBe the first to contribute. Concrete time/cost savings, with context. e.g. "Cut my code review backlog from 4h to 45m per week."
Curated lists that include this category
Two agents reasoning about the same location without a shared database will invent two different ground truths. emem solves this by representing each fact as a short, signed token — a content-addressed handle backed by a blake3 CID and canonical CBOR encoding. An agent stores a fact once, gets a token roughly 50 tokens long, and passes that token to any downstream agent. The receiver resolves it to the full signed record directly from the store by content hash, paying no upstream re-embedding or re-description cost. The token survives context compression: when a model summarizes a long session and drops the raw numbers, the handle stays intact and re-hydrates the exact bytes on demand.
The key architectural decision is that the token is an address, not a payload. Verification is fully offline — recompute the hash and ed25519 signature at /verify and the value stands independent of the server. Each fact carries a provenance class: direct sensor reading, deterministic index, model output, or human curated. This tamper-provenance labeling is per fact, not per session, which means a downstream agent knows not just what the value is but how it was produced — without trusting the upstream agent that sent it.
Emem’s first production substrate is earth observation and geospatial data: NDVI readings, raster fields, and spatiotemporal cubes are pre-filled and publicly checkable with no account. The token family has grown from single observations (emem:fact:) to place addresses, canonical object identities, signed bundles, raster arrays, and 4D time-series cubes — each resolving and verifying identically. The MCP integration requires a single JSON config block and no API key to read. Where emem breaks: single-token recall costs 5.8x more context than inlining the bare number, so agents exchanging high-frequency scalar values in tight context budgets will find the overhead significant. Teams in that scenario typically fall back to direct database lookups and lose the cross-vendor verifiability emem exists to provide.