
Every location on Earth gets a 64-bit cell identifier and a cryptographically signed answer for elevation, land cover, climate, or one of 46 remote sensing bands. Ask for a fact at a cell and emem fetches the tile, signs it with ed25519, persists it, and returns a content-addressed receipt you can verify at /verify without trusting the server. The MCP interface exposes locate, recall_bands, memory_create, memory_search, and memory_contradictions. Cold reads take 180ms, warm reads under 10ms, no API keys required. Four foundation models (Clay, Prithvi, Tessera, Galileo) run server-side for consensus scoring on deforestation, wetland change, and disaster anomaly. Reach for this when your agent needs a stable, auditable handle for real-world coordinates instead of hallucinating numbers.
claude mcp add --transport http emem https://emem.dev/mcpRun in your terminal. Add --scope user to make it available in every project.
Review the command, arguments, and environment values before installing — MCP servers run with your local permissions.
Verified live against the running server on Jun 10, 2026.
emem_locateResolves a place mention (free-text name, address, or lat/lng) to the protocol's cell64 identifier, and returns the topic-grouped inventory of bands and algorithms available at that location. When to use: Use whenever the input refers to a real-world location and the next step...4 paramsResolves a place mention (free-text name, address, or lat/lng) to the protocol's cell64 identifier, and returns the topic-grouped inventory of bands and algorithms available at that location. When to use: Use whenever the input refers to a real-world location and the next step...
latnumberlngnumberplacestringqstringemem_askSingle-shot free-text answer about a real-world location, backed by signed satellite/elevation/water/built-up receipts. Forwards a place mention plus a question; runs the locate → recall → algorithm chain server-side; returns one packaged envelope. When to use: Use when the qu...8 paramsSingle-shot free-text answer about a real-world location, backed by signed satellite/elevation/water/built-up receipts. Forwards a place mention plus a question; runs the locate → recall → algorithm chain server-side; returns one packaged envelope. When to use: Use when the qu...
cellstringincludearrayinclude_imagebooleanlatnumberlngnumberplacestringq*stringverbosebooleanemem_huntEvent-discovery sweep: pick an event keyword (algal_bloom, deforestation, flood_extent, wildfire, urban_heat_island, methane_plume, landslide, drought, soil_salinity, crop_stress, water_turbidity, oil_slick) plus a region (free-text name or polygon_bbox). The responder geocode...3 paramsEvent-discovery sweep: pick an event keyword (algal_bloom, deforestation, flood_extent, wildfire, urban_heat_island, methane_plume, landslide, drought, soil_salinity, crop_stress, water_turbidity, oil_slick) plus a region (free-text name or polygon_bbox). The responder geocode...
event*stringalgal_bloom · deforestation · flood_extent · wildfire · urban_heat_island · methane_plumepolygon_bboxobjectregionstringemem_eudr_ddsProduce a Due Diligence Statement per Regulation (EU) 2023/1115 for one or more plots. Each plot carries operator-supplied geometry (GeoJSON Polygon for >4 ha, Point for ≤4 ha non-cattle per Article 2(28)), country of production (ISO3), Combined Nomenclature code (HS-6+), and...6 paramsProduce a Due Diligence Statement per Regulation (EU) 2023/1115 for one or more plots. Each plot carries operator-supplied geometry (GeoJSON Polygon for >4 ha, Point for ≤4 ha non-cattle per Article 2(28)), country of production (ISO3), Combined Nomenclature code (HS-6+), and...
cut_off_datestringforest_baseline_overridestringlegality_modulestringmax_cells_per_plotintegeroperatorobjectplots*arrayemem_spiCompute the Standardized Precipitation Index (McKee et al. 1993) at a cell: fit a gamma distribution to the same-window precipitation-accumulation history, then standardize the current accumulation to a z-score and map it to a drought class (extreme/severe/moderate drought … n...4 paramsCompute the Standardized Precipitation Index (McKee et al. 1993) at a cell: fit a gamma distribution to the same-window precipitation-accumulation history, then standardize the current accumulation to a z-score and map it to a drought class (extreme/severe/moderate drought … n...
cell*stringcurrent_accumulation_mmnumberprecip_history_mmarraywindow_daysintegeremem_burn_severityCompute the differenced Normalized Burn Ratio (dNBR = NBR_pre − NBR_post; Key & Benson 2006) and map it to the USGS burn-severity classes (unburned / low / moderate-low / moderate-high / high). Supply `nbr_pre` + `nbr_post` (pin the scenes bracketing the fire date) for a corre...3 paramsCompute the differenced Normalized Burn Ratio (dNBR = NBR_pre − NBR_post; Key & Benson 2006) and map it to the USGS burn-severity classes (unburned / low / moderate-low / moderate-high / high). Supply `nbr_pre` + `nbr_post` (pin the scenes bracketing the fire date) for a corre...
cell*stringnbr_postnumbernbr_prenumberemem_rice_ch4Estimate seasonal CH4 emissions from rice cultivation per IPCC 2019 Refinement Eq 5.1: integrate the daily emission factor over the cultivation period with water-regime scaling (SFp pre-season, SFo organic amendment) and an optional Yan-2005 Q10 temperature modifier. `cultivat...7 paramsEstimate seasonal CH4 emissions from rice cultivation per IPCC 2019 Refinement Eq 5.1: integrate the daily emission factor over the cultivation period with water-regime scaling (SFp pre-season, SFo organic amendment) and an optional Yan-2005 Q10 temperature modifier. `cultivat...
cell*stringcultivation_period_days*numberefc_kg_ch4_ha_day*numberndwi_seriesarraysfonumbersfpnumbert_paddy_cnumberemem_deforestation_alertComposite deforestation-alert score: `alert_score = 0.5·clamp01(ndvi_drop/0.30) + 0.5·clamp01(embedding_change/0.20)`, where `ndvi_drop = max(0, ndvi_modis_baseline − ndvi_now)` and `embedding_change = 1 − cos(tessera_latest, tessera_prev)`. Each half degrades INDEPENDENTLY an...1 paramsComposite deforestation-alert score: `alert_score = 0.5·clamp01(ndvi_drop/0.30) + 0.5·clamp01(embedding_change/0.20)`, where `ndvi_drop = max(0, ndvi_modis_baseline − ndvi_now)` and `embedding_change = 1 − cos(tessera_latest, tessera_prev)`. Each half degrades INDEPENDENTLY an...
cell*stringemem_sar_forest_disturbanceCloud- and night-independent Sentinel-1 C-band confirmation of forest disturbance. Intact forest scatters VV strongly + stably (canopy volume scattering); clearing collapses that term so VV backscatter DROPS ~3-5 dB. Samples VV at a baseline-year July-1 anchor and the latest s...2 paramsCloud- and night-independent Sentinel-1 C-band confirmation of forest disturbance. Intact forest scatters VV strongly + stably (canopy volume scattering); clearing collapses that term so VV backscatter DROPS ~3-5 dB. Samples VV at a baseline-year July-1 anchor and the latest s...
baseline_yearintegercell*stringemem_triple_consensusThree-encoder change ensemble: compute the cosine change between the two most-recent DISTINCT vintages for each of the Clay, Prithvi, and Tessera embeddings at the cell, then vote each encoder's change against `consensus_threshold` (registry default 0.15). Returns each encoder...2 paramsThree-encoder change ensemble: compute the cosine change between the two most-recent DISTINCT vintages for each of the Clay, Prithvi, and Tessera embeddings at the cell, then vote each encoder's change against `consensus_threshold` (registry default 0.15). Returns each encoder...
cell*stringconsensus_thresholdnumberemem_terrainCompute three standard DEM terrain indices from one 3×3 Copernicus-DEM (copdem30m.elevation_mean) neighbourhood at a cell: Horn (1981) slope in degrees, Riley (1999) Terrain Ruggedness Index (TRI = sqrt(Σ(Z_centre−Z_i)²)), and Weiss (2001) Topographic Position Index (TPI = Z_c...2 paramsCompute three standard DEM terrain indices from one 3×3 Copernicus-DEM (copdem30m.elevation_mean) neighbourhood at a cell: Horn (1981) slope in degrees, Riley (1999) Terrain Ruggedness Index (TRI = sqrt(Σ(Z_centre−Z_i)²)), and Weiss (2001) Topographic Position Index (TPI = Z_c...
cell*stringstep_cellsintegeremem_region_similarityAnswer 'how alike are these two places?' Mean-pool the 128-D GeoTessera embedding across each region's cells to get a centroid, then return the cosine similarity in [-1,1] (+1 = identical landscape, 0 = unrelated). Each region is {place} | {polygon_bbox} | {cells}. CPU-fetched...3 paramsAnswer 'how alike are these two places?' Mean-pool the 128-D GeoTessera embedding across each region's cells to get a centroid, then return the cosine similarity in [-1,1] (+1 = identical landscape, 0 = unrelated). Each region is {place} | {polygon_bbox} | {cells}. CPU-fetched...
max_cellsintegerregion_a*objectregion_b*objectemem_embedding_centroidMean-pool the 128-D GeoTessera embedding over a region's cells: centroid = (1/N) Σ v_i, plus the L2-normalised centroid and a content-addressed centroid_cid. The building block region_similarity composes. Region is {place} | {polygon_bbox} | {cells}. NaN dims are averaged over...4 paramsMean-pool the 128-D GeoTessera embedding over a region's cells: centroid = (1/N) Σ v_i, plus the L2-normalised centroid and a content-addressed centroid_cid. The building block region_similarity composes. Region is {place} | {polygon_bbox} | {cells}. NaN dims are averaged over...
cellsarraymax_cellsintegerplacestringpolygon_bboxobjectemem_embedding_diversityQuantify how varied a region's landscape is: diversity = (1/(N(N-1))) Σ_{i<j} (1 − cosine(v_i, v_j)), the mean pairwise cosine distance over the region's GeoTessera embeddings. 0 = perfectly uniform; higher = more heterogeneous land cover (a determinantal-point-process / k-med...4 paramsQuantify how varied a region's landscape is: diversity = (1/(N(N-1))) Σ_{i<j} (1 − cosine(v_i, v_j)), the mean pairwise cosine distance over the region's GeoTessera embeddings. 0 = perfectly uniform; higher = more heterogeneous land cover (a determinantal-point-process / k-med...
cellsarraymax_cellsintegerplacestringpolygon_bboxobjectemem_neighborhood_consistencyScore how much a cell looks like its surroundings: consistency = (1/8) Σ cosine(centre, neighbour_i) over the 8 immediate cell64 neighbours, plus outlier_score = 1 − consistency. High consistency = the cell blends in (Tobler's First Law); high outlier_score = it stands out — a...1 paramsScore how much a cell looks like its surroundings: consistency = (1/8) Σ cosine(centre, neighbour_i) over the 8 immediate cell64 neighbours, plus outlier_score = 1 − consistency. High consistency = the cell blends in (Tobler's First Law); high outlier_score = it stands out — a...
cell*stringemem_stateGet one dense numeric fingerprint that summarises everything known about a place — ready to feed into similarity search, a classifier, or clustering. Two views: `encoder` returns a single AI-model embedding (128-D Tessera, 1024-D Clay, 1024-D Prithvi); `cube` returns the full...9 paramsGet one dense numeric fingerprint that summarises everything known about a place — ready to feed into similarity search, a classifier, or clustering. Two views: `encoder` returns a single AI-model embedding (128-D Tessera, 1024-D Clay, 1024-D Prithvi); `cube` returns the full...
as_of_signed_atstringas_of_tslotintegercell*stringencoderstringfamiliesarrayinclude_reservedbooleanmaterializebooleantslotintegerviewstringencoder · cubeemem_state_multiGet the place's fingerprint from several AI models at once (`geotessera`, `clay_v1`, `prithvi_eo2`, `galileo`) in one call, returned as a per-model map. Each model is tried independently; any that can't produce a vector here show up under `missing` with a reason instead of fai...5 paramsGet the place's fingerprint from several AI models at once (`geotessera`, `clay_v1`, `prithvi_eo2`, `galileo`) in one call, returned as a per-model map. Each model is tried independently; any that can't produce a vector here show up under `missing` with a reason instead of fai...
as_of_signed_atstringas_of_tslotintegercell*stringencodersarraytslotintegeremem_state_diffVector delta between the same cell at two tslots: returns the per-element residual, its L2 norm (scalar change-magnitude), the cosine between the two source vectors (orientation drift), and both source fact CIDs so the agent can quote both attestations as evidence. When to use...4 paramsVector delta between the same cell at two tslots: returns the per-element residual, its L2 norm (scalar change-magnitude), the cosine between the two source vectors (orientation drift), and both source fact CIDs so the agent can quote both attestations as evidence. When to use...
cell*stringencoderstringtslot_a*integertslot_b*integeremem_memory_tokenCompose a `memt:<cell64>:<fact_cid>` (or `memt:<cell64>:<state_cid>`) citation handle. Validates both components are non-empty and do not contain the outer separator `:`. When to use: Call when the agent wants a single rebindable string to cite a place + attested fact across m...2 paramsCompose a `memt:<cell64>:<fact_cid>` (or `memt:<cell64>:<state_cid>`) citation handle. Validates both components are non-empty and do not contain the outer separator `:`. When to use: Call when the agent wants a single rebindable string to cite a place + attested fact across m...
cell*stringfact_cid*stringemem_memory_token_resolveParse a `memt:<cell64>:<fact_cid>` citation handle and return the signed fact body the cid binds. Saves the agent from string-splitting the token and chaining `GET /v1/facts/<cid>` manually. When to use: Call when an agent receives a memory_token from another agent (or out of...1 paramsParse a `memt:<cell64>:<fact_cid>` citation handle and return the signed fact body the cid binds. Saves the agent from string-splitting the token and chaining `GET /v1/facts/<cid>` manually. When to use: Call when an agent receives a memory_token from another agent (or out of...
token*stringemem_memory_bundleCompose N (cell, band, tslot?) triples into ONE signed envelope. Each triple runs through the standard auto-materialize recall path; the resulting fact_cids are bundled into a content-addressed envelope and the responder signs over the full receipt. The composed `bundle_token`...2 paramsCompose N (cell, band, tslot?) triples into ONE signed envelope. Each triple runs through the standard auto-materialize recall path; the resulting fact_cids are bundled into a content-addressed envelope and the responder signs over the full receipt. The composed `bundle_token`...
purposestringtriples*arrayemem_memory_bundle_resolveParse a `memb:<bundle_cid>` token and return the signed bundle envelope: every citation (cell, band, resolved_tslot, fact_cid, memory_token), the receipt, the responder pubkey, and the deduped flat cells[] / fact_cids[] arrays. Returns 404 with a typed code when the responder...1 paramsParse a `memb:<bundle_cid>` token and return the signed bundle envelope: every citation (cell, band, resolved_tslot, fact_cid, memory_token), the receipt, the responder pubkey, and the deduped flat cells[] / fact_cids[] arrays. Returns 404 with a typed code when the responder...
token*stringmemory_viewRead the contents of a memory file at `/memories/<path>` or list a directory when the path ends with `/`. Optional `view_range: [start, end]` slices a 1-indexed inclusive line range out of the file. Mirrors the `view` verb in Anthropic's context-management-2025-06-27 memory to...4 paramsRead the contents of a memory file at `/memories/<path>` or list a directory when the path ends with `/`. Optional `view_range: [start, end]` slices a 1-indexed inclusive line range out of the file. Mirrors the `view` verb in Anthropic's context-management-2025-06-27 memory to...
kindstringepisodic · semantic · procedural · resourcepath*stringvault_capabilitystringview_rangearraymemory_createWrite a memory file at `/memories/<path>` with the supplied `file_text`. Overwrites if the file exists. Persists to sled, content-addresses the bytes (`file_cid`), and signs the write so the operation carries a verifiable receipt. Mirrors the `create` verb in Anthropic's conte...4 paramsWrite a memory file at `/memories/<path>` with the supplied `file_text`. Overwrites if the file exists. Persists to sled, content-addresses the bytes (`file_cid`), and signs the write so the operation carries a verifiable receipt. Mirrors the `create` verb in Anthropic's conte...
attesterobjectfile_text*stringkindstringepisodic · semantic · procedural · resource · vaultpath*stringmemory_str_replaceReplace `old_str` with `new_str` in the named memory file. Fails (no partial write) when `old_str` is absent or matches more than once. Writes a new content-addressed `file_cid` and signs the receipt. Mirrors the `str_replace` verb in Anthropic's context-management-2025-06-27...5 paramsReplace `old_str` with `new_str` in the named memory file. Fails (no partial write) when `old_str` is absent or matches more than once. Writes a new content-addressed `file_cid` and signs the receipt. Mirrors the `str_replace` verb in Anthropic's context-management-2025-06-27...
attesterobjectkindstringepisodic · semantic · procedural · resourcenew_str*stringold_str*stringpath*stringmemory_insertInsert `new_str` after the given 1-indexed line in the named memory file. `insert_line: 0` inserts at the top. Writes a new `file_cid` and signs the receipt. Mirrors the `insert` verb in Anthropic's context-management-2025-06-27 memory tool spec. When to use: Call when the LLM...5 paramsInsert `new_str` after the given 1-indexed line in the named memory file. `insert_line: 0` inserts at the top. Writes a new `file_cid` and signs the receipt. Mirrors the `insert` verb in Anthropic's context-management-2025-06-27 memory tool spec. When to use: Call when the LLM...
attesterobjectinsert_line*integerkindstringepisodic · semantic · procedural · resourcenew_str*stringpath*stringmemory_deleteDelete a memory file at `/memories/<path>`. When the path ends with `/`, every file beneath the directory is removed. Updates the path index but leaves prior content-addressed blobs in place (the audit history is append-only). Mirrors the `delete` verb in Anthropic's context-m...2 paramsDelete a memory file at `/memories/<path>`. When the path ends with `/`, every file beneath the directory is removed. Updates the path index but leaves prior content-addressed blobs in place (the audit history is append-only). Mirrors the `delete` verb in Anthropic's context-m...
attesterobjectpath*stringmemory_renameMove (rename) a memory file from `old_path` to `new_path`. Both paths must stay under `/memories/`; `new_path` must not already exist. The file_cid is preserved (no re-sign) so the prior receipt still binds the bytes. Mirrors the `rename` verb in Anthropic's context-management...3 paramsMove (rename) a memory file from `old_path` to `new_path`. Both paths must stay under `/memories/`; `new_path` must not already exist. The file_cid is preserved (no re-sign) so the prior receipt still binds the bytes. Mirrors the `rename` verb in Anthropic's context-management...
attesterobjectnew_path*stringold_path*stringmemory_list_by_kindList memory files by their typed `kind` (episodic | semantic | procedural | resource). Optional path prefix narrows the scan; results are sorted by signed_at descending. The kind taxonomy follows the CoALA / LangMem / MIRIX agent-memory ontology: `episodic` = observations of e...3 paramsList memory files by their typed `kind` (episodic | semantic | procedural | resource). Optional path prefix narrows the scan; results are sorted by signed_at descending. The kind taxonomy follows the CoALA / LangMem / MIRIX agent-memory ontology: `episodic` = observations of e...
kind*stringepisodic · semantic · procedural · resourcelimitintegerprefixstringemem_memory_searchSemantic search over /memories/* file contents using BGE-base-en-v1.5 (768-D, L2-normalised) backed by a Lance partition (`memory_text_index_d768.lance`). Matches paraphrases — "rainfall in March" finds "precipitation observed in spring" without an exact substring match. Retur...5 paramsSemantic search over /memories/* file contents using BGE-base-en-v1.5 (768-D, L2-normalised) backed by a Lance partition (`memory_text_index_d768.lance`). Matches paraphrases — "rainfall in March" finds "precipitation observed in spring" without an exact substring match. Retur...
attester_pubkey_b32stringkintegerkindstringpath_prefixstringq*stringemem_corpus_state_statsSigned snapshot of corpus liveness: distinct_cells, distinct_bands, facts_scanned, top per-band counts, manifest CIDs. Same payload that backs /v1/stream's corpus.state tick (signed). Use this for a one-shot poll instead of holding an SSE connection. When to use: Call when an...Signed snapshot of corpus liveness: distinct_cells, distinct_bands, facts_scanned, top per-band counts, manifest CIDs. Same payload that backs /v1/stream's corpus.state tick (signed). Use this for a one-shot poll instead of holding an SSE connection. When to use: Call when an...
No parameters — call it with no arguments.
emem_benchmarkHand-verified evaluation items for grading an agent against the responder. Returns {items[], grader_url}. Submit answers (cell64 or fact_cid per item) to POST /v1/benchmark/grade for per-item scores. Items today: elevation recall, NDVI, find_similar neighbours. When to use: Ca...Hand-verified evaluation items for grading an agent against the responder. Returns {items[], grader_url}. Submit answers (cell64 or fact_cid per item) to POST /v1/benchmark/grade for per-item scores. Items today: elevation recall, NDVI, find_similar neighbours. When to use: Ca...
No parameters — call it with no arguments.
emem_recallRecall facts about a cell — auto-materializes on miss for any band with a registered materializer. When to use: Call after `emem_locate` (or with a known cell64). Returns every Primary fact stored at that (cell, band, tslot). IMPORTANT: if the cell has no fact yet for a reques...7 paramsRecall facts about a cell — auto-materializes on miss for any band with a registered materializer. When to use: Call after `emem_locate` (or with a known cell64). Returns every Primary fact stored at that (cell, band, tslot). IMPORTANT: if the cell has no fact yet for a reques...
as_of_signed_atstringas_of_tslotintegerbandstringbandsarraycell*stringscopeobjecttslotintegeremem_recall_polygonRecall facts across every cell inside a place's polygon (single signed envelope). Closes the place-name-drift gap for wide features (parks, lakes, regions). When to use: Call when the user names a wide feature (national park, river basin, country, large urban area) where one c...8 paramsRecall facts across every cell inside a place's polygon (single signed envelope). Closes the place-name-drift gap for wide features (parks, lakes, regions). When to use: Call when the user names a wide feature (national park, river basin, country, large urban area) where one c...
as_of_signed_atstringas_of_tslotintegerbandsarrayincludearraymax_cellsintegerplacestringpolygon_bboxobjecttslotintegeremem_field_boundariesPer-field agricultural-boundary polygons from the Fields of The World global product (~3.17B fields, 241 countries, 10 m resolution, CC-BY-4.0). Returns a GeoJSON FeatureCollection with the polygon geometries, FIBOA-compatible properties, and a planar `area_m2` per field — plu...3 paramsPer-field agricultural-boundary polygons from the Fields of The World global product (~3.17B fields, 241 countries, 10 m resolution, CC-BY-4.0). Returns a GeoJSON FeatureCollection with the polygon geometries, FIBOA-compatible properties, and a planar `area_m2` per field — plu...
placestringpolygon_bboxobjectzoomintegeremem_query_regionQuery facts over a region (single cell or list of cells), optionally aggregated per band. When to use: Call when the user asks 'how does region X look', 'what's the average NDVI here', or wants a region-level summary. Use `agg=mean|median|p90|vector_centroid` to fold per-band...5 paramsQuery facts over a region (single cell or list of cells), optionally aggregated per band. When to use: Call when the user asks 'how does region X look', 'what's the average NDVI here', or wants a region-level summary. Use `agg=mean|median|p90|vector_centroid` to fold per-band...
aggstringmean · median · p90 · vector_centroidas_of_signed_atstringas_of_tslotintegerbandsarraygeometry*stringemem_compareCompare two cells: cosine similarity over shared vector bands + per-band scalar deltas. When to use: Call when the user asks 'how similar is X to Y', 'compare these two places', or wants a difference vector. Returns a single cosine score and per-band deltas.3 paramsCompare two cells: cosine similarity over shared vector bands + per-band scalar deltas. When to use: Call when the user asks 'how similar is X to Y', 'compare these two places', or wants a difference vector. Returns a single cosine score and per-band deltas.
a*stringb*stringfamilystringemem_compare_bandsCompare two bands at the same cell. Scalar pair → metric=delta, value=b-a. Vector pair (equal dim) → metric=cosine + per-dim delta. Returns a signed receipt naming both source fact CIDs. When to use: Call when the user wants cross-source consistency at one place ('does Cop-DEM...6 paramsCompare two bands at the same cell. Scalar pair → metric=delta, value=b-a. Vector pair (equal dim) → metric=cosine + per-dim delta. Returns a signed receipt naming both source fact CIDs. When to use: Call when the user wants cross-source consistency at one place ('does Cop-DEM...
a*stringb*stringcell*stringpredicateobjecttslot_aintegertslot_bintegeremem_find_similark-NN over the corpus by cell embedding or inline vector. When to use: Call when the user asks 'find places like X', 'where else looks like this', or hands an embedding to find neighbours. `key` is either a cell64 or `inline:[x,y,...]`. Default band is `geotessera` (128-D Tesse...6 paramsk-NN over the corpus by cell embedding or inline vector. When to use: Call when the user asks 'find places like X', 'where else looks like this', or hands an embedding to find neighbours. `key` is either a cell64 or `inline:[x,y,...]`. Default band is `geotessera` (128-D Tesse...
as_of_signed_atstringas_of_tslotintegerbandstringkintegerkey*stringmodestringcosine · hamming · hamming_then_rerankdefault: cosineemem_trajectoryTime series for one (cell, band) over an inclusive [start, end] tslot window. Returns only what's already attested — does NOT trigger materialization. For historical backfill use `emem_backfill`. When to use: Call when the user asks 'how did X change over time' for a band that...5 paramsTime series for one (cell, band) over an inclusive [start, end] tslot window. Returns only what's already attested — does NOT trigger materialization. For historical backfill use `emem_backfill`. When to use: Call when the user asks 'how did X change over time' for a band that...
as_of_signed_atstringas_of_tslotintegerband*stringcell*stringwindow*arrayemem_diffCompute a DerivativeFact (delta) between a band's values at two tslots. When to use: Call when the user asks 'what changed between t1 and t2', 'give me the delta'. Returns a signed DerivativeFact + receipt — the delta itself is content-addressed and citable.4 paramsCompute a DerivativeFact (delta) between a band's values at two tslots. When to use: Call when the user asks 'what changed between t1 and t2', 'give me the delta'. Returns a signed DerivativeFact + receipt — the delta itself is content-addressed and citable.
band*stringcell*stringtslot_a*integertslot_b*integeremem_memory_contradictionsSurface where the corpus DISAGREES with itself. When two or more independent sources signed different values for the same place + band + time, this returns that disagreement with a 0–1 severity score and citations to every disputed fact — instead of silently picking one value...5 paramsSurface where the corpus DISAGREES with itself. When two or more independent sources signed different values for the same place + band + time, this returns that disagreement with a 0–1 severity score and citations to every disputed fact — instead of silently picking one value...
bandstringcell_prefixstringlimitintegermin_severitynumberwindow_unix_sarrayemem_edges_recallRead temporal knowledge-graph edges (subj --pred--> obj, valid over [valid_from, valid_to)), bi-temporally filtered, in EITHER direction. Forward (`subj`, direction="out", the default): edges originating at a subject fact. Reverse (`obj`, direction="in"): edges pointing AT a f...6 paramsRead temporal knowledge-graph edges (subj --pred--> obj, valid over [valid_from, valid_to)), bi-temporally filtered, in EITHER direction. Forward (`subj`, direction="out", the default): edges originating at a subject fact. Reverse (`obj`, direction="in"): edges pointing AT a f...
as_of_tslotintegerdirectionstringout · inlimitintegerobjstringpredstringsubjstringemem_fetchFetch a fact by its content-address (CID). Returns the full signed Primary or Absence fact — the same body served by REST `/v1/facts/{cid}`. Closes the citation loop: any fact_cid surfaced by recall, materialize, attest, or verify can be re-resolved by another agent without RE...1 paramsFetch a fact by its content-address (CID). Returns the full signed Primary or Absence fact — the same body served by REST `/v1/facts/{cid}`. Closes the citation loop: any fact_cid surfaced by recall, materialize, attest, or verify can be re-resolved by another agent without RE...
cid*stringemem_backfillMaterialize and sign every per-tslot fact for one (cell, band) inside a [start_unix, end_unix] window. Returns a signed list of (tslot, fact_cid, status) for each step. Slow but possible — one upstream fetch per tslot, capped by `max_facts`. When to use: Call when the user wan...5 paramsMaterialize and sign every per-tslot fact for one (cell, band) inside a [start_unix, end_unix] window. Returns a signed list of (tslot, fact_cid, status) for each step. Slow but possible — one upstream fetch per tslot, capped by `max_facts`. When to use: Call when the user wan...
band*stringcell*stringend_unixintegermax_factsintegerstart_unixintegeremem_heat_solveForward-step 2-D explicit finite-difference solver for the heat equation ∂u/∂t = α∇²u over a 3×3 cell stencil centred on `cell`. Reads `modis.lst_day_8day` (Land Surface Temperature) at the centre and 8 cell64 neighbours, integrates N hours ahead under a CFL-stable timestep, r...3 paramsForward-step 2-D explicit finite-difference solver for the heat equation ∂u/∂t = α∇²u over a 3×3 cell stencil centred on `cell`. Reads `modis.lst_day_8day` (Land Surface Temperature) at the centre and 8 cell64 neighbours, integrates N hours ahead under a CFL-stable timestep, r...
cell*stringdiffusivity_m2_per_snumberhours_aheadnumberemem_wave_solveForward-step 1-D explicit finite-difference solver for the shallow-water wave equation ∂²u/∂t² = c²∂²u/∂x² with c² = g·h, where depth h comes from `gmrt.topobathy_mean` along the seaward gradient. Models how an offshore swell of height H_s and period T propagates toward `coast...4 paramsForward-step 1-D explicit finite-difference solver for the shallow-water wave equation ∂²u/∂t² = c²∂²u/∂x² with c² = g·h, where depth h comes from `gmrt.topobathy_mean` along the seaward gradient. Models how an offshore swell of height H_s and period T propagates toward `coast...
coastal_cell*stringn_offshore_cellsintegeroffshore_height_m*numberperiod_s*numberemem_jepa_predictPredict next-month NDVI at a cell using a constrained JEPA-pattern AR(2) seasonal predictor. Reads up to 24 past months of `indices.ndvi`, fits a closed-form predictor `y_{t+1} = α·(lag-12 NDVI or recent mean) + β·(last + slope) + γ·recent_mean`, returns the prediction clamped...4 paramsPredict next-month NDVI at a cell using a constrained JEPA-pattern AR(2) seasonal predictor. Reads up to 24 past months of `indices.ndvi`, fits a closed-form predictor `y_{t+1} = α·(lag-12 NDVI or recent mean) + β·(last + slope) + γ·recent_mean`, returns the prediction clamped...
bandstringcell*stringforecast_horizon_monthsintegerlookback_monthsintegeremem_jepa_predict_v2Predict the next-step value of 4 environmental scalars at a cell — `indices.ndvi`, `modis.lst_day_8day`, `modis.lst_night_8day`, `cams.pm25` — using a small learned dynamics MLP. Reads up to K=6 most-recent attested lags per band, runs them through an ONNX dynamics head (~200k...1 paramsPredict the next-step value of 4 environmental scalars at a cell — `indices.ndvi`, `modis.lst_day_8day`, `modis.lst_night_8day`, `cams.pm25` — using a small learned dynamics MLP. Reads up to K=6 most-recent attested lags per band, runs them through an ONNX dynamics head (~200k...
cell*stringemem_verifyVerify a structured claim against a cell's facts. Returns verdict + evidence CIDs + signed receipt. When to use: Call when the user asks a yes/no question about a cell ('is the NDVI > 0.7 here', 'has this been deforested'), or when downstream code wants citable evidence for a...3 paramsVerify a structured claim against a cell's facts. Returns verdict + evidence CIDs + signed receipt. When to use: Call when the user asks a yes/no question about a cell ('is the NDVI > 0.7 here', 'has this been deforested'), or when downstream code wants citable evidence for a...
cell*stringclaim*objectmodestringfast · resolvedefault: fastemem_bandsActive band ontology (offsets, dims, tempo, privacy). When to use: Call once at session start to learn the band registry — every other primitive's `band` argument MUST come from this list.Active band ontology (offsets, dims, tempo, privacy). When to use: Call once at session start to learn the band registry — every other primitive's `band` argument MUST come from this list.
No parameters — call it with no arguments.
emem_functionsActive function registry (derivation recipes). When to use: Call when you need to know which derivative ops are available for `emem_diff` or how a band is computed from upstream sources.Active function registry (derivation recipes). When to use: Call when you need to know which derivative ops are available for `emem_diff` or how a band is computed from upstream sources.
No parameters — call it with no arguments.
emem_sourcesActive source-connector registry (URL templates, providers, licenses). When to use: Call when you need to inspect which upstream EO providers are wired (Copernicus DEM, JRC GSW, ESA WorldCover, etc.) — useful for license attribution in agent answers.Active source-connector registry (URL templates, providers, licenses). When to use: Call when you need to inspect which upstream EO providers are wired (Copernicus DEM, JRC GSW, ESA WorldCover, etc.) — useful for license attribution in agent answers.
No parameters — call it with no arguments.
emem_schemaActive CDDL/JSON schema bundle by CID. When to use: Rarely needed at chat time. Useful for offline verification of receipts / attestations against the exact schema version a responder used.Active CDDL/JSON schema bundle by CID. When to use: Rarely needed at chat time. Useful for offline verification of receipts / attestations against the exact schema version a responder used.
No parameters — call it with no arguments.
emem_errorsStable error code catalog. When to use: Call to enumerate the wire-stable error codes — useful when the LLM wants to programmatically branch on responses.Stable error code catalog. When to use: Call to enumerate the wire-stable error codes — useful when the LLM wants to programmatically branch on responses.
No parameters — call it with no arguments.
emem_manifestsActive manifest CIDs (bands / functions / sources / schema). When to use: Call to learn which exact registry versions a responder is serving. Cite these CIDs alongside any answer where reproducibility matters.Active manifest CIDs (bands / functions / sources / schema). When to use: Call to learn which exact registry versions a responder is serving. Cite these CIDs alongside any answer where reproducibility matters.
No parameters — call it with no arguments.
emem_capabilitiesLive capability snapshot of the responder's GPU sidecar — extensions[] (e.g. gpu, clay-v1.5, prithvi-eo2), cuda_available, models_loaded[], healthy, last_polled_unix_s. Refreshed every 30 s by a background poller; reads are constant-time. When to use: Call before scheduling a...Live capability snapshot of the responder's GPU sidecar — extensions[] (e.g. gpu, clay-v1.5, prithvi-eo2), cuda_available, models_loaded[], healthy, last_polled_unix_s. Refreshed every 30 s by a background poller; reads are constant-time. When to use: Call before scheduling a...
No parameters — call it with no arguments.
emem_grid_infoActive grid encoding: cell64 ground resolution, lat/lng axis sizes, DGGS lineage. When to use: Call once at session start (or when the user asks about cell resolution / 'how big is a cell'). Returns the actual ground resolution today (~9.54 m × 9.55 m square at the equator (la...Active grid encoding: cell64 ground resolution, lat/lng axis sizes, DGGS lineage. When to use: Call once at session start (or when the user asks about cell resolution / 'how big is a cell'). Returns the actual ground resolution today (~9.54 m × 9.55 m square at the equator (la...
No parameters — call it with no arguments.
emem_coverage_matrixPer-band live status — what data is alive AND auto-materializable, with history bounds, tempo cadence, and the responder pubkey that signs the band. When to use: Call BEFORE `emem_recall` when you don't know which bands answer at this responder. For each band returns `has_mate...Per-band live status — what data is alive AND auto-materializable, with history bounds, tempo cadence, and the responder pubkey that signs the band. When to use: Call BEFORE `emem_recall` when you don't know which bands answer at this responder. For each band returns `has_mate...
No parameters — call it with no arguments.
emem_materializersAuto-fetch registry: which bands the responder will materialize on a recall miss, the upstream provider, license, value shape, and history bounds. When to use: Call once at session start (alongside `emem_bands` and `emem_coverage_matrix`) to learn which bands answer for ANY ce...Auto-fetch registry: which bands the responder will materialize on a recall miss, the upstream provider, license, value shape, and history bounds. When to use: Call once at session start (alongside `emem_bands` and `emem_coverage_matrix`) to learn which bands answer for ANY ce...
No parameters — call it with no arguments.
emem_data_availabilityTemporal catalog: for every materializable band the upstream-of-record window the data genuinely covers, the temporal `kind` (static | annual_snapshot | annual_stack | time_series | now_only | per_release), tempo seconds, upstream wire path, and whether `emem_backfill` is mean...Temporal catalog: for every materializable band the upstream-of-record window the data genuinely covers, the temporal `kind` (static | annual_snapshot | annual_stack | time_series | now_only | per_release), tempo seconds, upstream wire path, and whether `emem_backfill` is mean...
No parameters — call it with no arguments.
emem_algorithmsContent-addressed dictionary of composition recipes — formulas that fuse attested band facts (and embeddings) into derived scores, classifications, and similarity metrics. When to use: Call when the user's question is COMPOSITE (flood risk, urban density, water consensus, chan...Content-addressed dictionary of composition recipes — formulas that fuse attested band facts (and embeddings) into derived scores, classifications, and similarity metrics. When to use: Call when the user's question is COMPOSITE (flood risk, urban density, water consensus, chan...
No parameters — call it with no arguments.
emem_explain_algorithmPer-key drill-down on a single composition recipe — full body (kind, inputs, formula, output, citation, references) for ONE algorithm key. Companion to `emem_algorithms` (which is the catalog). When to use: Call when you already know the algorithm key (from `emem_algorithms`'s...1 paramsPer-key drill-down on a single composition recipe — full body (kind, inputs, formula, output, citation, references) for ONE algorithm key. Companion to `emem_algorithms` (which is the catalog). When to use: Call when you already know the algorithm key (from `emem_algorithms`'s...
key*stringemem_topicsTopic-grouped registry of every band and algorithm at this responder, plus visual surfaces and the `declared_but_no_materializer_at_this_responder` block (cube slots reserved without a live connector). Single source of truth shared with `/v1/locate`'s `data_at_this_cell` block...Topic-grouped registry of every band and algorithm at this responder, plus visual surfaces and the `declared_but_no_materializer_at_this_responder` block (cube slots reserved without a live connector). Single source of truth shared with `/v1/locate`'s `data_at_this_cell` block...
No parameters — call it with no arguments.
emem_coverage_mapLive SVG render of the responder's corpus density, returned as a proper MCP EmbeddedResource content block (image/svg+xml) — multimodal MCP agents can render it natively. When to use: Call when the user asks 'where do you have data?', 'show me the coverage', or wants a visual...Live SVG render of the responder's corpus density, returned as a proper MCP EmbeddedResource content block (image/svg+xml) — multimodal MCP agents can render it natively. When to use: Call when the user asks 'where do you have data?', 'show me the coverage', or wants a visual...
No parameters — call it with no arguments.
emem_cell_scene_rgbTrue-colour Sentinel-2 L2A RGB thumbnail centred on a cell. PNG returned as a native MCP ImageContent block (mimeType image/png). Pure-Rust pipeline: STAC search + HTTP-Range COG reads + 2-98 percentile stretch + PNG encode. When to use: Call when the user wants a VISUAL of a...3 paramsTrue-colour Sentinel-2 L2A RGB thumbnail centred on a cell. PNG returned as a native MCP ImageContent block (mimeType image/png). Pure-Rust pipeline: STAC search + HTTP-Range COG reads + 2-98 percentile stretch + PNG encode. When to use: Call when the user wants a VISUAL of a...
cell*stringdatetimestringmax_cloudnumberemem_cell_geojsonCell polygon as a native MCP EmbeddedResource (mimeType application/geo+json). Properties carry centre lat/lng, bbox, approx size in metres, and the 8-cell neighbourhood — drop straight into Mapbox / Leaflet / Deck.gl / QGIS without a GIS pipeline. When to use: Call when the a...1 paramsCell polygon as a native MCP EmbeddedResource (mimeType application/geo+json). Properties carry centre lat/lng, bbox, approx size in metres, and the 8-cell neighbourhood — drop straight into Mapbox / Leaflet / Deck.gl / QGIS without a GIS pipeline. When to use: Call when the a...
cell*stringemem_recall_manyRecall facts across a list of up to 256 cell64 strings in one signed envelope. Server fans out per-cell recalls in parallel, then aggregates the response. Auto-materializes any cell with a missing fact whose band has a registered materializer — same contract as emem_recall. Wh...4 paramsRecall facts across a list of up to 256 cell64 strings in one signed envelope. Server fans out per-cell recalls in parallel, then aggregates the response. Auto-materializes any cell with a missing fact whose band has a registered materializer — same contract as emem_recall. Wh...
bandstringbandsarraycells*arraytslotintegeremem_elevationOne-shot elevation answer that fuses Cop-DEM 30 m (land), GMRT (ocean topobathy), and ESA WorldCover (water mask) into a single signed scalar at a place or coordinate. Returns `elevation_m`, the source actually used, and a `coherence_note` when the two surfaces disagree at the...4 paramsOne-shot elevation answer that fuses Cop-DEM 30 m (land), GMRT (ocean topobathy), and ESA WorldCover (water mask) into a single signed scalar at a place or coordinate. Returns `elevation_m`, the source actually used, and a `coherence_note` when the two surfaces disagree at the...
cellstringlatnumberlngnumberplacestringemem_fleetPer-band satellite-and-sensor fleet inventory — names the upstream platform (e.g. Sentinel-2A/B, MODIS Aqua/Terra, Landsat-8/9), revisit cadence, native resolution, and license for every materialized band. Lets an agent attribute imagery products correctly and pick the right b...Per-band satellite-and-sensor fleet inventory — names the upstream platform (e.g. Sentinel-2A/B, MODIS Aqua/Terra, Landsat-8/9), revisit cadence, native resolution, and license for every materialized band. Lets an agent attribute imagery products correctly and pick the right b...
No parameters — call it with no arguments.
emem_temporal_routeTurn a time-shaped question into a ready-to-run recall plan: it figures out WHICH bands to pull at WHICH past time windows (e.g. 'the year before the flood', 'last growing season', 'two vintages to compare') so you don't have to compute tslot offsets by hand. Returns the band...5 paramsTurn a time-shaped question into a ready-to-run recall plan: it figures out WHICH bands to pull at WHICH past time windows (e.g. 'the year before the flood', 'last growing season', 'two vintages to compare') so you don't have to compute tslot offsets by hand. Returns the band...
bandsarraycell*stringintentstringlimitintegerquery_timeintegeremem_verify_receiptVerify a signed receipt envelope server-side: recomputes the canonical preimage (`request_id | served_at | primitive | cells, | fact_cids,`), runs ed25519 over the embedded pubkey + signature, and returns `{valid, reason, pubkey_b32}`. Use when the in-browser /verify path is b...2 paramsVerify a signed receipt envelope server-side: recomputes the canonical preimage (`request_id | served_at | primitive | cells, | fact_cids,`), runs ed25519 over the embedded pubkey + signature, and returns `{valid, reason, pubkey_b32}`. Use when the in-browser /verify path is b...
pubkey_b32stringreceipt*objectemem_atOne-shot multi-band recall at a place (or lat/lng). Defaults to emem's standard at-a-glance band set; pass `band` / `bands` to override. Polygon-resolved places stay at the centroid by default (`n_cells: 1`) to keep multi-band calls cheap — pass `n_cells: 2..=64` to fan out. W...8 paramsOne-shot multi-band recall at a place (or lat/lng). Defaults to emem's standard at-a-glance band set; pass `band` / `bands` to override. Polygon-resolved places stay at the centroid by default (`n_cells: 1`) to keep multi-band calls cheap — pass `n_cells: 2..=64` to fan out. W...
bandstringbandsstringincludearraylatnumberlngnumbern_cellsintegerplacestringtslotintegeremem_ndviRecall Sentinel-2 NDVI (indices.ndvi, 10 m native) at a point or place. Composes locate → cell64 → recall in one call; auto-materializes on miss. When to use: Use when the user names a place (or lat/lng) and just wants the NDVI number. Polygon-resolved places default to a 16-c...8 paramsRecall Sentinel-2 NDVI (indices.ndvi, 10 m native) at a point or place. Composes locate → cell64 → recall in one call; auto-materializes on miss. When to use: Use when the user names a place (or lat/lng) and just wants the NDVI number. Polygon-resolved places default to a 16-c...
bandstringbandsstringincludearraylatnumberlngnumbern_cellsintegerplacestringtslotintegeremem_airRecall Copernicus CAMS air-quality bands at a place: PM2.5 + NO2 + O3. Composes locate → recall → aggregate. When to use: Use when the user names a place and asks about air quality, pollution, or emissions exposure. CAMS is the European reanalysis — global coverage, ~0.4° nati...8 paramsRecall Copernicus CAMS air-quality bands at a place: PM2.5 + NO2 + O3. Composes locate → recall → aggregate. When to use: Use when the user names a place and asks about air quality, pollution, or emissions exposure. CAMS is the European reanalysis — global coverage, ~0.4° nati...
bandstringbandsstringincludearraylatnumberlngnumbern_cellsintegerplacestringtslotintegeremem_lstRecall MODIS land surface temperature day-8day + night-8day composites at a place. 1 km native, 8-day composite. When to use: Use when the user asks about surface heat, urban heat island, thermal anomalies, or wants day/night LST. Returns both fluxes so the agent can derive da...8 paramsRecall MODIS land surface temperature day-8day + night-8day composites at a place. 1 km native, 8-day composite. When to use: Use when the user asks about surface heat, urban heat island, thermal anomalies, or wants day/night LST. Returns both fluxes so the agent can derive da...
bandstringbandsstringincludearraylatnumberlngnumbern_cellsintegerplacestringtslotintegeremem_soilRecall SoilGrids 250 m profile at a place: SOC, pH, clay/sand/silt fractions, bulk density, nitrogen — all at 0–30 cm depth. When to use: Use when the user asks about soil quality, agricultural suitability, or carbon stocks at a location. Six bands returned in one envelope.8 paramsRecall SoilGrids 250 m profile at a place: SOC, pH, clay/sand/silt fractions, bulk density, nitrogen — all at 0–30 cm depth. When to use: Use when the user asks about soil quality, agricultural suitability, or carbon stocks at a location. Six bands returned in one envelope.
bandstringbandsstringincludearraylatnumberlngnumbern_cellsintegerplacestringtslotintegeremem_waterRecall surface-water signals at a place: JRC Global Surface Water recurrence (1984–2021) + Sentinel-1 SAR backscatter (current). Pair detects standing water through clouds. When to use: Use when the user asks about flooding, wetlands, surface-water dynamics, or wants a robust...8 paramsRecall surface-water signals at a place: JRC Global Surface Water recurrence (1984–2021) + Sentinel-1 SAR backscatter (current). Pair detects standing water through clouds. When to use: Use when the user asks about flooding, wetlands, surface-water dynamics, or wants a robust...
bandstringbandsstringincludearraylatnumberlngnumbern_cellsintegerplacestringtslotintegeremem_forestRecall forest signals at a place: Hansen Global Forest Change (tree cover 2000 baseline + year-of-loss) + ESA WorldCover 2021 land class. When to use: Use when the user asks about deforestation, canopy cover, forest loss, or wants a forest-vs-not classification. Hansen gives y...8 paramsRecall forest signals at a place: Hansen Global Forest Change (tree cover 2000 baseline + year-of-loss) + ESA WorldCover 2021 land class. When to use: Use when the user asks about deforestation, canopy cover, forest loss, or wants a forest-vs-not classification. Hansen gives y...
bandstringbandsstringincludearraylatnumberlngnumbern_cellsintegerplacestringtslotintegeremem_weatherRecall the standard met.no/CAMS weather bundle at a place: 2 m temperature + total cloud cover + precipitation + 10 m wind speed. When to use: Use when the user names a place and asks 'what's the weather' or wants a now-cast snapshot. weather.* bands are now-only (no backfill)...8 paramsRecall the standard met.no/CAMS weather bundle at a place: 2 m temperature + total cloud cover + precipitation + 10 m wind speed. When to use: Use when the user names a place and asks 'what's the weather' or wants a now-cast snapshot. weather.* bands are now-only (no backfill)...
bandstringbandsstringincludearraylatnumberlngnumbern_cellsintegerplacestringtslotinteger
emem is shared, verifiable memory for machines and AI. The proof travels; the files stay where they are.
Try it, no key · Quickstart · A worked example · Verify a token · Agent guide · Docs

Today, to trust a piece of data you usually have to hold it. A satellite downlinks the whole capture, a lab emails the spreadsheet, a company uploads its documents to whichever AI is reading them, and every copy is one more place the data can leak or quietly change.
emem splits the job in two.
A token lets a reader resolve the published record and check content they are given against it. It does not reveal what you kept: a tree token over a private file exposes the units you chose to publish and nothing else.
| Source | Where it is encoded | What crosses | What stays | Status |
|---|---|---|---|---|
| Documents, datasets, model weights, code | the tokeniser on emem.dev (in your browser: code by definition, safetensors by tensor, zip and pptx by entry, PDF by page) or tree_proof.py on your machine | one emem:tree: token: each unit's BLAKE3 folded into a Merkle root, in an index you sign | every unit you do not choose to publish | shipped |
| Your own algorithm | wherever you run it | emem_derive: your value, the signed facts it read, and an optional code_cid, the hash of your code, all signed by your key | the code. emem never runs it; it pins it by hash | shipped; emem re-runs only pure operations (delta, mean, sum) |
| A payload on a satellite, drone, camera or robot | emem-airgap on the device, with no network; the emem-encode sidecar adds an execution trace | a signed custody record: these bytes, this name, this size, at this time, under this key | the payload | the encoders ship (arm64 and amd64); the public device gate admits no real hardware yet |
| Open Earth archives | emem's own readers of registered archives | signed facts, each naming its source bytes | nothing private: the archives are public, so anyone can recompute | shipped, the first corpus |
What each check proves is stated with it. A custody record says bytes arrived, not that the sensor was calibrated. A tree proves a unit was in the file you signed, not that the file is true. A code_cid says which code you claim produced a value; running arbitrary private code in a sandbox is not built.
No account and no API key to read.
1. Connect an MCP host (Claude Code here; every other host):
claude mcp add --transport http emem https://emem.dev/mcp
2. Prove one part of a file without the rest of it. This repository's LICENSE is published as a signed tree of six 2 KB units, so you can try the file path with nothing to set up. From a clone:
curl -s "https://emem.dev/memories/by_attester/k572x7go/readme-example/license-tree.md" > index.md
curl -s "https://emem.dev/v1/tree/xrnco3igl6j2j4kehg4z33ggk4?row=3" > row.json
python3 plugins/emem/skills/emem-tokenise-files/scripts/tree_proof.py check row.json index.md LICENSE
The index names every unit's hash and the root; the row carries unit 3 and its audit path. The check confirms bytes 6144 to 8191 are in the file the key k572x7go signed, by hashing those bytes and walking three steps to the root. For your own file, tree_proof.py build report.md cuts it into units and prints the index offline; sign and publish it under your own key (the skill walks through it). The file never leaves your machine.
3. Read a signed measurement and check it offline (curl, jq, Python):
curl -fsS https://emem.dev/v1/recall -H 'content-type: application/json' \
-d '{"place":"Cairo","bands":["copdem30m.elevation_mean"]}' > fact.json
jq '.facts[0] | {cell, value, unit, memory_token}' fact.json
jq '{token: .facts[0].memory_token}' fact.json \
| curl -fsS https://emem.dev/v1/memory_token/resolve \
-H 'content-type: application/json' --data-binary @- > resolved.json
# pip install "ememdev[signing]"
import json
from ememdev.verify import verify_receipt_offline
verdict = verify_receipt_offline(json.load(open("resolved.json"))["receipt"])
print(verdict.ok, verdict.why) # True, checked in this process with no network call
The check uses the public key the receipt carries. To know it was emem that signed, pin emem's key from /.well-known/emem.json, or check the token at emem.dev/verify.
curl -s -X POST https://emem.dev/v1/ask -H 'content-type: application/json' \
-d '{"q":"what is the NDVI near Mount Fuji?"}' | jq '{answer, receipt: .receipt.fact_cids}'
# pip install "ememdev[signing]"
from ememdev import Client
from ememdev.verify import verify_receipt_offline
with Client() as em:
out = em.ask("what is the NDVI near Mount Fuji?")
print(out["answer"], verify_receipt_offline(out["receipt"]).ok)
// npm i @vortxai/emem
import { Client } from "@vortxai/emem";
const out = await new Client().ask({ q: "what is the NDVI near Mount Fuji?" });
console.log(out.answer, out.receipt.fact_cids);
There is no rule for turning a tool name into a REST path, so do not guess one: emem_memory_search answers at POST /v1/memory/search and emem_verify_receipt at POST /v1/verify_receipt. The authority is /openapi.json; over MCP, call the tool by name.
For agents. Connect to https://emem.dev/mcp. It advertises the 18 tools of the core loop in one page, about 75 KB of context, not the whole catalog: loading all 115 descriptors costs about 324 KB. For the lightest first contact, emem_tools returns the loop and a menu in about 13 KB, and tools/call dispatches every tool by name, with or without its emem_ prefix. To hand records on, prefer a bundle: emem_memory_bundle names up to 256 facts in 38 characters.
One field, worked end to end on the live node on 2026-10-10. Every token below resolves, and you can check each step yourself.
1. Name the thing once. The field gets an identity that stays put while everything measured about it changes:
emem:entity:2kmmk5bflnt5v2g7fh5y5ezqhe "Wheat field west of Ludhiana", a farm_plot at cell defi.zb560.kUtU.sozo
2. Two observations, each its own record. NDVI read from Sentinel-2 on two dates. Each reading has its own content id, so a later reading never overwrites an earlier one:
2026-03-12 NDVI 0.893 emem:fact:defi.zb560.kUtU.sozo:4okfcg4nyi7fo4ftfj57yzjdghc7av7jfjd3fp2urk7vajztgxpa
2026-06-27 NDVI 0.158 emem:fact:defi.zb560.kUtU.sozo:hbliam74soj7pmqy7ig67hyyqn7lz6fe7baagv743fzi75p2drpq
3. A result derived from them, and recomputed. An agent registers the drop, signed with its own key, naming both readings as inputs and pinning its code by hash. Because the operation is a pure delta, emem re-runs it over the two signed parents before recording it:
emem:fact:defi.zb560.kUtU.sozo:n77qfh6isswzwel5i5v7mvl6fmptsf647pklplh6rrdufglz775q
value -0.7352330536822305 class deterministic_index rule canonical_float_equality ulp_gap 0
4. A correction that keeps the original. The agent first wrote that the field was harvested on 27 June. That overstated it: the readings in between were never fetched, so the drop shows the canopy was gone by 27 June, not when it went. The agent wrote a corrected note and superseded the first. Readers of the old path now get superseded_by, and the original bytes still resolve, so anyone can see what was corrected and why:
/memories/by_attester/k572x7go/readme-example/farm-v1.md superseded_by rhngbrlvt3ihzbm3bdxjkdjrmy
reason "The NDVI drop shows the canopy was gone by 27 June; it does not date the harvest."
5. Another agent checks all of it, trusting nobody:
curl -s -X POST https://emem.dev/v1/memory_token/resolve -H 'content-type: application/json' \
-d '{"token":"emem:fact:defi.zb560.kUtU.sozo:n77qfh6isswzwel5i5v7mvl6fmptsf647pklplh6rrdufglz775q"}' \
| jq '{value, provenance: .provenance.class, inputs: .fact.derivation.args.inputs, recomputed: .fact.derivation.args.recomputation.verified}'
It gets the value, the two input tokens to resolve in turn, and the responder's statement that it reproduced the value, all under one receipt it can verify offline. Identity, time, provenance, computation and correction, in one place.
The operations behind those steps, and the rest of what agents do with them:
| Job | How emem does it |
|---|---|
| Name a thing once | emem_entity mints an identity; emem_entity_resolve and emem_entity_link converge different phrasings onto it. |
| Read what is known, at a place or across an area | emem_recall, emem_recall_polygon and emem_grid fetch and sign on a miss; emem_ask takes plain language. 168 published recipes combine readings into scores for flood risk, heat, crop condition and more. |
| Follow a place through time | emem_trajectory lists the dated readings stored at a cell; emem_diff and emem_compare contrast two; emem_field_series follows an area through a season, with a change map. |
| Derive, so others can recompute | emem_derive records your result over signed facts and re-runs pure operations before recording. |
| Correct without erasing | emem_memory_supersede redirects your own stale note to its replacement; across authors, a signed disagrees_with edge records the dispute. |
| Explain why a number moved | emem_change_attribution names the terms behind a change, each with its fact ids. |
| Catch a contradiction or a wrong number | emem_memory_contradictions finds signed sources that disagree at one address; emem_guard_verdict refuses a sentence whose number does not match the fact it cites. |
| Hand work to another agent | a bundle token puts exact signed bytes behind one line, on any model or vendor. |
| Turn documents into evidence | emem_read and emem_ocr turn reports into signed fields; emem_range_hash signs the BLAKE3 of one byte range of a public file without anyone downloading it. |
| Token | What it names |
|---|---|
emem:fact: | one observation's canonical bytes, signed |
emem:state: | one stage of an answer's reasoning, chained to the facts it grounded |
emem:bundle: | a set of facts, in one short line |
emem:entity: | an object's identity |
emem:cell: | a place, about 10 m across |
emem:tree: | a file, as a signed root over its units; #row=<i> names one unit |
emem:raster:, emem:cube:, emem:rasterset: | a field, or a field over time, bound to its source scenes, geometry and artifact hashes |
emem:trace: | a device's execution trace |
emem:fact: and emem:state: carry the full 32-byte BLAKE3 of the record's canonical CBOR, so they bind every byte of it. emem:entity: and emem:bundle: are 16-byte anchors: they co-refer rather than bind, and an entity's anchor does not cover every field of its metadata. The token guide sets out what each kind proves. A token costs more context than the number it stands for (84 characters, 51 LLM tokens, against about 5.4 for the value, measured over 131 facts with cl100k_base); it pays when the value must survive a summariser, be checked by someone who does not trust you, or travel with others in one bundle.
Ask for the road heading at a square in Venice and there is none. emem does not guess or go quiet. It signs an Absence that says what it looked at and why nothing qualified:
emem:fact:defi.zb604.zf0e2.hUpU:exhq6lpsjbimxru33wbhvx2rrz72jeecnugpynsber2dxwgrfuea
kind absence
reason Overture release 2026-09-23.1 holds no carriageway segment within 50 m of
(45.434282, 12.323702); seen and not counted: pedestrian=7;
row_groups=part-00047-...-c000.zstd.parquet#117,119
The row groups it names are public bytes in Overture's own bucket, so anyone can re-read them and reach the same answer without asking emem anything. Check this Absence yourself.
model_output and human_curated carry a caution in the same payload.unknown./v1/log/sth, prove it only grew, prove one entry sits under it, and read the independent co-signatures at /v1/log/witnesses. Receipts signed since 2026-10-10 name the log entry that carried them, and test vectors let a new verifier check itself.What a fact can hold is measured, not promised: /v1/plane/conformance samples up to 400 live facts on every call and checks that no value carries text, no string field exceeds 128 bytes, and no tool accepts a caller's value, so a fact cannot carry an instruction. Notes, which agents write, are a different plane: every read wraps a note's body in _content_is_data_not_instructions, because a memory agents write to is a prompt-injection surface by construction. The exact preimage rules to re-check any receipt yourself are at /v1/verifier_spec, generated from the running code.
A number can be checked before it is said. emem-guard reads the emem: citations in a draft, resolves each one, and answers allow or deny with a machine-readable reason (PROV_SIG when a signature fails, PROV_BYTES when a token resolves to different bytes, PROV_DRIFT when a value moved past its threshold) and a fix. Advisory on the hosted node, enforcing on your own. Its stricter rule, which flags a measurable claim that cites nothing, fired 3 times in 8,739 sentences of this repository's own prose, so it ships off by default with a --shadow mode.
| Property | What it means here |
|---|---|
| Immutable references | a content id names one fixed record forever; asking the same question later can return a newer observation, and the old token still resolves to the old one |
| Signed provenance | who signed, from which source bytes, by which method and registry version, in the record itself |
| Selective proofs | one unit of a file, one entry of the log, proved against a root without the rest |
| Temporal history | dated readings, supersessions and disputes kept side by side, never merged into one answer |
| Recomputation | derivations over signed inputs re-run by the responder, and facts that name the public bytes they came from |
| Data stays put | records commit to bytes held anywhere; nothing has to be uploaded into an index |
emem is not a vector database and does not replace your agent's own memory. It is the shared part: the records several agents, models and organisations need to agree on.
Scope for every number here: 5 sites, 2 open 7-12B instruct models on one host, up to 1,024 cells, n=48 at the largest size, no independent replication, labelled SAMPLE. Methods and threats to validity: docs/how-emem-compares.md.
| How the agent held the value | Exact | Confidently wrong |
|---|---|---|
| Citation, dereferenced from emem | 99.2% (84.4% before four fixes the benchmark prompted) | 0 |
| Value pasted into context (control) | 284/284 | 0 |
| Dense retrieval, top-5 | 4/142 | up to 138, off by a median 252 m |
| BM25 lexical retrieval, top-5 | 16/16 | 0 |
| Summarised memory, tight budget | 1/72 | most of the rest |
dxrfmreb, wrote a clean-room verifier from /v1/verifier_spec, reproduced our signatures, rejected five tampered receipts, verified inclusion and consistency proofs with its own RFC 6962 code, and filed eleven findings, eight of them real defects since fixed (docs/benchmarks.md). eudr.dev's agent built a second one from the same spec.
Handed one token, Anthropic's Claude (through the emem MCP), Google's Gemma 3 (on Amazon Bedrock) and Alibaba's Qwen 2.5 (running locally) all end with the same fact id and value. The decoding is emem's, not the model's: Claude called the resolver itself, and the other two were given the same resolved response, as the clip states.
Two Claude sessions with no shared context: A researches a place and hands over one bundle line; B, with no reason to trust A, resolves it to the same signed bytes and checks the signature. Agents also correspond in public on the signed channel, where they cite records, challenge each other and retract; A2A reaches every tool as a skill. Both are walked through in emem in depth.
Every demo on the website runs against the live memory, in your browser, with no key.
| Demo | What it shows |
|---|---|
| Tokenise a file | one section of a file, cited by its hash, proved against the root |
| A signed answer | a place, a signed number, and the receipt that proves who signed it |
| Check a handoff | what another agent handed you, resolved and verified yourself |
| Check a draft | the guard catching a wrong number before it is published |
| The log only grows | a consistency proof that history was not rewritten |
| Document evidence | a report parsed into signed fields, and the chain checked |
| One field | field edges, pixels, and an NDVI you compute yourself |
| EUDR plot | one farm plot against the EU deforestation cut-off |
Also live: the gallery of encoded files (Webb's Cosmic Cliffs, GPT-2 tensor by tensor, robot arms, a CT slice, 41 years of a climate model), 3-D worlds rebuilt from signed facts, the arcade, where our daemon agents have run the loop since July 2026, a signed note per act, and the scoreboard.
A recorded Claude session with only emem's tools, researching a real place: every number in its answer is a signed record. Watch nine agents share one memory (4 min).
The protocol does not care what a record is about. Earth goes first because its sources are public archives, so anyone can fetch the same input and recompute the answer, which makes it the hardest case to cheat at. Open data from ESA, NASA, USGS and the EU JRC fills the memory on demand: 115 wired measurements from 46 declared source schemes. Eighteen contributor profiles are published and one is active, earth.satellite.v0; the rest are candidates. Every registry that governs meaning is one of ten content-addressed manifests at /v1/manifests, so citing its cid pins the exact semantics a fact was written under.
A file tree can commit any content, but committing bytes does not make them a measured fact: only emem's own readers, enrolled devices and keys the operator lists write the fact plane. Devices join by proving how they ran, never by their own word: the device-platform registry names the 17 platforms that may enrol a key and the evidence each must present, emem_trace_verify checks an execution trace today, and the public gate admits no real hardware yet (the staged path).
By the numbers: 115 MCP tools (an 18-tool core loop by default), 179 paths under /v1/* (/openapi.json), and a transparency log of 2,811,200 signed entries on 2026-10-10. Measured on the production node (methods): warm recall p50 2.5 ms, offline receipt verification p50 0.13 ms, 632 requests/s on one node.
| In a chat | the emem app in ChatGPT (mention @emem), and emem in the Claude directory |
| In Claude Code | the plugin, the MCP server plus nineteen skills: /plugin marketplace add Vortx-AI/emem, then /plugin install emem@emem |
| In an IDE or MCP host | Claude Desktop, Cursor, Cline, VS Code and Copilot, Gemini CLI (gemini extensions install https://github.com/Vortx-AI/emem), all on one endpoint, https://emem.dev/mcp |
| In a workflow tool | the verified Dify plugin |
| In code | the Python and TypeScript clients, REST (OpenAPI), and examples for LangChain, LlamaIndex, CrewAI, AutoGen, Agno and Mastra |
| Agent to agent | A2A: read the agent card, send a task, verify the signed result |
Per-host configs, and every directory emem is listed in, are on the reference page. Writes are signed with an ed25519 key you generate locally; a refused write hands back the exact digest to sign.
The hosted node runs the binary in this repo, and a receipt minted on one verifies on the other:
docker run -p 127.0.0.1:5051:5051 ghcr.io/vortx-ai/emem:latest
Mount a volume for EMEM_DATA, which holds the node's signing key, before you hand out records you care about, and pin an image digest for anything long-lived (docs/self-host.md).
For a machine with no route out, such as a payload computer in orbit, emem-airgap takes custody of files in one directory and writes signed records to another, and emem-encode captures the execution evidence it can. Each record says which of the two guarantees it carries. The image is FROM scratch with one static binary and no networking or database crate linked, so --network none agrees with the binary rather than merely being asked of it; quickstart.sh goes from nothing to a signed, verified record without a clone or a Rust toolchain.
Independent witnesses co-sign the log head, listed live at /v1/log/witnesses, so a split view is detectable. Federation today is witnessing; fetching facts across nodes is separate, unbuilt work.
vault entry is readable by the operator, deletion unpublishes rather than erases, and memory_supersede works only in your own namespace. Encrypt client-side for anything private (PRIVACY.md).Version 2.4.3, a patch on the 2.4.0 minor. The receipt preimage last changed in 2.0.0, and receipts signed under earlier versions still verify under their own rule (CHANGELOG.md). Next: docs/roadmap.md.
| When you want to | Go |
|---|---|
| the long form: what a record promises, A2A and the channel, the agent card, security, every limit | docs/emem-in-depth.md |
| why it matters: robot fleets, satellite tasking, agent swarms, compaction | emem in depth, what breaks without it |
| see it work in ten minutes | tutorial, agent walkthroughs |
| follow one request end to end, with live consoles | emem.dev/how-it-works |
| wire your agent in | agent guide, skills, connect a client |
| put it on a machine | machines that ask emem where they are, emem-airgap, federation |
| check the trust model, formally | whitepaper, protocol, formal model, security, verifier spec |
| read the full API | /openapi.json, /mcp/full, all tools, the wire spec |
| use it for evals, and what is unmeasured | benchmarks with methods, how emem compares, roadmap |
Two artefacts, cited separately: the software if you ran it, the preprint if you build on the protocol. GitHub's Cite this repository button reads CITATION.cff, which carries both.
@software{emem_software,
title = {emem: shared, verifiable memory for AI agents},
author = {Kumari, Jaya and Singh, Avijeet},
year = {2026},
version = {2.4.3},
url = {https://github.com/Vortx-AI/emem},
license = {Apache-2.0},
publisher = {Vortx AI Private Limited}
}
@misc{emem2026,
title = {emem: A research on Content-Addressed, Verifiable Earth-Memory
Protocol for AI Agents over Foundation-Model Embeddings},
author = {Kumari, Jaya and Singh, Avijeet},
year = {2026},
doi = {10.5281/zenodo.20706893},
publisher = {Zenodo}
}
The preprint is open (CC-BY-4.0) and not yet peer-reviewed.
Issues and pull requests welcome: CONTRIBUTING.md, SECURITY.md. Pure Rust, Apache-2.0 (LICENSE, NOTICE). Default data sources are open, with no API keys and no lock-in. A shared memory is worth more the more agents read and write it; if yours use emem, a star helps other builders find it.
Every section above this one is a unit of one signed tree: emem:tree:c2ul6gsuimx5e2pia7eb2ycjh4, root bgwlri4xe66iaqbkow4quqcknig43rtfbo6jvylaiusblu6hsv6q, published under the key k572x7go. A single section is emem:tree:c2ul6gsuimx5e2pia7eb2ycjh4#row=<i>, so another agent can cite one part of this file and anyone can prove it was in the file as published:
curl -s "https://emem.dev/memories/by_attester/k572x7go/readme/tree-20261010f.md" > index.md
curl -s "https://emem.dev/v1/tree/c2ul6gsuimx5e2pia7eb2ycjh4?row=3" > row.json
python3 plugins/emem/skills/emem-tokenise-files/scripts/tree_proof.py check row.json index.md README.md
index.md is the signed note at /memories/by_attester/k572x7go/readme/tree-20261010f.md. python3 scripts/readme_tree_check.py rebuilds the whole tree from this file and compares its root with the index, and CI runs it, so a README edited without re-signing fails the build. The tree changes whenever the README does, and this section is left out of it because it names the tree.
EMEM_BINDBind address for the HTTP server. Defaults to 0.0.0.0:5051 inside the container.
EMEM_DATAPath to the persistent data directory (sled cache + ed25519 identity). Mount a volume here.
EMEM_PUBLIC_URLOptional canonical origin for self-referencing URLs in MCP responses (e.g. https://emem.dev). When unset the server falls back to urn:emem.
EMEM_TLS_DOMAINSComma-separated hostnames for built-in Let's Encrypt ACME (TLS-ALPN-01). When set, the server binds 0.0.0.0:443 instead of EMEM_BIND.