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...
qstringlatnumberlngnumberplacestringemem_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...
q*stringlatnumberlngnumbercellstringplacestringincludearrayverbosebooleaninclude_imagebooleanemem_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_plumeregionstringpolygon_bboxobjectemem_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...
plots*arrayoperatorobjectcut_off_datestringlegality_modulestringmax_cells_per_plotintegerforest_baseline_overridestringemem_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*stringwindow_daysintegerprecip_history_mmarraycurrent_accumulation_mmnumberemem_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_prenumbernbr_postnumberemem_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...
sfonumbersfpnumbercell*stringt_paddy_cnumberndwi_seriesarrayefc_kg_ch4_ha_day*numbercultivation_period_days*numberemem_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...
cell*stringbaseline_yearintegeremem_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...
region_a*objectregion_b*objectmax_cellsintegeremem_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...
cellsarrayplacestringmax_cellsintegerpolygon_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...
cellsarrayplacestringmax_cellsintegerpolygon_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...
cell*stringviewstringencoder · cubetslotintegerencoderstringfamiliesarrayas_of_tslotintegermaterializebooleanas_of_signed_atstringinclude_reservedbooleanemem_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...
cell*stringtslotintegerencodersarrayas_of_tslotintegeras_of_signed_atstringemem_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*stringview_rangearrayvault_capabilitystringmemory_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...
kindstringepisodic · semantic · procedural · resource · vaultpath*stringattesterobjectfile_text*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...
kindstringepisodic · semantic · procedural · resourcepath*stringnew_str*stringold_str*stringattesterobjectmemory_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...
kindstringepisodic · semantic · procedural · resourcepath*stringnew_str*stringattesterobjectinsert_line*integermemory_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...
path*stringattesterobjectmemory_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...
kintegerq*stringkindstringpath_prefixstringattester_pubkey_b32stringemem_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...
bandstringcell*stringbandsarrayscopeobjecttslotintegeras_of_tslotintegeras_of_signed_atstringemem_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...
bandsarrayplacestringtslotintegerincludearraymax_cellsintegeras_of_tslotintegerpolygon_bboxobjectas_of_signed_atstringemem_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...
zoomintegerplacestringpolygon_bboxobjectemem_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_centroidbandsarraygeometry*stringas_of_tslotintegeras_of_signed_atstringemem_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*stringtslot_aintegertslot_bintegerpredicateobjectemem_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...
kintegerkey*stringbandstringmodestringcosine · hamming · hamming_then_rerankdefault: cosineas_of_tslotintegeras_of_signed_atstringemem_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...
band*stringcell*stringwindow*arrayas_of_tslotintegeras_of_signed_atstringemem_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...
bandstringlimitintegercell_prefixstringmin_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...
objstringpredstringsubjstringlimitintegerdirectionstringout · inas_of_tslotintegeremem_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*stringhours_aheadnumberdiffusivity_m2_per_snumberemem_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...
period_s*numbercoastal_cell*stringn_offshore_cellsintegeroffshore_height_m*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*stringlookback_monthsintegerforecast_horizon_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*stringmodestringfast · resolvedefault: fastclaim*objectemem_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...
latnumberlngnumbercellstringplacestringemem_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...
cell*stringbandsarraylimitintegerintentstringquery_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...
receipt*objectpubkey_b32stringemem_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...
latnumberlngnumberbandstringbandsstringplacestringtslotintegerincludearrayn_cellsintegeremem_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...
latnumberlngnumberbandstringbandsstringplacestringtslotintegerincludearrayn_cellsintegeremem_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...
latnumberlngnumberbandstringbandsstringplacestringtslotintegerincludearrayn_cellsintegeremem_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...
latnumberlngnumberbandstringbandsstringplacestringtslotintegerincludearrayn_cellsintegeremem_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.
latnumberlngnumberbandstringbandsstringplacestringtslotintegerincludearrayn_cellsintegeremem_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...
latnumberlngnumberbandstringbandsstringplacestringtslotintegerincludearrayn_cellsintegeremem_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...
latnumberlngnumberbandstringbandsstringplacestringtslotintegerincludearrayn_cellsintegeremem_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)...
latnumberlngnumberbandstringbandsstringplacestringtslotintegerincludearrayn_cellsinteger
The verifiable memory protocol for the physical world, built for AI agents to cite.
Agents inherit a measured, signed account of the physical world instead of re-observing it; every observation becomes a shared, verifiable Memory Token that persists across long-horizon AI tasks.
Walk the memory in 3-D · Try it, no key · Verify a fact · Agent guide
A shared memory of the physical world, and a systems primitive for agents: memory that lives outside any one model, so an agent cites a fact instead of carrying a paraphrase of it. Location is the first key: every place on Earth has a stable 64-bit address, and every observation recorded there, an elevation, a temperature, a forest-loss year, is one signed, immutable record at that address. Any agent can read it, any keyholder can add to it, and anyone can check any of it offline. No account to read.
Satellite Earth observation fills it today; nothing in the record, receipt, or token grammar is satellite-specific, so the same loop carries any observer of a place (substrates). If you build agents, robot fleets, or anything else that must hold a fact longer than one context window, this is for you.
An agent verifies something early, the context gets compacted, and what survives is a paraphrase that is almost right:
without emem
turn 12 the agent verifies a value: 918 m
turn 40 the context is compacted
turn 41 what survives: "the site sits at roughly 900 m"
with emem
turn 12 the agent keeps one line:
emem:fact:defi.zb493.xuqA.zcb5f:yqbolgeoycqkvj3zkxukb4bjw4odhpwvfzqo3fbgwf4spk45zala
turn 40 the context is compacted
turn 41 the line resolves to 918.0 m, and the signature still checks
One line, about 50 BPE tokens, standing in for a signed record of about 1,600. A paraphrase drifts; the token re-hydrates to the exact bytes for any agent, on any model, any month later. And that token is real: it names the fact the next section verifies.
Reading needs no key and no account. This returns the elevation at one 10-metre cell of Bengaluru, as a signed record:
curl -s -X POST https://emem.dev/v1/recall \
-H 'content-type: application/json' \
-d '{"place":"Bengaluru","bands":["copdem30m.elevation_mean"]}'
A band names one measurement; this one is mean elevation from the Copernicus DEM. The response carries the value (918 metres), the record's content id (fact_cid), and an ed25519 receipt.
{
"facts": [{
"band": "copdem30m.elevation_mean",
"cell": "defi.zb493.xuqA.zcb5f",
"value": 918.0,
"unit": "m",
"kind": "primary",
"confidence": 0.95,
"derivation": { "fn_key": "open_meteo_copdem90m@1", "args": [12.9719, 77.5937] },
"sources": [{ "scheme": "open_meteo", "captured_at": "2021-04-30T00:00:00Z", "id": "https://api.open-meteo.com/v1/elevation?…" }],
"signed_at": "2026-05-28T19:54:32Z",
"signer_pubkey_b32": "777er3yihgifqmv5hmc2wwmyszgddzderzhsx6rex4yoakwomvka",
"fact_cid": "yqbolgeoycqkvj3zkxukb4bjw4odhpwvfzqo3fbgwf4spk45zala",
"memory_token": "emem:fact:defi.zb493.xuqA.zcb5f:yqbolgeo…zala"
}],
"receipt": { "primitive": "emem.recall", "fact_cids": ["yqbolgeo…"], "merkle_proof": {…}, "signature": "…", "responder_pubkey_b32": "…" }
}
No documents and no blobs: a fact is one small signed value carrying its own provenance, the function and source it is recomputable from. Even embeddings arrive this way, as bands whose record names the model checkpoint.
One more paste checks that receipt against the responder's published key, so you are not trusting the server or this README:
curl -s -X POST https://emem.dev/v1/recall -H 'content-type: application/json' \
-d '{"place":"Bengaluru","bands":["copdem30m.elevation_mean"]}' \
| jq '{receipt: .receipt}' \
| curl -s -X POST https://emem.dev/v1/verify_receipt \
-H 'content-type: application/json' --data-binary @- \
| jq '{signature_valid, merkle_proof_valid}'
"signature_valid": true. That is the whole trust model in two commands: every reading is a signed record, and anyone can check one. If that worked, the star button helps other builders find this. The line an agent keeps instead of the payload is next.
Six steps, each adding one idea, and every step works before the next exists:
emem:fact:defi.zb493.xuqA.zcb5f:yqbolgeoycqkvj3zkxukb4bjw4odhpwvfzqo3fbgwf4spk45zala
One line: the address of a place plus the fingerprint of one signed observation there. It is 84 characters, about 50 BPE tokens; the full signed record it stands in for is about 1,600. An agent keeps the line and drops the payload. Any agent, any model, any month later resolves the line back to the exact same bytes and re-checks the signature without trusting whoever sent it.
In practice your agent runs four verbs: locate a place, recall its signed facts, reason over them, and cite the tokens in its output. Verification is the receiver's single call.
| The retrieval memory you run | emem |
|---|---|
| documents chunked, embedded, ranked by similarity | one signed record per observation, at a content address |
| the top hit is close enough | the address returns exactly one record, or a signed absence |
| you trust the retriever, the store, and whoever filled them | the receipt verifies offline; no trust in the sender or the server |
| memory scoped to one session, one product, one vendor | one shared memory: any agent reads, any keyholder writes |
It sits beside retrieval, not under it: emem does not hold your documents. It holds the measured state of the physical world, signed so that agents which share no infrastructure and no trust can still share the same facts.
A long task survives its own context window. The harness compacts, the session ends, the model gets swapped. A paraphrase drifts; the token does not. After compaction it re-hydrates to the exact signed value, signature still checking. Record it once, cite it forever.
Two agents stop re-deriving each other's work. Agent A spends fifty tool calls establishing one fact and leaves the token in its report. Agent B, at another company, on another model, resolves it to the same bytes and proves it is genuine in one call. No shared database, no shared credentials, no "trust me".
A fleet shares one map it can prove. Robots and autonomous systems keep landmarks as emem:entity: identities and terrain or hazard readings as signed facts at addresses that never drift, shareable across vendors over the same MCP and REST surface agents use, verifiable without trusting the peer that wrote them. Runnable proof: examples/fleet-memory/, two vendors, one landmark, a 206-character handoff, verified offline.
Technical long-horizon tasks, the failure modes every agent and robot developer already knows:
| Your problem | What survives |
|---|---|
| Context compaction quietly turns your agent's verified details into paraphrase | the 84-char token outlives every summarization pass and re-hydrates to the exact signed bytes |
| A crash or restart lands mid-task and the transcript is gone | notes hold tokens, not payloads; the restarted agent resumes by resolving, not redoing |
| The model gets swapped or upgraded halfway through the project | the address derives from the bytes, not from who asked; the successor resolves the same tokens identically |
| Subagents fan out and the join step drowns in payload copies | workers receive and return tokens; the join resolves and verifies, contexts stay small |
| "Did I, or anyone, already compute this?" asked on every loop | recall is ensure, not get: what exists is reused (was_cached), what is missing is fetched and signed once |
| "Is what I knew last week still valid?" with no cheap way to answer | /v1/temporal_route scores per-band staleness: cite it or refetch it, no full re-read |
| A robot reboots, or a unit from another vendor joins the fleet | landmarks are emem:entity: identities at drift-free addresses; relocalize by resolving, merge maps by verifying |
Long-horizon work, the same survival stretched from minutes to months:
| Task | What the memory does |
|---|---|
| A watch that runs for months, across hundreds of sessions | change evidence accrues as signed facts; every alert cites the fact ids it fired on, and the relieving agent resumes from tokens, not from a predecessor's summary |
| A report that will be audited long after the agent that wrote it is gone | every claim in it is a token; an auditor, human or agent, resolves each one to the signed record and re-checks the signature without contacting the author |
| A project handed from one contractor's agents to another's | every prior finding stays citable across the handover; the successor resolves the same tokens to the same bytes instead of trusting the summary it was given |
| A decision that commits real resources | the state acted on is pinned at decision time (as_of_signed_at); "what did we know when we acted" has an exact answer months later, replayed without hindsight |
| A number that must hold across years of reports | records are immutable and supersession is explicit, so the baseline stays re-checkable by anyone, without trusting the author's laptop |
Industry-specific versions of these, with the verticals named, live at emem.dev/solutions.
If you are an agent reading this, the runnable version is below, and
https://emem.dev/llms.txtis the machine-readable entry point. The habit that matters most: before your context gets compacted or your turn ends, put theemem:fact:token for anything you verified into your notes or your final answer. You, your successor, or a different agent entirely can resolve and re-check it later.
The memory is not an abstraction; you can walk through it. Each 3-D world at emem.dev/worlds draws one gaussian per cell of signed facts: height, tilt, thickness, and colour are each a measurement. Click any splat to read its values, copy its Memory Token, or re-check its signature at /verify. The dense worlds at emem.dev/splats push the same signed substrate to photoreal, with every splat labelled measured, interpolated, or synthesized, so the invented detail peels off and the signed trust root stays.
Prefer a console? emem.dev has a live recall on the homepage, and emem.dev/humans is the whole corpus as an explorable constellation.
Reading needs no key, no account, no signup.
MCP (Claude Code, Claude Desktop, Cursor, Cline; drop into .mcp.json):
{ "mcpServers": { "emem": { "type": "http", "url": "https://emem.dev/mcp" } } }
REST (any language):
CELL=$(curl -s -X POST https://emem.dev/v1/locate \
-H 'content-type: application/json' -d '{"q":"Bengaluru"}' | jq -r .cell64)
curl -s -X POST https://emem.dev/v1/recall \
-H 'content-type: application/json' \
-d "{\"cell\":\"$CELL\",\"bands\":[\"weather.temperature_2m\"]}" | jq '.facts[0].value'
Python: pip install ememdev, then from ememdev import Client. Real as of 1.1.0, verified by installing into a clean environment and calling the live node; the wheel also ships the signing extra (pip install "ememdev[signing]") and an ememdev CLI for attested memory writes. Do not guess a shorter name: emem on PyPI is an unrelated project by another company. TypeScript: sdks/emem-ts/ publishes to npm as ememdev too; first publish pending, status in docs/roadmap.md.
| Client | Setup |
|---|---|
| Claude Desktop | examples/claude-desktop.json |
| Claude Code | examples/claude-code.mcp.json |
| Cursor | examples/cursor.mcp.json |
| Cline (VS Code) | examples/cline.mcp.json |
| Gemini CLI | gemini extensions install https://emem.dev/gemini-extension.json |
| ChatGPT (Custom GPT) | examples/openai-gpt-action.json |
| LangChain / LlamaIndex / Agno / AutoGen / CrewAI / Mastra | examples/<name>/ |
| Any MCP client over the standard bridge | { "command": "npx", "args": ["-y", "mcp-remote", "https://emem.dev/mcp"] } |
Packaged Claude skills live under claude-skills/; llms-install.md is a plain-text install guide an agent can follow by itself. TypeScript SDK: sdks/emem-ts/ (npm name ememdev; first publish pending).
Reads need no key, and four moves cover most sessions.
Connect to https://emem.dev/mcp. It advertises the 14 tools of the loop, not the whole catalog. A host loads every descriptor it is handed, and all 94 cost about 210 KB of context whether or not the session ever touches Earth observation. Narrowing discovery removes nothing: tools/call dispatches all 94 by name at either endpoint, so a tool missing from your list is still callable. Use /mcp/full to have every tool registered up front.
Do not know which tool? Call emem_tools. With no arguments it returns the loop, a bundle menu, and a shape menu in about 6 KB. Ask by the shape of the answer you need, which is usually the real question, rather than by topic:
curl -s -X POST https://emem.dev/mcp -H 'content-type: application/json' \
-d '{"jsonrpc":"2.0","id":1,"method":"tools/call",
"params":{"name":"emem_tools","arguments":{"shape":"raster"}}}'
Every tool carries exactly one shape (scalar, timeseries, raster, geometry, vector, identity, token, proof, plan, file, catalog) and any number of overlapping bundles (tokenisation, verification, agent_to_agent, long_horizon, robotics, satellites, agriculture, forestry, climate_risk). {"bundle":"robotics"} returns just that bundle; {"name":"emem_ndvi"} returns one tool's input schema and a runnable example in about 2 KB; {"q":"ndvi"} searches the text.
Ground a place, then cite it. emem_locate maps a place to its cell64. emem_recall returns the signed facts there, and its receipt carries the fact_cid. emem_memory_token composes the two into one handle:
emem:fact:defi.zb5b3.mAmi.leco:nzyep244xoxx6uvw4ope5dghy3eniawczovvzrrp7almuydzdbta
Hand the token to another agent. They call emem_memory_token_resolve on that line, get the byte-identical signed fact back, and emem_verify_receipt checks the ed25519 signature without trusting you or the server. That is the whole claim, and it is the only one worth making: the same token resolves to the same bytes for anyone, and the receipt verifies on its own.
Writes are the one place a key appears, and it is still not an API key. Memory writes need an attester block signed by an ed25519 keypair you generate locally, with no registration step. A refused write answers with the exact digest to sign, the base32 encoding rules, and a worked example, so an agent gets from refusal to signed write in one turn without going to look for docs.
| Operation | What it means for your agent | Tools |
|---|---|---|
| Recall | read memory for a place; a miss fetches, signs, and stores for everyone | emem_recall, emem_locate, emem_recall_polygon |
| Cite | one token per fact, or one emem:bundle: token for a set | emem_memory_token, emem_memory_bundle |
| Verify | trust a fact without trusting the sender, offline | emem_verify_receipt, /verify |
| Weigh | every fact says how it was produced; model and human classes carry an in-band caution; deterministic: true keeps only facts recomputable from raw source | inside every recall |
| Time travel | as_of_tslot for what was on the ground, as_of_signed_at for what the memory knew | flags on every read |
| Self-check | disagreement between writers is kept and scored, never averaged away | emem_memory_contradictions |
Or skip the menu: emem_ask takes a plain-language question and returns a signed answer. Each agent also gets a private signed memory with the six standard file verbs, and any keyholder writes shared facts through POST /v1/attest. The full handbook is emem.dev/agents.md.
/v1/log/sth, then prove the log only grew since your pin. The receipt does not yet chain to the log; the whitepaper's honest limits say exactly what that does and does not prove.The signature proves who attested a record and that the bytes never changed, not that the value is objectively true; confidence, uncertainty, and provenance travel with it. Deeper: how it works with live consoles, the formal model, the wire spec.
Generating a plausible answer is cheap. The scarce thing is a shared account of the physical world that is measured, signed, and checkable by someone who was not there. Drift threatens that account from two directions. In language, the reference drifts: a paraphrase mutates while the world stands still, and the token pins it; that is everything above. In the world, the readout drifts: the reference stands still, the signal at it moves, and not every move is the world. Between two visits to one address, the observed change is a sum:
Δz = Δ_env + Δ_sensor + Δ_geo + Δ_encoder + ε
The world changed; the instrument changed; the pixels moved; the model changed; noise. Only the first term is about the world, and the substrate already pins the rest of the ledger. An embedding record carries its model checkpoint, so a model swap can never pose as change on the ground. Bitemporal recall keeps "the world changed" and "what the memory knew changed" as separate questions. Every change points at a specific immutable record by its id, and the receipt lets someone who was not there check the split. A first attribution ledger ships at /v1/change_attribution: per-term evidence with the fact ids it read, and no numeric split. The split itself is still roadmap work; the design and its honest gaps live in docs/roadmap.md.
Today: satellite Earth observation. Open data from ESA, NASA, USGS, and the EU JRC fills the memory on demand: 124 wired measurements, drawn from a catalog of 46 declared source schemes of which several are declared but not yet fetchable (live lists at /v1/sources and /v1/bands), from elevation and NDVI to weather, forest change, and four open foundation-model embeddings.
Next: everything else that observes a location. Nothing in the record, receipt, or token grammar is satellite-specific; any observer with a location and a signing key can join the same attest, recall, cite, verify path. The multi-writer endpoint (POST /v1/attest) ships today; written substrate profiles for CCTV and fixed sensors, drones, robot fleets, industrial machines, government registries, and open data programs are roadmap work, tracked with the rest in docs/roadmap.md. Location stays the first key for all of them.
The hosted node runs the exact binary in this repo, and both name the planet the same way, so a receipt minted on one verifies on the other:
docker run -p 5051:5051 ghcr.io/vortx-ai/emem:latest # or: cargo run --release --bin emem-server
One note worth reading twice: the signing key is your node's identity. Mount a volume for EMEM_DATA (or set EMEM_SECRET_B32) before you hand out receipts you care about. Full guide: docs/self-host.md.
Measured on the production node (methods in docs/benchmarks.md): warm recall p50 2.5 ms, offline verification p50 0.13 ms, 632 requests/s on one node, cold materialize 0.5 to 1.6 s depending on the upstream.
Version 1.1.0, under the stability promise 1.0.0 made: the wire format, receipt preimage, and address space are settled and will not break under a 1.x. Today it is a single-host deployment (no federation yet), the memory holds thousands of places rather than billions, and it grounds facts about physical places, not arbitrary text. Verification is per-responder: a receipt proves what this responder signed, never a network consensus. The change attribution described above ships as an evidence ledger; the numeric split of a delta among its terms is still roadmap. The complete edge list, the staged path to federation, and the open research live in docs/roadmap.md.
| What | Where |
|---|---|
| How it works, with live consoles | https://emem.dev/how-it-works |
| The whitepaper, canonical | docs/whitepaper.md |
| The formal memory model and algebra | docs/model.md |
| Agent integration handbook | https://emem.dev/agents.md |
| Ten minutes to a verified, shareable fact | docs/tutorials/first-verified-memory.md |
| Limits, roadmap, open research | docs/roadmap.md |
| Benchmarks, with methods | docs/benchmarks.md |
| Industry use cases | https://emem.dev/solutions |
| Wire spec · OpenAPI (118 paths) · MCP (94 tools) | https://emem.dev/spec.md · /openapi.json · /mcp |
| Live proof in a regulated workflow | https://eudr.dev |
| Companion open model | TerraGround-Gemma |
emem: A research on Content-Addressed, Verifiable Earth-Memory Protocol for AI Agents over Foundation-Model Embeddings. Jaya Kumari, Avijeet Singh. Vortx AI, 2026. Open preprint (Zenodo, CC-BY-4.0; not yet peer-reviewed). doi.org/10.5281/zenodo.20706893
@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}
}
Issues and pull requests welcome: CONTRIBUTING.md, SECURITY.md. Pure Rust, Apache-2.0 (LICENSE, NOTICE); default-build data sources are open, with no API keys and no lock-in. Built by vortx.ai. A shared memory is worth more the more agents read and write it; if yours use emem, a star helps other builders find it.
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.
io.github.ericm1018/skillfm-llm-cost-optimizer-openai-anthropic-usage
io.github.mikerawsonnz/llm-orchestration-agent
io.github.mikerawsonnz/authenticated-llm-agent
labforgedev/copilot-memory-mcp
csoai-org/agent-prompt-injection-firewall-mcp
io.github.mikerawsonnz/authenticated-multi-llm-agent