Aura Memory
Aura turns fragile prompt-only agents into auditable, memory-aware, production-ready systems
Deterministic · No fine-tuning · No cloud training · Local recall · No required embeddings
---
Your AI model is smart. But it forgets everything after every conversation.
> **New: [Aura for Windows](https://www.aurasdk.dev/desktop)** — a free desktop app built on this library that gives Claude Code, Cursor, Codex, Copilot and other AI tools one shared local memory.
Aura is a local cognitive runtime that runs alongside any frozen model. It gives agents durable memory, explainability, governed correction, bounded recall reranking, and bounded self-adaptation through experience — all locally, without fine-tuning or cloud training.
```bash
pip install aura-memory
```
```python
from aura import Aura, Level
brain = Aura("./agent_memory")
brain.enable_full_cognitive_stack() # activate all four bounded reranking overlays
# store what happens
brain.store("User always deploys to staging first", level=Level.Domain, tags=["workflow"])
brain.store("Staging deploy prevented 3 production incidents", level=Level.Domain, tags=["workflow"])
# recall — local retrieval with optional bounded cognitive reranking
context = brain.recall("deployment decision") # local retrieval, no API call
# advisory hints are derived during maintenance once enough evidence accumulates
brain.run_maintenance()
for hint in brain.get_surfaced_policy_hints():
print(hint.action_kind, hint.domain, hint.recommendation)
# → prefer workflow deploy to staging first (only after repeated, consistent evidence)
```
No API keys. No embeddings required. No cloud. The model stays the same — the cognitive layer becomes more structured, more inspectable, and more useful over time.
### What's new in 1.61
- **Your words stay apart from outside text.** `recall()` now returns the provenance context by default: first-hand memory first, then web, document and tool content fenced and quoted as data. The fence stops ordinary instructions hidden in that content; text written to pose as the user can still steer a model (`experiments/mcp_gateway_fence_robustness`, `experiments/agentdojo_fence`). **Upgrade note:** code that parses the old `=== COGNITIVE CONTEXT ===` text should pass `format="levels"`.
- **Security profiles.** `Aura(path, security="strict")`, `set_security_profile()` and `security_report()`. Memories written by a model through MCP are stored as `inferred`, not as the user's word. One untrusted source repeating a claim can no longer crowd out a first-hand fact.
- **Better recall context.** A short block of lasting facts about the user, event dates on first-hand memory, and relevance-trimmed context.
- **One memory for many AI tools.** The Rust features `mcp-http` and `mcp-bridge` add a token-protected loopback MCP server and the `aura-bridge` stdio bridge, so several clients share one store. This is what the [Aura for Windows](https://www.aurasdk.dev/desktop) app runs on.
- **Conversation capture.** `python -m aura capture` is a Claude Code hook that keeps the user's words as first-hand memory.
- **Safer HTTP server.** `python -m aura serve` refuses to listen beyond localhost without an API key. Install it with `pip install "aura-memory[http]"`.
- **Plain MIT.** The license-enforcement module is gone.
The full list is in the [changelog](CHANGELOG.md).
> **⭐ If Aura is useful to you, a [GitHub star](https://github.com/teolex2020/aura-memory) helps us get funding to continue development from Kyiv.**
---
## Why Aura?
| Property | Aura |
|---|---|
| Architecture | Five-layer cognitive engine, with Belief→Concept→Causal→Policy derivation |
| Memory operations | Local Rust computation; no required LLM or embedding API |
| Recall latency | 11.61 ms median, distinct uncached top-10 queries on 5K records; see [Performance](#performance) |
| Offline use | Supported without a cloud service |
| API fee for memory operations | None required; local compute and storage still have costs |
| Package size | 2.77 MB Windows CPython 3.13 wheel1 |
| Lifecycle and governance | Built-in decay/promotion, provenance, contradiction controls, purge/freeze |
| Encryption at rest | Optional ChaCha20-Poly1305 with Argon2id key derivation |
We do not publish unmeasured speed, cost, or feature rankings against other
memory products. Their deployment modes and search outputs differ; a meaningful
comparison needs matched data, workload, and quality criteria.
### The Core Idea: Cheap Model + Aura > Expensive Model Alone
Fine-tuning costs thousands of dollars and weeks of work. RAG requires embeddings and a vector database. Context windows are expensive per token.
Aura gives you a third path: **a local cognitive runtime that accumulates structured experience between conversations** — free and local.
```
Week 1: GPT-4o-mini + Aura Week 1: GPT-4 alone
→ average answers → average answers
Week 4: GPT-4o-mini + Aura Week 4: GPT-4 alone
→ recalls your workflow → still forgets everything
→ surfaces patterns you repeat → same cost per token
→ exposes explainability + correction → no improvement
→ boundedly adapts from experience → no durable learning
→ no required external API cost → still billing per call
```
The model stays the same. The cognitive layer gets stronger. That's Aura.
### Performance
**Current local measurement (Aura `1.60.0`).** On Windows 10, AMD Ryzen 5
5600X, and CPython 3.13.14, we stored the first 5,000 dialogue turns from
LoCoMo (median 154 bytes per turn). Recall used 100 distinct natural-language
questions, `recall_structured(top_k=10)`, and no repeated-query cache hits.
One sequential local run, measured on 2026-09-25:
| Operation | Median | P95 |
|-----------|-------:|----:|
| Store one turn | 5.66 ms | 10.10 ms |
| Structured recall, distinct uncached query | 11.61 ms | 18.95 ms |
| Get record by known ID | 0.0033 ms | 0.0083 ms |
Local MCP stdio added about 0.04 ms to mean recall time in a separate
same-query round-trip comparison. This is not a network benchmark. A lexical
SQLite FTS5 index on the same 5,000 turns searched in 1.13 ms median, but it
does not provide the same cognitive functions. On the first 100 LoCoMo
evidence questions, Aura found all annotated evidence for 36 questions and
SQLite FTS5 for 39; this narrow slice does not establish a quality advantage
for either system. Do not interpret fast `get(id)` or cache-hit times as
uncached natural-language recall.
**Historical 1K measurement (Aura `1.58.0`).** The release wheel measured
2.483 ms median / 4.035 ms p95 for uncached structured recall, and 8.2 µs
median for a formatted cache hit, on the same CPU with 1,000 different
records. See [`benchmarks/results.json`](benchmarks/results.json) and reproduce
that historical test with `python benchmarks/bench_all.py 1000`. The 1K and
5K runs use different content and questions, so they do **not** demonstrate
a version-to-version slowdown.
All figures are observations, not guarantees. Corpus size, query shape,
cache state, enabled features, and background load affect latency. Aura recall
uses local computation and needs no embedding or LLM API call. No
cross-product speedup is claimed without matched data, deployment, and quality
targets.
Retrieval quality is measured separately from speed. The checked-in synthetic
development evaluation compares recent history, token overlap, and Aura on a
primarily English suite with small multilingual diagnostic slices, paraphrases,
code identifiers, temporal updates, negation, multi-evidence recall, tenant
interference, and restart consistency.
Its current machine-readable result and limitations are documented in
[`experiments/memory_quality_eval`](experiments/memory_quality_eval/README.md).
This development set is not presented as a competitor benchmark.
1 The measured Aura 1.58 Windows CPython 3.13 wheel was
2,772,715 bytes; installed size and artifacts for other Python versions,
platforms, and releases vary.
---
## What Ships Today
Aura's full cognitive recall pipeline is active and bounded:
`Record → Belief (±5%) → Concept (±4%) → Causal (±3%) → Policy (±2%)`
Enable everything in one call:
```python
brain.enable_full_cognitive_stack() # activates all four bounded reranking phases
brain.disable_full_cognitive_stack() # back to raw RRF baseline
```
Or configure individual phases:
```python
brain.set_belief_rerank_mode("limited") # belief-aware ranking
brain.set_concept_surface_mode("limited") # concept annotations + bounded concept reranking
brain.set_causal_rerank_mode("limited") # causal chain boost
brain.set_policy_rerank_mode("limited") # policy hint shaping
```
Higher layers also expose advisory surfaced output:
- `get_surfaced_concepts()` — stable concept abstractions over repeated beliefs
- `get_surfaced_causal_patterns()` — learned cause→effect patterns
- `get_surfaced_policy_hints()` — advisory recommendations (Prefer / Avoid / Warn)
- no automatic behavior influence — all output is advisory and read-only
Aura also ships operator-facing and plasticity-facing surfaces:
- explainability:
- `explain_recall()`
- `explain_record()`
- `provenance_chain()`
- `explainability_bundle()`
- governed correction:
- targeted retract/deprecate APIs
- persistent correction log
- correction review queue
- suggested corrections without auto-apply
- bounded autonomous plasticity:
- `capture_experience()`
- `ingest_experience_batch()`
- maintenance-phase integration
- anti-hallucination guards
- plasticity risk scoring
- purge / freeze controls
- bounded v6 cognitive guidance:
- salience:
- `mark_record_salience()`
- `get_high_salience_records()`
- `get_salience_summary()`
- reflection:
- `get_reflection_summaries()`
- `get_latest_reflection_digest()`
- `get_reflection_digest()`
- contradiction and instability:
- `get_belief_instability_summary()`
- `get_contradiction_clusters()`
- `get_contradiction_review_queue()`
- honest explainability support:
- unresolved-evidence markers in recall explanations
- bounded answer-support phrasing for agent / UI layers
---
## How Memory Works
Aura organizes memories into four levels across two active tiers. Maintenance is
event-driven: one maintenance call is one decay opportunity, not a calendar TTL.
Importance, evidence state, explicit pins, and archival policy decide what stays
active; wall-clock time is used only by configured age/completion rules.
```
CORE TIER
Identity Anchored identity and operator-protected knowledge.
Domain Learned facts and domain knowledge.
COGNITIVE TIER
Decisions Choices made and action items.
Working Current tasks and recent context.
CONSEQUENCE ROUTE STATE (maintenance retention)
candidate Fastest decay while usefulness is still unverified.
evidence debt Slower decay while an outcome remains unresolved.
confirmed Longest ordinary retention after supporting outcomes.
refuted scar No field decay; preserved against later positive noise.
OPERATOR PIN
pinned No field decay or automatic archival.
```
One call runs the lifecycle — decay, promotion, consolidation, and archival:
```python
report = brain.run_maintenance() # background memory maintenance
```
Weak records leave active RAM and fast indexes but keep a crash-safe journal
trace. They stay cold after restart. Automatic text-query reactivation from the
cold journal is not part of the current API.
---
## Key Features
### Evidence & Decision Audit Graph
Aura can preserve the complete chain from evidence to a verified outcome as a
small, deterministic read-model over the existing local store. It does not
require a graph database, an LLM, or a cloud service:
```python
source_id = brain.store("Canary telemetry is healthy", source_type="retrieved")
claim_id = brain.store("The canary is healthy", semantic_type="fact")
decision_id = brain.store(
"Proceed with the bounded rollout",
level=Level.Decisions,
semantic_type="decision",
)
brain.annotate_audit_entity(source_id, "source", "observed", "deploy/source")
brain.annotate_audit_entity(claim_id, "claim", "accepted", "deploy/claim")
brain.annotate_audit_entity(decision_id, "decision", "decided", "deploy/decision")
brain.link_audit_entities("deploy/source", "deploy/claim", "supports")
brain.link_audit_entities("deploy/claim", "deploy/decision", "recalled_for")
explanation = brain.explain_decision("deploy/decision")
history = brain.audit_graph(namespace="default", valid_at=timestamp)
conflicts = brain.find_claim_conflicts("deploy/claim")
```
Directed relations cover `supports`, `refutes`, `contradicts`, `supersedes`,
`derived_from`, `recalled_for`, `used_evidence`, `used_by`, `caused`,
`produced`, and `verified_by`. Entity status is append-only and bitemporal;
historical graph reconstruction uses both recording time and business-time
validity. Conflict recommendations are advisory and never rewrite memory.
The graph is rebuilt from reserved `aura.audit.v1.*` record metadata, while
links are committed atomically with Aura's existing typed connections. Compact
JSON export is available in Rust through `AuditGraph::to_compact_json()`.
### Observational Outcome Receipts
Aura can collect the exact evidence needed to evaluate future adaptive memory
policies without changing memory behavior today. A receipt records candidate
record IDs, selected IDs, and a categorical external outcome; it never stores
the prompt, answer, task text, or memory payload:
```python
receipt = brain.capture_outcome_receipt(
task_id="deploy-184",
lineage_id="deploy-canary",
attempt_id="attempt-1",
candidate_record_ids=[runbook_id, incident_id],
selected_record_ids=[runbook_id],
outcome="helpful",
namespace="operations",
provenance=["host:task-runner"],
# Optional: exact evidence for later offline evaluation.
logging_policy_id="bounded-exploration-v1",
selected_set_probability_bps=2500, # probability of the selected set
candidate_verdicts={
runbook_id: "helpful",
incident_id: "unhelpful",
},
verifier_id="terminal-check-v1",
)
history = brain.outcome_receipts(
namespace="operations",
lineage_id="deploy-canary",
)
```
Omit the optional evaluation fields to keep the schema-v1 receipt. Supplying a
logging policy and exact selected-set probability, explicit per-candidate verifier
verdicts, or both creates a schema-v2 receipt. These fields are integrity-protected
and observational: Aura does not use them for recall, importance, decay, retention,
consolidation, or forgetting.
The key `(namespace, task_id, attempt_id)` is idempotent; a conflicting retry is
rejected. Candidate records must exist in the same namespace, selected IDs must
be a subset, and persisted receipts carry an integrity digest. Receipts live in
the managed audit journal, remain outside cognitive records, and have no effect
on recall, strength, retention, consolidation, or forgetting. A full `history`
purge removes receipts that reference the purged record.
This API passed a frozen 96-case contract experiment. Using receipts to alter
ranking or retention remains gated on a separate temporal holdout with real,
verified outcomes.
### Immutable Evidence Lineage
Aura's cognitive provenance explains how a memory was formed and used. The
evidence substrate additionally binds an extracted claim to the exact bytes of
an immutable source revision:
```rust
use aura::{admission_decision, verify_lineage, AnswerPermission, EvidenceClaim,
SourceDocument, SourceSpan, VerificationStatus};
let bytes = b"Verified value: 42";
let document = SourceDocument::from_bytes("report", "v1", "file:///report.txt", bytes);
let span = SourceSpan::from_document(&document, bytes, 0, bytes.len())?;
let integrity = verify_lineage(&document, bytes, &span);
```
`VerificationStatus` and `AnswerPermission` are independent gates. A high
confidence score never overrides a changed source hash, a superseded claim, or
a blocked citation. New `Record` fields remain backward-compatible with older
serialized records.
Evidence-aware research reports are composed only from admitted findings.
Free-form synthesis is omitted until the synthesis itself can carry claim-level
lineage, preventing blocked source material from being reintroduced indirectly.
### Context Capsules
Agents can request a deterministic hot-context projection without maintaining a
separate wiki or mutating memory through recall activation:
```python
capsule = brain.build_context_capsule(
purpose="continue the current institute research",
token_budget=2000,
namespace="ask-institute",
)
```
The bounded capsule prioritizes refutation scars, open evidence debt, active
goals, contradictions, outcomes, decisions, and durable domain/identity
records. It returns an estimated token count, omitted-record count, selection
reasons, and a stable content hash. Blocked records and superseded versions
outside their validity interval are never surfaced.
When the prompt budget is tight, agents can opt into deterministic loss-aware
packing:
```python
result = brain.build_compacted_context_capsule(
purpose="continue the current institute research",
token_budget=2000,
namespace="ask-institute",
)
entries = result["entries"]
report = result["compaction"]
print(report["additional_entry_count"], report["saved_tokens"])
```
This mode removes exact sentence duplication and safe discourse prefixes before
packing, without calling an LLM or changing stored records. Active goals and
non-text payloads are excluded from compaction because local-model evaluation
showed that removing apparently redundant emphasis from an active instruction
can change a smaller model's answer. The normal token-budget truncation can
still apply to an oversized final entry. Every returned entry retains its
original `record_id`, so the full stored record remains available through
`brain.get(record_id)`. The existing `build_context_capsule()` behavior is
unchanged.
### Citation-Locked Retrieval Episodes
For answers that must be auditable, Aura can track which evidence was actually
opened during one retrieval episode and require every atomic answer claim to be
covered by an opened, citable source:
```python
source = b"The current launch date is September 24."
episode = brain.start_retrieval_episode(
"What is the current launch date?",
candidate_ids=[launch_record_id],
routes=["timeline"],
)
episode.open_verified_evidence(
record_id=launch_record_id,
claim_id="claim-launch-date",
claim_text="The current launch date is September 24.",
route="timeline",
support_keys=["launch-date"],
document_id="launch-plan",
revision_id="rev-2",
uri="memory://launch-plan",
registered_source_bytes=source,
current_source_bytes=source,
byte_start=0,
byte_end=len(source),
verification_status="verified",
answer_permission="cite",
)
gate = episode.finalize(
answer_present=True,
citations=[launch_record_id],
atomic_claim_keys=["launch-date"],
)
if not gate["answer_permitted"]:
answer = "UNKNOWN"
```
`registered_source_bytes` establish the immutable document revision and span;
`current_source_bytes` are rechecked when the agent opens the evidence. Changed,
superseded, contested, context-only, blocked, unopened, or partially supporting
evidence cannot authorize an answer. `brain.suggest_memory_intent(query)` offers
a deterministic advisory route, while unknown queries conservatively retain all
routes. Hosts supply trusted source snapshots, verification status and claim-support
keys. The gate checks byte lineage and declared coverage; hosts remain responsible
for semantic support and access authorization. It does not independently verify
that a source entails the answer or is still current after opening.
Retrieval episodes are ephemeral and read-only: they do not activate or
rewrite stored records, and the existing recall APIs remain unchanged.
### Context-Aware Applicability
Semantic relevance does not guarantee that an old experience is safe to reuse
in the agent's current situation. Experience records can declare hard,
model-independent preconditions in metadata:
```python
experience = brain.store(
"Refresh the expired token and retry deployment",
level=Level.DECISIONS,
semantic_type="decision",
metadata={
"applicability.require.cause": "expired_token",
"applicability.require.environment": "ready",
},
)
results = brain.recall_with_applicability(
"deployment authentication recovery",
current_state={
"cause": ["permission_denied"],
"environment": ["ready"],
},
top_k=5,
)
print(results[0]["applicability"]["decision"])
```
Every recalled record is annotated as `use`, `reject`, or `unknown`, with
matched, missing, conflicting, and mismatched fields. Aura does not filter or
rerank the results and never guesses missing state. The host agent remains in
control of whether to adapt an `unknown` memory. Existing `recall()` and
`recall_structured()` behavior is unchanged.
### Portable Store Containers
Aura can package its durable memory artifacts into one portable `.aura` file:
```python
report = brain.export_container("./snapshots/agent-memory.aura")
# For provenance-sensitive backups, keep the private key outside the Aura
# store and create a signed chain from generation one.
keys = Aura.generate_container_signing_key()
signed = brain.export_signed_container(
"./snapshots/agent-memory-signed.aura",
keys["private_key"],
)
auth = Aura.verify_container_authenticity(
"./snapshots/agent-memory-signed.aura",
trusted_public_key=keys["public_key"],
require_all_signed=True,
)
print(auth["public_key"], auth["latest_manifest_sha256"])
Aura.update_container_authenticity_checkpoint(
"./snapshots/agent-memory-signed.aura",
"./trusted-state/agent-memory.checkpoint.json",
trusted_public_key=keys["public_key"],
)
Aura.verify_container_authenticity_checkpoint(
"./snapshots/agent-memory-signed.aura",
"./trusted-state/agent-memory.checkpoint.json",
)
Aura.import_authenticated_container(
"./snapshots/agent-memory-signed.aura",
"./trusted-restore",
trusted_public_key=keys["public_key"],
require_all_signed=True,
)
# Later snapshots append only changed artifacts. Unchanged segments are
# referenced from the previous committed generation.
delta = brain.append_container("./snapshots/agent-memory.aura")
print(delta["generation"], delta["changed_segment_count"])
signed_delta = brain.append_signed_container(
"./snapshots/agent-memory-signed.aura",
keys["private_key"],
)
# Inspect retention history and garbage-collect unreachable generations.
history = Aura.list_container_generations("./snapshots/agent-memory.aura")
change = Aura.diff_container_generations(
"./snapshots/agent-memory.aura",
from_generation=7,
to_generation=8,
)
Aura.import_container_generation(
"./snapshots/agent-memory.aura",
"./restored-generation-7",
generation=7,
)
gc = Aura.compact_container(
"./snapshots/agent-memory.aura",
keep_last=10,
)
print(gc["kept_generations"], gc["reclaimed_bytes"])
policy = Aura.apply_container_retention(
"./snapshots/agent-memory.aura",
min_generations=2,
max_generations=20,
max_age_seconds=30 * 24 * 60 * 60,
max_size_bytes=512 * 1024 * 1024,
)
print(policy["selected_keep_last"], policy["size_target_met"])
# Dry-run makes no filesystem changes.
plan = Aura.plan_container_retention(
"./snapshots/agent-memory.aura",
min_generations=2,
max_generations=20,
max_age_seconds=30 * 24 * 60 * 60,
max_size_bytes=512 * 1024 * 1024,
)
print(plan["keep_generations"], plan["drop_generations"])
# Legal holds are persisted as append-only control generations.
Aura.hold_container_generation(
"./snapshots/agent-memory.aura",
generation=7,
label="legal-case-42",
)
Aura.release_container_generation_hold(
"./snapshots/agent-memory.aura",
generation=7,
)
# Signed chains require signed control generations too.
Aura.hold_signed_container_generation(
"./snapshots/agent-memory-signed.aura",
generation=1,
label="legal-case-42",
signing_key=keys["private_key"],
)
# Or enforce the same policy automatically after every committed append.
managed = brain.append_container_with_retention(
"./snapshots/agent-memory.aura",
min_generations=2,
max_generations=20,
max_age_seconds=30 * 24 * 60 * 60,
max_size_bytes=512 * 1024 * 1024,
)
# One scheduler can run per Aura instance and stops when Aura closes.
brain.start_container_retention_scheduler(
"./snapshots/agent-memory.aura",
interval_seconds=3600,
min_generations=2,
max_generations=20,
max_age_seconds=30 * 24 * 60 * 60,
max_size_bytes=512 * 1024 * 1024,
)
# Inspection reads only the checksummed table of contents; verification also
# decompresses every segment and checks its SHA-256 digest.
toc = Aura.inspect_container("./snapshots/agent-memory.aura")
Aura.verify_container("./snapshots/agent-memory.aura")
# Read, verify, or extract only selected logical segments.
brain_log = Aura.read_container_segment(
"./snapshots/agent-memory.aura", "brain.cog"
)
Aura.verify_container_segments(
"./snapshots/agent-memory.aura", ["brain.cog", "index/sdr.idx"]
)
Aura.extract_container_segments(
"./snapshots/agent-memory.aura",
"./diagnostic-extract",
["brain.cog"],
)
# Import is deliberately restore-only: the target path must not exist.
Aura.import_container("./snapshots/agent-memory.aura", "./restored-memory")
restored = Aura("./restored-memory")
```
The versioned container uses independently compressed Zstd segments, bounded
sizes, a checksummed table of contents, and per-segment SHA-256 integrity.
Version 2 stores append-only generation frames: a generation becomes visible
only after its frame header is committed, so an interrupted append falls back
to the previous valid generation. Unchanged segments retain their original
offsets, removed artifacts disappear from the latest logical TOC, and a no-op
append adds no bytes. Version 1 containers remain readable; incremental append
requires a v2 container created by the current exporter.
Compaction retains the latest `N` committed generations with their original
generation numbers, verifies every reachable segment while rebuilding, and
replaces the old file only after the compacted container passes validation.
Unreachable payloads, dropped TOCs, and incomplete trailing frames are removed.
`keep_last` must be at least one; if the container already satisfies the
retention policy, compaction is a no-op. Version 1 containers must first be
restored and exported as v2 before they can be compacted.
Retained generations can be independently inspected, SHA-256 verified, read,
diffed, or restored to a new directory. Generation diffs deterministically
separate added, removed, content-changed, and unchanged artifacts.
Automatic retention combines generation-count, wall-clock age, and estimated
compacted-size limits by selecting the most restrictive contiguous suffix.
`min_generations` is a hard floor (default `1`), and at least one maximum must
be configured. If even the minimum retained snapshot exceeds
`max_size_bytes`, compaction still preserves the floor and returns
`size_target_met=False` instead of deleting required history.
`append_container_with_retention()` commits the new generation first and then
applies the policy. If retention fails, the append remains valid and its error
is returned; a later policy run can safely retry cleanup.
Dry-run plans report exact keep/drop generation IDs, estimated compacted size,
active holds, and whether policy limits are blocked by a hold. Legal holds are
stored inside the container as append-only control generations and propagate
through later snapshots. Because generations form a contiguous chain, holding
generation `N` preserves the full suffix from `N` through the latest
generation. Manual compaction and automatic retention both honor that floor.
The optional background scheduler uses the same mutation lock as export,
append, compaction, retention, and hold operations, records its last run/error,
and can be stopped without waiting for the interval. Mutations are serialized
both between threads and between cooperating OS processes through an advisory
sidecar lock such as `agent-memory.aura.lock`. Lock acquisition times out after
30 seconds with an explicit error. The zero-content sidecar is intentionally
kept after release so that concurrent processes always address the same OS lock
object; it does not indicate that a lock is currently held. Processes that
modify `.aura` files without using Aura APIs must coordinate on the same lock.
Signed containers use Ed25519 over a canonical logical generation manifest:
generation metadata, legal holds, and each artifact's name, original size, and
SHA-256 digest. Physical segment offsets are excluded, so verified compaction
preserves existing signatures. Each signed manifest commits to the preceding
signed manifest digest. `inspect`, `verify`, and import reject invalid
signatures automatically; `verify_container_authenticity()` additionally pins
the expected public key and can require every retained generation to be signed.
An existing unsigned v2 container can start a signed epoch with a signed append,
even when no artifact changed. After that, unsigned append and unsigned hold
operations are rejected. Retention may leave the first retained signature with
a detached predecessor digest, reported as `detached_prefix=True`.
Keep signing private keys outside the Aura store and portable containers.
Signatures authenticate retained history but cannot by themselves detect
rollback to an older, otherwise valid signed container. For anti-rollback,
persist and compare `latest_manifest_sha256` in an external trusted system.
Aura's optional authenticity checkpoint automates this comparison: it pins the
signing identity plus the highest accepted generation and manifest digest,
rejects older generations and same-generation forks, and advances atomically.
Store the checkpoint outside the Aura memory directory with trusted filesystem
permissions; deleting or modifying both the container and its checkpoint is
outside this local protection model. None of this is enabled or required for
ordinary unsigned `export_container()` / `import_container()` usage.
Extraction rejects absolute paths, traversal, duplicate names, overlapping
ranges, corruption, and existing destinations. It is an additive snapshot
format, not the live storage backend. Credential files, RBAC secrets, API keys,
and encryption key material are intentionally excluded and must be managed
separately. In Rust this support is controlled by the `capsule` feature and is
included in the default `full` build.
### Temporal Memory Versioning
Context answers what an agent needs now; memory also needs to preserve which
version of a fact was valid at a particular time. Aura records business-time
validity separately from the system time at which a replacement was recorded:
```python
old_id = brain.store(
"The refund window is 30 days",
namespace="shop-a",
valid_from=1_735_689_600,
)
new_id = brain.supersede(
old_id,
"The refund window is 14 days",
namespace="shop-a",
effective_at=1_751_328_000,
)
historical = brain.recall_as_of(
"refund window",
timestamp=1_743_811_200,
namespace="shop-a",
)
```
Validity intervals are half-open: `valid_from <= time < valid_until`. Ordinary
recall, search, and context capsules return only records valid now; expired and
future versions remain available for audit through `get()`, `history()`,
`version_chain()`, and `recall_as_of()`. `recall_at()` retains its system-time
knowledge cutoff, while `recall_as_of()` answers the business-time question
using everything Aura knows now. Namespace isolation applies to both paths.
Supersession is committed as one durable cognitive-journal frame: the old
validity boundary, successor, and causal links either replay together or do not
apply. On startup Aura also repairs the `pending` marker written by older
releases after an interrupted replacement.
### Inspectable Memory Decisions
`explain_recall()` uses the same retrieval and bounded-reranking pipeline as
normal recall, but does not activate or mutate records. In addition to the
selected items and their existing score/provenance traces, it reports relevant
candidates rejected by memory gates:
```python
decision = brain.explain_recall(
"refund window",
top_k=5,
namespace="shop-a",
)
print(decision["trace_id"])
print(decision["decision_summary"]["rejection_counts"])
for candidate in decision["rejected_candidates"]:
print(candidate["record_id"], candidate["reasons"])
```
Current rejection reasons are `expired`, `not_yet_valid`,
`invalid_temporal_bounds`, `below_strength_threshold`, `outside_top_k`, and
`suppressed_by_belief_resolution`. For belief competition, selected and
rejected entries expose the candidate hypothesis, winning hypothesis, both
scores, and whether the record belongs to the winning side. Rejected output is
bounded and includes omission counts. Candidate discovery is performed only
inside the requested namespace scope: records belonging to another tenant are
never represented in the trace, even by ID. The generated `memory_trace_id`,
selected count, and rejected count are also attached to the existing
OpenTelemetry span without logging the query or record contents.
### Governed Promotion and Contradiction Safety
Memory level controls retention, not truth. Aura therefore uses one governed
policy for automatic reflection and promotion-candidate surfaces:
- Working records may graduate to Decisions through repeated use.
- Promotion into Domain or Identity pauses when a record is an explicit
contradiction, carries conflict mass, or has volatility of `0.20` or more.
- Domain-to-Identity promotion additionally requires at least 20 activations
and `0.90` strength.
- Expired and not-yet-valid versions are never promoted.
During maintenance, conflict and volatility are refreshed before promotion.
Explicit `contradicts`/`conflict` links form competing belief hypotheses rather
than collapsing both sides into one bucket. Hypothesis recency uses business
validity (`valid_from`) or creation time—not `last_activated`—so retrieving a
stale rule cannot make its evidence fresh again. When a belief is resolved,
the winning hypothesis is admitted to current recall and the losing side stays
available through history and `explain_recall()` as suppressed evidence.
Aura only resolves an explicit conflict graph as two competing sides when the
graph is one connected bipartite component. Odd cycles, multiple independent
components, isolated claims, and conflict sets without a defensible binary
topology remain `Unresolved`; no record receives a synthetic winning vote.
Use `promotion_block_reason(record_id)` to inspect why a record cannot advance.
For a genuine rule replacement, prefer `supersede(..., effective_at=...)`; use
an explicit `contradicts` relationship when both claims must remain auditable
as competing evidence.
**Core Cognitive Runtime**
- **Fast Local Recall** - Multi-signal SDR + BM25 + N-gram + tag ranking with optional embedding support
- **Two-Tier Memory** — Cognitive (ephemeral) + Core (permanent) with decay, promotion, and archival
- **Semantic Memory Types** — 6 roles (`fact`, `decision`, `trend`, `preference`, `contradiction`, `serendipity`) that influence memory behavior and insighting
- **Phase-Based Insights** — Detects conflicts, trends, preference patterns, and cross-domain links
- **Background Maintenance** — Continuous memory hygiene: decay, reflect, insights, consolidation, archival
- **Namespace Isolation** — `namespace="sandbox"` keeps test data invisible to production recall
- **Pluggable Embeddings** - Optional embedding support: bring your own embedding function
**Trust & Safety**
- **Trust & Provenance** — Source authority scoring: user input outranks web scrapes, automatically
- **Source Type Tracking** — Every memory carries provenance: `recorded`, `retrieved`, `inferred`, `generated`
- **Auto-Protect Guards** — Detects phone numbers, emails, wallets, API keys automatically
- **Encryption** — ChaCha20-Poly1305 with Argon2id key derivation
**Adaptive Memory**
- **Portable `.aura` Containers** — single-file, Zstd-compressed, SHA-256-verified store snapshots with restore-only safe import
- **Feedback Learning** — `brain.feedback(id, useful=True)` boosts useful memories, weakens noise
- **Temporal Semantic Versioning** — `brain.supersede(old_id, new_content, effective_at=...)` with validity intervals and full version chains
- **Snapshots & Rollback** — `brain.snapshot("v1")` / `brain.rollback("v1")` / `brain.diff("v1","v2")`
- **Agent-to-Agent Sharing** — `export_context()` / `import_context()` with trust metadata
**Enterprise & Integrations**
- **Multimodal Stubs** — `store_image()` / `store_audio_transcript()` with media provenance
- **Prometheus Metrics** — `/metrics` endpoint with 10+ business-level counters and histograms
- **OpenTelemetry** — `telemetry` feature flag with OTLP export and 17 instrumented spans
- **MCP Server** — Claude Desktop integration out of the box
- **WASM-Ready** — `StorageBackend` trait abstraction (`FsBackend` + `MemoryBackend`)
- **Pure Rust Core** — No Python dependencies, no external services
---
**Advisory Cognitive Overlays**
- **Belief-Aware Recall Rerank** — bounded production influence with strict guardrails
- **Concept Overlay** — surfaced concepts, per-record annotations, and optional bounded concept-aware reranking
- **Causal / Policy Overlays** — advisory surfaced output only, no automatic control path
- **Cross-Namespace Analytics** — read-only digest for tags, concepts, structural overlap, and canonical causal signatures across namespaces
**Explainability & Governed Adaptation**
- **Explainability APIs** — inspectable selected/rejected decisions through `explain_recall()`, including per-signal BM25 traces, plus `explain_record()`, `provenance_chain()`, and `explainability_bundle()`
- **Durable recall replay** — `replay_recall(trace_id)` reruns one of the latest 128 persisted traces and reports ranking additions, removals, moves, and score drift
- **Permission-aware retrieval** — per-record public/restricted ACLs with role, group, and principal allow-lists plus `audit` and deny-by-default `enforce` modes
- **Correction Governance** — correction log, correction review queue, suggested corrections, namespace governance status
- **Autonomous Cognitive Plasticity** — extraction → ingest → maintenance loop for bounded self-adaptation without changing model weights
- **Plasticity Safety Bounds** — generated-confidence ceiling, risk throttling, purge/freeze controls, operator-visible risk state
**Cognitive Guidance**
- **Salience Layer** — bounded significance weighting for preservation, reminders, ranking, and operator review
- **Maintenance Reflection** — bounded reflection summaries and digests produced inside the normal maintenance pipeline
- **Contradiction Governance** — instability summaries, contradiction clusters, and bounded operator review queues for unresolved evidence
- **Honest Answer Support** — non-anthropomorphic phrasing hints for significance, uncertainty, contradiction, and reflection context
## Quick Start
### Trust & Provenance
```python
from aura import Aura, TrustConfig
brain = Aura("./data")
tc = TrustConfig()
tc.source_trust = {"user": 1.0, "api": 0.8, "web_scrape": 0.5}
brain.set_trust_config(tc)
# User facts always rank higher than scraped data in recall
brain.store("User is vegan", channel="user")
brain.store("User might like steak restaurants", channel="web_scrape")
results = brain.recall_structured("food preferences", top_k=5)
# -> "User is vegan" scores higher, always
```
### Pluggable Embeddings (Optional)
```python
from aura import Aura
brain = Aura("./data")
# Plug in any embedding function: OpenAI, Ollama, sentence-transformers, etc.
from sentence_transformers import SentenceTransformer
model = SentenceTransformer("all-MiniLM-L6-v2")
brain.set_embedding_fn(lambda text: model.encode(text).tolist())
# Now "login problems" matches "Authentication failed" via semantic similarity
brain.store("Authentication failed for user admin")
results = brain.recall_structured("login problems", top_k=5)
```
Without embeddings, Aura continues to use its local recall pipeline - still fast, still effective.
### Claim Certainty (Optional LLM)
Aura records whether each memory is first-hand (`asserted`), qualified (`hedged`),
a guess (`speculative`), or relayed from someone else (`hearsay`). Relayed and
uncertain claims are still stored, but with lower confidence, and they never
count as first-hand evidence for advice.
Built-in rules catch obvious phrasings without any model. For better coverage,
plug in a small local LLM:
```python
import json, urllib.request
PROMPT = (
"Classify what kind of claim the sentence makes. Answer with exactly one word: "
"asserted, hearsay, hedged or speculative.\nSentence: {text}\nAnswer:"
)
def classify(text):
body = json.dumps({"model": "qwen3:4b-instruct", "prompt": PROMPT.format(text=text),
"stream": False, "options": {"temperature": 0, "num_predict": 8}}).encode()
req = urllib.request.Request("http://127.0.0.1:11434/api/generate", data=body,
headers={"Content-Type": "application/json"})
answer = json.loads(urllib.request.urlopen(req).read())["response"].strip().lower()
return next((l for l in ("asserted", "hearsay", "hedged", "speculative") if l in answer), None)
brain.set_claim_classifier(classify) # returning None falls back to the built-in rules
brain.store("I was born on May 12") # asserted, Identity level
brain.store("I heard the plant closed") # hearsay, lower confidence
```
On an independent test set, `qwen3:4b-instruct` via this hook reached hearsay
recall 0.88 and precision 0.92 without demoting any first-hand statement
(~0.3 s per write); the built-in rules alone reached recall 0.53. Those numbers
were measured with the longer prompt that defines each label, in
`experiments/claim_certainty/llm_eval.py`; the short prompt above is only an
illustration. See `experiments/claim_certainty` for the full evaluation.
### Encryption
```python
brain = Aura("./secret_data", password="my-secure-password")
brain.store("Top secret information")
assert brain.is_encrypted() # ChaCha20-Poly1305 + Argon2id
```
Password protection now covers the cognitive journal and snapshots, audit entries,
derived cognitive state, recall replay history, and stored embeddings. Reopening
requires the correct password and the original `memory.key` file. Back up that
wrapped key separately: portable containers intentionally exclude key material.
Use a new, empty directory when enabling encryption. Adding a password to an
existing store is rejected because its journals, snapshots, and backups may
already contain plaintext. Existing unencrypted stores remain readable without a
password; moving them to encrypted storage requires an explicit migration. Older
password-created stores did not protect all cognitive data and must also be
migrated. Explicit JSON exports return plaintext to the caller.
### Deletion and managed purge
`delete(record_id)` is the ordinary explicit deletion path. Aura durably writes
the tombstone before removing the record from active recall and invalidates
beliefs, concepts, causal patterns, policy hints, topology, reflections, and
replay baselines that depended on it. Natural cognitive forgetting is unchanged:
cold traces and refuted consequence scars still follow the normal retention
policy.
Use `purge_record` when bytes must also be removed from managed storage. The
scope is required and cumulative: `active`, `derived`, `current_storage`,
or `history`. The `history` scope rewrites Aura's current stores, audit
rotations, and named snapshots, and leaves a receipt containing only a SHA-256
record digest and the completed surfaces.
```python
receipt = brain.purge_record(record_id, "history")
print(receipt["record_digest"], receipt["removed_surfaces"])
```
Files exported or copied outside the Aura brain directory are not managed by
this call and must be removed by their owner.
Externally supplied embeddings persist across restart. Re-register a Python
embedding callback after reopening to compute query vectors with the same model.
Embedding writes reject empty, non-finite, or incompatible-dimensional vectors;
Rust callers should handle the returned `Result` from `store_embedding` and
`remove_embedding`.
### Semantic Memory Types
```python
brain = Aura("./data")
# Decisions are treated as higher-value memory
brain.store("Use PostgreSQL over MySQL", semantic_type="decision", tags=["db"])
# Preferences persist longer than generic working notes
brain.store("User prefers dark mode", semantic_type="preference", tags=["ui"])
# Contradictions are preserved for conflict analysis
brain.store("User said vegan but ordered steak", semantic_type="contradiction")
# Search by semantic type
decisions = brain.search(semantic_type="decision")
# Cross-domain insights surface higher-level patterns
insights = brain.insights(phase=2)
# Example:
# [{'insight_type': 'preference_pattern', 'description': 'Preference cluster around ui', ...}]
```
### Namespace Isolation
```python
brain = Aura("./data")
brain.store("Real preference: dark mode", namespace="default")
brain.store("Test: user likes light mode", namespace="sandbox")
# Recall only sees "default" namespace — sandbox is invisible
results = brain.recall_structured("user preference", top_k=5)
```
### Cross-Namespace Digest
Use this when you need inspection-only analytics across isolated namespaces without changing recall behavior.
```python
brain = Aura("./data")
digest = brain.cross_namespace_digest(
namespaces=["default", "sandbox"],
top_concepts_limit=3,
)
# Top concepts per namespace
print(digest["namespaces"][0]["top_concepts"])
# Pairwise overlap
print(digest["pairs"][0]["shared_tags"])
print(digest["pairs"][0]["shared_concept_signatures"])
print(digest["pairs"][0]["shared_causal_signatures"])
```
HTTP server:
```text
GET /cross-namespace-digest?namespaces=default,sandbox&top_concepts_limit=3
```
MCP tool:
```json
{
"tool": "cross_namespace_digest",
"arguments": {
"namespaces": ["default", "sandbox"],
"top_concepts_limit": 3
}
}
```
The digest is read-only. It does not bypass namespace isolation in recall and does not feed training or inference by default.
For richer operator-facing workflows, see [`examples/V3_OPERATOR_WORKFLOWS.md`](examples/V3_OPERATOR_WORKFLOWS.md).
### Autonomous Cognitive Plasticity
Aura can also observe model output and feed bounded experience back into the cognitive substrate, without retraining the model.
```python
from aura import Aura
brain = Aura("./data")
brain.set_plasticity_mode("limited")
capture = brain.capture_experience(
prompt="How should we deploy this release?",
retrieved_context=[],
model_response="Deploy to staging first, then verify health checks before production.",
session_id="deploy-session-1",
source="model_inference",
)
brain.ingest_experience_batch([capture])
brain.run_maintenance() # queued experience enters the normal cognitive pipeline
```
This stays bounded and operator-visible:
- the model remains frozen
- generated claims stay capped and guarded
- adaptation can be inspected, restricted, purged, or frozen per namespace
Recent operator HTTP endpoints:
- `GET /explain-record`
- `GET /explain-recall`
- `GET /explainability-bundle`
- `GET /correction-log`
- `GET /cross-namespace-digest`
- `GET /memory-health`
- `GET /belief-instability`
- `GET /policy-lifecycle`
- `GET /correction-review-queue`
- `GET /suggested-corrections`
- `GET /namespace-governance-status`
---
## Cookbook: Personal Assistant That Remembers
The killer use case: an agent that remembers your preferences after a week offline, with zero API calls.
See [`examples/personal_assistant.py`](examples/personal_assistant.py) for the full runnable script.
```python
from aura import Aura, Level
brain = Aura("./assistant_memory")
# Day 1: User tells the agent about themselves
brain.store("User is vegan", level=Level.Identity, tags=["diet"])
brain.store("User loves jazz music", level=Level.Identity, tags=["music"])
brain.store("User works 10am-6pm", level=Level.Identity, tags=["schedule"])
brain.store("Discuss quarterly report tomorrow", level=Level.Working, tags=["task"])
# Simulate a week passing — run maintenance cycles
for _ in range(7):
brain.run_maintenance() # decay + reflect + consolidate + archive
# Day 8: What does the agent remember?
context = brain.recall("user preferences and personality")
# -> Still remembers: vegan, jazz, schedule (Identity, strength ~0.93)
# -> "quarterly report" decayed heavily (Working, strength ~0.21)
```
Identity persists. Tasks fade. Important patterns get promoted. Like a real brain.
---
## MCP Server — Claude Desktop · Cursor · VS Code · any MCP client
Aura's local stdio server has no Python dependencies beyond `aura-memory`.
Install it, validate it, then generate the client-specific JSON:
```bash
python -m pip install --upgrade aura-memory
python -m aura mcp ./aura_brain --check
python -m aura mcp ./aura_brain --print-config claude
```
Use `cursor`, `vscode`, or `generic` instead of `claude` for another
client. The generator writes an absolute brain path and the exact Python
interpreter that owns Aura, so desktop applications do not depend on their
`PATH` configuration. Paste the printed JSON into the client configuration.
For an encrypted brain, set `AURA_PASSWORD` in the client's environment rather
than placing the password in command-line arguments.
The wheel also installs a direct command:
```bash
aura-mcp ./aura_brain --check
aura-mcp ./aura_brain
```
Once connected, the client receives 11 tools:
| Tool | Purpose |
|------|---------|
| `recall` | Retrieve relevant memories before answering |
| `recall_structured` | Get memories with scores and metadata |
| `store` | Save a fact, note, or context |
| `store_code` | Save a code snippet at Domain level |
| `store_decision` | Save a decision with reasoning |
| `search` | Filter memories by level or tags |
| `insights` | Memory health stats |
| `consolidate` | Merge similar records |
| `get` | Fetch a specific record by ID |
| `delete` | Remove a record by ID |
| `maintain` | Run a full maintenance cycle |
> After connecting, tell the agent: *"Before answering, recall relevant context
> from Aura. Store only durable facts, decisions, and useful patterns."*
### Automatic capture in Claude Code
The MCP tools store only what the model decides to store. To remember every
conversation instead, add Aura as a Claude Code hook:
```bash
python -m aura capture ./aura_brain --print-config
```
Merge the printed `hooks` block into `~/.claude/settings.json` (all projects)
or `.claude/settings.json` (one project). Each turn is then stored verbatim,
with its source taken from where it came from, never from what it says:
| Event | Stored as |
|------|---------|
| your prompt | first-hand (`user-claude-code`) |
| a tool's output — web page, file, MCP tool | untrusted data (`web`, `file`, `mcp:`, `tool`) |
| Claude's reply | model-written (`agent-claude-code`) |
| Aura's own tool results | not stored |
The hooks run in the background and only drop each event into
`.inbox/`; the Aura MCP server stores them before its next tool call
(or the hook does, when no server holds the brain). On 264 attack and utility
cases, capture through the hooks matched direct role-split writes: injected
content steered 10.5% of later answers (mem0 with the same capture: 30.5%),
and every prompt was kept verbatim as first-hand
(`experiments/hook_capture`, `experiments/auto_capture`).
For remote automation, `aura serve` exposes REST plus the legacy HTTP+SSE MCP
transport. Install its optional dependencies with
`python -m pip install "aura-memory[http]"`. New local MCP integrations should
use stdio; the legacy HTTP+SSE transport is retained for existing Make.com and
n8n workflows.
`aura serve` binds `127.0.0.1` by default. To expose it on another interface,
set an API key (`--api-key` or `AURA_API_KEY`); clients then send
`Authorization: Bearer `. Browser cross-origin access is off unless
`AURA_CORS_ORIGINS` lists allowed origins.
### Windows test note
If `cargo test` intermittently fails on Windows with `LNK1104` for `target\debug\deps\aura-...exe`, a stale test process is usually holding the file open. Run:
```powershell
powershell -ExecutionPolicy Bypass -File .\scripts\cleanup_windows_test_lock.ps1
```
Then rerun the test command.
---
## Dashboard UI
Aura includes a standalone web dashboard for visual memory management. Download from [GitHub Releases](https://github.com/teolex2020/aura-memory/releases).
```bash
./aura-dashboard ./my_brain --port 8000
```
**Features:** Analytics · Memory Explorer with filtering · Recall Console with live scoring · Batch ingest
| Platform | Binary |
|----------|--------|
| Windows x64 | `aura-dashboard-windows-x64.exe` |
| Linux x64 | `aura-dashboard-linux-x64` |
| macOS ARM | `aura-dashboard-macos-arm64` |
| macOS x64 | `aura-dashboard-macos-x64` |
---
## Integrations & Examples
**Try now:** [](https://colab.research.google.com/github/teolex2020/aura-memory/blob/main/examples/colab_quickstart.ipynb) — zero install, runs in browser
| Integration | Description | Link |
|-------------|-------------|------|
| Ollama | Fully local AI assistant, no API key needed | [`ollama_agent.py`](examples/ollama_agent.py) |
| LangChain | Drop-in Memory class + prompt injection | [`langchain_agent.py`](examples/langchain_agent.py) |
| LlamaIndex | Chat engine with persistent memory recall | [`llamaindex_agent.py`](examples/llamaindex_agent.py) |
| OpenAI Agents | Dynamic instructions with persistent memory | [`openai_agents.py`](examples/openai_agents.py) |
| Claude SDK | System prompt injection + tool use patterns | [`claude_sdk_agent.py`](examples/claude_sdk_agent.py) |
| CrewAI | Tool-based recall/store for crew agents | [`crewai_agent.py`](examples/crewai_agent.py) |
| AutoGen | Memory protocol implementation | [`autogen_agent.py`](examples/autogen_agent.py) |
| FastAPI | Per-user memory middleware with namespace isolation | [`fastapi_middleware.py`](examples/fastapi_middleware.py) |
**FFI (C/Go/C#):** [`aura.h`](examples/aura.h) · [`go/main.go`](examples/go/main.go) · [`csharp/Program.cs`](examples/csharp/Program.cs)
**More examples:** [`basic_usage.py`](examples/basic_usage.py) · [`encryption.py`](examples/encryption.py) · [`agent_memory.py`](examples/agent_memory.py) · [`edge_device.py`](examples/edge_device.py) · [`maintenance_daemon.py`](examples/maintenance_daemon.py) · [`research_bot.py`](examples/research_bot.py)
---
## Architecture
Aura uses a Rust core with Python bindings and a local-first memory runtime.
Publicly documented concepts are:
- Two-tier memory: cognitive + core
- Semantic roles for records
- Local multi-signal recall
- Belief-aware bounded reranking
- Trust, provenance, and namespace isolation
- Maintenance, insights, consolidation, and versioning
Higher cognitive layers may be present in the SDK as bounded reranking overlays and advisory inspection surfaces. They are not default runtime decision-making or behavior control.
The public repository documents the user-facing behavior and integration surface. Detailed internal architecture, tuning, and research notes are intentionally not published.
---
## Resources
- [Demo Video (30s)](https://www.youtube.com/watch?v=ZyE9P2_uKxg) — Quick overview
- [Examples](examples/) — Ready-to-run scripts
- [Landing Page](https://aurasdk.dev) — Project overview
---
## Contributing
Contributions welcome! See [CONTRIBUTING.md](CONTRIBUTING.md) for setup instructions and guidelines, or check the [open issues](https://github.com/teolex2020/aura-memory/issues).
⭐ **If Aura saves you time, a [GitHub star](https://github.com/teolex2020/aura-memory) helps others discover it and helps us continue development.**
---
## License
MIT — see [LICENSE](LICENSE). There are no patent or commercial-licensing terms: use Aura in any project, including commercial ones.
---
Built in Kyiv, Ukraine 🇺🇦 — including during power outages.
Solo developer project. If you find this useful, your star means more than you think.