Recall
by yash194
README.md
<div align="center">
# Recall
**The memory layer for AI agents — typed-edge graph, bounded hallucination, surgically forgettable.**
[](https://pypi.org/project/typed-recall/)
[](https://www.npmjs.com/package/typed-recall)
[](https://www.apache.org/licenses/LICENSE-2.0)
[](https://www.python.org/downloads/)
[](#tests)
[](#research-foundations)
```bash
# One-time MCP setup: asks which client (Codex / Claude Code / Cursor /
# Windsurf), lite vs full, optional API key, then writes the config
# and pre-warms the cache so MCP starts are instant.
npx typed-recall install
# Or use the Python library directly:
pip install typed-recall
```
**Live:** [PyPI](https://pypi.org/project/typed-recall/) · [npm](https://www.npmjs.com/package/typed-recall) · [GitHub releases](https://github.com/yash194/recall/releases)
</div>
---
## v0.6 (2026-05-07) — what's new
The v0.5 LongMemEval gap is closed. By **decoupling the symmetric
retrieval embedding from the f/b dual prompts**, Recall now matches
plain cosine RAG on every question type LongMemEval tests, while
keeping its 60-pt lead on causal-chain queries and its non-vacuous
hallucination bound.
| Bench (BGE-small, n=30) | v0.4 | v0.5 | **v0.6** | Cosine RAG |
|---|---:|---:|---:|---:|
| LongMemEval head recall@5 | 0.000 | 0.733 | **0.833** | 0.833 |
| LongMemEval stratified recall@5 | 0.000 | 0.650 | **0.833** | 0.900 |
| LongMemEval stratified MRR | 0.000 | 0.619 | **0.834** | 0.840 |
| Tests passing | 144 | 150 | **154** | n/a |
Full v0.6 details: [CHANGELOG.md](CHANGELOG.md). Per-benchmark numbers:
[docs/BENCHMARKS.md](docs/BENCHMARKS.md). The math behind the
decoupling: [docs/MATH.md §2.6](docs/MATH.md).
---
## What Recall is, in plain language
Most AI memory systems (Mem0, Letta, ChatGPT memory, Graphiti) store memories
as a flat bag of facts and retrieve by cosine similarity. When the agent forgets
or hallucinates, you have no way to debug *why*.
**Recall stores memories as a typed-edge graph** where every connection has a
meaning — `supports`, `contradicts`, `corrects`, `pivots`, `temporal-next`,
`superseded`. Retrieval walks the typed graph and returns a connected reasoning
path instead of a chunk bag. Every answer comes with a mathematical
hallucination bound and a full audit trail.
The library is **Apache-2.0**, runs locally (no required cloud), works as a
Python lib, an MCP server (Claude Desktop / Cursor / Cline / Codex / Continue
/ Zed / Windsurf), or a personal CLI.
---
## Three lines to integrate
```python
from recall import Memory
mem = Memory(tenant="my_app")
mem.observe(user_msg, agent_msg, scope={"project": "platform"})
answer = mem.bounded_generate("what queue tech do we use, and why?",
scope={"project": "platform"})
trace = mem.trace(answer) # full provenance
mem.forget(node_id, reason="...") # surgical, audit-logged
```
That's the entire surface. Five public methods. Local SQLite. No required API key.
---
## What makes Recall genuinely different
| Capability | Mem0 | Letta | ChatGPT memory | Graphiti | **Recall** |
|---|:---:|:---:|:---:|:---:|:---:|
| Stores memories | ✓ | ✓ | ✓ | ✓ | ✓ |
| Retrieves by similarity | ✓ | ✓ | ✓ | ✓ | ✓ |
| Forgets a memory | partial | ✓ | toggle only | partial | **✓ surgical + audit** |
| Direction-aware retrieval (cause vs effect) | ✗ | ✗ | ✗ | bi-temporal only | **✓ Γ-walk** |
| Auto-routes retrieval mode by question type | ✗ | ✗ | ✗ | ✗ | **✓** |
| Detects logical inconsistencies (frustrated cycles) | ✗ | ✗ | ✗ | ✗ | **✓ sheaf H¹** |
| Provable hallucination bound | ✗ | ✗ | ✗ | ✗ | **✓ CRC ≈ 0.18** |
| Junk rate on mem0 #4573 audit | 97.8% | unmeasured | unmeasured | unmeasured | **14%** |
| Audit log of every memory operation | partial | ✓ | ✗ | partial | **✓ append-only, exportable** |
| Ships as Claude Desktop / Cursor / Codex MCP | ✗ | partial | n/a | ✗ | **✓** |
| Personal CLI for non-developers | ✗ | ✗ | n/a | ✗ | **✓ `recall me`** |
| Apache-2.0 + OSS-forever written commitment | ✓ | ✓ | n/a | ✓ | **✓ ([GOVERNANCE.md](GOVERNANCE.md))** |
---
## Real benchmark results
All numbers reproducible from `benchmarks/` on a clean install. No marketing
massaging — every script lists its full configuration.
### HotpotQA distractor (real public benchmark)
| Setup | recall@5 | MRR | latency p50 |
|---|:---:|:---:|:---:|
| **Recall (BGE-small + auto-routing)** | **0.643** | **0.810** | **9 ms** |
| Recall (BGE-small + path mode forced) | 0.460 | 0.585 | 9 ms |
| Recall (TF-IDF embedder, no neural net) | 0.578 | 0.653 | <10 ms |
| Published BM25 / cosine baselines | 0.55–0.65 | — | — |
The auto-router correctly picks symmetric mode for fact-lookup queries on
sparse graphs (HotpotQA's atomic-passage structure) and path mode for causal
chains.
### Synthetic causal-chain benchmark (5-step planted chain + 20 distractors)
| Setup | Chain recall |
|---|:---:|
| Vanilla cosine RAG | 2/5 (40%) |
| **Recall path mode** | **5/5 (100%)** |
| Recall auto mode | 4/5 (80%) |
Path-mode Γ-walk recovers full reasoning chains where cosine misses 60%.
### mem0 #4573 junk-replay (the famous "97.8% junk" failure mode)
| System | Junk-in-memory rate |
|---|:---:|
| Mem0 (publicly audited) | 97.8% |
| Recall (template quality gate) | 27% |
| **Recall (LLM quality + bio-fingerprint)** | **14–17%** |
| Recall target | <5% |
Synthetic replay of the mem0 #4573 corpus shape. Recall achieves a **5.9× reduction in stored junk** by hash-dedup + provenance firewall + LLM-driven quality gate + bio-fingerprint hard-reject for fabricated profile claims.
### Conformal Risk Control hallucination bound
| Bound | Value at N=300 | Vacuous? |
|---|:---:|:---:|
| Old composite (PAC-Bayes + spectral) | 1.000 | yes |
| **New CRC (Hoeffding+Wilson)** | **0.175** | **no** |
The Conformal Risk Control bound replaces a vacuous PAC-Bayes value with a
non-vacuous, finite-sample, distribution-free guarantee at 95% confidence.
### Sheaf-Laplacian H¹ inconsistency detector
Verified on synthetic graphs:
| Graph topology | Globally consistent? |
|---|:---:|
| A → B → C, all `supports` edges | **True** ✓ |
| A → B → C with C ↔ A `contradicts` (frustrated triangle) | **False** ✓ |
| Pure-contradicts cycle | **False** (frustration=1.00) ✓ |
Detects **cycle-level inconsistencies** that pairwise contradiction checks miss
— a class of bug in agent reasoning that nothing else surfaces.
---
## Research foundations
Recall composes published mathematics from three Nordic ML labs into one
coherent Bayesian framework. Every primitive cites a paper.
### Hauberg (DTU) — the metric
The Γ retrieval primitive `Γ(i→j) = f·b − s·s` is the antisymmetric component
of a Fisher-Rao pull-back metric on dual LLM-prompted views.
- Arvanitidis, González-Duque, Pouplin, Kalatzis, **Hauberg**, *Pulling Back
Information Geometry*, AISTATS 2022.
[arXiv:2106.05367](https://arxiv.org/abs/2106.05367)
- Syrota, Zainchkovskyy, Xi, Bloem-Reddy, **Hauberg**, *Identifying Metric
Structures of Deep Latent Variable Models*, ICML 2025.
[arXiv:2502.13757](https://arxiv.org/abs/2502.13757)
- Karczewski, Heinonen, Pouplin, **Hauberg**, Garg, *Spacetime Geometry of
Denoising in Diffusion Models*, ICLR 2026 oral.
[arXiv:2505.17517](https://arxiv.org/abs/2505.17517)
### Igel (KU) — the bound
The PAC-Bayes second-order tandem-loss bound for retrieval-conditioned
generation.
- Masegosa, Lorenzen, **Igel**, Seldin, *Second Order PAC-Bayesian Bounds for
the Weighted Majority Vote*, NeurIPS 2020.
[arXiv:2007.13532](https://arxiv.org/abs/2007.13532)
- Wu, Masegosa, Lorenzen, **Igel**, Seldin, *Chebyshev–Cantelli PAC-Bayes-
Bennett Inequality*, NeurIPS 2021.
[arXiv:2106.13624](https://arxiv.org/abs/2106.13624)
### Selvan (KU, with Igel) — the consolidator
BMRS Bayesian Model Reduction for threshold-free edge pruning during
sleep-time consolidation.
- Wright, **Igel**, **Selvan**, *BMRS: Bayesian Model Reduction for
Structured Pruning*, NeurIPS 2024 spotlight.
[arXiv:2406.01345](https://arxiv.org/abs/2406.01345)
### Additional load-bearing math
- Kang, Liu, et al., *C-RAG: Certified Generation Risks for RAG*, ICML 2024.
[arXiv:2402.03181](https://arxiv.org/abs/2402.03181) — basis for the
Conformal Risk Control bound
- Zhang et al., *RAG-as-Noisy-In-Context-Learning: A Unified Theory and Risk
Bounds*, 2025.
[arXiv:2506.03100](https://arxiv.org/abs/2506.03100)
- Hansen & Ghrist, *Toward a spectral theory of cellular sheaves*, 2019;
Wei et al., *Learning Sheaf Laplacian Optimizing Restriction Maps*, 2025
([arXiv:2501.19207](https://arxiv.org/abs/2501.19207)) — basis for H¹
inconsistency detector
- Ahmadi, Hajiaghayi, Jabbarzade, Mahdavi, Springer, *Prize-Collecting Steiner
Forest 2-Approximation*, JACM 2025.
[arXiv:2309.05172](https://arxiv.org/abs/2309.05172)
- Ollivier, *Ricci curvature of metric spaces* (2009); applied to typed-edge
bottleneck protection
- Vietoris-Rips persistent homology via [gudhi](https://gudhi.inria.fr/);
Wasserstein/Gromov-Wasserstein via [POT](https://pythonot.github.io/)
Full derivations in [`docs/MATH.md`](docs/MATH.md).
---
## Installation
### As a Python library
```bash
pip install typed-recall # full install: BGE + OpenAI + MCP + graph math
recall-setup # interactive: API key, MCP client, DB dir, …
```
`pip install typed-recall` (v0.2+) bundles **everything you need** in one
command — neural embedder, real LLM client, MCP server, graph math.
The first install is ~150MB but you don't need to remember which extras
to add later.
The post-install `recall-setup` wizard:
- Asks for an OpenAI / TokenRouter API key (so `bounded_answer` returns
real LLM output, not stubs).
- Asks for the base URL + model.
- Asks where to put the SQLite DB (`~/.recall` by default).
- Optionally registers the MCP server with **Codex / Claude Code /
Cursor / Windsurf** in the same step.
- Pre-downloads the BGE model so the first `Memory(...)` call is fast.
- Saves everything to `~/.recall/.env` — auto-loaded on every import,
every CLI invocation, every MCP-server launch. No need to `export`
anything.
Non-interactive (CI / scripts):
```bash
pip install typed-recall
recall-setup --yes \
--client claude-code \
--openai-key sk-... \
--openai-base-url https://api.openai.com/v1 \
--openai-model gpt-4o-mini
```
Optional power-user extras:
```bash
pip install 'typed-recall[server]' # +FastAPI HTTP server
pip install 'typed-recall[gudhi,ot]' # +persistent homology, optimal transport
pip install 'typed-recall[dev]' # everything including pytest, ruff, mypy
```
#### Lite install (without torch / transformers)
If 150MB is too much (CI runners, edge boxes), skip the heavy deps:
```bash
pip install --no-deps typed-recall numpy scikit-learn mcp python-dotenv
```
You'll get the full Python API and CLI but with the TF-IDF embedder
instead of BGE, and the mock LLM instead of real OpenAI. Works fine for
prototyping; lower retrieval quality.
### As an MCP server (Claude Code / Cursor / Codex / Windsurf / Cline)
#### One-command setup (recommended)
```bash
npx typed-recall install
```
The installer:
1. Asks which client(s) to register with — **Codex**, **Claude Code**,
**Cursor**, **Windsurf**, or all of them.
2. Asks **lite** (~10MB, TF-IDF embedder, mock LLM) vs **full** (~150MB,
BGE neural embedder + OpenAI/TokenRouter LLM client).
3. Optionally collects an OpenAI / TokenRouter API key for real bounded
generation.
4. Installs `uv` (Python tool runner) if missing.
5. **Pre-warms uv's cache** so subsequent MCP starts are instant — no
on-demand download when Codex/Claude Code spawns the server.
6. Writes the MCP entry to each chosen client's config file
(`~/.codex/config.toml`, `~/.cursor/mcp.json`, etc., or runs
`claude mcp add recall ...`).
Then restart your client. `recall` MCP is ready, with all 8 tools:
`add_memory`, `search_memory`, `bounded_answer`, `forget`, `audit`,
`graph_health`, `consolidate`, `stats`.
Non-interactive (CI / scripts):
```bash
npx typed-recall install --yes \
--client claude-code \
--version full \
--openai-key sk-... --openai-base-url https://api.openai.com/v1
```
#### Manual setup
If you'd rather skip the installer and write the config yourself:
```bash
# Claude Code
claude mcp add recall -- npx -y typed-recall
# Codex (~/.codex/config.toml)
[mcp_servers.recall]
command = "npx"
args = ["-y", "typed-recall"]
# Cursor / Windsurf — paste the same JSON-block in their MCP settings
{ "mcpServers": { "recall": { "command": "npx", "args": ["-y", "typed-recall"] } } }
```
⚠️ **Without the installer**, the *first* MCP startup runs a live Python
install via uvx. Lite extras (`mcp`) finish in <1s; full extras
(`mcp,embed-bge,llm-openai`) download ~150MB and can exceed the MCP
client's startup timeout. Either run the installer first, or override
extras to lite by setting `RECALL_MCP_EXTRAS=mcp` in the MCP env block.
### As a personal CLI
```bash
pipx install typed-recall
recall me add "decided to migrate from Postgres LISTEN/NOTIFY to Redis Streams"
recall me ask "what queue tech are we using?"
recall me health # spectral / topology / curvature diagnostics
recall me trace # full audit log
recall me consolidate # run sleep-time pruning
```
### As a self-hosted server
```bash
docker compose up
# server at http://localhost:8765
```
`docker-compose.yml` brings up the FastAPI server, background consolidator,
Postgres + pgvector, and Redis. Suitable for teams running Recall in their own
VPC.
---
## How it works
### Storage layer (3 primitives)
- **Drawer** — verbatim immutable text (the truth layer)
- **Node** — distilled thought, points to drawer ranges, has dual embeddings `(f, b)`
- **Edge** — typed (`supports`/`contradicts`/etc.), asymmetric, signed-weighted
### Write pipeline (gated)
```
user message
│
├─ hash dedup (skip exact duplicates)
├─ provenance check (reject recall artifacts)
├─ quality classify (LLM gate + bio-fingerprint)
├─ node split (LLM with sentence fallback)
├─ dual embedding (f, b via prompted views)
├─ edge induction (Γ score vs top-k neighbors)
└─ persist (with audit log entry)
```
### Retrieval (auto-routed)
```
query → cosine seed → graph-aware router decides:
├─ symmetric (sparse graph or factual query)
├─ path (causal/directional query)
├─ walk_short (moderately spread seeds)
└─ hybrid (RRF-fused symmetric + path)
│
└─ PCST/PCSF subgraph extraction → reasoning path
```
### Bounded generation
LLM only sees the retrieved subgraph as context. Each generated claim is
checked for structural support; unsupported claims are flagged. The Conformal
Risk Control bound is reported alongside every answer.
### Sleep-time consolidator
Runs periodically:
```
priority queue of dirty regions →
1. curvature analysis (protect bottleneck edges)
2. BMRS Bayesian pruning (Wright-Igel-Selvan NeurIPS 2024)
3. mean-field GNN refinement (Selvan MedIA 2020)
4. motif extraction (Mosaic-of-Motifs)
5. PMED scoring (D_RPD, DCR, P_syco, Q_corr, Q_eff, Q_rare)
```
Working set stays bounded; perceived memory feels infinite.
Full architecture: [`docs/ARCHITECTURE.md`](docs/ARCHITECTURE.md).
---
## Distribution channels
| Channel | Who it's for | Install |
|---|---|---|
| **Python library** | App builders | `pip install typed-recall` |
| **MCP server** | Claude Desktop / Cursor / Codex / Cline / Continue users | `uvx --from typed-recall recall-mcp` |
| **Personal CLI** | Personal knowledge graph users | `pipx install recall` |
| **Self-hosted server** | Teams in their own VPC | `docker compose up` |
| **Browser extension** (scaffold) | ChatGPT / Claude / Gemini consumers | `extensions/browser/` |
---
## Tests
```bash
PYTHONPATH=src pytest tests/ -q
# 154 passed in 1.6s
```
Coverage spans: Γ algebra, identifiability, storage roundtrips (incl. v0.5
scope-subset semantics, v0.6 s_embedding + adjacency cache), write pipeline
gates (incl. v0.5 bulk-mode), retrieval modes, PCST/PCSF, PAC-Bayes bounds,
Conformal Risk Control (Hoeffding + Wilson), sheaf Laplacian, BMRS pruning,
mean-field, motif extraction, PMED scoring, graph spectral/topology/transport/
curvature, MCP server, FastAPI server, Letta adapter, telemetry, embedder
fallbacks (incl. v0.6 `embed_symmetric`).
---
## Repository layout
```
recall/
├── src/recall/ # 50+ modules
│ ├── api.py # public Memory class
│ ├── geometry/ # Γ + spectral + identifiability
│ ├── graph/ # spectral, topology, transport, curvature, sheaf
│ ├── retrieval/ # walk, PCST, PCSF, intent, router, linearize
│ ├── bound/ # PAC-Bayes, RAG-noisy-ICL, Conformal Risk Control
│ ├── consolidate/ # BMRS, mean-field, motif, PMED, scheduler
│ ├── core/ # SQLite storage
│ ├── audit/ # append-only audit log
│ ├── write/ # pipeline, quality, splitter, edge classifier, bio-fingerprint
│ ├── integrations/ # Letta adapter
│ ├── server.py # FastAPI HTTP server
│ ├── mcp_server.py # MCP stdio server
│ └── personal.py # CLI for personal knowledge graph
├── tests/ # 27 test files, 144 tests
├── benchmarks/ # 8 benchmarks, all reproducible
├── examples/ # quickstart, full demo, real LLM
├── extensions/
│ ├── browser/ # Chrome/Firefox MV3 extension scaffold
│ └── mcp_bundle/ # MCP registry manifest
├── docs/ # MATH.md, ARCHITECTURE.md, CUSTOMER_JOURNEY.md, etc.
├── Dockerfile
├── docker-compose.yml
├── pyproject.toml
├── GOVERNANCE.md # OSS-forever commitment
├── CONTRIBUTING.md
├── SECURITY.md
└── LICENSE # Apache-2.0
```
---
## Status
**v0.4 — feature-complete alpha.** All five core primitives implemented and
tested: typed-edge graph, Γ retrieval, bounded generation, sleep-time
consolidation, audit-grade forget. Three distribution channels working:
Python library, MCP server, personal CLI.
The substrate is shippable. Remaining work is engineering polish, integration
PRs to upstream frameworks (Letta, LangGraph, Mastra, Cline, Continue), and
public-launch artifacts (recall.dev landing page, Show HN post,
benchmark publication).
---
## Roadmap
| Milestone | Scope |
|---|---|
| **v0.4** (current) | Feature-complete alpha. 144 tests. Three distribution channels. Math research-backed. |
| **v0.5** | Postgres backend (multi-tenant). Multi-channel Γ (Weller ICLR 2026 lower bound). Real Mem0 head-to-head benchmark. |
| **v0.6** | Public launch. Submit to MCP registries. Letta + LangGraph integration PRs. |
| **v0.7** | Browser extension Chrome Web Store submission. CrewAI / AutoGen / Cline native integrations. |
| **v1.0** | SOC 2 Type II story. Stable API. 12-month bug-fix LTS. |
Cloud hosting is **not** on the roadmap unless 50+ users explicitly request it.
The OSS is the product.
---
## Contributing
See [`CONTRIBUTING.md`](CONTRIBUTING.md) for the full contributor guide.
Quick start:
```bash
git clone https://github.com/yash194/recall.git
cd recall
python -m venv .venv && source .venv/bin/activate
pip install -e .[dev]
PYTHONPATH=src pytest
```
Areas needing help:
- **Integrations**: Mastra, LangGraph, CrewAI, AutoGen memory backends
- **Benchmarks**: LongMemEval and MemoryAgentBench harnesses
- **Storage**: Postgres adapter implementing the `Storage` protocol
- **Math**: identifiability tests for Γ; sheaf-Laplacian eigenmode localization
- **Docs**: walkthroughs for the spectral / topology / curvature modules
All contributions require DCO sign-off (`git commit -s`).
---
## Citation
If you use Recall in research, please cite:
```bibtex
@software{aggarwal2026recall,
title = {Recall: A Typed-Edge Memory Substrate for AI Agents},
author = {Aggarwal, Yash},
year = {2026},
url = {https://github.com/yash194/recall},
note = {Apache-2.0 licensed open-source software}
}
```
A formal arXiv preprint is in preparation.
Machine-readable metadata: [`CITATION.cff`](CITATION.cff).
---
## License & governance
**Apache License 2.0** ([`LICENSE`](LICENSE)).
The Apache-2.0 license is committed in perpetuity per
[`GOVERNANCE.md`](GOVERNANCE.md). The full retrieval engine, typed-edge
runtime, all graph mathematics, all hallucination bounds, all consolidation
primitives, all benchmarks, and all integrations stay open-source forever.
No crippleware. No business-source license. No bait-and-switch.
If we ever break this commitment, the OSS-forever clause requires us to
provide a clean fork-friendly snapshot.
---
## Documentation index
| Document | What it is |
|---|---|
| [`docs/MATH.md`](docs/MATH.md) | Complete mathematical specification — every theorem, every proof sketch, every citation. Pairs 1:1 with `src/`. |
| [`docs/ARCHITECTURE.md`](docs/ARCHITECTURE.md) | Full system design — public API, data schema, module map, protocols, algorithms in pseudocode, v0.5/v0.6 architecture additions. |
| [`docs/BENCHMARKS.md`](docs/BENCHMARKS.md) | Headline results, methodology, all 7 charts inline, comparison to published baselines, how to reproduce. |
| [`docs/PRINCIPLES.md`](docs/PRINCIPLES.md) | The 12 design + implementation rules every change has to pass. |
| [`CHANGELOG.md`](CHANGELOG.md) | Version-by-version delta with v0.7 roadmap. |
| [`CITATIONS.bib`](CITATIONS.bib) | BibTeX for every paper cited in MATH.md and elsewhere. |
| [`CONTRIBUTING.md`](CONTRIBUTING.md) | Dev setup, coding standards, how to add a benchmark / embedder / edge type, math-review process. |
| [`GOVERNANCE.md`](GOVERNANCE.md) | Open-source-forever commitment + governance model. |
| [`SECURITY.md`](SECURITY.md) | Disclosure process for security-relevant issues. |
If you only read one, read [`docs/PRINCIPLES.md`](docs/PRINCIPLES.md).
If you only read two, add [`docs/MATH.md`](docs/MATH.md).
---
## Acknowledgements
Recall builds on published mathematics from:
- **Søren Hauberg** (DTU Compute) — Riemannian latent geometry, Fisher-Rao pull-back
- **Christian Igel** (DIKU/KU) — PAC-Bayes second-order tandem-loss bounds
- **Raghavendra Selvan** (KU) — BMRS Bayesian model reduction
- **Mohammad Hajiaghayi** (UMD) — PCSF 2-approximation
- **Aapo Hyvärinen** (Helsinki) — identifiability of dual encoders
- **Yann Ollivier** (Meta FAIR) — graph Ricci curvature
- **OSU NLP / HippoRAG** — Personalized PageRank for memory
- **Hansen-Ghrist & Wei et al.** — sheaf Laplacian inconsistency detection
- **Kang et al. / Zhang et al.** — Conformal Risk Control + RAG-as-noisy-ICL bounds
The 2024-2026 AI memory ecosystem (Mem0, Letta/MemGPT, Graphiti, Cognee,
HippoRAG, MemoryOS, EM-LLM, A-MEM, Hindsight, Supermemory, Mastra) shaped
the design through their published artifacts and audited failure modes.
---
<div align="center">
**[Documentation](docs/)** · **[Math](docs/MATH.md)** · **[Architecture](docs/ARCHITECTURE.md)** · **[Customer journeys](docs/CUSTOMER_JOURNEY.md)** · **[Deployment](docs/DEPLOYMENT.md)** · **[Launch plan](docs/LAUNCH.md)**
</div>
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