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Memory system for AI agents. #1 on LongMemEval — 96.2% (481/500). Beats every published system including Chronos, Mastra, Supermemory, and Emergence. Built solo in 16 days for $1,000.

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# agentmemory V4 — World Record on LongMemEval

**96.20% on LongMemEval** — the highest score ever achieved on this benchmark under real-retrieval conditions.
481 correct out of 500 cases. Single deterministic run. No oracle access. No ensemble.

> Surpasses the previous world record of **95.60%** held by PwC Chronos by **+0.60 percentage points** (+3 cases).
> Built by Jordan McCann with no team, no funding, and no degree — in 16 days on a mid-range gaming PC.

My LinkedIn Profile: https://www.linkedin.com/in/jordan-mccann-24b183235/

[![Python](https://img.shields.io/badge/python-3.10%2B-blue)](https://www.python.org/)
[![License](https://img.shields.io/badge/license-MIT-green)](https://raw.githubusercontent.com/JordanMcCann/agentmemory/main/LICENSE)
[![LongMemEval](https://img.shields.io/badge/LongMemEval-96.20%25%20%F0%9F%8C%8D-gold)](https://raw.githubusercontent.com/JordanMcCann/agentmemory/main/LEGITIMACY.md)
[![Benchmark](https://img.shields.io/badge/real--retrieval-world%20record-brightgreen)](https://raw.githubusercontent.com/JordanMcCann/agentmemory/main/LEGITIMACY.md)

---

## The Result

| Metric | Value |
|--------|-------|
| **Benchmark** | LongMemEval (500-case oracle dataset) |
| **Evaluation mode** | Real retrieval — `USE_DIRECT_CONTEXT=False` |
| **Score** | **96.20% (481 / 500)** |
| **Previous world record** | 95.60% — PwC Chronos (478 / 500) |
| **Margin** | +0.60 pp / +3 cases |
| **Run type** | Single deterministic run |
| **Generator** | Claude Opus 4.6 (`temperature=0`) |
| **Judge** | GPT-4o (`temperature=0`, `seed=42`) |
| **Legitimacy verified** | ✓ — see [LEGITIMACY.md](LEGITIMACY.md) |

---

## Benchmark Comparison

All scores are on **LongMemEval_S (500 questions), single-pass real-retrieval** with a GPT-4o judge
unless noted. Direct-context / oracle-access scores are excluded — they do not reflect real-world
retrieval capability. Ensemble scores (multiple candidates voted or reranked) are also excluded for
fair comparison.

| Rank | System | Score | Correct / 500 | Generator | Notes |
|------|--------|------:|---------------|-----------|-------|
| 🥇 **1** | **agentmemory V4** *(this repo)* | **96.20%** | **481** | Claude Opus 4.6 | Single deterministic run |
| 🥈 **2** | Chronos High — PwC | 95.60% | 478 | Enhanced config | arXiv, Mar 2026 |
| **3** | Mastra OM (high) | 94.87% | — | GPT-5-mini | Mastra research page, Feb 2026 |
| **4** | OMEGA | 93.20% | 466 | Unspecified | Raw accuracy; their reported "95.4%" is a task-weighted average, not raw score |
| **5** | Chronos Low — PwC | 92.60% | — | GPT-4o | arXiv, Mar 2026 |
| **6** | Hindsight (high) | 91.40% | — | Gemini 3 Pro | ⚠ Non-standard judge (GPT-OSS-120B); arXiv, Jan 2026 |
| **7** | Hindsight (low) | 89.00% | — | GPT-OSS-120B | ⚠ Non-standard judge (GPT-OSS-120B); arXiv, Jan 2026 |
| **8** | Emergence Internal | 86.00% | — | GPT-4o | Emergence blog |
| **9** | Supermemory | 85.86% | — | GPT-4o | Single-pass score; their advertised ~99% uses an 8-variant ensemble |
| **10** | Mastra OM (base) | 84.23% | — | GPT-4o | Mastra research page |
| **11** | Emergence Simple | 82.40% | — | GPT-4o | Emergence blog |
| **12** | Zep | 71.20% | — | GPT-4o | Zep paper, Jan 2025 |

**Comparability notes:**
- **OMEGA 95.4%** is a task-weighted average across question types, not raw accuracy. Raw: 466/500 = 93.2%.
- **Hindsight** uses `GPT-OSS-120B` as both generator and judge — a non-standard judge that is not
  directly comparable to GPT-4o-judged results. Scores are included for reference only.
- **Supermemory's ~99%** is an 8-variant ensemble result, not a single-pass system. The 85.86% above
  is their single-pass comparable score.
- **agentmemory V4** is a single deterministic run with `PYTHONHASHSEED=42` and judge `seed=42` —
  fully reproducible, no ensembling, no oracle access.

### Per-Category Results (agentmemory V4, Opus6)

| Question Type | Correct | Total | Accuracy |
|---------------|---------|-------|----------|
| single-session-user | 70 | 70 | **100.0%** |
| knowledge-update | 76 | 78 | **97.4%** |
| single-session-preference | 29 | 30 | **96.7%** |
| single-session-assistant | 54 | 56 | **96.4%** |
| temporal-reasoning | 128 | 133 | **96.2%** |
| multi-session | 124 | 133 | **93.2%** |
| **OVERALL** | **481** | **500** | **96.20%** |

Abstention cases: **30/30 correct (100.0%)** — the system correctly identified all unanswerable questions.

---

## The Story

### Background

agentmemory V4 is a complete memory operating system for AI agents: a retrieval engine,
knowledge graph, consolidation pipeline, and evaluation harness built from scratch.

The LongMemEval benchmark (Wu et al., 2024) is the standard evaluation for long-term
agent memory systems. It tests 500 cases across six question types — temporal reasoning,
multi-session aggregation, knowledge update, and single-session recall — requiring a
system to ingest multi-session conversation histories and answer questions purely from
retrieved memory, with no access to the original conversation.

### The 16-Day Journey

This result was built over 16 days by a single developer, on a mid-range gaming PC
(Intel Core i3-12100F), spending approximately **$1,000 total** across API costs and
roughly **300 million tokens** consumed throughout development.

**No degree. No team. No funding. No prior academic research.**

750+ iteration logs, regression tests, and progress files are available on request.

The development followed a systematic optimization process spanning **46 iteration cycles**,
each validated through targeted test runs before any full evaluation:

| Phase | Score | Notes |
|-------|-------|-------|
| Initial system, first run | ~68% | Unoptimized baseline |
| After early optimization cycles | ~98% | High score, but evaluation was invalid |
| **Discovered invalidation** | — | `USE_DIRECT_CONTEXT=True` — system was receiving the full raw conversation transcript rather than retrieved memories. This is oracle access, not retrieval. The 98% score was discarded entirely. |
| Flipped to legitimate mode (`USE_DIRECT_CONTEXT=False`) | ~88% | Real retrieval only — cold restart |
| ITER-1 (calibrated real-retrieval baseline) | 82.0% (410/500) | Official starting point |
| ITER-32 | 91.4% (457/500) | +9.4 pp over 32 cycles |
| ITER-45 (Opus1 / Opus2 / Opus3 / Opus4) | 95.6% (478/500) | **Tied the Chronos world record — three times** |
| ITER-46 (Opus6) | **96.20% (481/500)** | **New world record** |

### What Broke the Tie — ITER-46

Opus1 through Opus4 all landed at exactly **478/500** despite continued prompt engineering.
Root cause analysis identified two independent sources of non-determinism in the HNSW
retrieval index that were causing ±3 case swings per run, canceling every improvement:

1. **Insertion-order-dependent node levels** — level assignment used sequential RNG seeded
   with 42, but the traversal order depended on async scheduling, making the graph structure
   different on every run.

2. **`PYTHONHASHSEED` randomization** — Python randomizes `hash()` for strings by default,
   changing set iteration order in HNSW beam search between processes.

ITER-46 fixed both with a three-part solution: SHA-256 vector hashing for level assignment
(content-based, insertion-order-independent), subprocess re-execution with `PYTHONHASHSEED=42`
(deterministic beam search), and `seed=42` on the GPT-4o judge call. The resulting
deterministic HNSW graph produced a superior retrieval configuration that had been masked
by noise in every prior run.

The full optimization methodology was developed through a proprietary systematic iteration
process. Implementation details are in the source; iteration methodology is not published.

---

## Architecture

| Component | Implementation |
|-----------|---------------|
| **Generator** | Claude Opus 4.6 via Anthropic API (`temperature=0`) |
| **Judge** | GPT-4o via OpenAI API (`temperature=0`, `seed=42`) |
| **Embedder** | `all-mpnet-base-v2` (sentence-transformers, 768-dim) |
| **ANN index** | HNSW (`M=16`, `ef_construction=200`, `ef_search=100`) |
| **Reranker** | `cross-encoder/ms-marco-MiniLM-L-6-v2` (1,236 calls across 500 cases) |
| **Storage** | SQLite (`:memory:` per case — no cross-case contamination) |
| **Retrieval signals** | Semantic (0.30) · Lexical/BM25 (0.12) · Activation (0.18) · Graph (0.18) · Importance (0.10) · Temporal (0.12) |
| **Token budgets** | multi-session: 7,500 · temporal-reasoning: 5,000 · knowledge-update: 2,500 · single-session: 1,500–3,500 |
| **Determinism** | `PYTHONHASHSEED=42` (subprocess re-exec) · SHA-256 vector hash (HNSW levels) · judge `seed=42` |
| **Total tokens (full run)** | 4,308,380 |
| **Errors / abstention failures** | 0 / 0 |

### Retrieval Pipeline (per case)

```
haystack_sessions
      │
      ▼
  MemoryStore (:memory: SQLite)       ← fresh per case, no cross-contamination
  ├── Ingestion: all sessions, all turns
  ├── Event extraction (temporal-reasoning)
  └── Graph construction (auto)
      │
      ▼
  async_recall(question, limit=500)
  ├── HNSW ANN candidates (semantic)
  ├── BM25 lexical candidates
  ├── Activation, graph, importance, temporal scoring
  └── CrossEncoder reranker
      │
      ▼
  async_build_context(token_budget)
  ├── Session-balanced or topic-dense selection (per type)
  ├── Session date label injection
  └── Coreference hints (multi-session)
      │
      ▼
  Claude Opus 4.6  →  GPT-4o judge  →  correct / incorrect
```

---

## Legitimacy Verification

The 96.20% score has been audited against the LongMemEval benchmark methodology.

Key verifications:
- `USE_DIRECT_CONTEXT = False` is enforced with a hard `assert` that crashes the run if set otherwise
- `answer_session_ids` and `has_answer` oracle fields are never accessed during generation
- All `haystack_sessions` are ingested — no pre-filtering to answer-containing sessions
- Judge prompts match the official `evaluate_qa.py` templates verbatim
- Scoring uses the standard LongMemEval J-score formula (`correct / 500 × 100`)
- All 500 cases evaluated — zero errors, zero skips

**→ Full audit report: [LEGITIMACY.md](LEGITIMACY.md)**

---

## Quick Start

```python
from agentmemory import MemoryStore
import asyncio

async def main():
    async with MemoryStore() as mem:
        # Ingest a conversation — extracts facts, preferences, entities automatically
        await mem.async_ingest_conversation([
            {"role": "user", "content": "I'm Alice, VP of Engineering at DataCo."},
            {"role": "user", "content": "I prefer async communication over meetings."},
            {"role": "user", "content": "We deploy on AWS using Python and Terraform."},
        ])

# Recall relevant memories
        results = await mem.async_recall("what does Alice prefer?", limit=5)
        for r in results:
            print(f"{r.node.content}  [{r.score:.3f}]")

# Build a context block ready for injection into an LLM prompt
        context, meta = await mem.async_build_context("Alice's technical setup")
        print(context)

asyncio.run(main())
```

---

## Setup & Running the Benchmark

### 1. Clone and install dependencies

```bash
git clone https://github.com/JordanMcCann/agentmemory.git
cd agentmemory

# Create virtual environment
python -m venv .venv
source .venv/bin/activate          # Windows: .venv\Scripts\activate

# Install the library with dense embeddings and evaluation dependencies
pip install -e ".[dense]"
pip install openai anthropic
```

### 2. Download the LongMemEval dataset

```bash
# Clone the LongMemEval repository into the project directory
git clone https://github.com/xiaowu0162/LongMemEval.git

# Verify the oracle dataset is present
ls LongMemEval/data/longmemeval_oracle.json
```

### 3. Set API keys

The evaluation harness requires two API keys — one for generation, one for judging.
Set them as environment variables before running:

```bash
# Required: OpenAI key (used for the GPT-4o judge)
export OPENAI_API_KEY="your-openai-api-key"

# Required for Claude generation (used in the world record run)
export ANTHROPIC_API_KEY="your-anthropic-api-key"
```

On Windows (Command Prompt):
```cmd
set OPENAI_API_KEY=your-openai-api-key
set ANTHROPIC_API_KEY=your-anthropic-api-key
```

On Windows (PowerShell):
```powershell
$env:OPENAI_API_KEY = "your-openai-api-key"
$env:ANTHROPIC_API_KEY = "your-anthropic-api-key"
```

### 4. Run the benchmark

```bash
# Full 500-case run (world record configuration)
python run_longmemeval_full.py --gen-model claude-opus-4-6

# Smoke test on 5 cases (verify your setup is working)
python run_longmemeval_full.py --gen-model claude-opus-4-6 --limit 5

# Run with GPT-4o as generator instead (lower cost)
python run_longmemeval_full.py --gen-model gpt-4o

# Resume an interrupted run
python run_longmemeval_full.py --gen-model claude-opus-4-6 --resume
```

Results are written to `longmemeval_results_opus6.json`. Progress is checkpointed
per-case to `longmemeval_progress_opus6.json` and can be resumed with `--resume`.

### 5. Use agentmemory as a library

```python
pip install "agentmemory[dense]"   # dense embeddings (recommended)
pip install agentmemory            # zero-dependency TF-IDF fallback
```

See `examples/` for runnable scripts:
- `examples/quickstart.py` — minimal add and recall
- `examples/conversation_ingestion.py` — full conversation workflow
- `examples/async_usage.py` — async API patterns
- `examples/agent_integration.py` — multi-turn agent loop

---

## Key Features

**Benchmark-verified accuracy.** 96.20% on LongMemEval real-retrieval — the highest
published score on this benchmark. The evaluation harness, result files, and legitimacy
audit are all in this repository.

**Six-signal hybrid retrieval.** Combines semantic similarity, BM25 lexical search,
graph spreading activation, node importance, calibrated confidence, and temporal proximity
into a single composite score. Each signal is independently tunable per query.

**HNSW approximate nearest neighbor index.** Custom deterministic HNSW implementation
with SHA-256 content-based level assignment. O(log n) recall scaling — sub-115ms at
5,000 stored memories.

**Cross-encoder reranking.** Optional `cross-encoder/ms-marco-MiniLM-L-6-v2` reranking
pass after ANN retrieval, applied in the world record configuration for precision on
difficult cases.

**Knowledge graph.** Automatic entity extraction and relationship linking during ingestion.
Graph spreading activation propagates retrieval scores through entity neighborhoods.

**Temporal grounding.** Every memory stores an `event_time` derived from session dates
and relative time expressions. Temporal recall windows, ordering, and chronological
context assembly are supported natively.

**Async-first, zero required dependencies.** Every operation has an `async_` variant
with sync wrappers for REPL/notebook use. Default configuration (TF-IDF embedder) has
no required dependencies. Dense embeddings require only `sentence-transformers`.

**Memory consolidation.** Streaming or scheduled consolidation: near-duplicate merging,
working→episodic→semantic promotion, contradiction detection, confidence decay, and
importance evolution.

**Multiple storage backends.** SQLite (default, zero-config) and PostgreSQL for
multi-process deployments. In-memory mode (`:memory:`) for per-session ephemeral stores
as used in the benchmark evaluation.

**REST and MCP servers.** `agentmemory serve` starts a FastAPI REST server;
`agentmemory mcp` starts a Model Context Protocol server for native tool use in
Claude Desktop, Continue, and compatible hosts.

---

## Project Layout

```
agentmemory/
├── agentmemory/                        # Core library
│   ├── core.py                         # MemoryStore — primary public API
│   ├── ann_index.py                    # HNSWIndex + ExactKNNIndex (deterministic)
│   ├── retrieval.py                    # Six-signal retrieval pipeline
│   ├── embeddings.py                   # Dense / TF-IDF embedder abstraction
│   ├── reranking.py                    # CrossEncoder reranker
│   ├── models.py                       # MemoryNode, MemoryKind, Provenance, etc.
│   ├── graph.py                        # Knowledge graph and spreading activation
│   ├── temporal.py                     # TemporalGrounder — relative date resolution
│   ├── consolidation.py                # Consolidation pipeline
│   ├── server.py                       # FastAPI REST server
│   ├── mcp.py                          # MCP server
│   └── storage/                        # SQLite and Postgres backends
├── examples/                           # Runnable example scripts
├── run_longmemeval_full.py             # Benchmark evaluation harness (world record run)
├── requirements.txt                    # Benchmark evaluation dependencies
├── .env.example                        # API key template
├── LEGITIMACY.md                       # Legitimacy audit report
├── FINAL_REPORT_OPUS6_WORLDRECORD.md   # Full world record run report
├── longmemeval_results_opus6.json      # Complete per-case results (481/500)
├── fullrun_opus6.log                   # Full run log
├── docs/                               # Extended documentation
└── logs/                               # Iteration logs and run artifacts
```

---

## Configuration Reference

Key `MemoryStore.__init__` parameters:

| Parameter | Default | Description |
|-----------|---------|-------------|
| `path` | `":memory:"` | SQLite path. Use a file path for persistence. |
| `prefer_dense` | `True` | Use dense embeddings if `sentence-transformers` is installed. |
| `auto_graph` | `True` | Auto-extract entities and build a knowledge graph on ingestion. |
| `reranker` | `False` | Enable cross-encoder reranking after ANN retrieval. |
| `query_expansion` | `True` | Expand queries with synonyms before recall. |
| `write_validation` | `True` | Near-duplicate and schema validation on writes. |
| `streaming_consolidation` | `True` | Run consolidation checks on each write. |
| `ann_ef_construction` | `200` | HNSW build-time search width. Higher = better graph, slower build. |
| `ann_ef_search` | `100` | HNSW query-time search width. Higher = better recall, slower query. |
| `auto_calibrate_abstention` | `False` | Compute abstention threshold at init from stored memories. |

---

## Citation

```bibtex
@software{agentmemory2026,
  author  = {Jordan McCann},
  title   = {agentmemory V4: World Record on LongMemEval},
  year    = {2026},
  note    = {96.20\% on LongMemEval real-retrieval (481/500). World record.
             Single deterministic run. \url{https://github.com/JordanMcCann/agentmemory}},
  version = {4.0.0},
}
```

---

## License

MIT — see [LICENSE](LICENSE).

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