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agentmemory-evals

Public benchmarks for agentmemory's hybrid memory stack (BM25 + embeddings + consolidation + graph).

Two families, both reproducible:

  • LongMemEval — public 500-question retrieval benchmark over multi-session chat
  • coding-agent-life-v1 — in-house corpus of 15 fictional Claude Code sessions for a Rust CLI project (shipctl), with 15 hand-graded queries covering bug fixes, refactors, preferences, and multi-session causal reasoning

Adapters

Adapter Backend API key needed
grep Tokenized substring match none
vector OpenAI text-embedding-3-small + cosine OPENAI_API_KEY
agentmemory Running agentmemory server, smart-search endpoint none (auth optional via AGENTMEMORY_SECRET)

Sandbox first

Running the agentmemory adapter against your real ~/.agentmemory directory pollutes the eval with pre-existing memories AND pollutes your real store with eval test data. Always sandbox.

eval/scripts/sandbox.sh spins up a clean agentmemory + iii-engine on ports 3411/3412 with state in /tmp/agentmemory-eval-sandbox/, exports AGENTMEMORY_BASE_URL, and tears down on exit.

source eval/scripts/sandbox.sh
npm run eval:coding-life -- --adapters grep,agentmemory

Requires iii v0.11.2 on PATH (agentmemory pin). If you already have a different version installed, install the pinned build into ~/.local/bin and make sure that directory comes first on PATH:

mkdir -p ~/.local/bin
curl -fsSL https://github.com/iii-hq/iii/releases/download/iii/v0.11.2/iii-aarch64-apple-darwin.tar.gz | tar -xz -C ~/.local/bin
export PATH="$HOME/.local/bin:$PATH"  # add to ~/.zshrc or ~/.bashrc for persistence

Quickstart

coding-agent-life-v1 (in-house, no download)

# grep baseline, no sandbox needed
npm run eval:coding-life -- --adapters grep

# add agentmemory + vector (sandbox + OpenAI key)
source eval/scripts/sandbox.sh
OPENAI_API_KEY=sk-... npm run eval:coding-life -- --adapters grep,vector,agentmemory

LongMemEval _s (public, 278MB download)

mkdir -p ~/datasets/longmemeval
curl -Lo ~/datasets/longmemeval/longmemeval_s.json \
  https://huggingface.co/datasets/xiaowu0162/longmemeval/resolve/main/longmemeval_s

source eval/scripts/sandbox.sh

# Stratified sample of 10 per type (fast iteration, ~$0.20 OpenAI cost)
OPENAI_API_KEY=sk-... LONGMEMEVAL_PATH=~/datasets/longmemeval/longmemeval_s.json \
  npm run eval:longmemeval -- --stratify 10

# Full 500 questions × 3 adapters (~$2 OpenAI cost)
OPENAI_API_KEY=sk-... LONGMEMEVAL_PATH=~/datasets/longmemeval/longmemeval_s.json \
  npm run eval:longmemeval

Repo layout

eval/
├── README.md
├── runner/
│   ├── types.ts                   Adapter, Question, RankedDoc, ScoreRow
│   ├── score.ts                   P@K, R@K, aggregation
│   ├── load.ts                    LongMemEval JSON → Question[]
│   ├── adapters/
│   │   ├── grep.ts                tokenized substring baseline
│   │   ├── vector.ts              OpenAI embeddings + cosine
│   │   └── agentmemory.ts         POST /agentmemory/{remember,smart-search}
│   ├── longmemeval.ts             public benchmark runner
│   └── coding-life.ts             in-house benchmark runner
└── data/
    └── coding-agent-life-v1/
        ├── sessions.json          15 fictional sessions (~6KB)
        └── queries.json           15 queries with gold session IDs

Reports land in eval/reports/<bench>/ (gitignored): scores.ndjson + summary.json.

Published scorecards land in docs/benchmarks/YYYY-MM-DD-<bench>.md.

Writing a new adapter

  1. Implement Adapter<State> from eval/runner/types.ts: 
    import type { Adapter } from "../types.js";
export const myAdapter: Adapter<MyState> = {
  name: "my-adapter",
  async init(sessions, config) { /* index */ return state; },
  async query(q, state, k) { /* search */ return ranked; },
};
  2. Register in eval/runner/{longmemeval,coding-life}.ts ADAPTERS map.
  3. Run against coding-agent-life-v1 to sanity-check before committing OpenAI spend on LongMemEval.

Why a benchmark for agentmemory

agentmemory ships BM25 + embeddings + consolidation + graph retrieval. Numbers from those layers should be measured against grep/vector baselines so the value of each layer is provable.

The in-house corpus is small on purpose (15 sessions) — covers single-session, multi-session, preference, and temporal question types without taking 15 minutes to run. LongMemEval gives the public-comparison axis.