Agents
trpc-agent-go
Go framework for production agent systems: graphs, tools, memory, A2A, MCP.
★ 1.8k starsApache-2.0
View guide →A benchmark for comparing AI agent memory systems head-to-head.
agent-memory-benchmark is an open-source ai agents project by vectorize-io (vectorize-io/agent-memory-benchmark on GitHub). It has 144 GitHub stars and 47 forks, is released under the NOASSERTION license, and was last updated on GitHub on 7 October 2026.
Know what you want the agent to accomplish before touching any code.
Clone the repo and read the README's requirements section.
Install what's needed and add API keys or configuration.
Run the quickstart example, then iterate from there.
Same tests for every system.
Apples-to-apples memory evals.
Recall quality, not generic chat.
Inspect how scores are computed.
From the Hindsight team.
Updated as the field moves.
Download the source and move into the project folder.
git clone https://github.com/vectorize-io/agent-memory-benchmark.git
cd agent-memory-benchmarkIsolate dependencies, then install requirements.
python -m venv .venv && source .venv/bin/activate
pip install -r requirements.txtLaunch using the entry point shown in the README.
python -m agent_memory_benchmark # check README for the exact commandIt is an open-source project at vectorize-io/agent-memory-benchmark with 47 forks. Check the contributors tab and recent commits to judge how active maintenance is.
GitHub lists no license for this repo, so legally it's all rights reserved — check the README or ask the maintainer before building on it.
How well agent memory systems recall and use information — standardized tasks for head-to-head comparison.
Researchers and vendors who want memory performance as numbers, not claims.
Clone the repo and follow the README quickstart — the README is the best starting point.
Go framework for production agent systems: graphs, tools, memory, A2A, MCP.
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