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Evolving Agents

One empty outline on the left, drawn but never filled, becoming five solid shapes on the right — each traced back to where it came from

The loop is solved. What happens after the fork is not.

A plugin for the Claude Agent SDK that versions an agent's evolution — diff it, merge it, refuse to ship it when the eval fails.

License


The gap

The Agent SDK runs the loop, ships the tools, manages context, and enforces permissions. It also branches: fork gives you a second session starting from a copy of the first.

Nothing brings the two back together. There is no merge, no diff between two sessions, nothing that refuses to promote an agent whose eval regressed, and the transcript is plain JSONL that anyone can edit. Sessions persist the conversation — the skills, subagents, model and goal that produced it are not versioned alongside it.

That gap is this repository.

Agent SDK This plugin
Branch a session fork
Compare two branches avcs_diff
Rejoin them avcs_merge, with a --reconcile seam for goal and trace
Refuse to ship a regression avcs_freeze, which fails unless the eval passes
Prove a transcript wasn't edited Ed25519-signed commits
Keep the trace past compaction summarised away archived by the PreCompact hook

Install

from claude_agent_sdk import query, ClaudeAgentOptions

options = ClaudeAgentOptions(
    plugins=[{"type": "local", "path": "/path/to/evolving-agents/plugin"}],
)

Full details in plugin/README.md. The MCP server has zero runtime dependencies — standard library only, with a test that fails if that ever stops being true.

What is here

plugin/ The Agent SDK plugin: MCP server + three hooks
packages/agentvcs/ The version control itself — 214 tests, no dependencies
packages/memory/ Structured recall above the SDK's flat .claude/ memory files. Works; measures no better than naive matching — see PLAN.md
demos/robot/ A 2D robot that evolves its own skills, versioned with agentvcs
legacy/eat/ The Evolving Agents Toolkit, 2025. Kept readable; see below

Why this repository has a 2025 in it

It was the Evolving Agents Toolkit: 18,680 lines across twelve subsystems — a component library, an agent bus, smart memory, an evolution loop, and a governance layer called Firmware. Backed by MongoDB Atlas.

It had three test functions.

That number is the story. EAT was not a product that failed; it was an architecture written down and never pinned to anything that could contradict it.

Most of it is now deleted, because the SDK does it better — docs/WHAT-WAS-DELETED.md lists the 10,165 lines and what replaced each one. Firmware asked a model to "never use dangerous imports" in a string; a PreToolUse hook returning permissionDecision: "deny" stops the call whatever the model decided. We do not ship our own version of a problem that is already solved.

What survives in legacy/eat/ is the ancestry of what ships today. Read evolution/ if you read one thing: 337 lines that closed the loop between an agent changing and that change being kept, with nothing to verify the change was an improvement. avcs_freeze is the same idea with the missing half added.

Evidence

Claims here are measured, including the ones that came back flat.

  • The dual-embedding resolver does not help. EAT indexed every component twice — once for what it is, once for what it is for. Rebuilt and measured: 80% acc@1 either way, no difference. The second axis is genuinely distinct (cosine = 0.753); it just buys nothing on a modern encoder.
  • This organisation's "byte-identical wire format" claim was wrong. Two of three opcode regexes matched; HALT had diverged in a way that changes what parses. Found by writing the test instead of repeating the sentence.

Breaking change

pip install git+https://github.com/EvolvingAgentsLabs/evolving-agents no longer installs an evolving_agents package — this is a monorepo now. Install what you want directly:

pip install ./packages/agentvcs

The 2025 package sits at legacy/eat/ with its original setup.py, unchanged.


By Matias Molinas and Ismael Faro · Apache 2.0

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Your toolkit for autonomous, evolving agent ecosystems. Create, execute, govern, and evolve agents that learn from experience, collaborate, build new capabilities, and operate safely within defined guardrails.

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