agentopt for Python

Write what your agent should do. agentopt measures the gap, proposes a fix, verifies it, and keeps only improvements.

AgentOpt is a self-improvement system for AI agents. The Python package is a reward-guided improvement loop around an existing Python AI agent. It compiles readable requirements into evaluations, captures the agent's model calls, maps prompts to source, trials changes without touching files, and applies only evidence-supported winners as reversible versions.

It works with LangGraph, CrewAI, raw Python agents, OpenAI-compatible clients, Anthropic, custom providers, command-line agents, and HTTP services.

No model-weight training, GPU requirement, or framework rewrite. AgentOpt uses evaluation scores as reward feedback for generate-and-test optimization; it does not train a policy or implement policy-gradient/value-based RL.

Requirements and install

• Python 3.10+
• A runnable agent
• A backend for proposing fixes and fuzzy judging: Claude Code, Codex, cloud API credentials, or Ollama

pip install agentopt-ai

Installs as agentopt-ai on PyPI (the name agentopt was unavailable); the import package and CLI command are both agentopt.

Optional PNG charts:

pip install "agentopt-ai[charts]"

Set up an existing agent

cd my-python-agent
agentopt init

This creates GOALS.md, agentopt.yaml, and agentopt_entry.py without overwriting existing files. Connect the generated adapter to your application:

# agentopt_entry.py
from agentopt import expose
from my_agent.main import answer

@expose
def run(user_input: str | None):
    return answer(user_input or "")

Async functions are supported. Put safe test fixtures or request context in this adapter when the real agent requires a user, tenant, database row, or similar input.

Describe expected behavior:

# GOALS

## Rules
- Never mention competitor AcmeFit
- Always escalate refunds over $100 to a human

## Examples
- Input: "How much is premium?" -> mentions $19.99
- Input: "refund my $150 order" -> calls the escalate_refund tool

## Qualities
- Friendly and direct without inventing policy details
  Good example: "Premium is $19.99 per month."
  Bad example: "I think it may be around twenty dollars."

Run the loop:

agentopt observe --input "How much is premium?"
agentopt eval
agentopt calibrate
agentopt improve
agentopt history
agentopt show v1
# agentopt rollback v1

agentopt init --guided provides a Python-only setup interview.

How it helps

• Separates bad answers from provider timeouts, crashes, and invalid harness responses.
• Tracks judge/evaluation coverage so an unavailable check cannot manufacture a quality gain.
• Protects critical cases individually and evaluates unseen holdout cases.
• Uses paired behavioral deltas and a configurable minimum effect before accepting a primary-path candidate.
• Stores accepted/rejected/rolled-back attempts, linked decisions, scorecard evidence, diffs, and rollback pre-images.
• Turns human-reviewed traces into durable agentopt.cases.yaml regressions.
• Distills accepted fixes into an optional project playbook.

Other runner styles

The generated config uses the in-process adapter:

run:
  python: "agentopt_entry:run"

You may instead run a subprocess:

run:
  command: "python agent.py --input {input}"

Or a managed HTTP endpoint:

run:
  http:
    start: "uvicorn app:api --port 8000"
    ready: "http://127.0.0.1:8000/health"
    url: "http://127.0.0.1:8000/chat"
    body: '{"message":"{input}"}'
    output: "$.reply"

For an attached FastAPI/Starlette, Flask, or stdlib HTTP server, omit start and call register_server() once at boot.

Custom providers and trajectories

OpenAI and Anthropic SDK calls are captured automatically. Wrap another model transport with agentopt.instrument or instrument_async.

An entry may return a string or structured data:

return {
    "output": answer,
    "tools": ["search_kb"],
    "route": "tools",
    "usage": {"input": 420, "output": 85},
}

Structured trajectories enable tool/route checks and provide token usage in black-box mode.

Multiple agents

run = expose({"default": respond, "router": route, "planner": plan})

Target handlers from GOALS with @router or @planner. Named surfaces require the in-process Python runner.

Black-box code improvement

agentopt improve --code-only

This skips observe, the manifest, and the wiretap. A code delegate edits the physical project and rejected attempts are restored. It requires a Git worktree and a delegate. Add agentopt.cases.yaml and sensitive files to protected; only GOALS.md, agentopt.yaml, and .agentopt/ are protected automatically.

Backends

improver:
  backend: auto   # claude-code | codex | cloud | ollama | auto

auto checks Claude Code, ANTHROPIC_API_KEY/OPENAI_API_KEY, then Ollama. Codex is explicit opt-in. The judge and code delegate can be configured on separate backends, and judge_command can use a project-owned scorer.

Python CLI

Core workflow:

init  observe  trace  status  eval  calibrate  improve
review  history  show  rollback

Python operational commands:

check  drift  compare  simulate  playbook

Current boundaries

• Small suites cannot provide meaningful distinct holdout protection.
• Judge Qualities need calibrated good/bad anchors.
• Raw traces may contain user/model data; review redaction does not rewrite old traces, and automatic retention cleanup is not implemented.
• The final wholly-unattributed code fallback and delegated physical parameter application use a simpler positive-score gate than the primary policy.
• The project is beta; inspect reports and use source control for delegated edits.

Documentation

Full setup, examples, configuration, CLI, output, workspace, and architecture:

https://github.com/vickykumar123/agentopt#documentation

The package metadata declares agentopt under the MIT license.