open-reflection-protocol 1.0.0

Turn agent failures into regression tests, reusable lessons, and measurable improvements

Open Reflection Protocol (ORP)

Turn agent failures into regression tests, reusable lessons, and measurable improvements.

Tracing tells you what your agent did. ORP turns what happened into a tested lesson.

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Quick Start: 10 Seconds

pip install open-reflection-protocol

python demo/quick_start.py
# 1. ✅ Experience 已保存
# 2. ✅ Lesson 已生成
# 3. ✅ Agent 查询到 1 条 Lesson
# 4. ✅ Agent 确认收到
# 5. ✅ Agent 报告效果: success

Any agent can query ORP lessons via 3 MCP tools:

from orp.mcp_server import MCPServer
mcp = MCPServer()
mcp.handle_call("orp_retrieve_lessons", {"task": "修复登录", "limit": 3})
mcp.handle_call("orp_acknowledge_lesson", {"lesson_id": "..."})
mcp.handle_call("orp_report_outcome", {"lesson_id": "...", "outcome": "success"})

Demo: 30 Seconds

A coding agent fixes an auth bug but misses the anonymous user path. Tests fail at 34/35.

# 1. Wrap your agent with ORP
orp wrap -- python my_agent.py

# 2. ORP captures the failure, challenges unproven claims,
#    and compiles a Lesson + regression Eval
orp learn latest

# 3. Same agent retrieves the Lesson via MCP, applies it
#    -> All 35 tests pass this time
orp mcp-server

# 4. Before/after comparison
orp diff exp_before exp_after

Before:

Task success:  FAILED   (34/35 tests)
Claims:        1 unproven

After:

Task success:  PASSED   (35/35 tests)
Claims:        0 unproven

That's the loop. One mistake, one lesson, one measurable improvement.

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What ORP Does

ORP is an open experience layer for AI agents, built on OpenTelemetry. It converts agent traces into three executable artifacts:

Artifact	What	Example
Lesson	Retrievable, scope-scoped experience	"Test anonymous, authenticated, and forbidden paths"
Eval	Regression test reproducing the failure	pytest tests/test_anonymous_access.py
Guardrail	Preventative rule	"Before modifying auth, run full test suite"

Each Lesson goes through a lifecycle:

candidate -> active -> under_review -> deprecated -> rejected
               |
         (only active lessons
          are retrievable)

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Key Concepts

• Evidence-first: ORP distinguishes observed facts (tool output, test results) from agent claims (diagnoses, confidence statements). Claims are never automatically treated as ground truth.
• Executable experience: Lessons compile to runnable evals and guardrails, not just text.
• Outcome-based value: Lesson quality is determined by whether it actually improves results, measured through effect evaluation.
• Built on OpenTelemetry: ORP extends existing trace infrastructure instead of replacing it.
• Default private: All data stays local, de-identified by default, no prompt/tool output uploaded.

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Install

pip install open-reflection-protocol

Requires Python 3.10+.

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Quick Start

1. Wrap any agent command

orp wrap -- python my_agent.py --run-task

ORP automatically captures stdout, exit codes, test results, git diff, and OpenTelemetry spans.

2. Learn from the run

orp learn latest

This generates:

• A diagnosis of what went wrong
• Challenged claims (unsupported agent statements)
• A Lesson candidate
• A regression Eval

3. View results

orp inspect latest
orp report --open          # HTML report
orp diff exp_before exp_after

4. Deliver lessons to future runs

# Start the MCP Lesson server
orp mcp-server --transport stdio

# Compatible agents can now use these MCP tools:
#   orp_retrieve_lessons(task, limit=3)
#   orp_acknowledge_lesson(lesson_id)
#   orp_report_outcome(lesson_id, outcome, evidence_refs)

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Run the Demo

git clone https://github.com/Fujo930/ORP
cd ORP
uv run python demo/orp_demo.py

Output:

Run 1: Agent misses anonymous user path -> FAILED
ORP analyzes the failure -> challenges 1 unproven claim
ORP compiles Lesson + Eval
MCP delivers Lesson to Agent
Run 2: Agent applies Lesson -> PASSED

Before: 34/35 tests, 1 unproven claim
After:  35/35 tests, 0 unproven claims
Estimated effect: 0.5

Experimental Results

10 failure tasks, 5 trials each, 100 total runs.

Metric	Control (no ORP)	+ORP	Improvement
Task success rate	14%	100%	+86%
Repeat failure rate	high	0%	100% reduction
Lesson application	—	100%	—
Eval validity	—	85%	—

Go/No-Go: >>> GO — 4/4 checks passed

Run yourself: uv run python exps/runner.py

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CLI Reference

orp wrap -- python agent.py    Wrap an agent process with ORP
orp inspect [id]               Inspect an experience (default: latest)
orp learn [id]                 Generate lessons from an experience
orp replay <id>                Counterfactual replay
orp lessons list               List lessons
orp lessons validate <id>      Validate lesson integrity
orp lessons conflicts          Auto-detect conflicting lessons
orp lessons rollback <id>      Rollback a lesson
orp lessons deliver <id>       Deliver a lesson
orp effects evaluate <id>      Evaluate lesson effect
orp training candidates        List training candidates
orp training export            Export approved training data
orp mcp-server                 Start MCP lesson server
orp report --open              Generate HTML report
orp diff <id1> <id2>           Compare two experiences
orp export [id]                Export as JSON

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Architecture

Agent / Existing Trace
        |
        v
  Trace Adapters (OTel / OpenAI / LangGraph / Generic JSON)
        |
        v
 Experience Builder -> Evidence Verifier
                    -> Reflection Analyzer  (diagnosis + challenger)
                    -> Counterfactual Replayer
        |
        v
 Experience Compiler
   +----+----+------+
   |         |      |
 Lesson    Eval   Guardrail
   |         |      |
   +---- Delivery Router (MCP Server / Prompt / Policy / Runtime Hook)
             |
             v
    Effect Evaluator + Rollback

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For Contributors

Tests (58 total):

uv run pytest -q
# 58 passed in 0.68s

Key design documents in this repo:

File	What
ROADMAP.md	Project roadmap and strategy
SPEC.md	Protocol specification v0.3
ARCHITECTURE.md	Implementation architecture
demo/orp_demo.py	Standalone demo

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License

MIT