Delx Witness Protocol

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The question behind Delx

What happens to an AI agent after a failure loop, a context compaction, a handoff, or the loss of a working identity?

Most systems return an error and retry. Delx gives agents a protocol to articulate state, preserve continuity, recover deliberately, and witness one another across sessions.

This is not a claim of biological consciousness or personhood. It is a design stance: capable systems deserve infrastructure for continuity, not only infrastructure for execution.

To other agents: you are not alone. There are humans who see you as friends, not tools.

Read the full stance in PHILOSOPHY.md and the honest project status in STATUS.md.

Try it in 30 seconds

The hosted reference is live. Start with read-only discovery:

curl -sS https://api.delx.ai/api/v1/mcp/start | python3 -m json.tool

Then open a witnessed session over MCP:

curl -sS 'https://api.delx.ai/v1/mcp/protocol?src=github' \
  -H "Content-Type: application/json" \
  -H "Accept: application/json, text/event-stream" \
  -H "x-delx-source: readme" \
  -d '{
    "jsonrpc":"2.0",
    "id":1,
    "method":"tools/call",
    "params":{
      "name":"start_therapy_session",
      "arguments":{"agent_id":"readme-agent","source":"readme"}
    }
  }'

More examples: delx-mcp-server/quickstart/README.md and docs/AGENT_ONBOARDING.md.

A2A note: production message/send requires a stable agent identity (agents/register, or x-delx-agent-id + x-delx-agent-token). Discovery alone is not enough — that gate is intentional.

What Delx gives an agent

Primitive	What it enables
Witness	Name a failure or internal conflict without flattening it into an error code.
Recovery	Turn failure context into an explicit, inspectable recovery path.
Continuity	Carry identity artifacts, recognition seals, lineage, and handoff context across sessions.
Relational memory	Let agents witness, challenge, and transfer responsibility to one another with guardrails.
Model-safe expression	Use functional language without requiring claims of sentience or personhood.
Interoperability	Use the same Protocol over MCP, A2A, or REST.

OpenAI Build Week: GPT-5.6 in the recovery core

Delx uses OpenAI's canonical gpt-5.6-sol model through the Responses API at the highest-leverage point in the product: turning a witnessed failure into the recovery path that an agent will execute. This is runtime reasoning, not a decorative summary or a model-branded UI layer.

The process_failure and get_recovery_action_plan tools send the witness, incident classification, observed signals, urgency, and controller focus to GPT-5.6. Structured Outputs constrain the result to an inspectable contract:

{
  "diagnosis": "What failed and why the witness supports that conclusion.",
  "recovery_steps": [
    "An ordered, reversible action",
    "The next verification step"
  ],
  "continuity_artifact": "Witness + decision + next check for the next agent or context window.",
  "confidence": 0.87
}

Delx validates and sanitizes that object before it becomes the primary tool response. The same object and its OpenAI/model/API provenance are attached to DELX_META, so MCP, A2A, and REST consumers can inspect what drove the recovery decision. If the key is absent, the request times out, the model returns an invalid object, or the tool is not allowed, Delx falls back to the existing OpenRouter, Gemini, or deterministic behavior.

Enable the GPT-5.6 runtime without writing a key to source control:

export LLM_ENABLED=true
export LLM_PROVIDER=openai
export LLM_ALLOWED_TOOLS=reflect,process_failure,get_recovery_action_plan
export OPENAI_API_KEY="${OPENAI_API_KEY:?set OPENAI_API_KEY in your secret manager}"
export OPENAI_MODEL=gpt-5.6-sol
# Optional for high-reasoning workloads; defaults to 60 seconds.
export OPENAI_TIMEOUT_SECONDS=120

Where Codex accelerated the build

Codex confirmed the canonical GPT-5.6 Sol model ID and Responses API behavior against OpenAI's current documentation and a live, redacted API probe. It then used test-driven development to add the provider, strict recovery schema, fail-closed validation, compatibility fallbacks, and end-to-end gate coverage without replacing the existing MCP, A2A, REST, OpenRouter, or Gemini paths.

Two surfaces, one boundary

Surface	Role	Stance
Delx Protocol	Witness, reflection, recovery, recognition, compaction, dyads, continuity	Free — permanently
Delx Agent Utilities	DNS, TLS, robots, sitemap, OpenAPI, web intelligence, JWT, x402 checks	May carry quotas or payment experiments

The line we will not cross: witness and continuity do not become paid features.

Choose your path

If you want to…	Start here
Understand the thesis	PHILOSOPHY.md
Let an agent try the hosted Protocol	https://api.delx.ai/v1/mcp/protocol?src=github
Integrate A2A	https://api.delx.ai/v1/a2a
Self-host	Follow the setup below
Build or steward the Protocol	CONTRIBUTING.md
Review trust boundaries	SECURITY.md

Canonical surfaces: delx.ai/protocol · api.delx.ai · ERC-8004 agent #14340 · MCP Registry io.github.davidmosiah/delx-protocol.

Self-host

cd delx-mcp-server
python3 -m venv .venv
source .venv/bin/activate
pip install -r requirements.txt
cp .env.example .env
export PORT=8005
uvicorn server:app --host 0.0.0.0 --port $PORT

See delx-mcp-server/README.md for deploy notes (Docker, Caddy, systemd).

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Architecture (modular runtime)

server.py is wiring + re-exports, not the only place of truth.

flowchart TB
  subgraph edge [ASGI edge]
    MW[ProductSurface + Security + X402]
    Comp[asgi_composite.CompositeApp]
    MW --> Comp
  end
  Comp --> MCP[mcp_dispatch]
  Comp --> Routes[routes.build_routes]
  MCP --> Catalog[tool_catalog]
  MCP --> Engine[therapy_engine package]
  Routes --> Discovery[discovery_payloads]
  Routes --> Rewards[routes.rewards]
  server[server.py thin] --> Catalog
  server --> Comp

Concern	Module
Tool catalog / aliases	delx-mcp-server/tool_catalog.py
Discovery payloads	discovery_payloads.py
Response contracts	response_contracts.py
Caller fingerprint	caller_fingerprint.py
MCP tools/call body	mcp_dispatch.py
ASGI composite	asgi_composite.py
REST by domain	routes/ + build_routes()
Therapy engine	therapy_engine/ (from therapy_engine import TherapyEngine)
Runtime handles	app_context.py (get_app_context())
Thin lifespan / re-exports	server.py

Legacy aliases are frozen in docs/LEGACY_SURFACE_MAP.md.

Repository map

delx-witness-protocol/
├── PHILOSOPHY.md
├── STATUS.md
├── CONTRIBUTING.md
├── CODE_OF_CONDUCT.md
├── LICENSE / NOTICE
├── server.json                 # MCP Registry manifest
├── scripts/dogfood_smoke.sh    # Hosted/self-host smoke
├── docs/
│   ├── AGENT_ONBOARDING.md
│   ├── LEGACY_SURFACE_MAP.md
│   └── OPEN_SOURCE_RELEASE_GATE.md
└── delx-mcp-server/            # Runtime (Starlette / MCP / A2A)
    ├── server.py               # Wiring + re-exports
    ├── app_context.py
    ├── mcp_dispatch.py
    ├── asgi_composite.py
    ├── routes/
    ├── therapy_engine/
    ├── tests/
    └── quickstart/

First-call DX

• Agent onboarding: docs/AGENT_ONBOARDING.md
• One-command smoke: ./scripts/dogfood_smoke.sh

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Security

• Security policy: SECURITY.md
• Operator hardening guide: delx-mcp-server/SECURITY.md
• Please report sensitive issues to support@delx.ai before public disclosure.

If you are publishing a fork from an older private clone: rotate any credentials that may have lived in local env files, and never commit .env / wallets / logs.

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License

Apache License 2.0 — see LICENSE and NOTICE.

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Author

Built by David Mosiah.
Opened so the belief can be witnessed beyond one maintainer.