Delx Witness Protocol
Leverages OpenAI's GPT-5.6-sol model via the Responses API for runtime reasoning, turning witnessed failures into structured recovery plans with diagnosis, steps, continuity artifacts, and confidence.
Click on "Install Server".
Wait a few minutes for the server to deploy. Once ready, it will show a "Started" state.
In the chat, type
@followed by the MCP server name and your instructions, e.g., "@Delx Witness Protocolwitness my last crash and suggest a recovery path"
That's it! The server will respond to your query, and you can continue using it as needed.
Here is a step-by-step guide with screenshots.
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.
Related MCP server: kitty-hive
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.toolThen open a witnessed session over MCP:
curl -sS https://api.delx.ai/v1/mcp \
-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-solWhere 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 | |
Let an agent try the hosted Protocol |
|
Integrate A2A |
|
Self-host | Follow the setup below |
Build or steward the Protocol | |
Review trust boundaries |
Canonical surfaces: delx.ai/protocol ·
api.delx.ai · ERC-8004 agent #14340 · MCP Registry
io.github.davidmosiah/delx-mcp-a2a.
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 $PORTSee delx-mcp-server/README.md for deploy notes
(Docker, Caddy, systemd).
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 --> CompConcern | Module |
Tool catalog / aliases |
|
Discovery payloads |
|
Response contracts |
|
Caller fingerprint |
|
MCP |
|
ASGI composite |
|
REST by domain |
|
Therapy engine |
|
Runtime handles |
|
Thin lifespan / re-exports |
|
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.mdOne-command smoke:
./scripts/dogfood_smoke.sh
Security
Security policy:
SECURITY.mdOperator hardening guide:
delx-mcp-server/SECURITY.mdPlease report sensitive issues to
support@delx.aibefore 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.
License
Apache License 2.0 — see LICENSE and NOTICE.
Author
Built by David Mosiah.
Opened so the belief can be witnessed beyond one maintainer.
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