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CHAP Coordinator

User RatingsBe the first to rate and review this MCP server! Enrichment pendingWe haven’t run our AI enrichment pass on this listing yet, so the overview, use cases, and FAQ below may be sparse or missing. We work through the catalog over time β€” check back soon.
View Repository

Auditable records of human decisions over AI agent work. Approvals, edits, overrides, escalations.

Quick Install

Automated & IDE Setup

Copy the AI prompt to install this server into Claude Code, Cursor, or another agent β€” or use 1-click editor setup below.

Add to CursorAdd to VS Code
Manual Client & Custom JSON ConfigExpand JSON β–Ύ

Client Config & Setup

Choose your client or environment
Target File:~/Library/Application Support/Claude/claude_desktop_config.json
claude_desktop_config.json
{
  "mcpServers": {
    "chap-coordinator": {
      "command": "npx",
      "args": [
        "-y",
        "chap-coordinator"
      ]
    }
  }
}

πŸ’‘ Paste the JSON block into your client's configuration file under mcpServers, then restart the application.

Install Directory Badge Claim listing AlternativesπŸ’» More in Developer Tools

Documentation Overview

Collaborative Human-Agent Protocol (CHAP)

Latest release PyPI package npm package 23 of 23 conformance vectors passing Specification licensed CC BY 4.0 Code licensed Apache 2.0

The open protocol for humans and agents doing accountable work together.

CHAP gives approvals, overrides, handoffs and escalations a shared, auditable shape across MCP and A2A.

Install Β· 90-second tour Β· Scenarios Β· Implementations Β· Wiki Β· Discussions Β· Paper

Star CHAP on GitHub to help more implementers find and test the protocol


Same scenario, two stacks. Without CHAP: six tools holding fragments of one decision (OpenAI logs expired, Zendesk thread, Slack scrolled past, Linear comments, webhook tail, Notion runbook), 45 minutes across four UIs to answer 'what did the agent draft and why did we approve it?'. With CHAP: three hash-linked envelopes (task.create β†’ artefact β†’ decide.override) joined by prev_hash, one audit.read call, 30 seconds.


You have agents doing real work. Drafting code reviews, triaging tickets, suggesting settlements, reviewing contracts. A human approves, edits, or rejects each one. Right now, that decision lives in your application code, your chat threads, your ticket comments, and your head. When something goes wrong six weeks later, reconstructing what happened costs you forty-five minutes and is half guesswork.

CHAP gives you one place to put those decisions and one shape to put them in. The agent's draft is an artefact. The human's edit is a structured override with a diff, a rationale, and tags you control. The whole thing chains together by content hash. You query the chain instead of grepping logs across four UIs.

The chain survives key rotation, log expiry, and people leaving; one audit.read call returns the whole thing. The overrides your reviewers were already making accumulate into supervision data you'd otherwise have to commission. When approvals must be non-repudiable, security-signed/1.0 adds OIDC-bound signatures with a signature_meaning you define, and audit-scitt/1.0 anchors the chain in an external transparency log, verifiable without trusting your servers. And CHAP sits beside MCP and A2A rather than replacing them: MCP for tools, A2A for other agents, CHAP for the shared work with humans.

That's the whole pitch.

The 90-second tour

A solo developer using Cursor to review pull requests. The bot flags a "warning" the developer disagrees with. Here's the whole exchange, end to end. The clip below runs in about 23 seconds across six labelled steps; the matching code is right underneath.

Six-step CHAP Core+Review walkthrough with a progress bar and step indicator across the top. Step 1: Setup (workspace, two participants, a task). Step 2: Drafting (agent drafts a response). Step 3: Pending review (review.request with the draft artefact). Step 4: Override (human disagrees: diff, rationale, tags). Step 5: Audit chain (hash-linked replay, prev_hash continuous). Step 6: Two months in (override learning report shows framework-pattern as the top tag, pointing the next prompt revision at the right problem).

And here's the code, every line of it. One continuous story in two languages; pick whichever stack you actually use.

1. Spin up a workspace. An embedded coordinator with SQLite persistence, two participants, a workspace:

TypeScriptPython
server.ts
import { Coordinator } from "@brightbeamai/chap-coordinator";
import { SqliteStore } from
  "@brightbeamai/chap-coordinator/storage/sqlite";

const coord = new Coordinator({
  store: new SqliteStore("./chap.db"),
});

coord.api.workspace.create({
  workspace: "wsp_pr_reviews",
  profiles:  ["core/1.0", "review/1.0"],
});

coord.api.participant.join({
  workspace: "wsp_pr_reviews",
  from:      "human:me@local",
  type:      "human",
});

coord.api.participant.join({
  workspace: "wsp_pr_reviews",
  from:      "agent:cursor#v1",
  type:      "agent",
});
server.ts
from chap_coordinator import Coordinator
from chap_coordinator.storage.sqlite \
    import SqliteStore

coord = Coordinator(store=SqliteStore("./chap.db"))

def send(method, params):
    return coord.dispatch({
        "jsonrpc": "2.0", "id": method,
        "method": method, "params": params,
    })

send("workspace.create", {
    "workspace": "wsp_pr_reviews",
    "profiles":  ["core/1.0", "review/1.0"],
})

send("participant.join", {
    "workspace": "wsp_pr_reviews",
    "from":      "human:me@local",
    "type":      "human",
})

send("participant.join", {
    "workspace": "wsp_pr_reviews",
    "from":      "agent:cursor#v1",
    "type":      "agent",
})

2. The bot drafts, you override. Wire your existing Cursor integration to emit envelopes:

TypeScriptPython
server.ts
// The bot's review is the output of a task.
const { task_id } = coord.api.task.create({
  workspace: "wsp_pr_reviews",
  from:      "agent:cursor#v1",
  assignee:  "agent:cursor#v1",
  kind:      "code_review",
  input:     { pr_id: "PR-482" },
});

coord.api.task.complete({
  workspace: "wsp_pr_reviews",
  from:      "agent:cursor#v1",
  task_id,
  output:    cursorReview,
});

coord.api.review.request({
  workspace: "wsp_pr_reviews",
  from:      "agent:cursor#v1",
  task_id,
  artefact:  cursorReview,
  to:        "human:me@local",
});

// You disagree with one comment. Override it.
coord.api.decide.override({
  workspace:        "wsp_pr_reviews",
  from:             "human:me@local",
  task_id,
  intent_preserved: true,
  diff: [{ op: "replace",
           path: "/comments/0/severity",
           value: "info" }],
  rationale: "False positive. Framework " +
             "convention, not a bug.",
  tags: ["false-positive",
         "framework-pattern-misread"],
});
python
# The bot's review is the output of a task.
r = send("task.create", {
    "workspace": "wsp_pr_reviews",
    "from":      "agent:cursor#v1",
    "assignee":  "agent:cursor#v1",
    "kind":      "code_review",
    "input":     {"pr_id": "PR-482"},
})
task_id = r["result"]["task_id"]

send("task.complete", {
    "workspace": "wsp_pr_reviews",
    "from":      "agent:cursor#v1",
    "task_id":   task_id,
    "output":    cursor_review,
})

send("review.request", {
    "workspace": "wsp_pr_reviews",
    "from":      "agent:cursor#v1",
    "task_id":   task_id,
    "artefact":  cursor_review,
    "to":        "human:me@local",
})

# You disagree with one comment. Override it.
send("decide.override", {
    "workspace":        "wsp_pr_reviews",
    "from":             "human:me@local",
    "task_id":          task_id,
    "intent_preserved": True,
    "diff": [{"op":    "replace",
              "path":  "/comments/0/severity",
              "value": "info"}],
    "rationale": "False positive. Framework "
                 "convention, not a bug.",
    "tags": ["false-positive",
             "framework-pattern-misread"],
})

About the surfaces. TypeScript ships a typed facade (coord.api.*) so every method gets full autocomplete and compile-time checks. Python keeps the JSON-RPC envelope shape on the surface (coord.dispatch({...})) and consumers wrap it however suits the call site; a send() helper is the idiom the Python tests use. Both paths emit identical wire bytes; the audit chain is byte-for-byte the same regardless of which client made the call.

3. Two months in, analyse what you've been doing. The reference repo ships an analytics script in both languages that reads the audit chain (over HTTP or straight from your SQLite file) and groups overrides:

bash
# TypeScript reference, against the SqliteStore from step 1:
$ npm --prefix reference/core-plus-review run analyze -- --db ./chap.db wsp_pr_reviews

# Python reference, same idea:
$ python3 reference/python/analyze_overrides.py --db ./chap.db wsp_pr_reviews

Override Learning Report
========================
Total overrides: 47

Read the full README on GitHub β†’

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Frequently Asked Questions about CHAP Coordinator

Add the following block to your claude_desktop_config.json under mcpServers: "mcpServers": { "chap-coordinator": { "command": "npx", "args": ["-y", "CHAP Coordinator"] } }

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Technical Specs & Signals

CategoryπŸ’»Developer Tools
More technical detailsExpand β–Ύ
TransportSTDIO
RuntimeNode.js
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27Quality signal: Emerging Β· 27/100How this signal is calculated β–Ύ
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Not scored for repo-hosted servers β€” we can't reach the running server, only its GitHub page. Hosted MCP endpoints are health-checked live.

Verified ownership8/20
Documentation & tools11/30
Adoption & activity1/15
Community engagement0/10

A guidance signal from public completeness & health data β€” not a user rating. New listings start lower and rise as they add docs, get verified, and grow adoption. Signals we can't observe for a listing are skipped, not counted against it.

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