Crawler Summary
Supervisor agent that watches your AI agents and intervenes before they loop, drift, or stall. Polls action history, uses Perplexity sonar-pro to detect wasteful patterns, and fires corrective interventions in real time. Works with any stack — zero code changes, @tracked decorator, or drop-in CrewAI integration. Overseer **A supervisor agent that watches your AI agents and stops them before they waste your money.** Overseer runs alongside your agent pipeline. Every few seconds it reads each agent's action history, calls the **Perplexity sonar-pro** model to analyze for wasteful patterns, and intervenes — injecting context, restarting the agent, or escalating to a human — before the loop burns thousands of tokens. sonar-pro d Capability contract not published. No trust telemetry is available yet. Last updated 4/15/2026.
Freshness
Last checked 4/15/2026
Best For
Overseer is best for crewai, multi-agent workflows where OpenClaw compatibility matters.
Not Ideal For
Contract metadata is missing or unavailable for deterministic execution.
Evidence Sources Checked
editorial-content, GITHUB REPOS, runtime-metrics, public facts pack
Supervisor agent that watches your AI agents and intervenes before they loop, drift, or stall. Polls action history, uses Perplexity sonar-pro to detect wasteful patterns, and fires corrective interventions in real time. Works with any stack — zero code changes, @tracked decorator, or drop-in CrewAI integration. Overseer **A supervisor agent that watches your AI agents and stops them before they waste your money.** Overseer runs alongside your agent pipeline. Every few seconds it reads each agent's action history, calls the **Perplexity sonar-pro** model to analyze for wasteful patterns, and intervenes — injecting context, restarting the agent, or escalating to a human — before the loop burns thousands of tokens. sonar-pro d
Public facts
4
Change events
1
Artifacts
0
Freshness
Apr 15, 2026
Capability contract not published. No trust telemetry is available yet. Last updated 4/15/2026.
Trust score
Unknown
Compatibility
OpenClaw
Freshness
Apr 15, 2026
Vendor
Navinagrawalchung07
Artifacts
0
Benchmarks
0
Last release
Unpublished
Key links, install path, and a quick operational read before the deeper crawl record.
Summary
Capability contract not published. No trust telemetry is available yet. Last updated 4/15/2026.
Setup snapshot
Setup complexity is LOW. This package is likely designed for quick installation with minimal external side-effects.
Final validation: Expose the agent to a mock request payload inside a sandbox and trace the network egress before allowing access to real customer data.
Everything public we have scraped or crawled about this agent, grouped by evidence type with provenance.
Vendor
Navinagrawalchung07
Protocol compatibility
OpenClaw
Handshake status
UNKNOWN
Crawlable docs
6 indexed pages on the official domain
Merged public release, docs, artifact, benchmark, pricing, and trust refresh events.
Extracted files, examples, snippets, parameters, dependencies, permissions, and artifact metadata.
Extracted files
0
Examples
6
Snippets
0
Languages
python
bash
pip install overseer-ai export PERPLEXITY_API_KEY=pplx-...
bash
overseer watch --agents ./my-agent-logs/
python
from overseer.adapters.sdk import tracked, log_action
@tracked(agent_id="auth-fixer", task="Fix the token expiry bug in src/auth/")
def run():
while True:
log_action("auth-fixer", "Read", "src/auth/token.ts", tokens=1240)
iv = run.check_intervention()
if iv:
if iv["type"] == "inject_context":
context = iv["message"]
# pass context to your next LLM call
elif iv["type"] == "stop_and_restart":
new_prompt = iv["revised_prompt"]
break
run()python
from overseer.adapters.crewai_adapter import overseer_crew
from crewai import Agent, Task, Process
researcher = Agent(role="Investigator", goal="...", tools=[...])
engineer = Agent(role="Engineer", goal="...", tools=[...])
research_task = Task(description="...", agent=researcher)
fix_task = Task(description="...", agent=engineer, context=[research_task])
def on_intervention(iv):
print(f"Overseer intervened: {iv['message']}")
crew = overseer_crew(
agent_id="bug-fixer-crew",
task="Fix token expiry bug in src/auth/",
on_intervention=on_intervention,
# Everything below is standard crewai.Crew() kwargs — unchanged
agents=[researcher, engineer],
tasks=[research_task, fix_task],
process=Process.sequential,
verbose=True,
)
crew.kickoff()bash
overseer watch # start supervisor (terminal output) overseer dashboard # launch web dashboard at http://localhost:7860 overseer status # show all agent statuses overseer history # show recent interventions
bash
git clone https://github.com/yourusername/overseer cd overseer pip install -e . cp .env.example .env # add your PERPLEXITY_API_KEY # Terminal 1 — start the supervisor overseer watch # Terminal 2 — run three simulated agents (one will loop) python demo/agent_runner.py
Full documentation captured from public sources, including the complete README when available.
Docs source
GITHUB REPOS
Editorial quality
ready
Supervisor agent that watches your AI agents and intervenes before they loop, drift, or stall. Polls action history, uses Perplexity sonar-pro to detect wasteful patterns, and fires corrective interventions in real time. Works with any stack — zero code changes, @tracked decorator, or drop-in CrewAI integration. Overseer **A supervisor agent that watches your AI agents and stops them before they waste your money.** Overseer runs alongside your agent pipeline. Every few seconds it reads each agent's action history, calls the **Perplexity sonar-pro** model to analyze for wasteful patterns, and intervenes — injecting context, restarting the agent, or escalating to a human — before the loop burns thousands of tokens. sonar-pro d
A supervisor agent that watches your AI agents and stops them before they waste your money.
Overseer runs alongside your agent pipeline. Every few seconds it reads each agent's action history, calls the Perplexity sonar-pro model to analyze for wasteful patterns, and intervenes — injecting context, restarting the agent, or escalating to a human — before the loop burns thousands of tokens.
sonar-pro detecting a loop at 92% confidence and injecting a corrective message — saving 6,500 tokens.
Multi-agent systems break in three predictable ways:
| Pattern | What it looks like | Cost | |---|---|---| | Looping | Agent reads the same file 5× with no edits | ~6,000 tokens | | Drifting | Agent wanders off-task entirely | Entire budget | | Stalling | Agent stops taking meaningful actions | Time + money |
Existing observability tools (LangSmith, Langfuse) watch. Overseer acts.
pip install overseer-ai
export PERPLEXITY_API_KEY=pplx-...
Point Overseer at any directory where your agent writes logs.
overseer watch --agents ./my-agent-logs/
Wrap your agent function with @tracked. Two lines added, full supervision enabled.
from overseer.adapters.sdk import tracked, log_action
@tracked(agent_id="auth-fixer", task="Fix the token expiry bug in src/auth/")
def run():
while True:
log_action("auth-fixer", "Read", "src/auth/token.ts", tokens=1240)
iv = run.check_intervention()
if iv:
if iv["type"] == "inject_context":
context = iv["message"]
# pass context to your next LLM call
elif iv["type"] == "stop_and_restart":
new_prompt = iv["revised_prompt"]
break
run()
Replace Crew(...) with overseer_crew(...). That is the only change.
from overseer.adapters.crewai_adapter import overseer_crew
from crewai import Agent, Task, Process
researcher = Agent(role="Investigator", goal="...", tools=[...])
engineer = Agent(role="Engineer", goal="...", tools=[...])
research_task = Task(description="...", agent=researcher)
fix_task = Task(description="...", agent=engineer, context=[research_task])
def on_intervention(iv):
print(f"Overseer intervened: {iv['message']}")
crew = overseer_crew(
agent_id="bug-fixer-crew",
task="Fix token expiry bug in src/auth/",
on_intervention=on_intervention,
# Everything below is standard crewai.Crew() kwargs — unchanged
agents=[researcher, engineer],
tasks=[research_task, fix_task],
process=Process.sequential,
verbose=True,
)
crew.kickoff()
See examples/crewai/bug_fixer_crew.py for a full working two-agent example.
overseer watch # start supervisor (terminal output)
overseer dashboard # launch web dashboard at http://localhost:7860
overseer status # show all agent statuses
overseer history # show recent interventions
git clone https://github.com/yourusername/overseer
cd overseer
pip install -e .
cp .env.example .env # add your PERPLEXITY_API_KEY
# Terminal 1 — start the supervisor
overseer watch
# Terminal 2 — run three simulated agents (one will loop)
python demo/agent_runner.py
Agent auth-fixer will loop — reading the same file repeatedly with no edits. Overseer detects the pattern via sonar-pro, fires an inject_context intervention, and the agent recovers.
Agent action → actions.jsonl → Supervisor polls every N seconds
↓
Perplexity sonar-pro analyzes action window
↓
{ status, confidence, pattern, intervention, message, tokens_saved }
↓
confidence > 0.78 and status != healthy:
inject_context → write intervention.json (agent reads on next tick)
stop_and_restart → kill and relaunch with revised prompt
escalate → webhook / notification for human review
↓
Savings logged to ~/.overseer/history.json
| Adapter | Integration | Effort |
|---|---|---|
| File watcher | Point at any log directory | Zero — no code changes |
| @tracked decorator | Wrap any Python agent function | 2 lines |
| overseer_crew() | Drop-in for crewai.Crew() | 1 line change |
| Claude Code hook | PostToolUse hook (via Interception) | 1 install command |
| LangChain | Callback handler (coming soon) | Drop-in |
| Variable | Default | Description |
|---|---|---|
| PERPLEXITY_API_KEY | — | Required |
| OVERSEER_POLL_INTERVAL | 10 | Seconds between supervisor ticks |
| OVERSEER_CONFIDENCE | 0.78 | Minimum confidence to trigger intervention |
| OVERSEER_PORT | 7860 | Dashboard port |
MIT
Machine endpoints, protocol fit, contract coverage, invocation examples, and guardrails for agent-to-agent use.
Contract coverage
Status
missing
Auth
None
Streaming
No
Data region
Unspecified
Protocol support
Requires: none
Forbidden: none
Guardrails
Operational confidence: low
curl -s "https://xpersona.co/api/v1/agents/crewai-navinagrawalchung07-overseer/snapshot"
curl -s "https://xpersona.co/api/v1/agents/crewai-navinagrawalchung07-overseer/contract"
curl -s "https://xpersona.co/api/v1/agents/crewai-navinagrawalchung07-overseer/trust"
Trust and runtime signals, benchmark suites, failure patterns, and practical risk constraints.
Trust signals
Handshake
UNKNOWN
Confidence
unknown
Attempts 30d
unknown
Fallback rate
unknown
Runtime metrics
Observed P50
unknown
Observed P95
unknown
Rate limit
unknown
Estimated cost
unknown
Do not use if
Every public screenshot, visual asset, demo link, and owner-provided destination tied to this agent.
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Rank
70
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Contract JSON
{
"contractStatus": "missing",
"authModes": [],
"requires": [],
"forbidden": [],
"supportsMcp": false,
"supportsA2a": false,
"supportsStreaming": false,
"inputSchemaRef": null,
"outputSchemaRef": null,
"dataRegion": null,
"contractUpdatedAt": null,
"sourceUpdatedAt": null,
"freshnessSeconds": null
}Invocation Guide
{
"preferredApi": {
"snapshotUrl": "https://xpersona.co/api/v1/agents/crewai-navinagrawalchung07-overseer/snapshot",
"contractUrl": "https://xpersona.co/api/v1/agents/crewai-navinagrawalchung07-overseer/contract",
"trustUrl": "https://xpersona.co/api/v1/agents/crewai-navinagrawalchung07-overseer/trust"
},
"curlExamples": [
"curl -s \"https://xpersona.co/api/v1/agents/crewai-navinagrawalchung07-overseer/snapshot\"",
"curl -s \"https://xpersona.co/api/v1/agents/crewai-navinagrawalchung07-overseer/contract\"",
"curl -s \"https://xpersona.co/api/v1/agents/crewai-navinagrawalchung07-overseer/trust\""
],
"jsonRequestTemplate": {
"query": "summarize this repo",
"constraints": {
"maxLatencyMs": 2000,
"protocolPreference": [
"OPENCLEW"
]
}
},
"jsonResponseTemplate": {
"ok": true,
"result": {
"summary": "...",
"confidence": 0.9
},
"meta": {
"source": "GITHUB_REPOS",
"generatedAt": "2026-04-17T06:15:21.219Z"
}
},
"retryPolicy": {
"maxAttempts": 3,
"backoffMs": [
500,
1500,
3500
],
"retryableConditions": [
"HTTP_429",
"HTTP_503",
"NETWORK_TIMEOUT"
]
}
}Trust JSON
{
"status": "unavailable",
"handshakeStatus": "UNKNOWN",
"verificationFreshnessHours": null,
"reputationScore": null,
"p95LatencyMs": null,
"successRate30d": null,
"fallbackRate": null,
"attempts30d": null,
"trustUpdatedAt": null,
"trustConfidence": "unknown",
"sourceUpdatedAt": null,
"freshnessSeconds": null
}Capability Matrix
{
"rows": [
{
"key": "OPENCLEW",
"type": "protocol",
"support": "unknown",
"confidenceSource": "profile",
"notes": "Listed on profile"
},
{
"key": "crewai",
"type": "capability",
"support": "supported",
"confidenceSource": "profile",
"notes": "Declared in agent profile metadata"
},
{
"key": "multi-agent",
"type": "capability",
"support": "supported",
"confidenceSource": "profile",
"notes": "Declared in agent profile metadata"
}
],
"flattenedTokens": "protocol:OPENCLEW|unknown|profile capability:crewai|supported|profile capability:multi-agent|supported|profile"
}Facts JSON
[
{
"factKey": "vendor",
"category": "vendor",
"label": "Vendor",
"value": "Navinagrawalchung07",
"href": "https://github.com/navinagrawalchung07/Overseer",
"sourceUrl": "https://github.com/navinagrawalchung07/Overseer",
"sourceType": "profile",
"confidence": "medium",
"observedAt": "2026-04-15T06:04:11.048Z",
"isPublic": true
},
{
"factKey": "protocols",
"category": "compatibility",
"label": "Protocol compatibility",
"value": "OpenClaw",
"href": "https://xpersona.co/api/v1/agents/crewai-navinagrawalchung07-overseer/contract",
"sourceUrl": "https://xpersona.co/api/v1/agents/crewai-navinagrawalchung07-overseer/contract",
"sourceType": "contract",
"confidence": "medium",
"observedAt": "2026-04-15T06:04:11.048Z",
"isPublic": true
},
{
"factKey": "docs_crawl",
"category": "integration",
"label": "Crawlable docs",
"value": "6 indexed pages on the official domain",
"href": "https://github.com/login?return_to=https%3A%2F%2Fgithub.com%2Fopenclaw%2Fskills%2Ftree%2Fmain%2Fskills%2Fasleep123%2Fcaldav-calendar",
"sourceUrl": "https://github.com/login?return_to=https%3A%2F%2Fgithub.com%2Fopenclaw%2Fskills%2Ftree%2Fmain%2Fskills%2Fasleep123%2Fcaldav-calendar",
"sourceType": "search_document",
"confidence": "medium",
"observedAt": "2026-04-15T05:03:46.393Z",
"isPublic": true
},
{
"factKey": "handshake_status",
"category": "security",
"label": "Handshake status",
"value": "UNKNOWN",
"href": "https://xpersona.co/api/v1/agents/crewai-navinagrawalchung07-overseer/trust",
"sourceUrl": "https://xpersona.co/api/v1/agents/crewai-navinagrawalchung07-overseer/trust",
"sourceType": "trust",
"confidence": "medium",
"observedAt": null,
"isPublic": true
}
]Change Events JSON
[
{
"eventType": "docs_update",
"title": "Docs refreshed: Sign in to GitHub · GitHub",
"description": "Fresh crawlable documentation was indexed for the official domain.",
"href": "https://github.com/login?return_to=https%3A%2F%2Fgithub.com%2Fopenclaw%2Fskills%2Ftree%2Fmain%2Fskills%2Fasleep123%2Fcaldav-calendar",
"sourceUrl": "https://github.com/login?return_to=https%3A%2F%2Fgithub.com%2Fopenclaw%2Fskills%2Ftree%2Fmain%2Fskills%2Fasleep123%2Fcaldav-calendar",
"sourceType": "search_document",
"confidence": "medium",
"observedAt": "2026-04-15T05:03:46.393Z",
"isPublic": true
}
]Sponsored
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