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Openobserve Api

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fcakyon
openobserve-api

This skill should be used when user asks to "query OpenObserve", "create OpenObserve dashboard", "edit OpenObserve panel", "fetch OpenObserve logs", "run OpenObserve search", "list OpenObserve streams", "ingest into OpenObserve", or works with OpenObserve Cloud / self-hosted via REST API. Covers auth, search/SQL, streams, dashboards (CRUD + per-panel ops), the v8 panel JSON schema, and known pitfalls (re-aggregation, hash concurrency, microsecond timestamps).

Overview

Publisherfcakyon
Repositoryclaude-codex-settings
Skill nameopenobserve-api
Stars
1.1K
Forks
109
Bundled files
16
LicenseApache-2.0
Links
  • Markdown instructions

    A SKILL.md file the model loads on demand, so it only costs tokens when a request actually matches.

  • Works with any LLM

    AI skills are plain Markdown, not provider-specific code, so this works with GPT, Claude, Gemini, Grok, or a local model.

  • 16 bundled files

    Scripts, templates, and references the model can read while it works. Files are read-only and never executed.

  • Open source

    Published by fcakyon on GitHub. Read the source before you install it.

Installation

Install the Openobserve Api AI skill in TypingMind to use it with any LLM, or drop it into another agent that reads SKILL.md.

1

Install in TypingMind

TypingMind installs a skill straight from its GitHub folder — it reads SKILL.md, bundles the resource files, and stores the result locally.

  1. Open the app and go to Plugins → Skills.
  2. Choose "Install from GitHub".
  3. Paste the skill folder URL below and confirm.
  4. Enable the skill in any chat where you want it available.
Plugins → Skills → Add skill → From GitHub URL, then paste the folder URL and press Continue.
2

Install in another agent

Any agent that reads the Agent Skills format can use this skill — copy the folder into that agent's skills directory.

Claude Code — .claude/skills
git clone --depth 1 https://github.com/fcakyon/claude-codex-settings.git /tmp/claude-codex-settings
mkdir -p .claude/skills
cp -r /tmp/claude-codex-settings/plugins/openobserve-skills/skills/openobserve-api .claude/skills/openobserve-api
Restart Claude Code after copying so it picks up the new skill.

Use it in TypingMind

Enable Openobserve Api in any TypingMind chat and the model takes it from there. Its name and description sit in the system prompt, and the moment a request matches, the model loads the full instructions itself — you never invoke it by hand, and it costs no tokens until it is actually used.

The model loads Openobserve Api on its own as soon as a request matches it.

Works with any AI model

AI skills are plain Markdown instructions rather than provider-specific code, so Openobserve Api is not tied to the model it was written for. Install it once in TypingMind and use it with GPT-5, Claude, Gemini, Grok, DeepSeek, Mistral, Llama, or a local model you run yourself — all on your own API keys.

  • Loaded only when it is needed

    The system prompt carries just the name and description. The instructions are fetched on the first matching request, so an idle skill costs nothing.

  • Switch models mid-chat

    Because the skill is instructions rather than code, changing model does not break it — the next model reads the same SKILL.md.

Skill instructions

This is the SKILL.md content the model loads. Read it before installing — a skill is instructions your model will follow.

OpenObserve REST API Skill

Programmatic OpenObserve usage for AI agents. Talk to any OpenObserve instance (Cloud or self-hosted) using curl and the documented REST API. No CLI required — there is no first-party OpenObserve CLI.

Retrieval First

Your knowledge of OpenObserve API shapes may be outdated. Prefer retrieval over pre-training:

SourceHow to retrieveUse for
Docs repogh api repos/openobserve/openobserve-docs/contents/docs/reference/api/{path}.md -q .content | base64 -dAuthoritative request/response samples
Server sourcegh api repos/openobserve/openobserve/contents/src/handler/http/request/dashboards/mod.rs -q .content | base64 -dEndpoint paths, query params, status codes
Panel schemagh api repos/openobserve/openobserve/contents/src/config/src/meta/dashboards/v8/mod.rs -q .content | base64 -dExact panel JSON structure (Rust structs)

When docs and server source disagree, trust the server source — handlers ship faster than docs.

1. Auth

HTTPS basic auth with email + password. There is no token endpoint.

bash
# Method 1: curl -u shorthand
curl -u "you@example.com:PASSWORD" "https://eu1.openobserve.ai/api/<org>/streams"

# Method 2: explicit header
TOKEN=$(printf '%s' "you@example.com:PASSWORD" | base64)
curl -H "Authorization: Basic $TOKEN" "https://eu1.openobserve.ai/api/<org>/streams"

Endpoints below assume BASE=https://<host>/api/<org> and AUTH="-u you@example.com:PASSWORD".

2. Search / Query — POST $BASE/_search

Optional query string ?type=logs|metrics|traces (default logs).

bash
curl $AUTH -H 'Content-Type: application/json' \
  "$BASE/_search?type=logs" \
  -d '{
    "query": {
      "sql": "SELECT host_name, COUNT(*) AS n FROM \"my_stream\" GROUP BY host_name ORDER BY n DESC",
      "start_time": 1777000000000000,
      "end_time":   1777999999000000,
      "from": 0,
      "size": 100
    },
    "search_type": "ui"
  }'
  • Timestamps are microseconds (Unix epoch × 1_000_000). Always set start_time/end_time — missing them scans everything.
  • search_typeui | dashboards | reports | alerts — affects rate limits and audit logs.
  • Pagination: from (offset) + size (limit, max ~10000 per request).
  • Response: { took, hits[], total, from, size, scan_size }.
  • SQL flavor: DataFusion / Arrow SQL. Identifiers in double quotes ("stream_name"), strings in single quotes ('value').
  • Time-bucketed group by: SELECT histogram(_timestamp, '5 minute') AS ts, COUNT(*) FROM "stream" GROUP BY ts ORDER BY ts.
  • Term aggregation: SELECT k8s_namespace, COUNT(*) FROM "stream" GROUP BY k8s_namespace.
  • Full-text: match_all('text'), str_match(field, 'text'). Default full-text fields: log, message, msg, content, data, json.
  • PromQL on metrics: POST $BASE/prometheus/api/v1/query_range.
  • Trace context window: GET $BASE/{stream}/_around?key=<ts_us>&size=N.

3. Streams — GET $BASE/streams

bash
# List
curl $AUTH "$BASE/streams?fetchSchema=false&type=logs"

# Schema
curl $AUTH "$BASE/streams/<stream>/schema?type=logs"

# Update settings
curl $AUTH -X PUT -H 'Content-Type: application/json' "$BASE/streams/<stream>/settings" -d '{"partition_keys":["host_name"]}'

# Delete
curl $AUTH -X DELETE "$BASE/streams/<stream>?type=logs"

Field types: Utf8 | Int64 | Float64 | Timestamp | Boolean. Timestamp field is always _timestamp (microseconds).

4. Dashboards — GET|POST|PUT|DELETE $BASE/dashboards

All take ?folder=<folder_id> (default default).

bash
# List
curl $AUTH "$BASE/dashboards?folder=default"

# Get one (returns versioned wrapper {v1..v8, version, hash, updatedAt})
curl $AUTH "$BASE/dashboards/<dashboard_id>?folder=default"

# Create — body is the UNWRAPPED inner v8 object
curl $AUTH -X POST -H 'Content-Type: application/json' \
  "$BASE/dashboards?folder=default" \
  -d @dashboard-v8.json

# Update — REQUIRES the current hash for optimistic concurrency
HASH=$(curl -s $AUTH "$BASE/dashboards/<id>?folder=default" | jq -r .hash)
curl $AUTH -X PUT -H 'Content-Type: application/json' \
  "$BASE/dashboards/<id>?folder=default&hash=$HASH" \
  -d @updated-dashboard.json

# Delete
curl $AUTH -X DELETE "$BASE/dashboards/<id>?folder=default"

# Move between folders
curl $AUTH -X PUT -H 'Content-Type: application/json' \
  "$BASE/folders/dashboards/<id>" \
  -d '{"from":"default","to":"<target_folder_id>"}'

Critical: PUT/POST body must be the unwrapped inner v8 object, not the full {v1..v8, version, hash} wrapper. The server returns the wrapper but expects you to send only the inner object back.

bash
# Read, mutate, write — the correct pattern
RAW=$(curl -s $AUTH "$BASE/dashboards/<id>?folder=default")
HASH=$(echo "$RAW" | jq -r .hash)
echo "$RAW" | jq '.v8 | .title = "New Title"' \
  | curl -s $AUTH -X PUT -H 'Content-Type: application/json' \
    "$BASE/dashboards/<id>?folder=default&hash=$HASH" -d @-

A 409 Conflict response means the hash is stale — refetch and retry.

Per-panel operations (v8 only — return new hash)

bash
# Add panel
curl $AUTH -X POST -H 'Content-Type: application/json' \
  "$BASE/dashboards/<id>/panels?folder=default&hash=$HASH" \
  -d '{"panel": {...}, "tabId": "default"}'

# Update panel
curl $AUTH -X PUT -H 'Content-Type: application/json' \
  "$BASE/dashboards/<id>/panels/<panel_id>?folder=default&hash=$HASH" \
  -d '{...panel...}'

# Delete panel
curl $AUTH -X DELETE \
  "$BASE/dashboards/<id>/panels/<panel_id>?folder=default&hash=$HASH&tabId=default"

5. Panel JSON (v8)

The dashboard tree: dashboard.tabs[].panels[]. Each panel:

json
{
  "id": "panel-1",
  "type": "table",
  "title": "Per-host stats",
  "description": "",
  "queryType": "sql",
  "queries": [
    {
      "query": "SELECT host_name, COUNT(*) AS n FROM \"my_stream\" GROUP BY host_name ORDER BY n DESC",
      "vrlFunctionQuery": "",
      "customQuery": true,
      "fields": {
        "stream": "my_stream",
        "stream_type": "logs",
        "x": [
          { "label": "Host", "alias": "host_name", "column": "host_name", "color": null, "aggregationFunction": null }
        ],
        "y": [
          {
            "label": "Count",
            "alias": "n",
            "column": "n",
            "color": null,
            "aggregationFunction": null,
            "treatAsNonTimeseries": true
          }
        ],
        "z": [],
        "breakdown": [],
        "filter": { "filterType": "group", "logicalOperator": "AND", "conditions": [] }
      },
      "config": { "promql_legend": "", "layer_type": "scatter", "weight_fixed": 1, "limit": 0, "min": 0, "max": 100 }
    }
  ],
  "config": {
    "show_legends": true,
    "decimals": 2,
    "unit": "currency",
    "unit_custom": "USD"
  },
  "layout": { "x": 0, "y": 0, "w": 48, "h": 14, "i": 1 }
}

Panel type values

metric (single big number) · table · bar · h-bar · stacked · h-stacked · line · area · area-stacked · scatter · pie · donut · heatmap · gauge · geomap · maps · sankey · html · markdown.

Layout grid

The grid is 96 columns wide (verified via inspection of returned panel layouts on April 2026 OpenObserve Cloud). Older docs mention 192 or 48 — when in doubt, GET an existing dashboard from the same org and copy the w values you see. Heights are unitless rows (h: 7 = small metric panel; h: 14 = standard table).

Useful config keys

KeyEffect
decimalsNumber of decimal places for all numeric columns (0 = integers).
unitnumbers | currency | bytes | seconds | milliseconds | microseconds | nanoseconds | percent | percent-1
unit_customWhen unit=currency, ISO code like USD.
show_legendsBoolean, charts only.
legends_positionright | bottom.
axis_border_showBoolean.
line_interpolationsmooth | linear | step-start | step-end.
connect_nullsBoolean — line/area only.
top_resultsCap series count for line/bar (e.g. 10).
mark_line[{name, type:'avg'|'max'|'min', value}] — horizontal reference lines.

6. Critical pitfalls

6a. Re-aggregation when customQuery: true — the most common bug.

If your hand-written SQL already contains COUNT(*), SUM(...), AVG(...) etc., every entry in fields.y (and fields.x) must set aggregationFunction: null. Default 'sum' causes OpenObserve to wrap the already-aggregated column in another aggregation client-side, producing duplicate rows and wildly inflated numbers.

jsonc
// WRONG — produces duplicate rows
"y": [{"column":"messages", "aggregationFunction":"count"}]

// RIGHT — SQL already did the aggregation
"y": [{"column":"messages", "aggregationFunction":null, "treatAsNonTimeseries":true}]

6b. Multiple fields.y on Table panels — each Y entry can render as a separate series/row. For a Table that should display one row per group, put only one entry in fields.y (any one column); the renderer will then display all SQL columns as table columns.

6c. Metric panels with customQuery: true — must explicitly map the result column to fields.y:

json
"y": [{"label":"Value", "alias":"value", "column":"value", "aggregationFunction":"sum", "treatAsNonTimeseries":false}]

The metric panel needs to know which column is the number to display.

6d. ROUND + wildcard timestamp expansion — OpenObserve's planner sometimes auto-injects _timestamp into queries that wrap SUM(col) in ROUND(...), producing Column "_timestamp" must appear in the GROUP BY clause errors. Workaround: drop ROUND() and use the panel's decimals config instead, or pre-cast: CAST(SUM(...) AS DOUBLE).

6e. Hash-based concurrency on PUT — every successful PUT changes the dashboard hash. If you mutate a dashboard from two scripts back-to-back, the second one needs to refetch. Always re-GET before each PUT to grab the current hash.

6f. start_time/end_time are microsecondsDate.now() * 1000, not milliseconds. Off-by-1000× returns no hits but no error.

7. Folders / alerts / ingestion

Folders (v2 API):

bash
curl $AUTH "$BASE/folders/dashboards" # list
curl $AUTH -X POST "$BASE/folders/dashboards" -d '{"name":"my-folder"}'
curl $AUTH "$BASE/folders/dashboards/name/<folder_name>" # lookup by name

folder_typedashboards | alerts | reports.

Alerts:

bash
curl $AUTH "$BASE/{stream}/alerts" # list per-stream
curl $AUTH -X POST "$BASE/{stream}/alerts" -d '{...}'
# templates and destinations are referenced by alert definitions:
curl $AUTH "$BASE/alerts/templates"
curl $AUTH "$BASE/alerts/destinations"

Ingestion (POST your own data in):

bash
# JSON
curl $AUTH -X POST "$BASE/<stream>/_json" -d '[{"event":"foo","level":"info"}]'

# Multi-line JSON (one per line)
curl $AUTH -X POST "$BASE/<stream>/_multi" --data-binary @file.ndjson

# Elasticsearch bulk
curl $AUTH -X POST "$BASE/_bulk" --data-binary @bulk.txt

# OTLP HTTP
curl $AUTH -X POST "$BASE/v1/logs" -d @otlp-logs.json
curl $AUTH -X POST "$BASE/v1/traces" -d @otlp-traces.json
curl $AUTH -X POST "$BASE/v1/metrics" -d @otlp-metrics.json

# Loki
curl $AUTH -X POST "$BASE/loki/api/v1/push" -d @loki.json

# Prometheus remote-write (binary protobuf)
curl $AUTH -X POST "$BASE/prometheus/api/v1/write" --data-binary @write.pb

8. Common recipes

Get top hosts by message count (last 24h):

bash
NOW=$(($(date +%s) * 1000000))
DAY=$((NOW - 86400 * 1000000))
curl $AUTH -H 'Content-Type: application/json' \
  "$BASE/_search?type=logs" \
  -d "{\"query\":{\"sql\":\"SELECT host_name, COUNT(*) AS n FROM \\\"my_stream\\\" GROUP BY host_name ORDER BY n DESC\",\"start_time\":$DAY,\"end_time\":$NOW,\"size\":50}}"

Add a metric panel to an existing dashboard (single-shot, hash-aware):

bash
DASH_ID=<dashboard_id>
HASH=$(curl -s $AUTH "$BASE/dashboards/$DASH_ID?folder=default" | jq -r .hash)
curl $AUTH -X POST -H 'Content-Type: application/json' \
  "$BASE/dashboards/$DASH_ID/panels?folder=default&hash=$HASH" \
  -d '{
    "tabId": "default",
    "panel": {
      "id": "p-cost",
      "type": "metric",
      "title": "Total cost (USD)",
      "queryType": "sql",
      "queries": [{
        "query": "SELECT SUM(CAST(cost_usd AS DOUBLE)) AS value FROM \"my_stream\"",
        "customQuery": true,
        "fields": {
          "stream":"my_stream", "stream_type":"logs",
          "x":[], "z":[], "breakdown":[],
          "y":[{"label":"Value","alias":"value","column":"value","aggregationFunction":"sum","treatAsNonTimeseries":false}],
          "filter":{"filterType":"group","logicalOperator":"AND","conditions":[]}
        },
        "config":{}
      }],
      "config": {"unit":"currency","unit_custom":"USD","decimals":2},
      "layout": {"x":0,"y":0,"w":32,"h":7,"i":99}
    }
  }'

Build a complete dashboard from scratch: GET an existing dashboard's panel JSON as a template (it's the safest way to learn the exact field shapes the server will accept), then mutate the tabs[0].panels array and PUT the unwrapped v8 body back. See the references/recipes/build-dashboard.sh script that ships with this skill for a working example.

9. SDKs / clients (no first-party CLI)

LanguageRepoStatus
Pythongithub.com/openobserve/openobserve-python-sdkActive
Gogithub.com/openobserve/openobserve-go-clientZincObserve-era, partial
Helm chartgithub.com/openobserve/openobserve-helm-chartActive
OTel collector distrogithub.com/openobserve/openobserve-otel-collectorActive

For most agent tasks, plain curl against the REST API is the right tool — the SDKs add little value over an HTTP request and lag the server feature set.

References

Bundled files

The model reads these on demand while the skill is loaded. They are exposed as readable files and are never executed.

Frequently asked questions

What does the Openobserve Api AI skill do?

This skill should be used when user asks to "query OpenObserve", "create OpenObserve dashboard", "edit OpenObserve panel", "fetch OpenObserve logs", "run OpenObserve search", "list OpenObserve streams", "ingest into OpenObserve", or works with OpenObserve Cloud / self-hosted via REST API. Covers auth, search/SQL, streams, dashboards (CRUD + per-panel ops), the v8 panel JSON schema, and known pitfalls (re-aggregation, hash concurrency, microsecond timestamps).

Why use Openobserve Api on TypingMind?

Because you install it once and use it with any model. Openobserve Api is plain Markdown rather than provider-specific code, so the same skill runs on GPT-5, Claude, Gemini, Grok, or a local model — and you can switch model mid-chat without it breaking. TypingMind runs on your own API keys, so you pay providers directly instead of a per-seat subscription, and your skills and chats stay in your own storage.

How do I install Openobserve Api in TypingMind?

Open Plugins → Skills → Install from GitHub in TypingMind and paste https://github.com/fcakyon/claude-codex-settings/tree/main/plugins/openobserve-skills/skills/openobserve-api. TypingMind reads its SKILL.md and bundles its files and installs it as a skill you can enable per chat.

Which AI models can use Openobserve Api?

Any model you connect in TypingMind. AI skills are plain Markdown instructions rather than provider-specific code, so GPT, Claude, Gemini, Grok, and local models can all load this skill when a request matches it.

How many AI models can I use with Openobserve Api?

As many as you like. As long as a model supports skills, you can use Openobserve Api with it — GPT, Claude, Gemini, Grok, DeepSeek, Mistral, Llama and more — all on TypingMind with your own API keys.

Is the Openobserve Api AI skill free?

Yes. It is published on GitHub by fcakyon under the Apache-2.0 license. You only pay your own AI provider for the tokens you use.

What are AI skills?

An AI skill is a reusable instruction bundle that teaches an AI model how to do one specific task. It follows the open Agent Skills format: a SKILL.md file with a name and description, plus any scripts, templates or reference files the model may need. The model reads the instructions only when your request matches the skill, so an installed skill costs nothing until it is used.

How are AI skills different from plugins or MCP servers?

A plugin or MCP server gives a model new tools to call — code that runs somewhere and returns a result. An AI skill gives the model knowledge and process instead: how to approach a task, which steps to follow, what good output looks like. Skills are plain Markdown, so they need no server, no API key and no runtime, and they work with any model.

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