otel-genai-graph 0.1.0

OpenTelemetry GenAI spans → queryable Neo4j graph. Mixed-vendor cost attribution, agent delegation, tool blast radius — one command, zero Cypher.

otel-genai-graph

Turn your OpenTelemetry GenAI traces into a graph you can actually ask questions of. Cost per model, agent delegation, tool blast radius, session structure — one command each, zero Cypher, mixed vendors, real tokens.

See it in one command

Once you've loaded the bundled fixtures plus any live captures you've made, this is cross-vendor cost attribution in a single command:

$ python tools/render_graph.py --from-neo4j --query cost_by_model --format table

provider   model                   calls  input_tokens  output_tokens
---------  ----------------------  -----  ------------  -------------
anthropic  claude-sonnet-4-5       7      1760          830
openai     qwen2.5:7b              7      541           75
anthropic  claude-opus-4-7         1      500           300
openai     text-embedding-3-small  1      20            0
openai     nomic-embed-text        1      7             0

That row mixing Anthropic (synthetic fixtures) with openai/qwen2.5:7b (real local Ollama) and openai/nomic-embed-text (real embeddings) is the point — every provider, classic or cutting-edge, ends up on the same graph coordinate system.

What it does

• Ingests OTLP/JSON resourceSpans from any GenAI-instrumented app — OpenAI, Anthropic, Gemini, Azure OpenAI (classic / v1 / Foundry unified), Ollama, Groq, and anything OpenAI-SDK-compatible.
• Maps to a typed graph: Session → Operation → Model / Tool / DataSource, Agent ─INVOKED→ Operation, Agent ─DELEGATED_TO→ Agent.
• Writes idempotently to Neo4j via MERGE on natural keys. Re-ingesting the same trace is a no-op.
• Streams live or loads files — ships a SpanExporter (plug into any TracerProvider) and a file loader (python -m otel_genai_graph.load trace.json …).
• Explores without Cypher — a library of 10 saved queries (cost_by_model, session_tree, agent_delegation, failed_tools, …) exports to interactive HTML (cytoscape.js, single file, no install), node-link JSON, GraphML, or CSV/ASCII tables.
• Speaks legacy and canonical v1.37 — gen_ai.system → gen_ai.provider.name, generate_content → chat, plus a priority-ordered conversation.id fallback list (session.id, langsmith.trace.session_id, traceloop.association.properties.*). Canonical emitters always win; everything else gets pulled into the same graph.
• Enforces 7 shape-independent invariants on every graph — edge endpoint types, Session uniqueness, DAG property of parent / delegation edges, no orphan Models / Tools / DataSources, token-count and time-ordering sanity.

60-second quickstart

git clone https://github.com/kums1234/otel_genai_graph_exporter.git && cd otel-genai-graph
python3 -m venv .venv && . .venv/bin/activate
pip install -e ".[dev]"
pytest                                    # 833 unit/integration tests

# optional: live Neo4j + load the six sample fixtures
docker run -d --name otel-neo4j \
    -p 17474:7474 -p 17687:7687 \
    -e NEO4J_AUTH=neo4j/testtest neo4j:5

# configure connection + API keys once — shell env vars always win
cp .env.example .env          # then uncomment / fill in what you need

python -m otel_genai_graph.load tests/fixtures/*.json
# explore — no Cypher required:
python tools/render_graph.py --from-neo4j --query overview \
    --output /tmp/overview --format html && open /tmp/overview.html

Configuration

Every CLI (loader, tools/render_graph.py, capture scripts) reads its settings from environment variables. Copy .env.example → .env at the project root, uncomment the blocks you need, and they get auto-loaded.

• Works from any working directory — the loader walks up to find .env.
• Shell env always wins (override=False). CI / container env vars can't be shadowed by a committed or stale .env.
• Soft dep: python-dotenv is in dependencies; if it's ever missing (minimal container), load_env() silently no-ops and you fall back to explicit exports.
• .env is gitignored (.env, .env.local); only .env.example is committed.

Minimum viable .env for the quickstart:

NEO4J_URI=bolt://localhost:17687
NEO4J_USER=neo4j
NEO4J_PASSWORD=testtest

Explore without Cypher

Most users don't want to write Cypher. tools/render_graph.py bundles a curated library of the questions people actually ask, and exports the answer as an interactive HTML viewer, a structured JSON/GraphML file, or a plain ASCII table.

Discover

python tools/render_graph.py --list-queries
python tools/render_graph.py --list-queries --tag cost
python tools/render_graph.py --describe-query session_tree

The v0.1 library (10 queries, full list in docs/saved-queries.md):

Question	Query name	Output type
Which agents delegated to which?	agent_delegation	graph
What did I load?	overview	graph
Full hierarchy of one conversation	session_tree	graph
Which tools failed, and what did they touch?	failed_tools	graph
Agent ↔ DataSource access map	data_source_usage	graph
Token spend per (provider, model)	cost_by_model	table
Token spend per session	cost_by_session	table
Token spend per agent (incl. delegated sub-agents)	cost_by_agent	table
Tool call counts, ranked	tool_usage	table
Calls by vendor	provider_distribution	table

Sample: "where is my token spend going?"

python tools/render_graph.py --from-neo4j --query cost_by_model --format table

With the bundled fixtures + a bit of real Ollama traffic loaded:

provider   model                   calls  input_tokens  output_tokens
---------  ----------------------  -----  ------------  -------------
anthropic  claude-sonnet-4-5       7      1760          830
openai     qwen2.5:7b              7      541           75
anthropic  claude-opus-4-7         1      500           300
openai     text-embedding-3-small  1      20            0
openai     nomic-embed-text        1      7             0

Pipe it into anywhere — spreadsheets, BI, Slack, a Grafana panel:

python tools/render_graph.py --from-neo4j --query cost_by_model --format csv    > cost.csv
python tools/render_graph.py --from-neo4j --query cost_by_model --format jsonl  | jq '.'

Sample: "multi-turn session across three traces, collapsed"

python tools/render_graph.py --from-neo4j \
    --query session_tree --param session_id=ollama-multi-demo \
    --output /tmp/multi --format html
open /tmp/multi.html

Three chat turns, three distinct trace_ids, one shared gen_ai.conversation.id — collapsed to a single Session node by the mapper. This is what you can't do with a linear tracer: a turn-by-turn graph that persists identity across retries, async agents, and background batches. Real capture, not a synthetic fixture.

Sample: "show me exactly what happened in that tool call"

python tools/render_graph.py --from-neo4j \
    --query session_tree --param session_id=real-1776842869-0 \
    --output /tmp/tool --format html
open /tmp/tool.html

A real qwen2.5:7b on local Ollama requested get_current_time, it was evaluated locally, the model produced the final answer. The graph shows the whole flow: Agent ─INVOKED→ invoke_agent ─PARENT_OF→ two chat + one execute_tool, with EXECUTED and CALLED edges fanning out to the shared Model and Tool nodes. 8 nodes, 11 edges, 0 invariant violations.

Every HTML output is one self-contained file (cytoscape.js loaded from CDN, everything else inline). Email it to a non-technical stakeholder; they get pan / zoom / hover / click for node properties. No server, no Neo4j required on the receiving end.

Sample: any fixture, offline, no Neo4j

python tools/render_graph.py --fixture tests/fixtures/multi_agent.json \
    --output /tmp/multi-agent --format all
# → /tmp/multi-agent.{dot,html,json,graphml} in one shot

--format all covers the four dep-free formats. Add .svg / .png output with brew install graphviz (or apt-get install graphviz).

Output formats

Format	Flag	Shape	When to use
html	--format html	graph	Share with humans. Interactive, single file.
json	--format json	graph	Feed into D3, Observable, another graph viz, or your tests.
graphml	--format graphml	graph	Gephi / yEd for force-directed layout + centrality metrics.
dot	--format dot	graph	Pipe into your own dot for PDFs / papers.
svg/png	--format svg	graph	Docs / READMEs. Requires graphviz installed.
table	--format table	table	Terminal output for aggregations.
csv	--format csv	table	Spreadsheets, BI tools.
jsonl	--format jsonl	table	Pipe into jq, stream-processors, dashboards.

The CLI refuses to render a table query as HTML or a graph query as CSV, so you can't accidentally produce junk output.

Custom Cypher — for when the saved library isn't enough

python tools/render_graph.py --from-neo4j \
    --cypher "MATCH (a:Agent)-[:DELEGATED_TO*]->(b:Agent) RETURN a, b" \
    --output /tmp/delegations --format html

Graph schema (v0.1)

Node	Natural key	Sourced from
Session	id	gen_ai.conversation.id (+ legacy fallbacks)
Agent	id	gen_ai.agent.id
Model	(provider, name)	gen_ai.provider.name + gen_ai.response.model
Tool	name	gen_ai.tool.name
DataSource	id	gen_ai.data_source.id
Operation	span_id	span id

Edge	Direction	Emitted when
CONTAINS	Session → Operation	Operation carries a conversation id
EXECUTED	Operation → Model	Op type ∈ {chat, text_completion, embeddings}
INVOKED	Agent → Operation	Op type ∈ {invoke_agent, create_agent}
CALLED	Operation → Tool	Op type = execute_tool
RETRIEVED_FROM	Operation → DataSource	Op carries gen_ai.data_source.id
PARENT_OF	Operation → Operation	span has parent_span_id
DELEGATED_TO	Agent → Agent	child invoke_agent under a different parent Agent
ACCESSED	Agent → DataSource	agent-owned Op retrieves from a data source

See docs/schema.md for the full reference, extension seams, and open questions for v0.2.

Raw Cypher (power users)

If the saved-query library doesn't cover your question, the Neo4j browser (http://localhost:17474) is always available. A handful of useful queries for reference, each also available as a saved query:

// agent delegation graph across all loaded traces
// (saved query: agent_delegation)
MATCH p = (a:Agent)-[:DELEGATED_TO*]->(b:Agent) RETURN p

// cost / token attribution per (provider, model)
// (saved query: cost_by_model)
MATCH (o:Operation)-[:EXECUTED]->(m:Model)
RETURN m.provider, m.name,
       count(*)                  AS calls,
       sum(o.input_tokens)       AS in_tok,
       sum(o.output_tokens)      AS out_tok
ORDER BY calls DESC

// blast radius, which agents/sessions did a failing tool touch?
// (saved query: failed_tools — graph shape)
MATCH (t:Tool)<-[:CALLED]-(op:Operation {status:"ERROR"})
      <-[:PARENT_OF*]-(ancestor:Operation)
      <-[:CONTAINS]-(s:Session)
RETURN t, s, collect(DISTINCT ancestor) AS affected_ops

To run one-off ad-hoc Cypher through the export pipeline (HTML/JSON/etc.):

python tools/render_graph.py --from-neo4j \
    --cypher "MATCH (o:Operation {status:'ERROR'}) RETURN o" \
    --output /tmp/errors --format html

Validation pipeline

Four independent data sources catch different bug classes:

1. Hand-written fixtures, 6 canonical cases in tests/fixtures/*.json. Each ships an expected_graph block with hand-counted node/edge totals; the mapper has to match exactly.
2. Parametric synthesizer, tests/generate_traces.py emits thousands of deterministic OTLP traces across five shapes (simple, agent_tool, multi_agent, rag, multi_turn) with a chaos mode that introduces dropped attributes, orphaned children, reordered spans, and corrupted trace_ids. The mapper must stay correct on clean traces and must not crash on chaos.
3. Real-SDK capture, tests/capture_real_traces.py calls actual LLMs (Anthropic, OpenAI, Gemini, Azure OpenAI classic / v1 / Foundry, Ollama) with a BudgetGuard that refuses to run past a cap. Five shapes: chat, agent_tool, embeddings, multi_turn, tool_call (with real tool execution).
4. Upstream-instrumentor capture, tests/capture_with_instrumentor.py runs spans through Python Contrib's own OpenAIInstrumentor / GoogleGenAiSdkInstrumentor and dumps what they emit. This is how we found that the Google instrumentor emits the pre-v1.37 gen_ai.system="gemini" + operation.name="generate_content" shape, now handled by the mapper's legacy-compat table.

All four feed into a shape-independent invariant test suite that checks 7 structural properties across every captured graph. See docs/mapping.md for the full contract and tests/test_invariants.py for the enforcement.

Supported providers

Out of the box, with the bundled capture scripts:

Provider	CLI --provider value	Notes
Anthropic	anthropic	ANTHROPIC_API_KEY
OpenAI	openai	OPENAI_API_KEY (+ OPENAI_BASE_URL for Ollama/Groq)
Azure OpenAI	azure_openai	classic /openai/deployments/… path
Azure OpenAI v1	azure_openai_v1	new /openai/v1/ path (Foundry portal)
Azure AI Inference	azure_inference	Foundry unified inference endpoint
Google AI Studio	google	GEMINI_API_KEY; free tier works
Ollama	openai + OPENAI_BASE_URL=http://localhost:11434/v1/	local, unlimited, $0

Free paths: Google AI Studio (no credit card, 1,500 requests/day on Flash), Ollama (local), Groq free tier.

Streaming export

from opentelemetry import trace
from opentelemetry.sdk.trace import TracerProvider
from opentelemetry.sdk.trace.export import BatchSpanProcessor
from otel_genai_graph.exporter import Neo4jGenAIExporter
from otel_genai_graph.neo4j_sink import Neo4jSink

sink = Neo4jSink("bolt://localhost:17687", "neo4j", "testtest")
sink.connect()
sink.ensure_schema()

provider = TracerProvider()
provider.add_span_processor(BatchSpanProcessor(Neo4jGenAIExporter(sink)))
trace.set_tracer_provider(provider)
# any instrumented code below this line streams into the graph

See tests/live_export_demo.py for a runnable walkthrough.

Status

v0.1, reference implementation, 833 tests passing. All components live: schema, mapper (with legacy-compat canonicalisation), Neo4j sink (MERGE-based, idempotent), SpanExporter, file loader, cost table, synthesizer, real-API capture, upstream-instrumentor capture, invariants, saved-query library, HTML / JSON / GraphML / CSV / ASCII-table export.

Known open questions, tracked in docs/schema.md:

• Resource nodes aren't currently emitted (schema has the label; mapper skips it to keep v0.1 simple).
• Streaming spans (partial output): we take the final span only.
• Cost attribution lives as Operation properties, not a dedicated Budget node, deferred until a concrete UX asks for it.

Contributing

See CONTRIBUTING.md for the dev loop, adding fixtures, adding provider adapters, and the invariant contract.

Contributors

Maintained by @kums1234. Any non-trivial contribution — code, docs, bug report, test fixture, real-trace capture, provider adapter, design feedback — earns a row in CONTRIBUTORS.md. Instructions for getting listed are in that file.

License

Apache-2.0.