agentpile 0.1.0

The first release.

Added

• ap.load(name) for 13 public agent-trace datasets, each at a pinned revision and checked against the run count in its manifest: Who&When, MAST, TRAIL, tau-bench, tau2-bench, AgentRewardBench, AgentDojo, SWE-rebench OpenHands, SWE-agent (Nebius), SWE-Gym OpenHands, SWE-smith, ADP and CatchBench PRE. With limit=N, a Hugging Face dataset downloads only the files its first N runs come from (five AgentRewardBench runs fetch about 25 MB of its 30 GB); a later full load fetches the rest and runs the count check.
• ap.load(path) and ap.load(record) for ATIF v1.7, OpenAI-style message lists, OTLP JSON (OpenInference and OTel GenAI attributes), MCP tools/list results, OpenAI and Anthropic tool lists, and agentpile config records. The format is detected, and an input that more than one reader claims is an error.
• The run record: an ATIF v1.7 trajectory with an extra["agentpile"] extension for source identity, labels with targets, agents and tools with permissions, source spans, and the source values behind every stand-in ATIF requires. A configuration without execution is an agentpile-config/0 record. Harbor 0.22.0's ATIF models accept every trajectory checked: the first 3,000 runs of each dataset, or all of a smaller one.
• Run.events(): messages, tool calls and tool results in the order the source recorded them, with each event's address in the record.
• ap.derive_deps(run, rules=[...]) with six named, versioned rules (tool_call_id_v1, full_context_v1, resource_chain_v1, last_writer_v1, prior_reads_v1, value_flow_v1), and Run.with_deps(rules=[...]), which returns a copy whose steps carry the entries.
• run.to_json() and run.to_otlp(); an OTLP export reads back to an equal record, and a run loaded from a trace writes each source span once.
• ap.show(run): a terminal view of one run, with labels beside the events they target and the record's stand-in and pairing marks; rich output with the view extra, plain text without it.
• ap.table(runs, level) for flat rows of runs, events or labels (standard library), ap.to_pandas(runs, level) (pandas extra), and ap.to_networkx(run, rules), an event graph with pairings and inferred dependencies as edges (graph extra).
• ap.send_otlp(runs, endpoint): posts each trajectory's OTLP JSON to a tracing backend such as Jaeger or MLflow, and switches to protobuf for one that answers 415, such as Arize Phoenix (needs opentelemetry-proto from the otel extra).
• ap.capture(agent=...) and ap.config_from(agent) for Pydantic AI (2.58.0 and 2.59.0) and the OpenAI Agents SDK (0.24.0, through openinference-instrumentation-openai-agents 2.5.3). Spans from any other version of the instrumentation (Pydantic AI's own, or OpenInference's for the Agents SDK) raise UnsupportedVersion unless allow_untested_version=True, which builds the run and marks it.
• ap.validate(record), the JSON schema of the extension, and plugin registries for readers, writers and datasets (ap.register and the agentpile.readers, agentpile.writers and agentpile.datasets entry-point groups).
• One download cache, ~/.cache/agentpile/ (AGENTPILE_CACHE), with ap.cache_info() and ap.clear_cache().

Install: pip install agentpile (PyPI).