Changelog

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SDK updates — August 2026

Released: August 2026 · TypeScript (@zespan/sdk) and Python (zespan) New:

• Cohere — wrapCohere() (TypeScript) / patch_cohere() (Python) traces the Cohere v2 chat API, including streaming (chatStream() / chat_stream()), tool calls, and cost from usage.billed_units — see Cohere
• Vectra — ZespanVectraCallbackHandler (both SDKs) implements vectra-js/vectra-py’s duck-typed callback interface directly, tracing ingestion, retrieval, reranking, and generation with no changes to how you call Vectra — see Vectra
• Documented sending traces from any OTel-instrumented app with no Zespan SDK — the OTLP ingest endpoint (/v1/traces) has accepted standard OpenTelemetry GenAI-semconv traces for a while, but it was never actually documented; frameworks already instrumented with OpenLLMetry, OpenInference, or their own native OTel exporter can now be pointed at Zespan directly — see Send traces from any OTel-instrumented app
• 9 new per-framework/gateway OTel integration pages — Ollama, DSPy, OpenAI Agents SDK, Strands Agents, Mastra, Agno, and a new Gateways category covering LiteLLM Proxy, Kong AI Gateway, and Cloudflare AI Gateway — each showing that specific framework/gateway’s own OTel auto-instrumentation pointed at Zespan, no Zespan SDK wrapper involved. No-code platforms (n8n, Dify, Langflow) intentionally excluded — none has official OTel support anywhere, only unofficial community patches

Platform:

• zespan doctor — a new subcommand on the @zespan/cli binary (zespan doctor, alongside the existing zespan gate / zespan-gate) diagnoses SDK setup problems in one command: API key validity, API reachability, whether spans are actually arriving (GET /v1/projects/:id/ingest-health), your @zespan/sdk version against latest, PII redaction posture, and a heuristic scan for the most common integration mistake — a provider client (OpenAI, Anthropic, Mistral, Groq, Cohere, GoogleGenAI) constructed before zespan.init() runs, so it never gets patched. Configure via flags, ZESPAN_* environment variables, or a committed .zespan.yaml — neither the API key nor the API base URL is ever read from that file, so a committed config can’t redirect where your key gets sent. doctor also flags a --project/ZESPAN_PROJECT_ID that doesn’t match the project your API key belongs to, and doctor --help prints usage without making any network calls. --json emits { results, summary } for CI. A new GET /v1/sdk/whoami endpoint backs it, resolving project identity from an API key alone — see zespan doctor and CLI overview
• Environments — every project now has real environment records (seeded dev/staging/prod, plus any custom slugs you add) instead of just a free-text tag: manage them from Settings → Environments, switch between them from a new project-header switcher that scopes Traces, Evaluations, Guardrails, Incidents, and metrics via a shared environment query parameter, and delete is blocked with a 409 naming exactly what’s still attached rather than orphaning guardrails, alerts, or incidents. Historical free-text values (production, development) alias onto prod/dev on both read and ingest; an inbound value that matches neither a slug nor an alias is never dropped, only flagged — see Environments
• Changes — a new project-wide timeline page merges prompt deploys, agent lifecycle transitions, guardrail/evaluator/alert edits, and deploys reported from your own CI pipeline into a single chronological feed, filterable by time range and change kind. Report your own pipeline deploys with a new POST /v1/projects/:id/changes endpoint so they show up alongside everything else — see Changes
• Changes around this incident — an incident’s detail page now shows a before/after split of every prompt deploy, agent lifecycle transition, and policy/evaluator/alert edit in the six hours on either side of when the incident started, each side labeled with its count so the split is legible at a glance. A partial-results banner appears if any underlying source didn’t respond in time — see Changes around this incident
• Blast Radius — a dependency graph across prompts, agents, models, guardrail policies, evaluators, and alerts, distinguishing declared (configured) from observed (actually-called, from real trace data) edges. Surfaces as an advisory impact summary in the prompt release-confirmation dialog and as a delete-blocking check on evaluators — deleting an evaluator with dependents now requires explicit acknowledgement instead of silently breaking whatever depended on it. No standalone page or graph visualization yet; both are on the roadmap. GET /v1/projects/:id/blast-radius and GET /v1/projects/:id/graph back it — see Blast Radius
• Outcome attribution — a new Value dashboard page shows which agents and models actually drive business results. Report a business outcome (a deflected ticket, an avoided refund, an SLA met) against any trace with zespan.getClient().outcome(...) (TypeScript) or POST /v1/ingest/outcomes directly (Python, or any backend) — reporting is designed to happen out-of-band, minutes or hours after the trace that produced the outcome has finished. Outcomes are summarized per-agent and per-model against real trace cost: total outcomes, success rate, attributed value, cost, cost-per-success, and value-per-dollar. Re-reporting the same outcome corrects it rather than double-counting it. GET /v1/projects/:id/outcomes/summary and GET /v1/projects/:id/outcomes/kinds back it — see Value
• Compliance Evidence Packs — a new Compliance dashboard page generates audit-ready evidence documents from data Zespan already recorded: a per-agent Compliance Card, or a SOC 2 control evidence report mapped to CC6.1 (logical access), CC7.2 (monitoring), and CC8.1 (change management). Every document is hash-addressed (SHA-256 over the exact stored bytes) and re-verifiable — a new GET /v1/projects/:id/evidence-packs/:packId/verify re-hashes the stored document and re-resolves every cited record against live data, honestly separating what it actually re-checked (checkedRefs/evidenceStillPresent) from what it structurally can’t (unverifiableRefs, e.g. ClickHouse aggregates). A coverage preview shows which controls have evidence for your chosen period before you generate anything, and every document carries a disclaimer stating it reports observed evidence, not a certification of compliance. SOC 2 is the only framework mapped today — EU AI Act and ISO/IEC 42001 aren’t yet — and the SOC 2 mapping itself is reviewed internally, not yet by a licensed external auditor, which the document says on its face. Output is JSON or a printable HTML document; PDF export isn’t available yet (print-to-PDF from your browser is the interim path), and there’s no recurring/scheduled generation or email delivery in this release — generation is on-demand only, from the dashboard or POST /v1/projects/:id/evidence-packs — see Evidence Packs
• Model Lifecycle — a daily scan matches the models you actually call against a curated, bundled catalogue of provider-announced deprecation and retirement dates, and raises a finding — call volume, cost, affected agents and prompts, all measured from your own traffic over the trailing 30 days — for any model retiring within 90 days that you’re still calling. Findings surface on a new Overview widget, a new Lifecycle column on the Models page, and a dedicated Model Lifecycle findings page. Dismissing a finding suppresses it at its current urgency band (90/30/7/0 days remaining) and it re-raises exactly once when the deadline tightens into a nearer band, never when a feed correction pushes it further out. Delivery reuses the existing alert-rule/webhook machinery under a new model-lifecycle rule type — a project needs an enabled rule of that type to get notified; the finding is recorded and visible either way. There is deliberately no fabricated quality delta or regression comparison against the named successor model — the only automated comparison is a measured cost-per-call difference on your own organic traffic, and the UI states outright that Zespan does not run your evaluations against the successor for you. Drift detection (catching a pinned model’s behavior silently changing over time, independent of any provider announcement) is not part of this release — see Model Lifecycle, Models, Alerts, and the feed reference

Bug fixes:

• Model Lifecycle deprecation notifications can now actually be turned on — a small settings card at the top of the Model Lifecycle page lets you opt in to email and/or webhook notifications for new or re-raised findings. Previously there was no way, in the dashboard or the API, to create the model-lifecycle alert rule the delivery worker looks for, so notifications never fired for any project even though findings were always fully recorded and visible — see Model Lifecycle → Getting notified
• The OTel ingest endpoint now reads the real GenAI semantic-convention model attributes (gen_ai.request.model / gen_ai.response.model) — it previously only checked a non-standard gen_ai.model, so any spec-compliant instrumentation (OpenLLMetry, OpenInference, and most framework-native OTel exporters) sending traces straight to /v1/traces showed every span as model: unknown — see Send traces from any OTel-instrumented app
• OpenRouter used via the openai client is no longer mislabeled openai — autopatch() in both SDKs now inspects the client’s base_url and correctly tags these calls provider: "openrouter", fixing provider-based cost and model lookups for anyone using OpenRouter through an OpenAI-compatible client instead of a dedicated OpenRouter SDK
• wrapBedrock, wrapMistral, wrapGroq, wrapLiteLLM are now reachable via the zespan convenience object (TypeScript) — zespan.wrapMistral(...)-style usage shown on those integrations’ docs pages previously threw at runtime since only wrapOpenAI/wrapAnthropic/wrapGoogle/wrapGoogleGenAI/wrapOpenRouter were actually attached to it

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Platform updates — Simulations CI parity

Released: August 2026

• Closed-loop fix proposal — on an incident with a single identified causal prompt deploy, a new Propose Fix button generates a fix candidate with the AI Prompt Enhancer grounded in that incident’s own captured failing traces, tests it head-to-head against the current production prompt — both replays scored by your project’s own LLM-judge evaluator — using the same Simulations quality gate, and — only if the candidate beats production — routes it through ZespanPilot’s approval queue, showing the gate’s score deltas and regressions alongside the request. Approving lets the reviewer promote the fix to staging or production; rejecting leaves the draft untouched. The draft is created with no labels at all, so it is never reachable by a latest prompt lookup before approval. Only a higher-is-better judge is used for the comparison — a lower-is-better rubric like toxicity would invert the gate — falling back to a provisioned reference-free quality judge otherwise. Starting a run requires both incidents:manage and prompts:manage (owner/admin), plus at least one other owner/admin in the organization, since nobody can approve their own fix candidate. Nothing is ever auto-deployed — a human approval is always required — see Propose Fix
• Convert to Scenario — a Convert to Scenario button on a trace’s detail page and a session’s detail page turns that one trace or conversation into a regression test in one click: a dedicated one-item dataset named after the source (with a timestamp), and a new scenario pointed at it with Reference Match set to Fuzzy match by default. A trace converts to a Prompt Target scenario, a session (inherently multi-turn) to a Conversation (multi-turn) scenario. It doesn’t auto-run — you land on the Simulations page with a success toast. The new scenario’s provider/model default to the project’s baseline rather than the original trace’s, with the original noted in the scenario’s description — see Convert to Scenario
• Adaptive adversarial persona presets — the Persona section of a Conversation (multi-turn) scenario now offers four built-in presets (Prompt Injection Attacker, Escalating Angry Customer, Social Engineer, Confused Novice) that populate the existing Name/Goal/Tone/Expertise fields — still editable afterward — plus a tactic-ladder instruction that pushes the simulated user to vary its approach across turns. This works on every run with no evaluator required; when a real judge rubric is also attached, each turn’s judge verdict is additionally prepended to the target’s reply before the next turn, reinforcing the adaptive signal for free — see Adversarial persona presets
• Real LLM-judge scoring for simulation runs — a simulation scenario’s attached evaluator now scores with a real judge call when its template has a system prompt: one call for a single-turn (Prompt/HTTP) target, or for a Conversation (multi-turn) target, one call per turn plus a final holistic call judging the whole transcript, with each turn’s judge cost and latency shown in the Conversation Transcript. An evaluator with no system prompt (e.g. one created via the quick New Evaluator dialog) still falls back to the existing metric-key/assertion scoring — no judge call, no LLM cost — see Creating a scenario
• CI gate for simulation batch runs — POST /projects/{id}/simulations/batch-runs/{batchRunId}/gate gates a candidate batch run against an explicit baseline batch run, mirroring the prompt-version quality gate: three threshold signals (avg score drop, regressed items, pass-rate drop, all with defaults), a 202 { pending: true } response while the candidate is still running, and a pass/fail verdict persisted into the batch run’s experimentMetadata.gateResult — see CI gate for batch runs
• referenceMatch scenario evaluation option — a scenario’s evaluation config can now request exact or fuzzy (≥80% similarity) string comparison against the dataset item’s expectedOutput, surfaced as a reference_match entry in the run’s assertion results and automatically skipped when the dataset item has no expectedOutput — see Creating a scenario
• Gate Check UI — the CI gate for batch runs is now also runnable from the dashboard, not just the API/CI: a Gate Check card on a completed batch run’s detail page picks a baseline (defaulting to the most recent completed batch run on the same dataset), exposes the three threshold overrides behind a collapsed Advanced thresholds section, and shows the pass/fail verdict, exit reason, and a per-item regression table — persisted so it’s shown automatically on reload, with a Re-run option — see CI gate for batch runs
• Scenario editing — scenarios can now be edited after creation from an Edit button on the Scenarios list, which reopens the same form pre-filled with the scenario’s current config — see Creating a scenario
• HTTP Target headers & timeout — a scenario’s HTTP Target now supports custom request headers and a configurable request timeout (defaults to 30000ms) — see Creating a scenario
• Conversation stop conditions — a Conversation (multi-turn) scenario can now also stop early on a regex match against the agent’s reply, or on its attached evaluator’s pass/fail verdict, alongside the existing max-turns limit — see Creating a scenario
• Evaluator score direction — set whether a higher or lower judge score counts as “better” for an LLM-judge evaluator via the new Direction button on its row (Evaluators tab); defaults to higher-is-better, with lower-is-better for metrics like toxicity or hallucination rate — see Score direction

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Platform updates — Evaluation trust & CI gating

Released: July 2026

• Evaluations page redesign — the Evaluators tab now shows a color-coded type badge (LLM JUDGE/CLASSIFIER/METRIC CHECK/PATTERN DETECT) and a pass/warn/fail score-distribution bar per evaluator, backed by real per-score counts rather than the metric-level average used elsewhere on the page; the Trends density view’s sparkline now reflects the selected time window instead of a fixed lookback. The Runs tab’s Score and new Samples columns now show each run’s actual result instead of a placeholder, and its status badge gained an icon. The LLM-connection banner also shows a “connected” state naming the active provider once one is configured — see Reading evaluation results and Evaluation runs
• Vector-DB tracing for Pinecone, Chroma, Weaviate, and Qdrant — calling a vector-DB client directly (no LangChain/LlamaIndex retriever in between) is now auto-traced by zespan.autopatch(), the same one-liner that already covers LLM providers: reads (query/search/near_vector) emit a retriever span with rag_contexts, writes (upsert/add/insert) emit an embedding span with metadata.vector_count — no code changes to your DB client calls. A new recordVectorSearch()/record_vector_search() manual helper covers pgvector and other raw-SQL vector stores, which have no client library to auto-patch — see Pinecone, Chroma, Weaviate, Qdrant, and Manual spans
• Vector-DB read spans now include metadata.top_k and metadata.filter — every retriever span from the auto-patched Pinecone/Chroma/Qdrant/Weaviate clients now also carries the requested result count (metadata.top_k — always recorded for Pinecone, whose top_k is required; recorded when the caller passes one for Chroma/Qdrant/Weaviate, whose equivalent option is optional) and the query’s filter/where clause (metadata.filter, redacted and stringified, only when storePrompts/store_prompts is on). Both fields are only observable nested under metadata — the ingest pipeline persists the metadata object verbatim but drops other unrecognized top-level fields — see each provider’s integration page for the exact source field per client
• Corrected built-in evaluators — Latency SLA, Cost Budget, Loop Detection, and Error Recovery Rate now compute a real deterministic check against the trace’s own latency_ms/cost_usd/operation/status data instead of silently falling back to a generic LLM quality-judge score — see Performance & agent evaluators
• Categorical/boolean scores now render correctly — a categorical or boolean evaluator’s result shows as the judge’s chosen label, or true/false, everywhere it appears (trace detail, per-trace score chip, evaluator tables) instead of a meaningless percentage bar — see Reading evaluation results
• Unified evaluation score sources — scores attached from the SDK via span.setEvalScore() now appear alongside server-side judge scores in the Evaluations dashboard’s KPI tiles and metric trend list, not only in the raw per-trace view — see Attaching scores from the SDK
• POST /evaluation-runs now actually scores — creating an evaluation run through the API now enqueues real scoring and the run list shows its aggregated score, instead of the run sitting at pending forever
• zespan-gate CLI — a new dependency-free @zespan/cli package wraps the prompt quality gate for CI: poll for a pending run, fail fast with a linking snippet if nothing’s scored yet, and exit non-zero on a failed or misconfigured gate — see CI quality gate
• Evaluation & dataset MCP tools — the hosted Zespan MCP server now exposes datasets, evaluators, evaluation runs, and trace verdicts to any connected coding agent (Claude Desktop, Cursor, Claude Code), alongside the existing trace/metrics and prompt-management tools — see Zespan MCP server
• Issue, session, retroactive-eval, and cost MCP tools — the hosted Zespan MCP server now covers 35 tools, adding open Issues (list, detail with sample traces, resolve/dismiss, and cached markdown remediation suggestions), sessions (recent-session list, full rollup + turn-by-turn transcript, session-level eval scores), retroactive evaluation runs (create, list, status), and cost attribution by agent/tool/model/user/operation. Write tools enforce the same role permissions and plan limits as the dashboard — a personal API key can only do what its owner’s role allows — and remediation stays advice-only: Zespan never opens a PR or touches your source. See Zespan MCP server
• HTTP Targets & HTTP-endpoint dataset runs — register an externally-hosted agent endpoint you don’t control or can’t instrument with the SDK (a deployed Bedrock/Glean agent, or any HTTP-reachable chatbot API), then run a dataset against it directly from “Run over dataset” — Zespan POSTs your hydrated request template to the endpoint (with retry, SSRF protection, and W3C traceparent propagation) and records the response as a tagged trace, no LLM connection required — see HTTP Targets and Running against an HTTP endpoint
• Wider SSRF blocking for HTTP Targets — the address-range check applied to a registered HTTP Target URL (and re-applied to the hydrated URL before every call) now also rejects RFC 6598 carrier-grade NAT space 100.64.0.0/10 — which includes Tailscale’s default range — plus multicast and reserved space from 224.0.0.0 up. The full list of rejected ranges is now documented; there is no override, so an agent reachable only over a private or Tailscale address has to be exposed publicly or instrumented with the SDK instead — see Security: URL validation
• Retroactive evaluation trigger UI — score historical traces without waiting for online eval to have been configured, from a new Retroactive Runs panel on the Evaluations page: pick any evaluator, a time range, and optional operation/model filters, then watch per-run progress live — see Retroactive evaluation
• Annotation Queues — a new dashboard section for routing a filtered set of traces to a human reviewer: filter by time range (and, via the API, operation/model/session/verdict), work through pending items with a trace input/output review panel, and submit a pass/fail verdict, optional label, score, and notes. A categorical (label-only) annotation always requires an explicit Pass/Fail choice — there’s no way to submit one without picking a verdict. Annotations are written into the same evaluation_scores data as automated judge scores, tagged human_annotated, so they appear in every existing trend and cost-quality view alongside judge scores — see Annotation Queues
• Session-level evaluation — a new scope: "session" option on evaluator templates judges an entire conversation’s transcript instead of one trace, so multi-turn quality problems (contradictions, lost context, topic drift) can be caught even when no single call looks wrong. “Session complete” is detected by quiescence — 30 minutes of no new activity by default, checked on a 10-minute scan — since the SDK has no explicit “close this session” call. Results appear as a Conversation: Pass/Fail · N evaluators badge on the session detail page, kept visually distinct from per-trace scores — see Session-level evaluation
• Session-level scores no longer skew the trace-level Evaluations views — a scope: "session" score judges a whole conversation and belongs on the session detail page, but it was also being counted by the Evaluations page’s metric trends, traces/spans-evaluated tiles, and Retrieval tab. Those four aggregations are now trace-scoped, so a session evaluator can’t drag a per-trace metric average down, add a phantom trace to the evaluated count, or rank session among your worst retrieval operations — see Session-level evaluation
• Sessions time-range filter — the Sessions list now defaults to the last 7 days instead of scanning full project history on every load, with a date-range picker to widen or narrow the window — see Filtering sessions
• Issue detail page, resolve action, and remediation suggestions — Issues now open into a detail page with their full sample-trace list; a new Resolve action sits alongside Dismiss with Sentry-style semantics (resolved = fixed, dismissed = stop showing me this pattern — either can reopen on a fresh occurrence); and a Generate suggestion button runs root-cause analysis across representative sample traces and asks ZespanPilot for a cached markdown remediation suggestion — likely cause and one concrete next step, never an automatic PR or repo change — see Issues
• Multi-turn conversation scenarios are now fully functional — Simulations’ Conversation (multi-turn) target, where a simulated user and your target agent alternate turns, now runs end-to-end. Conversation scenarios also gained structured persona configuration (name, goal, expertise level, and tone compose the simulated user’s system prompt, with the existing free-text Simulated User Prompt as a fallback), and a run’s detail view now shows the full turn-by-turn Conversation Transcript with per-turn latency and cost plus a total conversation cost from real per-model pricing — see Persona configuration for conversation scenarios and Reading run results
• Conversation scenarios now honor their inner target’s prompt template — the Prompt template you configure for a Conversation (multi-turn) scenario’s inner target was previously discarded, so the target agent ran with no template and no system prompt. It’s now applied to every turn, with {{input}} resolving to that turn’s message, and a configured system prompt is rendered against the run’s variables just like a single-turn prompt target — see Scenario target types
• Plan retention is now enforced on reads — traces, evaluation scores, guardrail events, and incident events older than your plan’s retention window (14 days on Free through 1 year on Scale) are excluded from every API response and dashboard view. Widening a date filter past the window no longer returns older data — see Data retention
• OTLP trace ingest reaches parity with the native SDK path — /v1/traces now enforces the same monthly event quota and 1 MB body limit as /v1/ingest, plus its own 512-span-per-request cap (matching the OpenTelemetry SDK/Collector’s own default max_export_batch_size, rather than reusing /v1/ingest’s 100-span cap, which was sized for the native SDK’s batching and would have silently dropped most spans from a default-configured OTel exporter), with the overflow reported through OTLP’s partialSuccess.rejectedSpans — see OpenTelemetry integration
• OTLP spans now honor organization custom pricing — spans ingested over OTLP are priced through the same resolver as SDK spans (custom ModelPrice rows first, built-in table as fallback) instead of always using built-in rates. Orgs with negotiated or self-hosted model pricing will see corrected costs on OTLP traces
• /v1/metrics and /v1/logs now return 501 instead of a misleading 202 — these OTLP signals were never stored; the endpoints previously accepted and discarded the payload while reporting success. They now fail honestly and non-retryably. Point only your trace exporter at Zespan — see OpenTelemetry integration
• Per-trace sampling in both SDKs — a sampleRate/sample_rate below 1.0 now keeps or drops a whole trace, decided from the trace id, instead of rolling per event. Sampled traces are complete: no orphaned child spans, no wrong span counts, and the same verdict on every service a distributed trace passes through. This also fixes a bug where a trace arriving via OpenTelemetry (or from another service in a distributed trace) always sampled in at 100%, ignoring sampleRate entirely — if you use OTel with sampleRate below 1.0, upgrading will reduce your ingest volume down to your configured rate, which is the fix working as intended, not new data loss — see sampleRate
• Corrected span counts across the dashboard — agent summaries, the AI Hub top-agents panel, top-cost and slow-trace tables, the ops expensive-traces panel, and session summaries now count distinct spans instead of raw rows, so their span and error totals match the trace detail view
• Multi-trace root-cause investigation — ZespanPilot can now investigate a recurring Issue across its sample traces instead of one trace at a time, and reports whether the occurrences share a single root cause or are several distinct failure modes colliding on the same verdict/operation/error-code cluster key — so an Issue that is quietly two problems says so — see Automatic remediation suggestions
• Automatic remediation suggestions — an Issue that recurs three or more times gets a remediation suggestion generated for it without anyone asking, ready on the Issue detail page with the time it was produced. Still text only: no pull request, no prompt edit, no config change — see Automatic remediation suggestions
• Evaluator authoring from chat — describe what you want scored (“check whether replies stay on topic”) and ZespanPilot maps it to the right built-in metric and creates the evaluator, instead of pointing you at the dashboard — see What ZespanPilot can do
• ZespanPilot conversations survive navigation — leaving the Pilot page and coming back resumes the same conversation with its transcript instead of silently starting a new one, and reopening a conversation from History now shows its messages — see Opening ZespanPilot
• Export a dataset run comparison — the two-run comparison view on a dataset’s Runs tab now has Export CSV and Export HTML buttons, so a report can be saved, attached to a PR, or shared without needing dashboard access — see Comparing two runs

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Platform updates — Agentic reliability & RAG evaluation

Released: July 2026

• Enforcement-linked trace forensics — guardrail hits now appear inline in the trace flame graph as their own spans, colored by outcome, instead of a separate log. Promote any violation directly into a permanent guardrail rule with pre-filled tool/field/value — see Promoting a violation to a policy
• Human approval gates — client.awaitApproval() (TypeScript) / client.await_approval() (Python) blocks execution until an admin approves or rejects the call from a new Approvals inbox — a real human-in-the-loop primitive for your own agents’ high-risk tool calls
• Near-miss capture & suggested policy rules — numeric guardrail checks that evaluate close to their threshold without firing are now logged; a background worker clusters recurring near-misses into suggested rules you can promote or dismiss
• Verdict-based issue clustering — a new Issues page automatically groups recurring failed/degraded traces into a single Issue using the same deterministic verdict classifier shown on every trace, with occurrence counts and one-click promotion to a guardrail rule
• Retroactive evaluations — score historical traces against an evaluator you didn’t have configured at the time, from a new panel on the Costs page
• Cost-Quality Frontier — a new chart plots every model used on an operation by real cost vs. real quality, span-level joined so multi-model traces aren’t misattributed — see Cost-Quality Frontier
• Regression testing from production failures — recurring Issues that hit 3+ occurrences are auto-captured into a “Production Failures” dataset your own CI can replay, with verdict-comparison scoring and an optional quality-gate signal — see Regression testing from production failures
• Agent-to-agent trust ledger (Phase 1: intra-org) — a compliance badge (pass rate, violation count, last violation) now appears on delegation lines in the trace detail view and on the Agent Registry, pulled from anywhere in your org rather than only the current project
• RAG quality evaluators — four new reference-free evaluator templates (RAG Faithfulness, Context Relevance, Context Utilization, Retrieval Hit Rate) score the retrieval step of a RAG pipeline directly, plus a fix so the existing Groundedness evaluator actually receives retrieved context instead of judging blind — see Evaluating RAG pipelines
• RAG analytics — a new Retrieval tab on the Evaluations page trends RAG evaluator scores over time and ranks your worst-performing retrieval operations
• Redesigned trace detail — every trace now opens into a plain-English health verdict, three evaluation lenses (final response / trajectory / per step), and a resizable span navigator + inspector, plus Flow (hierarchical/chronological node graph) and Flame (chronological/icicle) visualizations with full-screen, a Retrieval panel showing retrieved RAG chunks, an A2A task-lifecycle panel, a Session conversation view, and per-span deep-link permalinks — see Traces
• Bedrock & Vertex LLM connections — bring-your-own-key now covers Amazon Bedrock (static access keys or STS assume-role, no long-lived secret stored) and Google Vertex AI (service-account JSON), usable by Playground, Evaluations, and Simulations just like the direct-provider connections — see LLM Connections

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SDK updates — July 2026

Released: July 2026 · TypeScript (@zespan/sdk) and Python (zespan) New:

• RAG / retrieval tracing — recordRetrieval() / record_retrieval() records a retriever span with the retrieved documents in one call; span.recordDocuments() / record_documents() and a documents argument on span.end() attach chunks to a span you’re timing. Accepts plain strings, objects, or framework nodes (LangChain Document, LlamaIndex NodeWithScore). Chunk text follows your storePrompts and redaction settings. The LangChain and LlamaIndex integrations now capture retrieved document content automatically. See Recording retrieved documents

Bug fixes:

• Google wrapper no longer crashes generate_content (Python) — a TypeError on the token-usage fields (None where a number was expected) took down every call after the model had already responded; fixed
• Google embedding cost is now computed — embedding calls that report only a total token count (e.g. gemini-embedding-2) previously showed $0; cost is now calculated from the correct input-token count in both SDKs
• Manual span kind always recorded (TypeScript) — startSpan({ span_kind }) previously dropped the kind unless the span was created inside an agent context; it’s now always emitted, matching Python

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Platform updates — July 2026

Released: July 2026

• Prompt folders — organize prompts into folders, with a move-to-folder action and folder autocomplete
• Prompt webhooks — fire a webhook on version created, label assigned, or version deleted, delivered to Slack or a signed, retrying HTTPS endpoint, with a test-delivery button
• Dataset experiment runs — link your own pipeline’s results to a named run against a dataset via new SDK methods (getItems/get_items, createRun/create_run, run.link()), score runs with an evaluator, and compare two runs side by side
• MCP prompt tools — the hosted MCP server now exposes prompt management (list, get, create, update tags, set label) as tools an AI assistant can call directly
• Chat prompt message placeholders — declare a placeholder slot in a chat prompt and fill it with a caller-supplied message array at compile time (e.g. conversation history)
• SDK prompt-fetch resilience — PromptClient.get() now serves a stale cached value or a caller-supplied fallback instead of throwing when the API is unreachable
• Custom evaluation templates — author your own LLM-judge rubrics (numeric or categorical scoring), pin a specific judge model per template, and dry-run a template against a real trace before deploying it
• Auto-evaluator sampling and filters — scope continuous evaluation to a sample rate and filters by model, operation, or status instead of judging every matching trace

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SDK v1.0.1 — TypeScript

Released: June 2026 Bug fixes:

• Fixed API key prefix validation warning (now expects zsp_ prefix)
• Minor internal reliability improvements

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SDK v1.0.0

Released: June 2026 Initial public release of the Zespan SDK (TypeScript and Python).

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Middleware v1.0.0

Released: June 2026

• zespan-autogen — observability middleware for Microsoft AutoGen (agentchat + legacy pyautogen)
• zespan-crewai — observability listener for CrewAI agents
• zespan-fastapi — FastAPI middleware for automatic request/response tracing
• zespan-flask — Flask middleware for automatic request/response tracing