A developer CRM with a real API and real webhooks on every object event.
Strkr ships a documented REST API, a GraphQL endpoint, outbound webhooks on every create, update, and delete across every object, HMAC-signed deliveries, automatic retry with replay, scoped API tokens, a published OpenAPI 3.1 spec, and typed SDKs for TypeScript, Python, Go, and Ruby. Integration is a first-class product surface, not an add-on charged by a third party and billed by the operation.
What a credible developer CRM owes its builders in 2026
The nine primitives that separate an API from a marketing claim.
Most CRM vendors list "open API" on the feature page. The useful question is which of those APIs survive contact with a real engineering team: an engineer who needs to build a billing-sync job that will run every ten minutes for the next decade, a platform team that has to issue scoped credentials for a dozen downstream services without compromising audit, a security review that wants HMAC signing and replay protection, and a founding engineer who refuses to write another iPaaS gateway just because the CRM did not want to ship webhooks for the custom object they built last quarter. In 2026, the bar for a credible developer CRM is a specific list: REST, GraphQL, outbound webhooks on every object event, retry with replay, HMAC signing, scoped tokens with granular permissions, a published OpenAPI spec, typed SDKs, and a sandbox environment that mirrors prod. If every one of those is a check-box item, integration ships in a sprint. If any one of them is missing, the integration load shifts to Zapier, to a bespoke middleware tier, or to a never-finished internal library. The nine primitives below are the ones to walk every vendor through before you sign. Strkr ships all nine on every paid tier.
REST API
Resource-oriented endpoints on every object.
Every object in Strkr, including custom objects, has a full REST surface at /v1/{object}. GET for list and detail, POST for create, PATCH for partial update, DELETE for soft delete, and bulk variants for high-volume jobs. The URLs follow the resource name you defined in the UI, so a custom object named "renewal_milestone" shows up at /v1/renewal_milestones with no admin ticket, no beta flag, and no custom-object API tier upcharge. Pagination is cursor-based with a stable sort key. Rate limits are documented per endpoint and visible in response headers. Error payloads include a stable error code, a human-readable message, and a request ID that threads through the server logs.
GraphQL endpoint
One query, one round trip, deep object graphs.
A single POST to /graphql returns the exact shape your UI needs. Fetch a contact with the parent account, the related open deals, the last five activities, and the owner profile in one request. The schema is introspectable, so Apollo Studio, GraphiQL, and any typed codegen tool work out of the box. Field-level permissions are enforced in resolvers the same way they are in REST, so a token with scoped visibility sees the same slice through GraphQL that it would through REST. Query depth and complexity are capped per token, and the server returns a cost estimate in the response extensions so your client can self-govern before it hits a limit.
Outbound webhooks
Every create, update, delete, every object.
Register a webhook endpoint, pick the object and the event type, and Strkr starts posting signed payloads within seconds. Every standard object and every custom object fires on created, updated, and deleted events. Field-level change deltas ship inside the payload so your consumer does not have to re-fetch to figure out what moved. The event list includes stage transitions, assignment changes, status transitions, and permission changes, so a downstream system can react to the semantic event, not just the raw row diff. Consumers can filter by object, by event, or by a JSON predicate against the payload, which keeps noisy events out of your queue.
HMAC signing
Every delivery signed with a tenant secret.
Every webhook delivery includes an X-Strkr-Signature header computed as HMAC-SHA256 of the raw body using the tenant's rotating webhook secret. Your consumer verifies the signature before processing, and any request that fails verification is dropped. The timestamp is included and signed, so replay attacks are rejected by comparing it to a short acceptance window. The secret rotates on demand from the admin UI without downtime: a second active secret is published, both are honored for a grace window, and the old one is retired on your schedule. Signing is on by default, not an opt-in, and the admin UI blocks saving a webhook endpoint until a secret is attached.
Retry and replay
Transient failures do not become data loss.
When your endpoint returns a non-2xx response or times out, Strkr retries with exponential backoff over a 24-hour window. Every attempt is recorded in a delivery log that shows the request body, the response code, the response body, and the latency. From the log, an admin or an operator with the webhooks.replay permission can replay a single event, a batch of events, or a time range. Replay is the recovery pattern when your consumer had a bug and dropped events for ninety minutes; no support ticket, no custom script, no asking a vendor to re-emit the stream.
Scoped API tokens
One token, one job, one blast radius.
Create a token for your billing job with scope limited to deal.read and account.read. Create another for your enrichment service with contact.write only. Create a third for your reporting warehouse with read access on all objects and no write access anywhere. Tokens carry an owning user identity so writes attribute correctly in the audit log, an expiry date, an IP allowlist, a per-token rate limit, and a label that shows up next to every request in the API log. Rotating a token is one click. Revoking a token is one click. The blast radius of a leaked credential collapses from "the whole CRM" to "the one job that owned it."
OpenAPI 3.1 spec
A machine-readable contract, versioned.
The full REST surface ships as a published OpenAPI 3.1 document at /openapi.json. Point Postman at it, generate a typed client in your language of choice, feed it to your CI gate so a breaking change in prod fails a canary before a deploy. The spec includes request and response schemas for every endpoint, auth requirements, rate-limit headers, and example payloads. A changelog page lists every schema change with a semver bump and a deprecation window, so a client written against v1.3 keeps working through v1.9 and only fails after the published removal date.
Typed SDKs
TypeScript, Python, Go, Ruby, all first-party.
Each SDK is generated from the same OpenAPI spec the public docs render, published to the native package manager, and version-pinned alongside the server. The TypeScript client ships full types for every object including your tenant's custom objects via a one-command codegen step. The Python client is importable in a Jupyter notebook for a one-off data pull. The Go client is production-ready for a high-volume background worker. The Ruby client fits a Rails app. All four follow the same auth and pagination conventions, so a developer who learns one has learned all four.
Sandbox environment
A full-fidelity staging tenant, not a toy.
Every Pro and Scale tenant gets a sandbox tenant with the same schema, the same custom objects, the same automations, and the same permission model as production. Point your integration test suite at sandbox, run destructive tests without touching live customer records, promote the integration to prod when it passes. The sandbox has its own API keys, its own webhook endpoints, and its own data seed options. Full-fidelity sandbox is the single hygiene feature that separates a developer CRM from an API bolted onto a product that never expected to be integrated.
How the Strkr API is structured underneath
Native, consistent, versioned, auditable.
The API from a mature developer CRM is not a layer bolted onto the UI. It is the single interface that both the UI and your integrations consume. In Strkr, the web app itself calls the same endpoints a third-party integration would, with the same auth model, the same permission checks, and the same rate limits. This matters because it eliminates the entire class of "the UI shows one thing, the API returns another" bug that plagues legacy CRM platforms where the UI runs on a privileged internal path while the public API ships against a stale schema. The upshot is predictability. If a value is visible to a user in the UI, it is readable through the API with their token. If a field is editable in the UI, it is writable through the API with their token. If an automation fires on a UI edit, it fires on an API edit. One system, one contract, one audit log.
Single surface
The UI and your integration use the same API.
There is no "internal API" that the Strkr web app uses and a "public API" that lags behind. Every page in the UI is powered by the same /v1 endpoints that your integration calls. New fields show up in the API the moment they ship in the UI. New objects become addressable the moment they are created in the admin. Features and the API never drift because there is nothing to drift against.
Permission-aware
Your token sees exactly what its owner can see.
Every API request runs under an owning identity, either a user or a service account, inheriting the full permission model: role, team, ownership scope, field-level visibility, record-level sharing. A token that belongs to a sales rep returns the slice of pipeline that rep would see in the UI. A token that belongs to a platform service account with reporting-only perms returns the aggregate shape and refuses the field-level writes. No privileged bypass, no hidden escape hatch, no "admin API key" that undermines the whole security model.
Audit trail
Every write logged, with token and request ID.
Every API write records the token label, the owning user, the request ID, the IP address, and the before and after values on the activity log. A manager auditing a deal's history sees which edits came from a human in the UI, which came from a flow, which came from the AI assistant, and which came from an API token. The audit shape is identical across all four actor types, so a single query covers every write path. Compliance teams get one review surface for every data mutation that ever happened on the tenant.
Versioned
Deprecations announced, dates published.
The REST API is versioned at /v1. Breaking changes ship as /v2 with a published deprecation window that is a minimum of twelve months. Non-breaking additive changes ship inside the current version. Every deprecation lands with a documented replacement, a migration guide, and a Deprecation header in the response so your logs catch it before your pager does. The deprecation inbox is a dashboard your platform team can subscribe to, not a vendor newsletter you missed.
Idempotent writes
Retry a POST without creating duplicates.
Pass an Idempotency-Key header on any write. If the request succeeds, the response is cached against that key for 24 hours. If your worker crashes mid-request and retries, the second call returns the first response instead of creating a duplicate record. The pattern eliminates the biggest cost center in integration code: the dedup logic you had to write around every CRM that assumed the network was reliable. Billing jobs, lead syncs, and bulk imports all become retryable by default.
Rate limits
Documented per endpoint, visible in headers.
Every response includes X-RateLimit-Limit, X-RateLimit-Remaining, X-RateLimit-Reset, and X-RateLimit-Resource headers. The limits are per token, per endpoint class. Bulk endpoints have different budgets than detail endpoints. 429 responses include a Retry-After header, so your client backs off at exactly the right moment. For high-volume integrations, a Scale-tier add-on raises the limit against a published pricing matrix, not a bespoke conversation with sales.
Bulk endpoints
High-volume jobs do not run at 1 QPS.
/v1/bulk/{object}/create, /v1/bulk/{object}/update, and /v1/bulk/{object}/upsert accept up to 10,000 records per request. The API returns a job ID. Poll the job status, or register a webhook on job completion. The pattern is designed for the warehouse-to-CRM back-sync, the yearly data cleanup, and the migration from a prior CRM. No support ticket to raise the limit for a one-time migration, no "contact sales" to unlock bulk access, no admin forum thread complaining that a 50,000-row sync has been running for seven hours.
Stable IDs
Record IDs never change, even on merge.
A record ID assigned on creation is stable for the life of the record. On merge, the surviving record keeps its ID and the merged record's ID becomes a documented alias that resolves to the survivor. Your integration never has to rewrite its foreign keys because a user clicked "merge duplicate" in the UI. External ID fields exist as a first-class concept on every object, so your billing system's customer ID lives alongside the Strkr ID and either resolves the record.
Custom objects
Full API coverage, no gated upcharge.
When an admin creates a custom object in the UI, the REST and GraphQL endpoints exist immediately at /v1/{object_name}. Webhooks fire on create, update, and delete. The SDK codegen picks up the new object. The OpenAPI spec includes it. There is no "custom object API" tier charged separately, no engineering ticket to expose the object, no manual API registration step. If you can create a shape in the UI, it is reachable through the full API surface the same minute.
Webhook feature maturity, an honest look
What vendors claim versus what their webhooks actually do.
Every CRM lists webhooks on the feature page. Fewer explain the delivery guarantees. Here is how the common claims hold up on the way from the marketing page to a 2 AM pager rotation when your consumer has been down for four hours and a hundred thousand events have piled up upstream. Webhook maturity is a specific checklist, and the gap between a toy webhook and a production one is the difference between a stable integration and the thing your platform team deprecated after the third outage.
Delivery guarantee
Mature when at-least-once is contractual.
A webhook that fires once and gives up is a fire-and-forget notification. A webhook with 24 hours of retry, exponential backoff, and a replay button is at-least-once delivery. Strkr ships the second shape. Several competing products ship the first shape and leave the data-reconciliation problem to the consumer, which means your consumer has to run a nightly polling job on top of the webhook stream to catch misses, which defeats the entire point of webhooks.
Event payload
Mature when the delta ships inside the body.
A webhook body that is just an ID forces every consumer to make a GET round trip to figure out what changed. A webhook body with the full object plus the field-level diff lets the consumer react in constant time. Strkr ships the full object and the diff. Several competitors ship the ID alone, which doubles the API load on your tenant and your consumer and leaves a race condition when the object changes again between the webhook and the follow-up GET.
Signing
Mature when it rotates without downtime.
An unsigned webhook can be spoofed by anyone who guesses the URL. A signed webhook with a static secret is only slightly better because rotating the secret means a maintenance window. Strkr supports two active secrets at once with a grace window, so a rotation is zero-downtime. The old secret stays valid for a configurable overlap, then gets revoked on your schedule. Most competing products force a maintenance pause for every rotation, which is why most tenants never rotate.
Replay
Mature when you can rebuild your consumer from scratch.
Replay is only useful if the retention window is long enough to recover from a real outage. Strkr retains delivery logs for 30 days on all paid tiers and 90 days on Scale and Enterprise. A new consumer deployed to catch up on missed events can replay the whole last-30-days stream in one operation. Many competitors cap replay at 24 hours, which covers routine blips and nothing else; a real outage during a holiday weekend goes past the window.
Filtering
Mature when noisy events can be filtered server-side.
If every update event on every record fires every webhook, consumers that only care about a stage transition have to filter 95 percent of the stream in-memory. Strkr supports JSON predicates on the subscription, so your consumer only receives the events it cares about. Several competing products require you to consume the whole firehose and filter client-side, which wastes bandwidth, inflates your consumer's compute, and increases the chance of a dropped message during a backlog.
Ordering
Mature when events on a single record arrive in order.
Out-of-order deliveries cause the classic bug where the consumer sees "deal moved to closed-won" before "deal moved to negotiation." Strkr serializes delivery per record ID, so events on a single record arrive in commit order even when the consumer is behind. Cross-record order is best-effort because strict global ordering would cap throughput. The guarantee matches what consumers actually need, which is per-record determinism.
Observability
Mature when every delivery is inspectable in the UI.
A webhook that fails silently and only shows up in a vendor-side log you cannot see is a debugging nightmare. Strkr ships a full delivery log in the admin UI with filter by endpoint, status, event type, time range, and response code. Click a row to see the request headers, body, response code, response body, and timing. An on-call engineer debugs a failed delivery in 30 seconds, not three hours and a support ticket.
Dead-letter handling
Mature when exhausted retries do not disappear.
A webhook that fails 24 hours of retry on Strkr lands in a dead-letter queue, visible in the UI, with a one-click re-enqueue once the consumer is fixed. The admin gets an email after the first exhausted retry and a daily digest if the queue is non-empty. Many competing products drop the message silently after retries exhaust, which means your consumer has data loss and no visibility that it happened.
The integration pricing trap
How vendors turn basic integration into a line item.
The integration pricing problem is not the sticker. It is the structure. Metered API calls, gated custom-object APIs, "Operations Hub" tiers, iPaaS prerequisites, and per-connection surcharges turn a built-in capability into a budgetary surprise. The Strkr pattern is the opposite shape. The API is included on every paid tier. Webhooks are included on every paid tier. Custom-object APIs are included on every paid tier. The seat price is the integration price. No separate integration SKU, no per-operation meter, no "contact sales" to turn on your own data.
API call metering
The invoice that scales with success.
Several enterprise CRMs meter API calls against a daily or monthly quota and charge for overages. The incentive is upside-down: a successful integration that doubles traffic also doubles the invoice. Strkr publishes per-endpoint rate limits, not a quota meter. A high-volume Scale tenant pays a documented limit uplift against a published pricing matrix. The invoice is predictable; integration success is not punished.
Gated custom-object APIs
Pay again for the shape you already defined.
Some CRM products expose custom objects in the UI on a mid tier but gate the API access on the top tier. Build a custom object in month 3, discover in month 4 that integrating it costs an upgrade, discover in month 6 that the upgrade requires an annual commit. Strkr exposes every object, standard or custom, through the API on every paid tier. Shape it, address it, no second invoice.
Operations Hub tier
Data sync behind a separate SKU.
HubSpot sells Operations Hub as a separate product line for integration-heavy workloads: programmable automations, data sync, bulk operations. For an engineering team that just wants to call the API against the CRM they already paid for, the Ops Hub line is a 15-to-30 percent uplift. Strkr includes the same capabilities on every paid tier. The CRM you bought is the integration platform you bought.
iPaaS prerequisite
The middleware you did not ask to buy.
Salesforce's enterprise integration story routinely assumes MuleSoft, which is a separate product line with a six-figure floor. Teams that just wanted to post leads into Salesforce from their marketing site discover that the "right" path is a MuleSoft flow. Strkr does not need middleware. The REST and webhook surface is designed for direct integration from your own code, your own worker, your own warehouse.
Zapier dependency
A $20-per-task meter that is not your meter.
Many smaller CRMs ship a thin API and encourage Zapier for anything non-trivial. Zapier is a credible iPaaS for teams without engineers, and we support it as a legitimate option; the issue is the pricing shape, not the product. For an engineering team at scale, Zapier's per-task billing becomes a running tax on every automation. Strkr's native webhook surface replaces the Zap for an engineering team that would rather own the code. The Zap stays available for the small jobs; the heavy traffic moves to native.
Per-connection fees
Paying for the plumbing between your own systems.
Some vendors charge per connected app or per registered integration. The incentive is upside-down: consolidate connections to reduce the invoice, even when the right architecture is a separate integration per service. Strkr does not count connections. Register a hundred webhook endpoints across a hundred downstream services. The invoice does not notice.
Specialized iPaaS
Workato and Tray solve one shape well.
Workato and Tray are credible enterprise iPaaS platforms with strong UIs and large connector libraries. The buyer's math is whether to pay the iPaaS plus the CRM or ship the capability inside the CRM at the seat price. For a team with an engineering function that can write a webhook consumer in half a day, native Strkr integration consolidates spend and reduces vendor count. For a team without engineering, iPaaS is a reasonable choice; Strkr integrates with both.
Three Strkr API patterns in production
What the integration layer looks like on a real engineering team.
The documentation page shows a cURL example. The product in production looks like three specific patterns engineering teams run every day. These are not aspirational screenshots. These are the three Strkr API uses that save teams the most engineering time per month by a wide margin.
Billing sync
Stripe webhook in, Strkr write out, idempotent.
A Stripe webhook hits your integration worker on a subscription event. The worker calls PATCH /v1/accounts/{id} with an Idempotency-Key derived from the Stripe event ID. The write succeeds on first delivery, is a no-op on Stripe's retry, and lands in the Strkr activity log tagged with the integration's token label. The pattern replaces a weekly reconciliation batch with a sub-second sync and eliminates the classic "Stripe says active, CRM says cancelled" support ticket. Across a 50,000-account tenant, the sync runs for pennies in compute and holds under the published rate limits.
Warehouse back-sync
Snowflake nightly batch into Strkr via bulk upsert.
Your analytics team computes a per-account health score in Snowflake every night. A dbt job writes the result to a staging table. A Python worker reads the staging table and posts to /v1/bulk/accounts/upsert with 10,000 records per request, keyed by external_id. Strkr returns a job ID, the worker polls completion, and the health field is current by 7 AM every morning. No iPaaS, no Zapier, no third-party tool to credential and audit; one Python script, 80 lines, cron-scheduled, done.
Internal tooling
Your admin UI reads and writes through the public API.
Your platform team builds an internal workflow tool that opens the right Strkr record from inside your own support console, writes a note, updates a stage, and closes the ticket. The tool uses a scoped service-account token with contact.read, deal.read, and activity.write. Every write shows up in Strkr's activity log tagged with the token label and the acting user. Deep-link rendering uses the GraphQL endpoint for a one-round-trip page load. The pattern turns Strkr into a programmable record of truth that your internal tools consume the same way your reps do.
A real REST API, a real GraphQL endpoint, real webhooks on every object event. On every paid tier.
Starter includes the full REST and GraphQL surface, outbound webhooks on standard objects, scoped API tokens, HMAC signing, and the published OpenAPI spec. Pro adds webhooks on custom objects, bulk endpoints, 30-day replay retention, and the full-fidelity sandbox tenant. Scale and Enterprise raise rate limits, extend replay retention to 90 days, and unlock the integration-focused support SLA. The seat price is the integration price, every tier, every month, with no surprise overage invoice. Open the docs and ship your first webhook consumer before lunch.
Does the Strkr API cost extra, or is it included in the seat price?
It is included. The full REST API, the GraphQL endpoint, outbound webhooks on every object event, HMAC signing, retry and replay, scoped API tokens, bulk endpoints, the OpenAPI spec, and the typed SDKs all ship on every paid tier at no additional charge. There is no "Operations Hub" line, no per-operation meter, no gated custom-object API tier, no per-connection fee. The seat price listed on the pricing page is the full price for both the CRM and the integration surface. Rate limits are published per endpoint and raise on the Scale tier against a documented pricing matrix, not a bespoke sales conversation.
How does the Strkr API compare to Salesforce and HubSpot?
Salesforce has one of the deepest APIs in the market, but much of the enterprise integration story assumes MuleSoft, which is a separate product line with a six-figure floor. HubSpot ships a credible API on mid-tier plans but gates integration-heavy capabilities behind Operations Hub, which is a separate paid line that scales with usage. Strkr includes the full API, GraphQL, webhooks, bulk endpoints, and custom-object API access on every paid tier with no separate integration SKU. For an engineering team that wants to call the CRM directly from its own code, the Strkr pattern consolidates spend and removes a vendor.
Why does Strkr never recommend Zapier?
Zapier is a legitimate iPaaS, and Strkr supports it for teams that want a UI-driven automation layer; the issue is not the product, it is the positioning. A CRM that ships a thin API and tells its customers to "just use Zapier" has outsourced its integration story to a vendor that bills per task. For an engineering team at scale, Zapier's per-task billing becomes a running tax on every automation. Strkr's native API, GraphQL, and webhook surface replaces the Zap for anything an engineering team can write a small worker for. The Zap stays available for one-off jobs; the heavy traffic moves to native integration at the seat price.
What delivery guarantees do Strkr webhooks give?
At-least-once delivery with exponential backoff over a 24-hour retry window. Every delivery is HMAC-signed with a per-tenant rotating secret, includes a signed timestamp for replay protection, and ships the full object plus the field-level diff in the payload so consumers do not need a follow-up GET. Exhausted retries land in a dead-letter queue visible in the admin UI with one-click re-enqueue. Events on a single record arrive in commit order; cross-record order is best-effort. Delivery logs retain for 30 days on all paid tiers and 90 days on Scale and Enterprise, with full replay for any time range within retention.
How are API tokens scoped and audited?
Every API token has a label, an owning identity, an expiry date, an IP allowlist, a per-token rate limit, and a scope expressed as per-object permissions and record-level filters. Writes attribute to the owning identity in the activity log with the token label visible. Reads are rate-limited per token and show in the audit log with the request ID. Rotating or revoking a token is one click. The blast radius of a leaked credential collapses from "the whole CRM" to "the one job that owned it." All writes through the API are indistinguishable in the audit log from writes through the UI, so compliance review is a single query across both paths.
Is there a sandbox environment for testing integrations?
Yes. Every Pro, Scale, and Enterprise tenant gets a full-fidelity sandbox with the same schema, the same custom objects, the same automations, and the same permission model as production. The sandbox has its own API keys, its own webhook endpoints, and its own data-seed options so you can run destructive tests without touching live records. Promote an integration from sandbox to prod when it passes your test suite. Starter tier supports a scratch tenant for API testing; the full-fidelity sandbox lives on Pro and above because it carries a non-trivial infrastructure footprint.
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