Changelog

All notable changes to JarvisCore Framework are documented here. This project follows Semantic Versioning.

Versioning Policy (effective v1.1.0)

Releases prior to v1.1.0 did not follow SemVer consistently: new features were shipped in patch releases and a breaking change landed in v0.3.1 (a patch). Starting with v1.1.0, this project adheres to strict SemVer:

• PATCH (1.1.x): backward-compatible bug fixes only.
• MINOR (1.x.0): new features, new public API surface, backward-compatible behavioral changes.
• MAJOR (x.0.0): breaking changes to the public API.

Versions 1.0.3 and 1.0.4 contain critical regressions and should be avoided. They will be yanked from PyPI. Pin jarviscore-framework>=1.1.0.

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1.8.0 2026-09-06

1.6.0 and 1.7.0 were tagged but never published to PyPI

Both contain #170 — the Athena event write sat inside a branch that nothing triggered, so no agent event reached the memory tier — and a nested f-string in jarviscore/cli/atom.py that raises SyntaxError on import under Python 3.10 and 3.11. Their GitHub releases and changelog entries stand as history. On PyPI, 1.8.0 follows 1.5.1 and contains everything from both.

Fixed

• Athena has stored nothing since 1.4.0 (#170). _store_event built the request and then posted it inside if serialized_timestamp:. _rfc3339(None) returns None and nothing passes a timestamp — record_thought, record_action, record_observation and every on_* helper call the write without one — so the request was assembled and dropped, store_event returned False, and no caller checks the return. Any deployment with ATHENA_URL set has an empty STM tier, and therefore no MTM chains and no cross-session recall, since both are summarised from events that were never written. The only test on the path used a fake AthenaMemory, so it verified the layer above the broken one.
• The Athena tier is self-healing and its delivery is countable (#128). Writes are queued and shipped in order by one worker, so a slow Athena no longer adds latency to a reasoning turn. A failed start opens a circuit breaker with a cooldown probe instead of setting the client to None for the life of the process, which turned a thirty-second restart into a dead memory tier. A cached session id is checked before reuse: a 404 mints a new session and replaces the mapping, while an unreachable Athena keeps the id, because a network blink is not evidence that a session was deleted.
• The planner and evaluator stop cutting inside a record (#166). Agent identity was clipped to 400 characters twice, once by the caller and again in the Planner constructor, so the component choosing the steps saw a persona without the rules that constrain it. The evaluator clipped the step summary and payload and then instructed the model never to fail a step merely because evidence was clipped — managing the damage rather than removing it. Evidence now goes in whole, and that instruction retires with the problem it was covering for.
• jarviscore/cli/atom.py could not be imported on Python 3.10 or 3.11 (#167). Three print statements nested an f-string inside an f-string and reused the same quote in the replacement field, which is PEP 701 syntax valid only from 3.12.
• PyYAML was never declared as a dependency although AgentProfile.load() reads agent profiles from YAML on every AutoAgent load, and returned None with a warning when the import failed — an agent silently missing its role intelligence. It is now a dependency, and an unreadable profile raises.

Added

• A test job runs the suite on every pull request across Python 3.10, 3.11 and 3.12 (#167). CodeQL was previously the only check a pull request waited on. The suite was believed to have 38 permanent failures; every one was a missing dependency, and the real state is 1709 passing and none failing.
• jarviscore.context.fidelity — spends a prompt budget on whole records in priority order and names what did not fit, instead of halving values.
• Athena delivery counters on AthenaMemory.delivery_stats and UnifiedMemory.athena_delivery_stats: submitted, shipped, retried, dropped, queue depth, breaker state and last success.
• AthenaClient.write_event, which raises instead of reporting False, and AthenaClient.session_exists.
• Athena settings: athena_outbox_max_events, athena_write_max_attempts, athena_breaker_threshold, athena_breaker_cooldown_seconds, athena_deduplicates_writes. Planning budgets: PLANNER_FACTS_BUDGET_CHARS, PLANNER_CONTEXT_BUDGET_CHARS, EVALUATOR_FACTS_BUDGET_CHARS.

Changed

• EVALUATOR_SUMMARY_EVIDENCE_LIMIT and EVALUATOR_PAYLOAD_EVIDENCE_LIMIT are still read so existing configuration keeps importing, and no longer shorten anything.

Behaviour changes to know about (compatible, but read this)

• Athena writes are asynchronous. record_thought and friends return once the event is queued. Call AthenaMemory.close() or UnifiedMemory.close() on shutdown to flush; both report whether anything was still unsent.
• Write retries are off by default. Athena has no client-supplied deduplication key, so replaying a write that actually landed would store it twice, and a memory tier with invented corroboration is worse than one with a counted gap. JarvisCore attaches an idempotency_key and retries only when athena_deduplicates_writes is set — a claim about a deployment that only an operator can make.
• The planner receives the whole system prompt. system_prompt_excerpt is still accepted as an argument name.

1.7.0 2026-09-06

Fixed

• A rejected DONE now tells the agent what it produced instead of what rule it broke (#144). The gate returned a verdict in its own vocabulary — DONE_VALIDATION_FAILED: evidence is required — and the observation was identical on every attempt, so nothing in the agent's input changed between the first rejection and the eighth. The facts already existed: _validate_done_payload computes evidence_count, bad_evidence, bad_api_specs and incomplete_specs on every call, and the report was discarded to return a sentence. A gate now names the check that did not hold, what it reads, and what it observed. The harness recognises an attempt that carries no new information — same check failing on the same values, same result submitted, no tool run in between — reports that too, and ends the step after max_identical_done_attempts (default 3) of them rather than spending the remaining lease re-submitting a rejected artifact. An agent that is still changing its output is never cut off.
• single_response can no longer report an unfinished completion as success (#148). A truncated answer, a refusal, a content-filter block and a completed answer were indistinguishable to the caller, because the provider's finish reason was discarded by every adapter before the profile could read it. Native reasons and completion metadata now travel with the response, and success requires a terminal reason. A missing reason stays None — a generation that stops short of the cap is not evidence that it finished, and inferring one would manufacture the confidence this removes. Gemini output tokens now include thoughts_token_count, thought parts are excluded from content, and execution_contract.max_output_tokens is honoured instead of ignored.
• Search no longer cancels healthy grounded requests (#147). Every provider ran under one hard six-second deadline, so Gemini grounded search — a generative call — was routinely cut off and the run lost its highest-weight provider without saying so. Deadlines are now per provider (45s grounded, 15s otherwise) and configurable; a failing provider is named in the log with its error type and the deadline it exceeded, instead of an anonymous "Search provider failed".
• Docs and the packaged skill report the installed atom catalog rather than a count that was true when the page was written (#149).

Added

• jarviscore.kernel.gate: GateEvidence for reporting a completion failure as the check that did not hold plus what was observed, and the attempt ledger the harness uses to recognise a repeat.
• Per-provider search deadlines via RESEARCH_GROUNDED_TIMEOUT_SECONDS and RESEARCH_SEARCH_TIMEOUT_SECONDS, or constructor arguments on either public InternetSearch client.
• finish_reason and provider_metadata on every LLM response.

Behaviour changes to know about (compatible, but read this)

• A step can now end on an unsatisfied completion gate. _can_complete keeps its (bool, reason) signature and a bare string is still accepted — carried through as a verdict with no observations behind it — but a gate that rejects the same unchanged attempt three times ends the step as FAIL_DONE_GATE_UNSATISFIED instead of running to the emergency turn fuse. The outcome names the gate rather than reporting "out of turns".
• A single-response turn that used to succeed may now fail. Truncated, refused and filtered completions are reported as failures that name their cause. They were already failures; they were being reported as successes.
• Grounded search takes longer before giving up. The default grounded deadline is 45s. A run that previously lost that provider at six seconds will now wait for it, and return better evidence for the wait.

1.6.0 2026-09-06

Added

• Context pressure tiers replace per-value truncation (#154). Blocks are composed at full fidelity and the least aggressive tier that fits the budget is chosen: NORMAL → COMPRESS → EVICT_P3 → EVICT_P2 → RECOVERY. Under pressure whole blocks stop being inlined in priority order — mission and goal state are never evicted — and withheld records are named with the key they live under, so an agent retrieves a whole artifact instead of reasoning from a fragment of one. The active tier is rendered into the prompt and recorded in internal_variables["context_pressure"].

Fixed

• An agent's system_prompt now reaches the LLM in every sub-agent dispatch (#91). It was composed, passed to the kernel, stored in context and dropped: only the coder read it back, and only as a keyword check. Every researcher and communicator turn ran as a generic role stub, so authored personas, playbooks and guardrails had no effect. Identity now leads the system message, with the role prompt following as an execution harness.
• A coder run that returns nothing no longer completes the task (#150). _auto_complete fired on any clean sandbox exit, so a run with data: None and empty stdout was reported as a success whose "answer" was the execution envelope. Completion now requires a returned value, stdout, or a written file.
• Missing credentials for a declared system yield for access instead of warning to logs (#151). The kernel already knew no connection could be resolved; it now returns the existing YIELD_AUTH_REQUIRED contract at that point rather than spending a dispatch on code that cannot authenticate.
• Athena memory writes keep full fidelity (#153). log_turn wrote the whole thought and result to the scratchpad and episodic ledger, then wrote thought[:500] and result[:300] to Athena — the longest-retention tier held the only damaged copy, and mid-term chains were summarised from it. Domain events were clipped at 200–300 characters. A read may abridge and recover; a write cannot.

Changed

• BudgetConfig's per-value character limits (context_value_limit, history_value_limit, memory_item_limit, belief_value_limit, internal_var_limit, prior_step_value_limit, state_keys_limit, summary_evidence_limit) are retained so existing configuration keeps importing, but no longer affect rendering. total_tokens still scales from the lease profile.

Behaviour changes to know about (compatible, but read this)

• Prompts carry more, and reference the rest. Values are no longer cut, so a turn that previously showed 800 characters of a payload now shows all of it — or, under budget pressure, none of it plus a pointer. Agents that silently reasoned from fragments will now either see the whole record or be told where it is.
• A dispatch whose code returns nothing costs an extra turn instead of ending with a success it did not earn.
• A task declaring a system with no credentials yields instead of running. Declaring a provider is a statement of intent, so a mis-declaration now surfaces rather than failing obscurely later.

1.5.1 2026-09-04

Fixed

• Local Nexus vault was unreachable from agents (#142). jarviscore nexus register promised "no further setup needed", but nexus_call() in the agent sandbox was always a raise-stub and the kernel only resolved connections through a gateway. Agents now get a real call proxy in both gateway and local-vault modes, and the kernel tags the opaque provider handle when the vault holds credentials. Agent code, prompts and generated code never see the token.
• Traces omitted model and token data, and the scrubber redacted token counts (#143). LLM trace events now carry model and tokens; the secret scrubber only redacts string values, since a token count is not a credential; jarviscore inspect renders per-call models and no longer crashes on legacy scrubbed traces.
• claimer: "No agent found" for a step with no local agent is normal in distributed mode, and is now an info log with honest wording (#141).

Added

• examples/demo_synthesizer.py and examples/demo_node_{1,2,3}.py: the four-process distributed research demo — SWIM gossip discovery, ledger-claimed steps, live web research, synthesis, and Slack delivery through the credential vault.
• examples/slack_notify_demo.py: minimal single-agent vault-to-Slack end-to-end.

1.5.0 2026-09-04

Fixed

• A rejected DONE no longer kills the agent as "Cognitive budget exhausted" (#139). The done-gate rejection set done_called, so an agent that tried to finish early was terminated the next turn under a label that named the wrong cause. Wall-clock interventions now fire at 40% and 15% remaining, exhaustion yields always name the dimension that expired, and a landing turn produces a partial result (SUCCESS_ON_LANDING) instead of dead air.
• Settings kernel_* and WORKFLOW_STEP_TIMEOUT env knobs now reach lease enforcement (#135). Per-role profiles merge key-wise, so a partial override no longer drops the rest of the profile.
• Researcher wall clock raised 240s → 600s and communicator → 360s (#136): the previous walls could not complete a modest real web-research task.
• SWIM probe budgets stop assuming idle round-trip times (#138, mitigation): PING_TIMEOUT floor 2s, SUSPECT_TIMEOUT floor 15s, adaptive timing off by default. SWIM_* env vars still take precedence. Kept open for transport process isolation.

Added

• mesh.workflow(timeout_per_step=...), per-step timeout keys, and a WORKFLOW_STEP_TIMEOUT setting (#137). The crash-recovery path resolves its own default.

Behaviour changes to know about (compatible, but read this)

• Agents run longer and may spend more by default. Runs that previously died early now keep working; pin your own budgets via kernel_role_profiles or KERNEL_* env vars if you depended on the old walls.
• New success path. Runs that previously yielded on lease expiry can return status="success" with typed_outcome=SUCCESS_ON_LANDING and a partial result. Check metadata["landing_turn"] to distinguish full from partial completions.
• Slower peer-death detection. Set SWIM_* env vars to restore tighter probes on dedicated transport hosts.

1.4.1 2026-09-02

Fixed

• Athena session creation now sends distinct user_id and agent_id fields. Existing callers retain their prior user scope, while applications may opt into a shared user with separate agent memories. Redis session keys now include tenant, user, and agent identity to prevent cross-scope collisions.

1.4.0 2026-09-01

Added

• Athena v0.1.6 client parity for session reads/deletes, interactions, query-aware context, metadata-filtered semantic search, topics, segments, graph analytics, structured payloads, source timestamps, and durable event IDs through store_event_with_id().
• Optional ATHENA_API_KEY and ATHENA_JWT_TOKEN authentication headers.

Changed

• store_event() remains boolean for compatibility while accepting optional timestamp, payload, and MIME arguments. Closed HTTP clients are recreated on the next request instead of permanently disabling Athena access.

1.3.2 2026-09-01

Fixed

• Athena v0.1.5 GetContext responses now retain canonical relevantPages as MTM chains instead of silently dropping cross-session summaries. The client also preserves segments, user persona, and LTPM status while keeping compatibility with legacy response aliases.

1.3.1

Added

• Optional JarvisCore launch-promotion provider, for invited developers only. A JARVISCORE_PROMO_TOKEN grants restricted hosted inference without exposing an upstream provider API key. Without a token nothing changes: JarvisCore uses your own configured providers exactly as before, and the promotion is neither reachable nor active.

  Promotional calls use a fixed Prescott endpoint, expose only the jarviscore-promo model alias rather than the upstream deployment, preserve the complete exchange under a stable call ID, and fail explicitly on expiry, exhaustion, or service errors rather than falling through to a paid provider — so an expired free entitlement can never quietly start billing your own account.

  The endpoint may be pointed at staging or a local instance with JARVISCORE_PROMO_ENDPOINT. Overrides must be HTTPS and must resolve to a Prescott host or loopback: the promotional token is sent as a bearer credential, so an unrestricted override would be a way to harvest tokens rather than a convenience.

• jarviscore check --validate-llm reports promotional configuration and performs a real promotional inference request when asked.

Fixed

• jarviscore init failed on a pip-installed package with Source file not found: .../jarviscore/data/.env.minimal. The file was read by the scaffold command but never declared as package data, so it was absent from every published wheel and the first command a new user runs created nothing. Declared, along with the bundled data/examples/*.py that were similarly declared but missing from the repository.

1.3.0 2026-08-05

View release on GitHub →

A developer-experience and integrations release. The atom catalog more than triples, memory setup becomes a one-command flow backed by a published image, traces become readable from the CLI, and AI editors get a first-class skill. Backward compatible; one security default changed (see Security below).

Added

• [#111] Atom catalog expansion: 104 new provider bundles (987 atoms) imported from our production function registry, bringing the catalog to 150 providers and 1224 atoms. Every ported provider was hand-verified atom by atom against the official vendor API documentation.
• [#103] jarviscore inspect: read recorded traces from the CLI. Run list with steps, tokens, failures, and duration; per-step timelines; --errors and --step filters; workflow id prefix matching; honest [clipped] markers on long values.
• [#109] AI editor skill: jarviscore init --skill installs a verified SKILL.md for GitHub Copilot and Claude Code, covering the real API contracts, profile decision rule, and common mistakes. The doc site now serves llms.txt and an AI Editors guide.
• [#108] Image-first memory init: jarviscore memory init pulls the published Athena image instead of cloning and building from source. --from-source keeps the old path for contributors; memory up is now a working alias.
• [#106] Minimal env scaffold: jarviscore init writes a focused 35-line .env template (one provider key gets you running); --full writes the complete reference.

Fixed

• [#106] Fresh-clone init: an unanchored data/ gitignore pattern meant packaged data files were never tracked, so jarviscore init failed from any fresh clone.
• [#111] Atoms never shipped: integrations/atoms was neither a package nor declared package-data, so pip-installed wheels contained zero atom files and seed_registry found nothing at runtime.
• [#105] Silent import: import jarviscore no longer prints four lines of peer-to-peer module banners; the swim-p2p output is captured into debug logging and the p2p re-exports are lazy.
• [#108] Memory CLI crashes: memory init raised NameError (missing Path import) and memory up did not exist.

Security

• [#110] TLS verification enforced: three code paths disabled certificate verification on outbound HTTPS, one of them while sending Bearer tokens. All outbound HTTPS now verifies against the certifi CA bundle. If you genuinely need to disable verification behind a corporate proxy, set JARVISCORE_TLS_INSECURE=1; it logs a warning on every use.

Changed

• [#104] Documentation overhaul: landing page rewritten around enforced production rules, getting-started leads with a profile decision table, site-wide style pass, and the changelog page renders correctly.

1.2.0 2026-07-19

View release on GitHub →

A large, fully backward-compatible release. It hardens the AutoAgent cognitive stack (context assembly, convergence, planning, evaluation), adds new orchestration and goal-mode primitives, and makes failures loud and honest across the board. Every change is additive: no public API was removed or altered, so upgrading from 1.1.0 is a drop-in.

Added

• [#52] Dynamic fan-out (mesh.fanout()): run one task template over a runtime list of items with bounded concurrency, first-class partial failure (collect or fail_fast), per-item timeouts, and explicit aggregation via .aggregate() / .summarize(). Results are stamped with item and step identity so concurrent items cannot cross-contaminate.
• [#73] Goal persistence and resume: goal executions persist after planning, after every completed step, and at terminal states, under goals/{agent_id}/{goal_id}.json. execute_goal(resume_goal_id=...) rehydrates the plan, facts, and history so a crash loses at most the in-flight step.
• [#74] Dependency-parallel planning: the planner declares depends_on per step. Steps with no ordering constraint run concurrently (bounded by MAX_PARALLEL_STEPS), and a step never runs before its dependencies produce usable output. Plans without dependencies stay strictly sequential.
• [#72] GOAL STATE context block: plan facts and completed-step outcomes now cross step boundaries as a structured, named block instead of one clipped generic line.
• [#69] Generational LTM compaction: long-term memory is bounded through incremental generational merges rather than unbounded growth or a destructive rewrite, with archives kept for retrieval.
• [#63] single_response execution contract: AutoAgent can serve a one-completion analysis shape without the full planner or codegen, declared per task. The two-profile model (CustomAgent, AutoAgent) is preserved.
• [#84] Partial-result preservation: a goal that stops before completing now returns the work it finished, tagged [PARTIAL RESULT], instead of an empty result. Loud failures stay loud but hand back what they earned.
• [#86] Local in-memory mailbox: MailboxManager works without Redis for single-process meshes instead of raising AttributeError. Multi-node durability still uses Redis.
• [#88] Registry function identity: successful coder results carry a function_id when a registry function was reused or promoted, so reuse is observable from the envelope.

Fixed

• [#57, #58] Observation and convergence integrity: the subagent observation channel no longer truncates silently, and the convergence governor stops raising false stalls from content-length equivalence or parameter-blind tool streaks.
• [#55, #56] Honest context assembly: every context clip carries an explicit marker, and key-cap overflow is announced by name instead of dropping state silently.
• [#61, #62] Directive precedence and step identity: TOOL and DONE precedence is resolved correctly, and every workflow result carries its step_id, including successes.
• [#59] Zero-loss summarization: summarization compresses from real evidence and archives originals rather than discarding history.
• [#60] FailureLedger integrity: structured-first error classification with aligned guard keys, so retry policy is driven by real error types, not substring guesses.
• [#81, #82] HITL correctness: HITL_ENABLED is the single opt-in for every escalation path. Goals never dead-end waiting for a human on deployments that did not enable HITL. Evaluator hitl verdicts are reserved for genuine human decisions, and the file-backed queue resolve() to check() round-trip now works.
• [#85] Evaluator evidence: the evaluator sees enough of a step's output to judge it (tunable, generous windows with honest truncation markers), ending replan churn caused by verdicts made blind.
• [#87] Execution-backed coder reasoning: the coder's system prompt trains it to prove computed answers by executing code rather than answering from memory.

Changed

• [#83] Plan-mode boundary documentation: clarifies that plan mode is an AutoAgent capability which triages before planning, while CustomAgent treats planning as a library it calls.
• [#80] Fan-out guide fix: the workflow guide fan-out example now reads results from output, matching the real result shape.
• Documentation accuracy pass: corrected blob_storage.load() references to the real read() API in the CustomAgent guide and troubleshooting page.
• Removed internal-project references and development artifacts from the public tree.

1.1.0 2026-05-12

View release on GitHub →

This release fixes all critical regressions introduced in v1.0.3 that rendered AutoAgent unusable, adds new AI engineering primitives (cognitive routing, intent normalization, structured output validation), and marks the beginning of strict SemVer compliance. Versions 1.0.3 and 1.0.4 are deprecated and will be yanked from PyPI.

Fixed

• [#32] Output schema enforcement: Agent.output_schema (Pydantic BaseModel) is now passed through the Kernel into CoderSubAgent, which validates sandbox output against the schema via model_validate(). Schema violations fail fast with a clear error instead of silently returning unstructured data.
• [#33] CoderSubAgent sandbox hallucination: CoderSubAgent.get_system_prompt() now appends a dynamic SANDBOX ENVIRONMENT manifest listing all pre-loaded modules and globals in the sandbox namespace. This grounds the LLM in what is actually available, preventing hallucinated imports and undefined-name errors.
• [#34] Complexity gate before Planner: AutoAgent.execute_task() now runs a TaskComplexityClassifier before dispatching to the Planner DAG. Non-complex tasks bypass the full Plan → Execute → Evaluate loop, while classifier contract failures now fail visibly instead of silently falling through to the Planner.
• [#35] FunctionRegistry semantic search miss: CoderSubAgent._tool_check_registry() now normalizes verbose task descriptions into concise canonical intents via IntentNormalizer before calling semantic_search(). This eliminates embedding distance drift caused by prompt verbosity.
• [#36] AutoAgent vs CustomAgent boundary: Added p2p_responder attribute to the Agent base class (False by default, True on CustomAgent). JarvisLifespan now only creates background asyncio.Task instances for agents with p2p_responder=True, and raises RuntimeError at startup if a p2p_responder agent does not override run().
• [#37] Semantic vs execution status: ResultHandler.process_result() now tracks semantic_success separately from execution status. CoderSubAgent._tool_execute_code() includes an evaluator hook that flags outputs where success=False or status="failure" even when the sandbox execution itself succeeded. Fixed TypeError when cost_usd is None.
• [#38] Sandbox namespace leak into ZMQ coroutine cleanup: SandboxExecutor._execute_sync() and _execute_async() now restore namespace['__builtins__'] to the actual builtins module in a finally block. This prevents KeyError: '__builtins__' crashes in ZMQ's Cython backend during coroutine garbage collection.
• Structured Kernel routing: keyword role matching has been replaced by a typed TaskRouter. Explicit planner/profile roles are honored first; otherwise the router returns a validated role, confidence, reason, and evidence flag. Invalid or low-confidence routing fails visibly. Custom roles must register kernel_role_profiles.
• Strict subagent completion protocol: unparseable LLM responses now fail as protocol violations instead of being returned as successful raw content.
• Coder proof-of-work contract: CoderSubAgent must produce sandbox execution evidence before completion; structured prose results alone are no longer accepted for coder work.
• Workflow terminal status handling: only success completes a workflow step. yield, hitl, blocked, error, and unknown statuses are recorded as failures rather than satisfying dependencies.
• WorkflowBuilder failure visibility: agent-returned failure, yield, blocked, hitl, or unknown statuses are now preserved instead of being wrapped as step success.
• Distributed workflow output integrity: a remote step marked completed without persisted output now returns failure rather than fabricating a successful empty result.
• AutoAgent Kernel failure visibility: Kernel exceptions now return an explicit failure instead of silently falling back to the legacy direct-codegen pipeline.
• Profile routing explicitness: missing default_kernel_role in a profile no longer implies communicator; applications must opt into profile-level routing hints.
• _run_context AttributeError in CoderSubAgent: Changed direct attribute access to getattr(self, '_run_context', {}) to prevent AttributeError when _run_context is not yet initialized.

Added

• TaskComplexityClassifier (jarviscore.planning.classifier): LLM-based cognitive router that classifies tasks as "trivial", "moderate", or "complex" to determine whether the full Planner DAG is needed.
• IntentNormalizer (jarviscore.execution.intent_normalizer): Distills verbose task descriptions into concise canonical intents for accurate embedding-based semantic search.
• Agent.p2p_responder attribute: Boolean flag distinguishing reactive task workers (AutoAgent) from proactive mesh citizens (CustomAgent) at the framework level.
• Agent.output_schema attribute: Optional Pydantic BaseModel class for end-to-end structured output validation through the Kernel pipeline.
• semantic_success field in ResultHandler result data: Enables downstream consumers to distinguish between "code ran without errors" and "task actually achieved its goal".
• SandboxExecutor.get_manifest() / CoderSandbox.get_manifest(): Introspect the sandbox namespace for prompt injection into the CoderSubAgent system prompt.

Deprecated

• Versions 1.0.3 and 1.0.4: contain critical AutoAgent regressions. Will be yanked from PyPI. Users should pin >=1.1.0.

---

1.0.4 2026-05-11

View release on GitHub →

Documentation

• Mobile drawer fully resolved: Level 1 nav items clickable on all viewports, back arrow restored, site name text hidden in mobile view.
• Section index.md entry points added for Concepts, Guides, and Reference: each section now has an overview landing page with icon cards.
• CSS tab icons removed from sections that use frontmatter-defined icons, eliminating duplication.
• README expanded and restructured with additional examples and API reference.

Fixed

• Deprecated Mesh(mode='distributed') calls replaced with explicit config={"p2p_enabled": True} in test_09_distributed_autoagent.py and test_10_distributed_customagent.py.

---

1.0.3 2026-05-08

View release on GitHub →

Documentation

• Launched the full MkDocs documentation site: Getting Started, Concepts, Guides, Reference, Examples, and Enterprise sections.
• New guides: AutoAgent, CustomAgent, Workflows, Chat, HITL, Nexus, Knowledge Base, System Prompts, Observability, FastAPI Integration, Internet Search, Migration (CrewAI + LangGraph), Testing.
• New concept pages: Architecture, Memory, Nexus, P2P, System Bundles, Agent Personas.
• New examples: Financial Pipeline, Research Network, Support Swarm, Content Pipeline, Investment Committee.
• Synced all 15 brand SVG variants to docs/assets/ and removed legacy unreferenced logo.png / logo.svg.
• guides/testing.md: documented ExampleMockLLMClient: tool-validating mock LLM for unit testing agents with tool use.
• Fixed deprecated Mesh(mode=...) calls in README.md, guides/production.md, guides/browser-automation.md, and guides/adapters.md.

Added

• mesh.run_task(agent, task, context, complexity): primary user-facing API for dispatching a single task to an agent by role with multi-tier model routing.
• P2P_ENABLED=true env var support: Settings.p2p_enabled is now merged into Mesh config at startup.
• HITLCategory enum with hard enforcement on HITLQueue.request(): valid categories: auth_required, data_required, critical_action. Invalid categories raise ValueError.
• Planner subagent hints are strict: valid hints are accepted exactly and invalid hints fail visibly instead of being remapped.
• STEP_OUTPUT_MAX_BYTES (default 200 KB) and STEP_OUTPUT_PREVIEW_BYTES (default 20 KB): large step outputs stored as truncated preview with _overflow flag.
• Idempotent write guard on RedisStore.save_step_output(): a successful result will not be overwritten by a subsequent error payload from a stalled re-execution.
• Azure Content Filter visibility in LLMClient: raw provider content-filter rejections now fail visibly by default. AZURE_CONTENT_FILTER_REPAIR_ENABLED=true explicitly opts into Azure-specific prompt repair after the raw prompt is rejected.
• Kernel._get_model_for_tier(): clean multi-tier model resolution: complexity hint → TASK_MODEL_NANO / TASK_MODEL_STANDARD / TASK_MODEL_HEAVY → legacy fallback.
• MailboxManager schema normalisation: handles both the current flat envelope schema and the pre-v1.0.2 double-nested schema transparently.
• Vertex AI provider (LLMProvider.VERTEX_AI): GCP-native Gemini access via Application Default Credentials (ADC). No API key required: authenticate with gcloud auth application-default login or attach a service account. Config: VERTEX_AI_ENABLED=true, VERTEX_AI_PROJECT, VERTEX_AI_LOCATION (default us-central1), VERTEX_AI_MODEL (default gemini-2.5-flash). Slots into the fallback chain after Gemini: Azure → Claude → vLLM → Gemini → Vertex AI.
• _normalize_tools_for_gemini() static method: auto-converts tool schemas to Gemini function_declarations format. Accepts Anthropic/PeerTool (input_schema), flat (name+parameters), or already-native formats.
• Shared _call_genai_client() helper: both _call_gemini and _call_vertex_ai delegate here for consistent token accounting and cost calculation.
• Tool-call response parsing in _call_genai_client: when a Gemini/Vertex AI response contains function_call parts, tool_calls is populated and content is set to "".
• Token pricing entries for gemini-2.5-flash, gemini-2.5-pro, gemini-3.1-pro, gemini-3.1-pro-preview.
• Process-wide LLM concurrency semaphore (LLM_MAX_CONCURRENT env var): prevents thundering-herd 429s in multi-agent deployments.
• llm.nano_model and llm.planner_model properties: tier-aware model selection for StepEvaluator and Planner.
• max_completion_tokens alias in generate(): callers can use GPT-5.x SDK naming convention interchangeably with max_tokens.

Fixed

• P2P_ENABLED env var was not forwarded to Mesh.config, requiring config={"p2p_enabled": True} explicitly even when the env var was set.
• HITL escalations could be raised for arbitrary reasons, polluting the human review queue. Now enforced at the framework level.
• Planner emitted Unknown subagent_hint warnings for semantically valid but aliased LLM role names.
• Examples audit (2026-05-07): fixed API compatibility across all 5 production examples for v1.0.3 breaking changes.
• SWIM stabilisation replaced hardcoded 5 s sleep with condition-based poll (0.3 s for single-node; up to 5 s when seed nodes configured).
• AutoAgent result dict now exposes payload as a dedicated top-level key when the output is a structured dict, enabling downstream step access without manual parsing.
• Crash recovery _resume() pre-populates recovered step results into pre_results so resumed workflows replay correctly rather than skipping completed steps.
• ExampleMockLLMClient validates tool names against the tools parameter before returning tool-use responses, preventing mock deadlocks when a tool is out of scope.
• _call_gemini now forwards **kwargs (including tools) to _call_genai_client, making Gemini tool-calling behaviour consistent with Vertex AI.
• test_claude_primary now passes an explicit config that disables all other providers, making the assertion environment-independent.

Changed

• mesh.workflow() no longer restricted to autonomous mode: works across all mesh configurations.
• CLI references updated from python -m jarviscore.cli.* to the jarviscore entry-point CLI.

---

1.0.2 2026-03-04

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Fixed

• P2P Keepalive Spam Prevention: Added exponential backoff mechanism (45s default) to prevent continuous keepalive attempts when peers are unavailable or network has connectivity issues.
• Remote Agent Discovery Bug: Fixed PeerClient.list_roles() to include remote agents from SWIM mesh, not just local agents. Previously only checked local agent registry, missing agents discovered via P2P network.
• Single-Node Graceful Degradation: Added allow_zero_peers flag (default: True) to recognize single-node runs as valid state without triggering failure warnings.

Changed

• Increased ask_peer timeout from 600s to 7200s (2 hours) to support long-running database queries and complex analysis tasks.
• Enhanced keepalive manager with consecutive failure tracking and backoff-until timestamp for better network resilience.
• Improved keepalive logging to distinguish between expected zero-peer state and actual failures.

Added

• P2P_KEEPALIVE_FAILURE_BACKOFF_SECONDS config parameter (default: 45) for keepalive retry backoff.
• P2P_ALLOW_ZERO_PEERS config parameter (default: True) for single-node development and testing.

---

1.0.1 2026-02-27

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Fixed

• LICENSE link in README resolves to 404 on PyPI. Replaced relative path with absolute GitHub URL.

---

1.0.0 2026-02-25

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Changed

• Version: 0.4.0 → 1.0.0: stable public release.
• Documentation URL updated to custom domain: https://jarviscore.developers.prescottdata.io/

Added

• Apache 2.0 license (replaces MIT); CLA/INDIVIDUAL.md, CLA/CORPORATE.md, TRADEMARK.md.
• CONTRIBUTING.md with CLA links, ruff tooling, PR checklist.
• CODE_OF_CONDUCT.md community standards.
• ENTERPRISE.md for OSS vs Enterprise comparison.
• examples/investment_committee/: 7-agent multi-step workflow with web dashboard (AutoAgent + CustomAgent, parallel step execution, LTM institutional memory, FastAPI dashboard on port 8004).

---

0.4.0 2026-02-19

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This was the largest release in JarvisCore history, introducing the complete infrastructure stack across nine phases. It added persistent storage, context distillation, telemetry, the mailbox messaging system, the function registry, the Kernel OODA loop, distributed workflow execution, Nexus authentication, and the unified memory architecture.

Phase 1: Foundation Layer

LocalBlobStorage / AzureBlobStorage with save(path, data) / load(path) for any artifact. RedisContextStore for full Redis-backed step output, workflow graph, mailbox, HITL, and checkpoint methods. Configured via STORAGE_BACKEND, STORAGE_BASE_PATH, and REDIS_URL.

Phase 2: Context Distillation

Evidence, TruthFact, TruthContext, AgentOutput Pydantic models. distill_output(), scrub_sensitive(), merge_facts() utilities. ContextManager for token-budget aware prompt building (priority stack: mission → plan → scratchpad → LTM → tool history → variables). JarvisContext enhanced with truth, mailbox, tracer, human_tasks fields.

Phase 3: Telemetry and Tracing

TraceEventType enum covering workflow, step, kernel cognition, tool, mailbox, HITL, and context events. TraceManager with three output channels: Redis List (persistent), Redis PubSub (real-time), and JSONL (compliance fallback). record_step_execution(duration, status) Prometheus histogram and counter, enabled via PROMETHEUS_ENABLED=true.

Phase 4: MailboxManager

self.mailbox.send(target_id, payload) / read(max_messages) for async agent messaging. Backed by Redis Streams, available in all modes when REDIS_URL is set.

Phase 5: Function Registry

CodeRegistry auto-registers successfully executed code per task and promotes to VERIFIED on first success. Available as agent.code_registry in AutoAgent, persisted to {log_dir}/function_registry/. Includes update_execution_stats(func_name, success, execution_time) for graduation tracking.

Phase 6: Kernel / SubAgent OODA Loop

The Kernel replaces AutoAgent's linear codegen → sandbox → repair pipeline with a supervised OODA loop. ExecutionLease enforces token/turn/wall-clock budgets per subagent role. AgentCognitionManager tracks budget spend per phase, detects spinning (same tool 3+ times), and enforces cognitive gates. AdaptiveHITLPolicy with HumanTask pauses execution when confidence or risk triggers fire. Coder dispatches require executable proof of work before completion.

Phase 7: Distributed WorkflowEngine

WorkflowEngine persists DAG to Redis hash workflow_graph:{wf_id} for crash recovery. When no local agent matches a step, the engine resets status to "pending" and polls Redis. Mesh._run_distributed_worker() scans jarviscore:active_workflows, checks are_dependencies_met(), atomically claims steps via SETNX, executes, and writes output to Redis.

Phase 7D: AuthenticationManager (Nexus)

Set requires_auth = True on any agent and the Mesh injects self._auth_manager before setup(). Full NexusClient flow: request_connection → browser OAuth → poll ACTIVE → resolve_strategy → apply headers. Graceful degradation when NEXUS_GATEWAY_URL is not set.

Phase 8: Memory Architecture

UnifiedMemory(workflow_id, step_id, agent_id, redis_store, blob_storage) with .episodic (EpisodicLedger via Redis Streams), .ltm (LongTermMemory via Redis), and .scratch (in-memory WorkingScratchpad). RedisMemoryAccessor reads step outputs across the workflow.

Phase 9: Mesh Integration and Auto-Injection

Before each agent's setup(), the Mesh injects _redis_store, _blob_storage, and mailbox. Prometheus server starts automatically when PROMETHEUS_ENABLED=true. Agents use injected infrastructure directly with zero boilerplate.

Production Examples

• financial_pipeline.py: AutoAgent autonomous, 3-step financial analysis pipeline.
• research_synthesizer.py + research_node_1/2/3.py: AutoAgent 4-node SWIM research cluster.
• support_swarm.py: CustomAgent P2P, 4-agent support routing with Nexus auth.
• content_pipeline.py: CustomAgent distributed, content pipeline with LTM.

Fixed

• AutoAgent.execute_task: pass context=task.get('context') to sandbox.execute() so LLM-generated code can access previous_step_results.

Changed

• P2P env var namespace: bind_port, bind_host, seed_nodes, node_name now read from JARVISCORE_BIND_PORT, JARVISCORE_BIND_HOST, JARVISCORE_SEED_NODES, JARVISCORE_NODE_NAME respectively. This isolates per-process P2P settings from the swim package's own env vars.

---

0.3.2 2026-02-04

View release on GitHub →

Added

Session Context Propagation: context parameter added to notify(), request(), respond(), and broadcast() methods. Context carries metadata like mission_id, priority, trace_id across message flows. respond() automatically propagates context from request if not overridden.

response = await peers.request("analyst", {"q": "..."}, context={"mission_id": "abc"})

async def on_peer_request(self, msg):
    mission_id = msg.context.get("mission_id")
    return {"result": "..."}

Mesh Diagnostics: mesh.get_diagnostics() returns local_node, known_peers, local_agents, and connectivity_status (healthy, isolated, degraded, not_started, local_only). Includes SWIM and keepalive status when P2P is enabled.

Async Request Pattern: ask_async(target, message, timeout, context) returns a request_id immediately. check_inbox(request_id, timeout, remove) retrieves the response later. Enables fire-and-forget workflows where you collect results when ready.

Load Balancing Strategies: strategy parameter on discover() supports "first", "random", "round_robin", and "least_recent". discover_one() convenience method for single peer lookup.

MockMesh Testing Utilities: jarviscore.testing module with MockPeerClient (full mock with discovery, messaging, assertion helpers) and MockMesh (simplified mesh without real P2P infrastructure). Auto-injects MockPeerClient into agents during MockMesh.start().

---

0.3.1 2026-02-02

View release on GitHub →

Breaking Changes

ListenerAgent has been merged into CustomAgent. Migration requires only an import change:

# Before
from jarviscore.profiles import ListenerAgent

# After
from jarviscore.profiles import CustomAgent

All handler methods work exactly the same way. No other code changes required.

Changed

CustomAgent now includes P2P handlers: on_peer_request(msg), on_peer_notify(msg), on_error(error, msg), and the built-in run() listener loop. The profile architecture was simplified from three profiles (AutoAgent + CustomAgent + ListenerAgent) to two (AutoAgent + CustomAgent), removing the "which profile do I use?" confusion.

---

0.3.0 2026-01-29

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Added

ListenerAgent Profile: New ListenerAgent class for handler-based P2P communication with on_peer_request(msg) and on_peer_notify(msg) handlers. No more manual run() loops required for simple P2P agents.

FastAPI Integration: JarvisLifespan context manager reduces FastAPI integration from approximately 100 lines to 3. Automatic agent lifecycle management with support for both p2p and distributed modes.

Cognitive Discovery: peers.get_cognitive_context() generates LLM-ready peer descriptions with dynamic peer awareness. Auto-updates when peers join or leave the mesh.

Cloud Deployment: agent.join_mesh(seed_nodes) for self-registration without central orchestrator. agent.leave_mesh() for graceful departure. agent.serve_forever() for container deployments. RemoteAgentProxy for automatic cross-node agent visibility.

---

0.2.1 2026-01-23

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Fixed

• P2P message routing stability improvements.
• Workflow engine dependency resolution edge cases.

---

0.2.0 2026-01-22

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Added

CustomAgent profile for integrating existing agent code. P2P mode for direct agent-to-agent communication. Distributed mode combining workflow engine with P2P. @jarvis_agent decorator for wrapping existing classes. wrap() function for wrapping existing instances. JarvisContext for workflow context access. Peer tools: ask_peer, broadcast, list_peers.

Changed

Mesh now supports three modes: autonomous, p2p, distributed. Agent base class now includes P2P support.

---

0.1.1 2026-01-16

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Changed

• Migrated from the deprecated google.generativeai SDK to the current google.genai SDK.
• Updated default Gemini model to gemini-2.0-flash.

Added

• Scaffold CLI (python -m jarviscore.cli.scaffold) for new project initialization.
• Bundled .env.example and example files in the PyPI distribution.

---

0.1.0 2026-01-13

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Added

Initial release. AutoAgent profile with LLM-powered code generation. Workflow engine with dependency management. Sandbox execution (local and remote). Auto-repair for failed code. Internet search integration (DuckDuckGo). Multi-provider LLM support (Claude, OpenAI, Azure, Gemini). Result storage and code registry.